ISBN 978-1-927797-45-7
ISSN 1198-273X

PICES SCIENTIFIC REPORTNo. 62, 2021

Oceanography of the Yellow Sea andEast China Sea

PICES Scientific Report No. 62
2021

Oceanography of the Yellow Sea
and East China Sea
edited by
Joji Ishizaka, Guebuem Kim, Jae Hak Lee, Su Mei Liu, Fei Yu and Jing Zhang

September 2021
North Pacific Marine Science Organization (PICES)
P.O. Box 6000, Sidney, BC, V8L 4B2, Canada
www.pices.int

PICES Scientific Reports

Published since 1993, the PICES Scientific Report series includes final reports of PICES expert
groups, proceedings of PICES workshops, data reports and reports of planning activities. Formal
peer reviews of the scientific content of these publications are not generally conducted.
PICES Scientific Reports can be found at: https://meetings.pices.int/publications/scientific-reports

This report was developed under the guidance of the PICES Science Board, its Phyical Oceanography
and Climate Committee and Technical Committee on Monitoring. The views expressed in this report
are those of participating scientists under their responsibilities.

Front cover:Satellite image of the Yellow Sea and East China Sea showing chlorophyll-a concentrations (red, thehighest) during July 10 and 19, 2018. Derived from data from the Second-generation Global Imager(SGLI) of Global Change Observation Mission-Climate (GCOM-C) of Japan Aerospace Exploration
Agency (JAXA).
This document should be cited as follows:
Ishizaka, J., Kim, G., Lee, J.H., Liu, S.M., Yu, F. and Zhang, J. (Eds.) 2021. Oceanography of the
Yellow Sea and East China Sea. PICES Sci. Rep. No. 62. North Pacific Marine Science Organization,
Sidney, BC, Canada, 298 pp.

Table of Contents
Contributing Authors ...................................................................................................................... vii
Preface ............................................................................................................................................. xi

Physical Oceanography................................................................................................................. 1
Preface .............................................................................................................................................. 2
1.1 Circulation ................................................................................................................................ 3
Xinyu Guo, Byoung-Ju Choi and Fangli Qiao ................................................................................. 3
1.1.1 Introduction ...................................................................................................................... 3
1.1.2 Bohai Sea and Yellow Sea circulation .............................................................................. 4
1.1.3 East China Sea circulation.............................................................................................. 14
1.1.4 Summary and future work .............................................................................................. 22
1.1.5 References ...................................................................................................................... 24
1.2 Tides and Waves ..................................................................................................................... 33
Dongfeng Xu, SungHyun Nam and Hailun He ............................................................................... 33
1.2.1 Introduction .................................................................................................................... 33
1.2.2 External tides .................................................................................................................. 34
1.2.3 Surface waves................................................................................................................. 36
1.2.4 Internal tides ................................................................................................................... 39
1.2.5 Nonlinear internal waves ................................................................................................ 41
1.2.6 Future work .................................................................................................................... 43
1.2.7 References ...................................................................................................................... 43
1.3 Water Masses.......................................................................................................................... 47
Fei Yu.............................................................................................................................................. 47
1.3.1 Introduction .................................................................................................................... 47
1.3.2 East China Sea................................................................................................................ 47
1.3.3 Yellow Sea...................................................................................................................... 51
1.3.4 Future work .................................................................................................................... 55
1.3.5 References ...................................................................................................................... 55
1.4 Ocean Mixing ......................................................................................................................... 62
Takeshi Matsuno, Hao Wei, Jae Hak Lee and Chuanjie Wei .......................................................... 62
1.4.1 Introduction .................................................................................................................... 62
1.4.2 Methods for estimating the turbulence intensity ............................................................ 63
1.4.3 Characteristics of mixing processes ............................................................................... 63
1.4.4 Concluding remarks ....................................................................................................... 71
1.4.5 Future work .................................................................................................................... 72
1.4.6 References ...................................................................................................................... 72

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iii

1.5 Atmosphere–Ocean Interaction .............................................................................................. 75
Atsuhiko Isobe ................................................................................................................................ 75
1.5.1 Introduction .................................................................................................................... 75
1.5.2 Two-way coupling process related to extratropical cyclones ......................................... 75
1.5.3 Two-way coupling processes longer than on a sub-weekly timescale ........................... 79
1.5.4 Future work .................................................................................................................... 82
1.5.5 References ...................................................................................................................... 82
1.6 Long-Term Variations of Physical Properties ......................................................................... 85
Sang-Wook Yeh and Jae-Hak Lee ................................................................................................... 85
1.6.1 Introduction .................................................................................................................... 85
1.6.2 Basic features of physical properties in the Yellow Sea and East China Sea ................. 86
1.6.3 Long-term variation of SST variability and sea level..................................................... 87
1.6.4 Summary ........................................................................................................................ 92
1.6.5 Future work .................................................................................................................... 92
1.6.6 References ...................................................................................................................... 93

Chemical Oceanography............................................................................................................. 97
Preface ............................................................................................................................................ 98
2.1 Dissolved Methane and Nitrous Oxide................................................................................... 99
Gui-Ling Zhang and Wang-Wang Ye .............................................................................................. 99
2.1.1 Introduction .................................................................................................................... 99
2.1.2 CH4 distribution in the East China Sea and Yellow Sea and the controlling factors .... 100
2.1.3 Distribution of N2O in the East China Sea and Yellow Sea andthe influencing factors................................................................................................... 102
2.1.4 Sea-to-air fluxes of CH4 and N2O in the East China Sea and Yellow Sea.................... 103
2.1.5 Future perspective ........................................................................................................ 104
2.1.6 References .................................................................................................................... 104
2.2 Nutrients Biogeochemistry ................................................................................................... 106
Su Mei Liu and Yu Umezawa ........................................................................................................ 106
2.2.1 Introduction .................................................................................................................. 106
2.2.2 Spatial and temporal variations of nutrients ................................................................. 107
2.2.3 Main sources and sinks of nutrients ............................................................................. 108
2.2.4 Nutrient budgets ............................................................................................................ 111
2.2.5 Long-term changes of nutrients.....................................................................................113
2.2.6 Isotopic applications in nutrient biogeochemistry studies .............................................114
2.2.7 Regeneration of nutrients ..............................................................................................115
2.2.8 Other nutrient sources ...................................................................................................116
2.2.9 Future perspective .........................................................................................................117
2.2.10 References .....................................................................................................................117
2.3 Hypoxia ................................................................................................................................ 123
Zhuoyi Zhu.................................................................................................................................... 123
2.3.1 The occurrence of hypoxia ........................................................................................... 123
2.3.2 Yellow Sea and East China Sea background ................................................................ 124
2.3.3 Overview of hypoxia in the Yellow Sea and East China Sea ....................................... 124
2.3.4 The biggest hypoxia event in the Yellow Sea and East China Sea ............................... 125
2.3.5 Impact of hypoxia ........................................................................................................ 131
2.3.6 Uncertainties, unknowns, and perspectives .................................................................. 132
2.3.7 References .................................................................................................................... 133

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2.4 Biogeochemical Tracers ....................................................................................................... 138
Jing Zhang and Guebuem Kim ..................................................................................................... 138
2.4.1 Introduction .................................................................................................................. 138
2.4.2 Water column ............................................................................................................... 138
2.4.3 Sediment....................................................................................................................... 143
2.4.4 Atmosphere .................................................................................................................. 144
2.4.5 Future perspectives ....................................................................................................... 146
2.4.6 References .................................................................................................................... 147
2.5 Persistent Organic Pollutants and Heavy Metal Pollution .................................................... 153
Xianguo Li and Yanbin Li ............................................................................................................. 153
2.5.1 Introduction .................................................................................................................. 153
2.5.2 Legacy and emerging persistent organic pollutants in sediments of the Yellow Sea
and East China Sea........................................................................................................ 154
2.5.3 Heavy metals in the Yellow Sea and East China Sea ................................................... 162
2.5.4 Conclusions and recommendations .............................................................................. 169
2.5.5 References .................................................................................................................... 170
2.6 Atmospheric Deposition ....................................................................................................... 182
Sai-Chun Tan and Chun-Ok Jo ..................................................................................................... 182
2.6.1 Introduction .................................................................................................................. 182
2.6.2 Atmospheric deposition in the Yellow Sea ................................................................... 183
2.6.3 Atmospheric deposition in the East China Sea............................................................. 184
2.6.4 Relationship between atmospheric deposition and biological production in
the Yellow Sea and the East China Sea ......................................................................... 186
2.6.5 Future perspective ........................................................................................................ 189
2.6.6 References .................................................................................................................... 189

Biological Oceanography ......................................................................................................... 195
Preface .......................................................................................................................................... 196
3.1 Pelagic Microbial Food Web ................................................................................................ 197
Wuchang Zhang, Chung Yeon Hwang, Hiroshi Koshikawa, Jae Hoon Noh and Yuan Zhao........ 197
3.1.1 Introduction .................................................................................................................. 197
3.1.2 Virioplankton................................................................................................................ 198
3.1.3 Heterotrophic bacteria .................................................................................................. 199
3.1.4 Picophytoplankton........................................................................................................ 203
3.1.5 Flagellates .................................................................................................................... 210
3.1.6 Ciliates...........................................................................................................................211
3.1.7 Tintinnids ..................................................................................................................... 212
3.1.8 Trophodynamics ........................................................................................................... 215
3.1.9 Planktonic community respiration................................................................................ 217
3.1.10 Synthesis and perspectives ........................................................................................... 217
3.1.11 References .................................................................................................................... 218
3.2 Phytoplankton....................................................................................................................... 224
Joji Ishizaka.................................................................................................................................. 224
3.2.1 Introduction .................................................................................................................. 224
3.2.2 Chlorophyll-a ............................................................................................................... 225
3.2.3 Primary production....................................................................................................... 228
3.2.4 New production and nitrogen fixation.......................................................................... 230
3.2.5 Community structure .................................................................................................... 231
3.2.6 Influence of the Three Gorge Dam ............................................................................... 236

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3.2.7 Influence of typhoons ................................................................................................... 236
3.2.8 Future perspective ........................................................................................................ 238
3.2.9 References .................................................................................................................... 238
3.3 Zooplankton ......................................................................................................................... 244
Guang-Tao Zhang......................................................................................................................... 244
3.3.1 Introduction .................................................................................................................. 244
3.3.2 Adaptation to distinct habitats ...................................................................................... 244
3.3.3 Responses to climate change ........................................................................................ 248
3.3.4 Anthropogenic impacts ................................................................................................ 249
3.3.5 Attractive issues and future perspective ....................................................................... 251
3.3.6 References .................................................................................................................... 252
3.4 Macrobenthos ....................................................................................................................... 255
Xinzheng Li and Yong Xu ............................................................................................................. 255
3.4.1 Introduction .................................................................................................................. 255
3.4.2 Yellow Sea.................................................................................................................... 256
3.4.3 East China Sea.............................................................................................................. 271
3.4.4 Summary and prospects ............................................................................................... 281
3.4.5 References .................................................................................................................... 282
3.5 Fish and Invertebrate Fisheries............................................................................................. 285
Weiwei Xian and Cui Liang .......................................................................................................... 285
3.5.1 Introduction .................................................................................................................. 285
3.5.2 Methods ........................................................................................................................ 285
3.5.3 Nekton assemblages in the Yangtze River estuary ....................................................... 286
3.5.4 Key species................................................................................................................... 290
3.5.5 Summary and future perspective .................................................................................. 297
3.5.6 References .................................................................................................................... 298

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Contributing Authors

Contributing Authors
Physical Oceanography
Byoung-Ju Choi
Chonnam National University
Korea
bchoi@jnu.ac.kr
Xinyu Guo
Ehime University
Japanguoxinyu@sci.ehime-u.ac.jp
Hailun He
Second Institute of Oceanography
Ministry of Natural ResourcesChinahehailun@sio.org.cn
Atsuhiko Isobe
Research Institute for Applied Mechanics
Kyushu University
Japanaisobe@riam.kyushu-u.ac.jp
Jae Hak Lee
Ocean Circulation and Climate Research Center
Korea Institute of Ocean Science and Technology
Korea
jhlee@kiost.ac.kr
Takeshi Matsuno
Research Institute for Applied Mechanics
Kyushu UniversityJapanmatsuno@riam.kyushu-u.ac.jp
SungHyun Nam
Seoul National University
Korea
namsh@snu.ac.kr
Fangli Qiao
First Institute of Oceanography
Ministry of Natural Resources
China
qiaofl@fio.org.cn

PICES Scientific Report No. 62

viiContributing Authors

Chuanjie Wei
Research and Development Center of Marine Environmental Engineering and Technology
Institute of Oceanography
Chinese Academy of SciencesChina
weicj@qdio.ac.cn
Hao Wei
School of Marine Science and Technology
Tianjin University
Chinaweihao@ouc.edu.cn
Dongfeng Xu
Second Institute of Oceanography
Ministry of Natural Resources
China
xudongfengsio@sio.org.cn
Sang-Wook Yeh
Hanyang University, ERICA
Korea
swyeh@hanyang.ac.kr
Fei YuInstitute of Oceanology
Chinese Academy of Sciences
China
yuf@qdio.ac.cn

Chemical Oceanography
Chun-Ok Jo
Reseach Institute of Oceanography, Anyang University
Korea
cojoo100@gmail.com
Guebuem Kim
Seoul National University
Korea
gkim@snu.ac.kr
Xianguo Li
College of Chemistry and Chemical Engineering/Key Laboratory of Marine Chemistry Theory
and Technology
Ocean University of China
China
lixg@ouc.edu.cn
Yanbin Li
College of Chemistry and Chemical Engineering/Key Laboratory of Marine Chemistry Theory
and Technology
Ocean University of China
Chinaliyanbin@ouc.edu.cn

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Contributing Authors

Su Mei Liu1,2
1
Key Laboratory of Marine Chemistry Theory and Technology Ministry of Education
Ocean University of China
2
Laboratory for Marine Ecology and Environmental Science
Qingdao National Laboratory for Marine Science and Technology
Chinasumeiliu@ouc.edu.cn
Sai-Chun Tan1,2
1State Key Laboratory of Numerical Modeling for Atmospheric Sciences andGeophysical Fluid Dynamics Institute of Atmospheric Physics
Chinese Academy of Sciences2
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters
Nanjing University of Information Science and Technology
China
sctan@mail.iap.ac.cn
Yu Umezawa
Tokyo University of Agriculture and TechnologyJapanumezawa@me.tuat.ac.jp
Wang-Wang Ye
Key Laboratory of Marine Chemistry Theory and Technology
Ministry of Education
Ocean University of China
China
yezikasuo@126.com
Gui-Ling Zhang
Key Laboratory of Marine Chemistry Theory and Technology
Ministry of Education
Ocean University of China
China
guilingzhang@ouc.edu.cn
Jing Zhang
University of ToyamaJapanjzhang@sci.u-toyama.ac.jp
Zhuoyi Zhu
Shanghai Jiao Tong University
Chinazhu.zhuoyi@sjtu.edu.cn

PICES Scientific Report No. 62ixContributing Authors

Biological Oceanography
Chung Yeon Hwang
Seoul National University
Korea
chung.hwang@snu.ac.kr
Joij Ishizaka
Institute for Space-Earth Environmental Research
Nagoya University
Japanjishizaka@nagoya-u.jp
Hiroshi Koshikawa
National Institute for Environmental StudiesJapankoshikaw@nies.go.jp
Xinzheng Li
Institute of Oceanology, Chinese Academy of Sciences
Chinalixzh@qdio.ac.cn
Cui Liang
China Institute of Oceaonology, Chinese Academy of Sciences
China
cui-liang@hotmail.com
Jae Hoon Noh
Korea Institute of Ocean Science and Technology
Korea
jhnoh@kiost.ac.kr
Weiwei XianInstitute of Oceaonology, Chinese Academy of Sciences
China
wwxian@qdio.ac.cn
Yong Xu
Institute of Oceanology, Chinese Academy of SciencesChinaxuyong@qdio.ac.cn
Guang-Tao Zhang
Institute of Oceaonology, Chinese Academy of Sciences
China
gtzhang@qdio.ac.cn
Wuchang Zhang
Institute of Oceaonology, Chinese Academy of Sciences
China
wuchangzhang@qdio.ac.cn
Yuan Zhao
Institute of Oceaonology, Chinese Academy of Sciences
China
yuanzhao@qdio.ac.cn

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Preface
The Advisory Panel for a Circulation Study of East Asian Marginal Seas (AP-CREAMS) of theNorth Pacific Marine Science Organization (PICES) has recognized a lack of an extensive review ofthe oceanography of the Yellow Sea and East China Sea. The Yellow Sea, including the Bohai Sea,and East China Sea are one of the most productive marine environments in the world, and they arealso known to be facing rapid environmental changes in recent decades with possible eutrophication,
climate change and other causes. It is also realized that there are many studies in this important area;
however, there is almost no review which is internationally available. Thus, AP-CREAMS
recommended to review the present conditions of the Yellow Sea and East China Sea and to preparea report which could be widely used by the marine sciences community. Here, we have prepared“Oceanography of the Yellow Sea and East China Sea” for publication in the PICES Scientific
Report series. Members of the editorial committee appreciate all the contributors of this report.
Joji Ishizaka, Guebuem Kim, Jae Hak Lee, SuMei Liu, Fei Yu, Jing Zhang
Editorial Committee

PICES Scientific Report No. 62xiPhysical Oceanography

PICES Scientific Report No. 62

1Physical Oceanography

PrefaceThis chapter summarizes the general understanding of physical oceanography in the Bohai, Yellow
and East China seas based on published literature. The chapter consists of six sections: currents,tides and waves, water masses, ocean mixing, atmosphere–ocean interaction and long-term
variations. The reviews generally indicate that the research trend had a turning period in the 1990s
in its scope. Before the 1990s, research focused largely on tides and water masses, though work tounderstand the current system had been continuing for a long time. In the 1990s, the understandingof the current system improved significantly. Since the 2000s, observations of small-scale events,
such as internal waves and frontal structures, have been growing and studies of ocean mixing,
especially vertical mixing, has made considerable progress. Recently, research on processes in theatmosphere–ocean interface have been increasing. This includes the effect of waves, surface fluxes
and tele-connections.
It is noted that the lack of time-series data and wintertime observations must be overcome toimprove our understanding of these seas in the next generation. All sections made similar remarksthat more in-situ observations need to be carried out to obtain long-term time series, and that
regional numerical models, including various coupled models, need to be improved. A suggestion tosee successful results for these demands in the future is collaboration of scientists from countries
surrounding these seas under an international framework such as PICES.
Jae Hak Lee

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Physical Oceanography

Circulation

1.1 Circulation
Xinyu Guo1, Byoung-Ju Choi2 and Fangli Qiao3
1

Ehime University, Japan
Chonnam National University, Korea
3First Institute of Oceanography, Ministry of Natural Resources, China2

1.1.1 Introduction
The Bohai Sea, the Yellow Sea (YS) and the East China Sea (ECS) lie on the largest continental
shelf in the northwestern Pacific Ocean (Fig. 1.1.1). The Bohai Sea and YS are separated by the
Bohai Strait, while the YS and the ECS are separated by a line from the mouth of the ChangjiangRiver to Jeju Island. With only one connection to the YS, the Bohai Sea becomes a semi-enclosed
marginal sea with an average depth of 18 m (Xia et al., 2019). The YS is also a semi-closed shelf
sea connected to the Bohai Sea to the north and the ECS to the south; it has an area of 380,000 km2
and extends from 31°40′N to 39°50′N and 119°10′E to 126°50′E. The YS has average andmaximum water depths of 44 and 140 m, respectively.

Bohai Strait

Jeju Strait

PN

Fig. 1.1.1 Bathymetric map of the Bohai Sea, the Yellow Sea, and the East China Sea. Continuous lines
denote isobaths in meters. Dashed line connecting the Changjiang River mouth and Jeju-do indicates thegeographic boundary between the Yellow Sea and East China Sea. PN denotes a section observed regularly
by the Japan Meteorological Agency. Modified from Lie and Cho (2016).

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Physical Oceanography

The ECS is connected to other marginal seas through the Tsushima Strait to the northeast and throughthe Taiwan Strait to the southwest and is open to the Pacific Ocean to the southeast; it has an area of
770,000 km2 and extends from 21°54′N to 33°17′N and 117°05′E to 131°03′E. The ECS has both ashelf area and sharp slope and deep water areas with average and maximum water depths of 370 and
2940 m, respectively. Two-thirds of the ECS is on the continental shelf, and this part can be separated
into an inner shelf, middle shelf, and outer shelf by the 50, 100, and 200 m isobaths, respectively,
which largely run parallel to the coastline over the continental shelf. Immediately outside of thecontinental shelf is the Okinawa Trough with a depth of 600 to 2000 m.
As one of the most studied seas in the world, there have been already many review papers on thecirculations in the Bohai Sea, YS and ECS (e.g., Guan, 1994; Su, 1998; Lie, 1999; Su, 2001; Ichikawa
and Beardsley, 2002; Lie and Cho, 2002; Isobe, 2008; Park et al. 2014, 2017). In this section, using a
combination of review papers and newly published papers related to this topic, we present acomprehensive description on the circulation pattern in this area and its related dynamics. Finally, we
present some outlooks on future works.

1.1.2 Bohai Sea and Yellow Sea circulation
Seasonal variation of circulation is prominent in the Bohai Sea and YS (Figs. 1.1.2, 1.1.3, and 1.1.4).
Winter circulation is controlled mostly by topography and northerly wind bursts with northward flowin the middle and southward flow along both coastal boundaries of the basin. In summer, circulation isgoverned by topography, tidal mixing, and stratification with three-layer structure (Naimie et al.,
2001; Xia et al., 2006; Lin et al., 2011). A basin-scale cyclonic gyre is formed in the upper layer of the
YS.

Fig. 1.1.2 Schematic pattern of circulation in the Bohai Sea, the YS and the ECS during (a) winter and(b) summer. JWC, YSWC, CCC, WKCC, and CDWF stand for Jeju Warm Current, Yellow Sea Warm
Current, Chinese Coastal Current, and West Korea Coastal Current, Changjiang Diluted Water Flow,
respectively. Modified from Park et al. (2017).

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Physical Oceanography

Circulation

Seasonal circulation
Winter circulation
Northerly and northwesterly winds are dominant in winter (Hsueh, 1988; Teague and Jacobs, 2000;
Lie and Cho, 2016). Northerly winds induce an anti-cyclonic gyre in the eastern half of the YS with
the northward Yellow Sea Warm Current (YSWC) along the western side of the Yellow Sea Trough,the southward West Korea Coastal Current (WKCC) at the eastern part of the YS (Fig. 1.1.3a), and acyclonic gyre in the western YS with the northward YSWC along the western side of Yellow Sea
Trough and the southwestward and southeastward Chinese Coastal Current (CCC) over the westernpart of the YS (Fig. 1.1.2) (Le et al., 1993; Takahashi et al., 1995; Naimie et al., 2001; Ma et al.,2006; Lin and Yang, 2011).

(a)

(b)

(c)

(d)

Fig. 1.1.3 Vertically (a) integrated circulation, (b) near surface current (z = 0.5 m), and (c) near bottom
current (z = 0.5 m above bottom) in January from the three-dimensional simulation of Naimie et al. (2001).
Reproduced with permission of Elsevier. (d) Mean SST (°C) in winter (DJF) from Advanced Very High
Resolution Radiometer (AVHRR) observations from 1985 to 2009. Blue solid circles denote the axis of awarm water tongue as the pathway of the YSWC. Black lines are the bathymetry with the dashed lines
representing 2.5-m intervals between 50 and 70 m. After Lin et al. (2011). Reproduced with permission ofWiley & Sons.

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Physical Oceanography

Although the interaction of topography, horizontal density gradient, and tidal mixing (tidal
rectification) can induce a weak YSWC without wind forcing (Lee and Beardsley, 1999), northerly
wind bursts accompanied by the propagating coastal shelf waves on the YSWC is the largest force
influencing the northward current (Hsueh et al., 1986; Takahashi et al., 1995; Naimie et al., 2001;
Ma et al., 2006; Qu et al., 2018). The YSWC is separated intermittently from only a portion of the
Jeju Warm Current (JWC) to the west of Jeju Island during the northerly winds bursts. The JWC is
the mean current flowing clockwise around Jeju Island in all seasons. The YSWC extension enters
the Bohai Sea along the northern side of Bohai Strait and separates to form an anti-cyclonic gyre in
the east and a cyclonic gyre in the west (Guan, 1994; Bian et al., 2016). Low salinity water flows
out to the YS along the southern side of Bohai Strait.

(b)

(a)

(c)

Fig. 1.1.4 Vertically (a) integrated circulation, (b) near surface current (z = 0.5 m), and (c) near bottom
current (z = 0.5 m above bottom) in July from the three-dimensional simulation of Naimie et al. (2001).

Summer circulation
In summer, the water column of the YS is highly stratified offshore where the bottom depth (h) is
deeper than about 60 m due to high solar radiation, while the water column remains relatively well
mixed in shallow coastal regions (h < 40 m) due to strong tidal mixing. The tidal front andalong-front jet form in the transition zone with depths between 30 and 60 m as a result of tidal
rectification due to the interaction between tidal mixing and the stratified ocean (Lee and Beardsley,
1999; Naimie et al., 2001; Liu et al., 2003; Kwon et al., 2011; Yuan et al., 2013). The horizontal

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Circulation

circulation in the summer YS has a three-layer structure: in the surface layer (0–4 m), the prevailingcurrent direction is northeastward; in the upper layer (4–40 m) it is dominated by a basin-scale
cyclonic gyre; and in the bottom layer (below 40 m) the water diverges from the center area andthere exists a weak southward current along the Yellow Sea Trough (Xia et al., 2006). Tidal mixing
plays a dominant role in the formation of upwelling, and the relative importance of tidal mixing andwind forcing for upwelling has been further examined in numerical experiments. The southerly
wind enhances the upwelling off the western coasts, but its overall inﬂuence on the whole YS
upwelling is less important than that of the tidal mixing (Lü et al., 2010). The along-front jet
produces a large, basin-wide cyclonic surface circulation along the 40- to 50-m isobaths in both thewestern and eastern YS, i.e., the CCC and WKCC, respectively (Fig. 1.1.2b).

Yellow Sea Warm Current
Average winter currents are northward on the western side of the Yellow Sea Trough (Isobe, 2008)(Figs. 1.1.2a, 1.1.3a, and 1.1.5). However, the YSWC is not a steady current but rather anintermittent (or episodic) current related to northerly wind bursts over the YS in winter (Hsueh,
1988; Ma et al., 2006; Isobe, 2008). The northerly wind fluctuates with a synoptic time scale (3–7days) over the YS in winter (Hsueh, 1988; Teague and Jacobs, 2000) with bursts that produce apressure gradient force to the north near the bottom of the ocean. As the northerly wind relaxes, thenorthward flowing YSWC gains strength along the Yellow Sea Trough, reaching a peak innorthward speed 1 to 2 days later (Figs. 1.1.6 and 1.1.7). The YSWC has been thought to form at theend of October and remain in place until early March. During the weak northerly wind periods
between two northerly wind bursts, a southward current flow appears at the upper and middle levels.
Hence, the current does not continually flow toward the north throughout the winter in the middle of
the Yellow Sea Trough.

Fig. 1.1.5 Time-averaged currents at mooring stations M2 (122°15.06'E, 34°40.35'N), M4 (122°45.48'E,
34°40.21'N), and M5 (123°00.49'E, 34°40.01'N) with the background temperature (°C) along 34°40'N.
Barotropic currents at M2, M4 and M5 are 2.40, 2.54, and 7.46 cm s–1, respectively. After Lin et al. (2011).
Reproduced with permission of Wiley & Sons.

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Physical Oceanography

Fig. 1.1.6 Time series plot of the northward component of the mid-depth current (solid line in bottom
panel) observed at the solid circle in the Yellow Sea in the upper panel from January to March 1986 (plotreproduced from Figure 6 in Hsueh, 1988). Broken line in the bottom panel is the northward component of
the reanalysis wind at the cross in the upper panel. Grey bars are depicted to emphasize a correlation
between southward winds and northward currents. Broken lines in upper panel indicate depth contours inmeters. After Isobe (2008). Reproduced with permission of Springer Nature.

Fig. 1.1.7 The time series of observed daily wind in 2006–2007 winter in north Yellow Sea (averaged in
the box from 34°N to 38°N, 122°E to 124°E) from (a) QuikSCAT data and near bottom residual current at
stations (b) M2, (c) M4, (d) M5, and (e) M6. After Lin et al. (2011). Reproduced with permission of Wiley& Sons.

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Direct current measurements
Hsueh (1988) found that the current was northward at the middle depth (38 m) and near the bottom(74 m) based on current meter mooring at the northern Yellow Sea Trough (124°E, 37°N) andbottom currents at 70 and 95 m depths were also northward based on a current meter positioned at
the eastern side of Yellow Sea Trough (124.75°E, 35.25°N) in winter of 1986. Winds were mostly
northerly from January to early March 1986. Teague and Jacobs (2000) monitored the YSWC using
an acoustic Doppler current profiler (ADCP) in the middle of the Yellow Sea Trough (124.033°E,
35.967°N) from July 1995 to January 1996. Observations indicated that there was southward flownear the surface (14 m), northward and northeastward flow at the middle depth (44 m), andnorthward flow near the bottom (72 m) from the beginning of November 1 to January 15 inresponse to northerly wind pulses. From April 1996 to May 1998, Lie et al. (2001) conducted six
Korea–China joint hydrographic surveys and four satellite-tracked drifter experiments in the YS.
They analyzed the observational data and concluded that the SWC is not a persistent mean current
and that Jeju Warm Current Water (JWCW) may be transported to the southwestern YS in winter by
the intermittently generated northwestward currents on the western slope of the Yellow Sea Trough.
Because the YSWC was suggested to flow along the western slope of the Yellow Sea Trough basedon hydrographic observation (Lie et al., 2001; Tang et al., 2001) and a numerical model (Takahashi
et al., 1995), five ADCPs were deployed on the western side of the central Yellow Sea Trough from
December 20, 2006 to January 23, 2007 (Yu et al., 2009; Lin et al., 2011). The YSWC was found toflow northwestward near the bottom with a speed of 4 to 10 cm s–1 on the western side of the Yellow
Sea Trough (Fig. 1.1.5). The core of the YSWC was suggested to be near the bottom. The monthly
mean flow of the current is northward from the surface to bottom on the western side of the Yellow
Sea Trough with intensification following strong northerly wind bursts. The lag of the YSWC
response to the northerly wind bursts was about 1 to 2 days (Lin et al., 2011). The YSWC was
induced mainly by the barotropic current (Hsueh, 1988; Lin et al., 2011).

Hydrographic evidence
The YSWC had been thought to be present year-round with the main path lying along the central
part of the Yellow Sea Trough (Nitani, 1972). However, the YSWC is prominent along the western
slope of the Yellow Sea Trough only in winter (Zang et al., 2003). Hydrographic data in the 1980s(Lie, 1986; Park, 1986) did not show any evidence of the northward intrusion of saline water into
the eastern YS in summer (Isobe, 2008). Mean surface salinity distribution from November to
February showed the location of the main path of the YSWC along the western slope of the Yellow
Sea Trough (Isobe, 2008). Vertical temperature and salinity profiles across zonal sections along
34°N, 35°N, and 36°N (Lie et al., 2009; Lin et al., 2011) revealed that a warm and saline core exits
at the western side of Yellow Sea Trough. Climatological mean SST data and SST snapshots inwinter from satellite observation also indicate the main path of the YSWC to be along the western
slope of the Yellow Sea Trough (Lie et al., 2001; Lie et al., 2009; Yu et al., 2009; Lin et al., 2011;
Wang et al., 2012; Park et al., 2017).

Theory and numerical modeling
Based on an early simple analytical model, a wind-induced pressure gradient in the lower layer
along the Yellow Sea Trough was proposed to generate the YSWC (Park, 1986; Seung et al., 2012).
When northwesterly winds blow over the YS in winter, the pressure gradient force dominates over
the wind stress in deep waters along the Yellow Sea Trough where the local depth exceeds a criticaldepth (53 m), producing upwind northward flow in the lower layer along the Yellow Sea Trough.

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The YSWC has been known to be accompanied by coastal shelf waves induced by northerly windbursts in winter (Hsueh and Pang, 1989; Takahashi et al., 1995). In a two-dimensional numericalexperiment, onset of a northerly wind in winter produced anti-cyclonic circulation in the eastern partof the YS and cyclonic circulation in the western part of the YS. However, only the anti-cyclonic
circulation can be amplified with the northerly wind, which produces steady anti-cyclonic
circulation in the YS, reaching equilibrium in about 2 days (Takahashi et al., 1995).
Three-dimensional modeling of wind-driven upwind flow in the YS showed that surface water was
moved southward out of the Bohai Sea by northerly wind bursts, which decreased the sea level in
the north relative to the sea level in the south (Riedlinger and Jacobs, 2000). The northward pressure
gradient force induces a bottom flow to the north in the opposite direction of the northerly wind.
This return flow is concentrated in the Yellow Sea Trough. Lin and Yang (2011) suggested that theYSWC was generated by trapped topographic waves in response to the northwesterly wind. Bottom
friction was considered to be important for arresting the waves based on barotropic numericalmodeling experiments. Three-dimensional simulation of the YS winter circulation with tidal forcing
performed to examine the evolution of the YSWC showed that flow is northward at depths lower
than 30 m along the western slope of the Yellow Sea Trough (Tak et al., 2016). Currents and winds
varied on timescales of 12 and 20 days. One day after the northerly wind burst, an upwind current in
the subsurface layer appeared in the center of the Yellow Sea Trough, while the downwind flow inthe surface layer decreased significantly in the three-dimensional model. Two days later, the upwind
flow in the subsurface layer shifted west of the Yellow Sea Trough due to cyclonic propagation ofcoastal shelf waves along the coast from the Korean coast to Chinese coast (Tak et al., 2016; Qu etal., 2018).

Jeju Warm Current
The JWC is the mean current that flows clockwise around Jeju Island and delivers warm and saline
water (Jeju Warm Current Water; JWCW) around Jeju Island in all seasons (Chang et al., 1995; Lieet al., 1998, 2000; Pang et al., 2003). The YSWC is separated sporadically from only a portion of
the JWC to the west of Jeju Island (124–125°E, 33.5–34°N) during strong northwesterly events inwinter (Zang et al., 2003; Lie and Cho, 2016). Origins of the warm and saline water are not yet
clearly known, but there are two possible origins. The first origin is the Kuroshio water which flows
onto the shelf across isobaths and mixes with shelf waters (Nitani, 1972; Zang et al., 2003; Park etal., 2017); the other is a continuation of the Taiwan Strait Current mixing with shelf waters in the
ECS (Lie et al., 2000).

Direct observation
Transport was estimated from current meters and ADCP measurements (Chang et al., 1995, 2000).
Transport of the JWC reaches a maximum in summer and fall and a minimum in winter and spring.
The strong core of the JWC moves away from Jeju Island to the middle of the strait during thesummer and fall. In the summer and fall, the volume transport of the JWC is 0.51 to 0.66 Sv with amaximum speed of 30.0 cm s–1 in the middle of the Jeju Strait (Chang et al., 2000). In winter andspring, the volume transport of the JWC is 0.37 to 0.45 Sv and the maximum speed was 17.0 cm s–1
near Jeju Island.
Several drifters were deployed south of Jeju Island (126.3°E, 32.4°N and 126.1°E, 32.8°N) from1991 to 1998. They floated clockwise around the west coast of Jeju Island and passed through the
Jeju Strait, confirming the pathway of the JWC (Lie et al., 2000). The current speed was about 5 to
40 cm s–1 west of Jeju Island and in the Jeju Strait.

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A current meter mooring was deployed just north of the strong frontal region (125.5°E, 34°N;
northwest of Jeju Island), and the current velocity was measured from February to April 1986. From
current meter observations, a relatively strong residual current with a speed of about 10 cm s-1 and a
direction varying from northeast to southeast was identified, which indicates eastward flow of thecurrent (Zang et al., 2003).

Hydrographic evidence
The JWC is a year-round current with seasonal variation (Lie et al., 2000). In winter, JWCW withrelatively high salinity expands to the west and is bounded by a strong thermohaline front in thenorth. It reaches the southern mouth of the Yellow Sea Trough. In summer and autumn, the JWCW
retreats to near Jeju Island and appears only in the lower layer. JWCW is affected by fresher waterflowing eastward from the ECS in summer. The JWCW is defined as water with salinity greaterthan 34.0 and the modified JWCW as water with salinity of 33.5 to 34.0 (Lie et al., 2000).

Chinese coastal current
The coastal current flows year-round along the 40- to 50-m isobaths from the western side of theBohai Strait to the mouth of the Changjiang River in the western YS (Su, 1998; Zang et al., 2003).
It is also called the Yellow Sea Coastal Current (Su, 1998).

Direct current measurements
In winter, drift bottles and drift cards released along the Chinese coast showed that coastal currents
flowed to the south along the 40- to 50-m isobaths in the western YS (Zhang et al., 1987). Most of
the drift bottles and drift cards released at 35°N in May and June moved north, but those released
along the slope of Changjiang Bank with bottom depths of 40- to 50-m moved to the south. Driftbottles and drift cards released at 36°N to 37°N in August moved to the south and arrived along theeast coast of China from 36°N to 32°N. There were a comparatively large number of drifters
deployed in summer, and the mean trajectory of the drifters revealed the summer circulation patterns
in the YS (Lie and Cho, 2016). In summer, coastal currents are southeastward from 38°N to 37°N
along the Chinese coast, flowing southwest from 37°N to 35°N and south from 35°N to 33°N along
the 40- to 50-m isobaths (Beardsley et al., 1992; Lie and Cho, 2016). Over the Changjiang Bank,
the surface current flows to the northeast toward Jeju Island while the subsurface (30–50 m) current
flows east or southeast on the slope of the Changjiang Bank (Beardsley et al., 1992; Lie and Cho,2016; Fig. 1.1.8b).

Theory and numerical modelingIn the deep region (h > 30 m) of the western YS, the water column is well mixed or verticallyhomogenous in winter. Tide residual currents flow to the southeast along the slope of the
Changjiang Bank (40–50 m isobaths) and east from the Changjiang River with a speed of about
5 cm s–1 due to tide and topography interactions (Lee and Beardsley, 1999; Naimie et al., 2001).
There are few direct measurements of surface currents in the coastal regions (h < 40 m) of thewestern YS (120–122°E, 34–36°N) in winter and summer. In summer, both direct observation andnumerical simulations indicate northwestward coastal currents in the shallow region (h < 40 m)(Fig. 1.1.4). Numerical models with realistic atmospheric forcing, topography, and coastline inputs
identify a northwestward, along-coast surface current (32–35°N) in the western shallow part (h <40 m) of the YS (Naimie et al., 2001; Kwon et al., 2011; Lee et al., 2011; Qiao et al., 2011). A

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surface drifter also indicated a northward coastal current in summer along the 20- to 30-m isobaths(Yuan et al., 2013). The water column is stratified in the deep ocean (h > 60 m) in summer, and tide
residual currents increase along the slope of the Changjiang Bank due to a southeastward jet along
the tidal mixing front, which is generated by interactions between density stratification and tidal
mixing (Lee and Beardsley, 1999; Naimie et al., 2001).

West Korea Coastal Current
The West Korea Coastal Current (WKCC) flows southward along the west coast of Korea in winterand northward in summer (Le et al., 1993; Guan, 1994; Park et al., 2017). It is also identified as the
Korean Coastal Current (KCC) (Kwon et al., 2011; Park et al., 2017).

Direct current measurements
During winter, southward downwind currents have been observed, using a current meter mooringand satellite-tracked drifters in the eastern YS. Lie (1999) observed a residual current flowing
mostly to the southwest with a speed of 7.6 and 6.1 cm s–1 at depths of 20 and 45 m, respectively, inthe eastern YS (125.5°E, 36°N) in March 1984. Hsueh (1988) also observed a bottom (64 m) current
flowing southward at 125.6°E, 36°N from January to April 1986. In the southeastern YS (125.5°E,
34.6°N), the residual current was toward the southeast for more than 40 days from March to April
1984 at depths of 20 and 45 m with a speed of about 3 and 2.6 cm s–1, respectively (Lie, 1999).
A satellite-tracked drifter (No. 9605) deployed at the southeastern corner of the YS (125.22°E,
35.05°N) in April 1996 moved southward to 124.77°E, 34.10°N with a speed of 4 to 9 cm s–1 (Tanget al., 2001; Zang et al., 2003). Another satellite-tracked surface drifter (No. 27276) was deployed at
a depth of 15 m in the coastal region in the southeastern corner of YS (125.51°E, 35.03°N) on
February 21, 1997, and it moved southwestward for 18 days with mean speed of 4 cm s–1 (Lie et al.,2001). Zang et al. (2003) claimed that the WKCC flows toward the south approximately along the
40- to 50-m isobaths on the eastern side of the YS, turns to the east near 34°N, and enters the Jeju
Strait along the northern edge of the thermohaline front. Subsurface currents were observed at thesoutheastern corner of the YS (124.1°E, 34.0°N) from a current meter mooring deployed from April
7 to 13, 1996 (Tang et al., 2001; Zang et al., 2003). The residual current at a depth of 40 m flowed
to the northeast in the first two days and turned to the east from the third day of observation. The
residual current at a depth of 80 m flowed to the northeast at the beginning of observations but
quickly turned to the southeast after 1 day and where it remained (Zang et al., 2003).
In summer, a boundary current was observed along the tidal front in the southeastern corner of theYS (125.24°E, 35.22°N) from June 22, to August 7, 1983 with a current speed of about 9 to 10 cm
s–1 along the front (Lie, 1986). Beardsley et al. (1992) identified the northward flow along theeastern boundary of YS from the trajectories of satellite-tracked drifting buoys (Fig. 1.1.8a). The
Korea Hydrographic and Oceanic Administration (KHOA) observed northward or northwestward
surface currents at 8 current meters deployed along the eastern boundary of the YS near the surface(5 m depth) in July and August 2007 (Fig. 1.1.8b) with a speed of about 10 cm s–1 (Kwon et al.,2011).

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(a)

(b)

Fig. 1.1.8 (a) West Korea Coastal Current (WKCC) flowing northward or northwestward along the west
coast of Korea at 5 m depth in summer. Solid vectors are observed by current meters in July and August
2007 and thin vectors are simulated mean currents in July by a three-dimensional model (Kwon et al.,2011). (b) Trajectories of near-surface drogue drifters tracked by satellite from July 1986 in the Yellow Sea
and the East China Sea. Numbers indicate the drifter identification number. After Beardsley et al. (1992)
and Lee and Beardsley (1999).

Hydrographic evidence
From winter to early spring, the WKCC transports coastal water toward the south along the 40- to
50-m isobaths in the eastern YS, and its velocity increases from north to south (Zang et al., 2003).
The current turns toward the east or southeast near 34°N and enters into the Jeju Strait along thenorthern edge of thermohaline front.
In summer, cyclonic circulation forms in the upper layer of the YS along the tidal front which is
generated by tidal mixing and vertical stratification due to solar radiation (Lie, 1986; Seung et al.,1990). However, the eastern boundary current, i.e., the WKCC in the YS is not a northward
continuation of the saline JWC in summer (Lie, 1986).

Theory and numerical modelingAfter relaxation of northerly winds during winter in the two-dimensional model simulation for theYS, coastally trapped waves travel counterclockwise around the YS and northward flow is enhanced
along the Yellow Sea Trough (Hsueh et al., 1986). In a two-dimensional model with multi-year
mean winter wind forcing and realistic bottom topography, a cyclonic gyre and an anti-cyclonicgyre were generated in the western and the eastern YS, respectively (Le et al., 1993). The basic
features of the simulated circulation were northward flow along the western side of the Yellow Sea
Trough and southward flows along the Chinese and Korean coasts. The southward coastal current
off the west coast of Korea was the strongest in the model simulation. Trajectories of Lagrangianparticles estimated from the simulated velocity demonstrated northward displacement of particles
along the Yellow Sea Trough, which represents the YSWC, and southward drifting of particles along
the eastern side of the Yellow Sea Trough, which represents the WKCC (Hsueh and Yuan, 1997;
Riedlinger and Jacobs, 2000; Naimie et al., 2001).

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Seung (1987) applied the geostrophic adjustment theory to the tidal front along the eastern boundaryof the YS in summer and estimated the northward transport of the WKCC at 0.01 Sv, which is oneorder of magnitude smaller than 0.27 Sv estimated by the three-dimensional model of Kwon et al.
(2011). Estimation of the YS surface circulation in summer by a diagnostic model using long-term
temperature and salinity data (Yanagi and Takahashi, 1993) identified cyclonic circulation in thesurface and middle layers. Satellite altimetry data and numerical modeling also confirmed thecyclonic surface circulation (Yanagi et al., 1997). The Coriolis force is balanced against the pressure
gradient across the front, which produces the WKCC flow northward along the tidal front (Naimie
et al., 2001; Xia et al., 2006). Tidal stirring and surface wind mixing were found to be major factorsthat control the summertime boundary currents along the bottom tidal front (Kwon et al., 2011).
Tidal stirring was essential to generating the bottom temperature front and boundary current. Wind
mixing made the boundary current wider and augmented its northward transport.

Bohai Sea coastal currents
The YSWC extension provides saline water to the Bohai Sea along the northern part of Bohai Strait
in winter. When the YSWC extension reaches the northwestern coast of the Bohai Sea, it separates
into a coastal current flowing westward along the west coast of Bohai Bay and a coastal current
flowing northeastward along the west coast of Liaodong Bay (Fig. 1.1.9). In winter, circulation is
generally cyclonic in Bohai Bay and anti-cyclonic in Liaodong Bay (Guan, 1994; Bian et al., 2016).
In summer, it is known that an anti-cyclonic circulation forms in Liaodong Bay. However, recent
observation and numerical model simulation indicate more complicated circulation in the Bohai Sea(Hainbucher et al., 2004; Xu et al., 2006; Xia et al., 2019).

Liaodong
Bay

Bohai
Bay

Fig. 1.1.9 General circulation in the Bohai Sea in (a) winter and (b) summer. After Guan (1994) and Bianet al. (2016).

1.1.3 East China Sea circulation
As with most ocean current systems, those in the ECS are mainly constrained by bathymetry andgenerally flow along isobaths. However, the currents flowing perpendicular to the isobaths also play
an important role in the exchange of water and materials between the shelf seas and the Pacific
Ocean. The vertical currents are weaker than horizontal ones by at least one order of magnitude but
are significant for primary production because the upward currents can carry large amounts ofnutrients from the lower to upper layers in the ocean. Schematics of the circulation pattern for the
ECS are shown in Figure 1.1.10. The along-isobath currents out of the shelf are the northeastward

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(f)

(g)

Fig. 1.1.10 Schematic circulation patterns of the Kuroshio and Kuroshio branch currents reproduced
from Lie and Cho (2016) (upper panels) showing reproductions from (a) Nitani (1972), (b) Beardsley et al.
(1985), (c) Guan (1994) for winter and summer; (d) Lie and Cho (2002), and (e) Kondo (1985) and
Ichikawa and Beardsley (2002) and seasonal surface circulation in (f) winter (November–April) and (g)summer (May–October) in the YS and the ECS given by Lie and Cho (2016). Reproduced with permissionof Elsevier, Springer Nature, and Wiley & Sons.

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Kuroshio and the southwestward countercurrent between the Kuroshio and the Ryukyu Islands. The
Kuroshio splits into two branches across the shelf break (Fig. 1.1.10e): the Kuroshio Branch Current
north of Taiwan (KBCNT) and the Kuroshio Branch Current west of Kyushu (KBCWK). In the
middle shelf of the ECS, the Taiwan Warm Current flows northeastward mainly along the isobaths
with different origins in summer and winter. The Taiwan Strait Current is strong in summer but
barely existent in winter. The Min-Zhe Coastal Current and Changjiang Diluted Water are located in
the inner shelf, and both flow in totally different directions in summer and winter. Upwelling in the
ECS mainly occurs northeast of Taiwan, along the southern coast of China, and off the ChangjiangRiver Estuary.

Currents along isobaths
The Kuroshio, Taiwan Strait Current, Taiwan Warm Current, and Min-Zhe Coastal Current are
constrained by bottom topography and flow generally along isobaths in the East China Sea.

Kuroshio
The Kuroshio is a strong western boundary current in the North Pacific Ocean. It originates from theNorth Equatorial Current and is characterized by high temperature and salinity. The Kuroshio enters
the ECS from east of Taiwan, flows northeastward along the shelf break, and exits the ECS throughthe Tokara Strait. The observations on the Kuroshio in the ECS were concentrated east of Taiwan,
the Tokara Strait and a section between them (PN line, Fig. 1.1.1).
The Kuroshio east of Taiwan has an impact of great significance to the downstream area. The
Kuroshio maximum velocity in this area ranges from 0.7 to 1.4 m s–1 based on nine ship-based
surveys conducted from 2012 to 2014 (Jan et al., 2015). The width of the Kuroshio is about 100 to
150 km (Liang et al., 2003; Hsin et al., 2008), and the thickness varies from 400 to 600 m (Jan et al.,2015). Based on observations from the moored current meter array PCM-1 in the World Ocean
Circulation Experiment from 1994 to 1996, Johns et al. (2001) estimated the volume transport of theKuroshio east of Taiwan to be 21.5 ± 2.5 Sv. This transport can be determined by the sea level
difference between Taiwan and the southern Ryukyu Islands (Johns et al., 2001; Chang and Oey,2011). Its variation has a dominant period of 100 days, which is related to the arrival of mesoscaleeddies (Zhang et al., 2001). Chang and Oey (2011) demonstrated that the Kuroshio volumetransport east of Taiwan is strong in fall and weak in spring, associated with the seasonal eddy
activity of the Subtropical Counter Current. However, the Kuroshio volume transport shows adifferent seasonal pattern that is strong in summer and weak in winter based on other observations(Tang and Yang, 1993; Chuang and Liang, 1994; Hsin et al., 2011).
The Kuroshio across the PN line is often considered as an indicator of the Kuroshio in the ECS.
Combining 23 months of inverted echo sounders and ADCP measurements, Andres et al. (2008)
showed that the northeastward Kuroshio at the PN line was focused near the continental shelf andthe maximum surface velocity is located 30 km offshore from the shelf break (Fig. 1.1.11). A
dual-core and even multi-core distribution of velocities was separately observed at the surface and210 dbar (Chen et al., 2009). Based on routine hydrographic measurements along the PN line
conducted by the Japan Meteorological Agency since 1972, the mean transport from 1973 to 2000
was 25.8 Sv with a maximum of 27.0 Sv in summer and a minimum of 23.9 Sv in autumn (Ichikawa
and Beardsley, 2002). Wei et al. (2013) extended this analysis to a period from 1955 to 2010 andreported an interannual variability of 2 to 5 years with an amplitude of 2.8 Sv. A model study by
Kagimoto and Yamagata (1997) showed that transport across the PN line is strong in summer andweak in winter.

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Fig. 1.1.11 (a) Mean and (b) standard deviation of Kuroshio velocity at the PN line as shown in Figure
1.1.1. Reproduced from Andres et al. (2008).

The Kuroshio in the Tokara Strait has little influence on the ECS but its current structure andvolume transport are important to the Kuroshio south of Japan. Feng et al. (2000) analyzed four
years of moored current meter data and reported a multicore structure in the Kuroshio in the Tokara
Strait. The volume transport of the Kuroshio in the Tokara Strait is similar to that at the PN line
(Wei et al., 2013). Using high resolution (an interval of 2 days in time and 2 km in space) ferryboat
ADCP data from 2003 to 2011, Zhu et al. (2017) reported a mean volume transport of 23.03 Sv forthe Kuroshio in the Tokara Strait, with a maximum volume transport (24.60 Sv) in July and aminimum (21.47 Sv) in November.
In addition to current velocity and transport, the position of the Kuroshio is also an importantvariable. The surface Kuroshio axis is located around the continental slope where the depth is 200 to1000 m (Lie and Cho, 2016). The principal variations in the Kuroshio axis occur northeast of
Taiwan where the axis shifts onshore in winter and offshore in summer (Sun and Su, 1994). The
seasonal meridional migration of the Kuroshio axis southwest of Kyushu is also apparent (Sun andSu, 1994), which is explained by the joint effect of baroclinicity and bottom relief (Guo et al.,2003).
There are two southwestward currents within the Kuroshio region. One is the countercurrent
beneath the Kuroshio (Ito et al., 1995; James et al., 1999) and the other is the countercurrent
between the Kuroshio and the Ryukyu Islands (James et al., 1999; Oka and Kawabe, 2003; Hisaki
and Imadu, 2009).

Taiwan Strait Current
The Taiwan Strait connects the ECS to the South China Sea (SCS). The Taiwan Strait Current flows
through the Taiwan Strait and is a major source of the Taiwan Warm Current. Typically, there arethree currents in the Taiwan Strait (Hu et al., 2010): the Chinese Coastal Current (CCC) and theKuroshio branch are in the western and eastern Taiwan Strait, respectively, and the extension of the
SCS water located in the western and central Taiwan Strait. The Taiwan Strait Current is stable andflows northeastward in summer but is weak and variable in winter (Chen et al., 2016).
There are several causes for the Taiwan Strait Current being weak and variable in winter. The CCC
in winter flows southward along the Chinese coast and reaches the Taiwan Strait. Severe
northeasterly winds in the winter may reverse the direction of the Taiwan Strait Current (Zhang etal., 2009; Chen et al., 2016). Liang et al. (2003) tracked the northward Kuroshio branch along theeastern side of the Taiwan Strait in both winter and summer using shipboard ADCP data from 1991
to 2000. Chen and Sheu (2006) demonstrated by satellite and in situ data that the northward
Kuroshio branch and water from the SCS could reach only the southern Taiwan Strait in winter.

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The mean transport through the Taiwan Strait is nearly zero or even southward in winter (Teague etal., 2003; Jan et al., 2006; Isobe, 2008; Chen et al., 2016). Qiu et al. (2011) analyzed the trajectories
of about 100 satellite-tracked surface drifters from 1989 to 2007 and clarified that in winter the
majority of them in the Taiwan Strait eventually turned southward. Thus, the Taiwan Strait Current,
in regard to surface drifters, is not persistently northward in winter.

Taiwan Warm Current
The current off the Min-Zhe coast in the southern ECS has strong seasonality and is referred to as
the Taiwan Warm Current. In summer, the Taiwan Warm Current flows northeastward off the coast
at a velocity of about 20 to 40 cm s−1 (Katoh et al., 2000; Guan and Fang, 2006). Upon arriving at
28°N, it bifurcates into two branches: an onshore branch which flows northward and turns to thenortheast off the mouth of the Changjiang River and an offshore branch which flows along the
100-m isobath and then joins the western side of the Kuroshio (Su and Pan, 1987). The Kuroshio
surface water and the Taiwan Strait Current water constitute the surface water of the Taiwan Warm
Current in summer (Weng and Wang, 1984; Su et al., 1994).
In winter, the Taiwan Warm Current flows in an upwind direction and reaches the Changjiang River
Estuary (Lian et al., 2016). This phenomenon was often observed in snapshot surveys rather than inclimatological hydrographic datasets, suggesting its episodic character in winter (Zhu et al., 2004).
Taiwan Warm Current water originates mostly from the Kuroshio onshore intrusion northeast of
Taiwan based on hydrographic and nutrient data, rather than from the weak wintertime Taiwan Strait
Current (Liang and Su, 1994; Chen, 2003; Chen and Sheu, 2006). Lian et al. (2016) suggested that
the Taiwan Warm Current originates farther from the mixing of the SCS water and Kuroshio branchwater south of Taiwan (Fig. 1.1.12), which is supported by hydrographic observations and isotopic
evidence (Liu et al., 2000; Jan et al., 2006; Chen, 2009).

Fig. 1.1.12 (a) Schematic diagram of routine monitoring stations and (b) Temperature-Salinity diagramwith reference historical curve of water masses of the South China Sea (SCS, red line), Kuroshio origin(black line) and historical data (1985–2003) in the northern Taiwan Strait in winter (shaded patches). See
Lian et al. (2016) for more information on data distribution in (b).

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Coastal current
The coastal current in the ECS is mainly the Min-Zhe Coastal Current that flows along the Min-Zhe
coast southwestward in winter and northeastward in summer (Fig. 1.1.3). As the buoyant coastal
current from the Changjiang River, the Min-Zhe Coastal Current is characterized by low salinity,
low temperature, and high nutrient content (Lü et al., 2006; Qiao et al., 2006), and it plays animportant role in transporting terrigenous materials to the open ocean.
From observations covering only one or a few sites (Guan, 1978; Zhu et al., 2004; Zhang et al.,2011; Zeng et al., 2012), it is difficult to estimate the transport of the Min-Zhe Coastal Current,
which is modulated by strong tidal currents. Model results showed that the transport is 0.12 Sv andthat 80% of the freshwater from the Changjiang River is transported by this current (Li and Rong,2012). Based on a well-designed cruise survey, the subtidal characteristics of the Min-Zhe Coastal
Current were comprehensively quantified (Wu et al., 2013). The Min-Zhe Coastal Current was
confined within the 40-m isobath and had a maximum surface speed of ~50 cm s–1. The lower
salinity suggests the transport of Changjiang-diluted water. The Min-Zhe Coastal Current isestimated to have a volume transport of ~0.215 Sv, which is ten times larger than runoff of the
Changjiang River, and it transports over 90% of the freshwater from the Changjiang (Wu et al.,2013). Primarily buoyancy and local winds drive the Min-Zhe Coastal Current, and the former
accounts for ~0.128 Sv of the observed volume transport (Wu et al., 2013).

Circulation across isobaths
Kuroshio intrusion
The main stream of the Kuroshio flows northeastward along the shelf break while a portion escapesfrom the constraint of steep topography and intrudes onshore. Two main areas of intrusion are
northeast of Taiwan and southwest of Kyushu.
The Kuroshio northeast of Taiwan intrudes onto the shelf after leaving the island (Chuang and Liang,
1994; Tang et al., 1999). The water intrusion is named the Kuroshio Branch Current north of Taiwan(KBCNT) (Ichikawa and Beardsley, 2002), which can reach as far as 26.5°N according to composite
of trajectories of drifting buoys (Lie and Cho, 2016) and then turns right to the main stream due tobottom friction and the topographic beta effect (Qiu and Imasato, 1990; Su, 2001). The rest of the
intruded water northeast of Taiwan goes farther onto the ECS shelf along the second route (Liu et al.,2014). The nearshore Kuroshio branch shows gradual upwelling from the shelf break to thecontinental shelf off the Changjiang River Estuary in summer as detected from in situ salinity data
(Yang et al., 2011, 2012).
The Kuroshio onshore intrusion northeast of Taiwan is strong and extends farther onshore in winter(Tang et al., 2000; Hsin et al., 2013). Ichikawa et al. (2008) demonstrated that the Kuroshio mainstream is strong and moves offshore in summer. Wu et al. (2014) showed a Hovmöller diagram ofclimatological monthly surface geostrophic velocity flowing perpendicular to the cross-shelf section
northeast of Taiwan (Fig. 1.1.13). The western edge of the current indicates the variability of the
intrusion as it gradually migrates seaward after winter and reaches the easternmost place in Julywhen the Kuroshio main stream velocity reaches a peak (> 0.8 m s–1).

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Fig. 1.1.13 Hovmöller diagram of monthly climatological surface geostrophic velocity of the Kuroshio
derived from altimeter data in the northeast of Taiwan, 25–27°N. Reproduced from Wu et al. (2014).

From observations, Tang and Yang (1993) and Chuang and Liang (1994) found that the currents
northeast of Taiwan in winter were not well correlated with local wind conditions. Oey et al. (2010)
and Wu et al. (2014) proposed that, in winter, surface heat flux rather than local wind conditions is
responsible for the Kuroshio intrusion northeast of Taiwan. The importance of the role of surface
heat flux is consistent with dynamical analysis of model results indicating that the change in density
field is a primary factor in the seasonal variability of the Kuroshio onshore flux at a fixed location
along the shelf break (Guo et al., 2006).
When the Kuroshio main stream reaches southwest of Kyushu, it turns right to the east and exits the
ECS through the Tokara Strait. As a result of conservation of potential vorticity, another Kuroshio
Branch Current west of Kyushu (KBCWK) forms and intrudes over the outer shelf across the shelf
break (Hsueh et al., 1996; Lie et al., 1998; Isobe, 2000). The KBCWK is traceable by its highsalinity, and bifurcates into three branches in the outer shelf (Lie and Cho, 2002). It has smaller
seasonal variability than the KBCNT (Lie and Cho, 2016).
In addition to the onshore intrusions at the two locations discussed above, the Kuroshio onshore
intrusion across the entire shelf break of the ECS has received more attention recently. This onshore
intrusion has strong seasonal variation, showing a maximum in autumn and a minimum in summer(Teague et al., 2003; Guo et al., 2006; Isobe, 2008), and shows distinct vertical variation that can beseparated into the surface Ekman layer, bottom Ekman layer, and a middle layer between the two
Ekman layers (Zhou et al., 2015; Ding et al., 2016; Wang and Oey, 2016; Zhang et al., 2018).
Strong seasonal variation in the Kuroshio onshore transport across the entire shelf break is
associated with the monsoon and changes in the density field (Chao, 1990; Guo et al., 2006), as well
as volume transport through the Taiwan Strait and Kuroshio transport east of Taiwan (Lee andMatsuno, 2007).

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Changjiang Diluted Water
The Changjiang River accounts for most of the freshwater discharge into the ECS. A major coastal
water mass, the Changjiang Diluted Water (CDW), is found in the surface layer and has salinity lessthan 32 (Su and Weng, 1994).
The CDW flows offshore to the Kuroshio frontal area in spring (Chang and Isobe, 2003). Usually
the CDW is confined to a thin surface layer with a thickness of 10 to 15 m. It extends farther
offshore to the northeast over the western shelf of the northern ECS in summer (Lie et al., 2003; Huet al., 2016), enters the Jeju Strait from the west (Beardsley et al., 1992), and goes through thewestern channel of the Tsushima Strait in August (Chang and Isobe, 2003). With the appearance ofthe northeasterly monsoon in autumn, the CDW retreats to the western YS and remains close to theChinese coast in a narrow band. In winter, it reaches the Taiwan Strait against the Taiwan Warm
Current (Hu, 1994; Chang and Isobe, 2003). Low salinity shelf water was observed in the Kuroshio
subsurface layer (~100 m depth) around the shelf break of the ECS (Isobe et al., 2004), suggesting a
possible discharge of the CDW to the open ocean.
Southwesterly or southerly winds cause the northeastward extension of the CDW in summer, whilestrong northeasterly winds cause the southwestward extension to the Taiwan Strait in winter (Changand Isobe, 2003; Lie et al., 2003). The area of the CDW in summer over the continental shelf isassociated with the Changjiang River discharge (Chen et al., 2009; Tseng et al., 2011). When the
discharge in August is at a low level over years, its area is more strongly correlated with southwesterly
winds (Bai et al., 2014).
A tracer experiment was carried out to assess the relative importance of outlets of the CDW (Changand Isobe, 2003). About 68% of the Changjiang discharge likely passes through the Tsushima Strait,
which is supported by hydrographic and ADCP observations at the Tsushima Strait (Isobe et al.,2002), and about 27% is transported through the Taiwan Strait in autumn and winter. An interesting
influence of CDW is that it promotes sea surface warming (Park et al., 2011; Moon et al., 2019) thatfurther likely affects precipitation over a large area, including Japan (Manda et al., 2014).

Vertical circulation
Upwelling is the upward movement of water that usually brings nutrient-rich water to the sunlit
upper layer. Thus, the upwelling area is usually recognized as having a lower sea surface
temperature (SST) and a higher chlorophyll a concentration than the surrounding water.

Upwelling northeast of Taiwan
The cold eddy northeast of Taiwan is characterized by low temperature and high salinity (Tseng etal., 2012). It appears as two or three cold cores in summer and autumn, and has large seasonalvariations (Xiu et al., 2001). Observational data revealed that the upwelling below 100 m depth isstrongest in summer. However, upwelling at about 30 m below the surface is stronger in winter thanin summer (Chang et al., 2009). Model results showed the largest upwelling velocity to be on theorder of 5 × 10–6 m s–1, and the strong upwelling was related to the Kuroshio onshore intrusion
northeast of Taiwan (e.g., Lee and Chao, 2003; Xiu and Huang, 2006).
The cold eddy northeast of Taiwan has also been recognized as a cold dome over the shelf northeastof Taiwan (Jan et al., 2011), and was confirmed by some high resolution numerical models(Gopalakrishnan et al., 2013). The cold dome is recognized as having a diameter of approximately
100 km and being centered around 25.625°N, 122.125°E when taking the temperature at a depth of

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50 m as the reference. The monsoon-driven winter intrusion of the Kuroshio onshore promotes
upwelling and transports more subsurface water to the cold dome than does the summer monsoon
period. The cold dome is more easily detected at the sea surface in summer than in winter (Jan et al.,
2001; Gopalakrishnan et al., 2013).

Coastal upwelling
The coastal upwelling phenomenon is observed off the Changjiang River Estuary with a center at
about 31°30′N, 122°40′E and a spatial longitude–latitude scale of 1° × 1° (Zhao et al., 2001).
During cruises in August 2000, sections off the mouth of the Changjiang River showed atemperature distribution of 20 to 23°C and salinity of about 34, suggesting the presence of
upwelling (Zhu, 2003; Zhu et al., 2003; Lü et al., 2006). Lü et al. (2006) suggested that tidal mixing
plays a major role in inducing upwelling off the estuary in summer. Topography, the ChangjiangRiver discharge, the Taiwan Warm Current, and wind are less important factors.
Along the Min-Zhe coast upwelling appears in June, becomes strongest in July and August, andwanes in late September (Lou et al., 2011). The SST of 24° to 27°C in summer in the upwelling
region is about 2° to 4°C lower than in surrounding waters (Hu and Zhao, 2007; Lou et al., 2011). In
addition, upwelling signals were detected as a high concentration of nutrients and low concentrationof dissolved oxygen from observational data in January 1999, in which a doming structure wasfound (Qiao et al., 2006).
According to the classical Ekman transport theory, southwesterly winds prevail along the eastern
Min-Zhe coast in summer, causing offshore transport of water in the upper layer, and thus inducing
coastal upwelling along the coast. Therefore, wind-driven offshore Ekman transport has been
regarded as the crucial mechanism for coastal upwelling in the ECS in summer. However, numericalexperiments suggest tidal mixing plays a predominant role in inducing this upwelling. Strong tidal
mixing results in a considerable horizontal density gradient across the tidal front, and upwelling is
induced as a branch of the secondary circulation which is stimulated by the cross-frontal density
gradient. The influence of wind on upwelling is small. In fact, wind forcing exerts negative
influences on upwelling by weakening the encroachment of the Taiwan Warm Current onto thecontinental shelf, which is totally different from the classical Ekman pumping (Lü et al., 2006).
Although the upwelling is maintained in winter, and the presence of a density (or salinity) front
between the coastal water and the Taiwan Warm Current is the primary controlling factor
responsible for upwelling in winter (Qiao et al., 2006), the wind, the Taiwan Warm Current, and thetidal mixing influence the extension and intensity of the density front in winter.
Three kinds of low-frequency waves, the Kelvin wave mode, the first shelf wave mode, and thesecond shelf wave mode, were identified from five mooring array observations in the coastal waterof ECS in winter of 2007. The periods of the waves varied from 2 to 10 days. The remotely
generated free Kelvin wave is a major factor to cause the sea level variation, while the Kelvin wave
and the locally generated first shelf wave work together to control the downwind flow. The wave
energy increases toward the direction of wave propagating and decreases to the offshore (Yin et al.,2014).

1.1.4 Summary and future work
The Bohai Sea, the YS and the ECS are marginal seas on the continental shelf of the northwestern
Pacific Ocean. Currents in the YS have noticeable seasonal variation. In winter, northerly winds
induce an anti-cyclonic gyre in the eastern YS and a cyclonic gyre in the western YS, both of which

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are composed of the northward YSWC along the Yellow Sea Trough and the southward West Korea
Coastal Current in the east and the southward Chinese Coastal Current in the west. The YSWC
flows northwest near the bottom with a speed of 4 to 10 cm s–1 on the western side of the Yellow
Sea Trough. The YSWC extension enters the Bohai Sea along the northern side of the Bohai Strait
and forms an anti-cyclonic gyre in the east and a cyclonic gyre in the west of the Bohai Sea. The
Jeju Warm Current is the mean current that flows clockwise around Jeju Island and delivers warmand saline water. The relaxation of northerly wind bursts accompanied by propagating coastal shelfwaves sporadically induces the northward intrusion of the YSWC from the Jeju Warm Current to thewest of Jeju Island. In summer, horizontal circulation in the YS has a three-layer structure:northeastward current in the surface layer (0–4 m), a basin-scale cyclonic circulation in the upper
layer (4–40 m) with boundary currents along the tidal mixing fronts, and a weak southward flow
along the Yellow Sea Trough in the bottom layer. The West Korea Coastal Current flows northwardwith a speed of about 10 cm s–1 along the tidal mixing front in the eastern YS and the Chinese
Coastal Current flows to the south along the 40- to 50-m isobaths. Over the shallow (h < 40 m)
western shelf of the YS, the surface current flows northeastward due to southerly winds.
In the ECS, the Kuroshio main stream flows northeastward along the shelf break over thecontinental slope where the bottom depth is 200–1000 m and its mean transport is about 25.8 Sv. An
onshore intrusion of the Kuroshio occurs across the entire shelf break of the ECS and it is weaker insummer and stronger in autumn. Two main regions of the Kuroshio intrusion are to the northeast of
Taiwan and southwest of Kyushu. The Taiwan Strait Current flows northeastward through the
Taiwan Strait and is a major source of the Taiwan Warm Current. The Taiwan Warm Current flows
northeastward off the Min-Zhe coast at a velocity of about 20 to 40 cm s−1 in summer. The Min-Zhe
Coastal Current flows along the Min-Zhe coast northeastward in summer and southwestward in
winter. It is confined within the 40-m isobath and transports the CDW. The CDW flows offshore to
the Kuroshio frontal area in spring and to the northeast over the western shelf of the northern ECS
in summer. It remains close to the Chinese coast in a narrow band in autumn and reaches the Taiwan
Strait in winter. Strong upwelling northeast of Taiwan is related to the Kuroshio onshore intrusion.
Upwelling off the Changjiang River Estuary in summer is induced by tidal mixing. At the Min-Zhe
coast, subsurface water upwells due to a density gradient by a tidal mixing front in summer and adensity front in winter.
In this review, we summarized the general understanding on the circulations in the Bohai Sea, YS
and ECS from a view of steady state to seasonal variations with coverage of literature on
observations, modeling, and processes. We did not include studies on the interannual variations andeven longer-term variations of circulations in the YS and ECS although they are very important tothe community. One reason for this choice is the lack of such studies, which inhibit summarizationand generalizations on the subject. It is easy to imagine the difficulties to solve this issue. However,with the accumulation of observed data in past decades and more modeling work based on a newgeneration surface wave-tide-circulation coupled models in the future, we expect a rapid advance on
this topic. The collaboration of scientists from countries surrounding the YS and ECS under aninternational framework such as PICES is absolutely necessary to accomplish this goal.

Acknowledgements
The authors thank PICES for providing us a chance for this review. X. Guo thanks support from Ms.
Jing Zhang who helped him to finish the first draft of ECS part. X. Guo was supported by
Grants-in-Aid for Scientific Research (MEXT KAKENHI grant numbers: JP15H05821/26287116).
B.-J. Choi was supported by the Basic Science Research Program through the National Research
Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A1B03935246
and NRF-2016R1A6A1A03012647). F. Qiao was supported by the International Cooperation

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Project of the Indo-Pacific Ocean Environment Variation and Air–Sea Interaction under Grant
GASI-IPOVAI-05 and the Qingdao National Laboratory for Marine Science and Technology underGrant 2016ASKJ16.

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Tides and Waves

1.2 Tides and Waves
Dongfeng Xu1, SungHyun Nam2 and Hailun He1
1
2

Second Institute of Oceanography, Ministry of Natural Resources, China
Seoul National University, Korea

1.2.1 Introduction
When we are facing the ocean, the distinct tide exists everywhere. Egbert and Ray (2000) estimatedthat 1 TW (terawatt), representing 25 to 30% of the total tidal dissipation occurs in the deep ocean,generally near areas of rough topography. This tidal dissipation slows the Earth’s rotation (Lambeck,1975) and causes the moon to retreat from the Earth (Kvale et al., 1999). Besides the total tidal
dissipation, Munk and Wunsch (1998) estimated that the wind provides the other 1.2 TW. These two
powers maintain the global abyssal density distribution, which prevents the ocean from turning intoa stagnant pool of cold salty water (Munk and Wunsch, 1998).
When tides interact with steep topography, internal waves usually can be generated by theinteraction of the tidal current with the topography, which is similar to the phenominon of a Lee
wave when winds interact with a high mountain. Generation of oceanic internal waves (IWs) at
inertial to buoyancy frequencies often include those at tidal periods (hereafter referred as internal
tides, ITs) can be an important sink of the energy provided from winds and tides, i.e., sink ofbarotropic tidal energy or energy conversion from barotropic to baroclinic tides. The East China Sea(ECS) shelf slope is known to be one of the most important generation regions of ITs or surface
tidal energy sinks with one of the strongest semidiurnal (M2) ITs among the world’s shelf slopes
generated (Baines, 1982; Kuroda and Mitsudera, 1995; Niwa and Hibiya, 2004; Park et al., 2006).
In particular, 2nd-mode as well as more typical 1st-mode IT structures have been observed as
generated at the shelf break in the central ECS during ebb tide periods (Kuroda and Mitsudera, 1995;
Park et al., 2006). Since the speed of barotropic tidal currents are often comparable or higher thanthe phase speed of the 2nd-mode ITs on the shelf or the northern ECS, ebb tides are optimal for the
IT generation. Although the ECS ITs are poorly understood in spite of their importance inredistributing heat, energy, and materials, here we review generation and propagation of ITs in the
ECS, based on a few observations and numerical models.
Surface wave studies are important in scientific and engineering communities from three aspects.
First, surface waves change the roughness of the sea surface, participate in air–sea exchange, andthen play an important role at the atmospheric and oceanic boundary layers (Song, 2009; He andChen, 2011; Li et al., 2013; He and Song, 2014; Song et al., 2015; He et al., 2018b). Second, themean state and the extreme value of surface waves supply basic information for coastal and offshore
design and also ship route design in the marginal sea (He et al., 2009; Li and He, 2013). Third, theimpact of surface waves on geomorphological phenomena are significant, and include coastal
shoreline stability and longshore migration of sediment.

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1.2.2 External tides
The Yellow Sea and East China Sea (YECS) tides have been extensively studied in recent decades(Fang, 1986), especially under the development of numerical modeling technology and the launchof the TOPEX/Poseidon altimetry satellite (Yanagi et al., 1997). Here we review some new progressin the study of the YECS, based on new observations and numerical models.
Egbert and Erofeeva (2002) assimilated TOPEX/Poseidon altimetric data into the generalized
inverse (GI) modeling of barotropic ocean tides, which forms the basis for the Oregon State
University (OSU) Tidal Inversion Software (OTIS). Here we present the M2 Co-amplitude (red dash,in cm) and Co-phase lag line charts of the M2 constituent (Fig. 1.2.1a). This result is similar to thatof Fang (1986) which was inferred from observation and calculation (Fig. 1.2.1b). The M2
semi-diurnal tidal ellipse (Fig. 1.2.2) is similar to that derived from observation (Chen, 1992). The
new ocean color satellite sensor, Geostationary Ocean Color Imager (GOCI), can provide more tidal
information on the YECS. Hu et al. (2016) used the GOCI data to derive surface M2 tidal currents,
which are validated with a comprehensive set of 28 surface drifters and four mooring observations(Fig. 1.2.3).

(b)
(a)

Fig. 1.2.1 (a) Co-amplitude (red dash, in cm) and Co-phase lag line (blue solid, in degrees referred to120°E) charts of M2 constituent for the YECS derived from the TPXO-8 of Oregon State University (OSU)
high-resolution regional tidal model with a 1/30 degree resolution. (b) Distribution of M2 tidal amplitude H(in cm) and phase lag g (in degrees and referred to 120°E) in the YECS. From Fang (1986).

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Fig. 1.2.2 M2 semi-diurnal tidal ellipses for the YECS derived from the TPXO-8 of Oregon State
University (OSU) high-resolution regional tidal model with a 1/30 degree resolution (red : anti-clockwise,
blue: clockwise).

Fig. 1.2.3 Comparison of M2 semidiurnal tidal ellipses between satellite (blue) and in situ observations
from four moorings (gray) and 28 drifter locations (red). Arrows denote the tidal phase relative to GMT.
From Hu et al. (2016). Reproduced with permission of Wiley & Sons.

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The tide plays a significant role in mixing in the YECS area. With the help of a three-dimensional
numerical model, the patch-like structure of low-salinity water detached from the Chanjiang diluted
water (CDW) is demonstrated to be related with intense tide-induced vertical mixing during thespring tide (Moon et al., 2010). Yang et al. (2017) studied the tidal straining within two different
stations which have different stratification environments in the ECS through field observation. Theirresults showed that the tidal straining induces a semi-diurnal switching between stable and unstablestratification at both stations. Near-bottom high-frequency velocity measurements further reveal thatthe dissipation rate of turbulent kinetic energy (TKE) is highly elevated during periods when
unstable stratification occurs.

1.2.3 Surface waves
Measurement of surface waves comes from multiple platforms which include buoy, radar andsatellite. Among them, wave buoy is the most reliable measurement platform. However, the numberof wave buoys is very rare in the YECS (Ebuchi and Kawamura, 1994). Radar retrieving supplies
fine grid resolution but limited spatial coverage (Chen et al., 2017; Shao et al., 2017). Then, satellite(TOPEX/Poseidon) data show significant wave heights (Ebuchi and Kawamura, 1994; Chen et al.,2004), but the cross-track resolutions are too coarse to match the demand for a marginal sea(Fig. 1.2.4). Therefore, due to the insufficiency of observations, numerical models are widely usedin regional surface wave studies (Chen et al., 2013; He and Xu, 2016; He et al., 2018a). Benefitingfrom suitable parameterization of physical terms (or source terms; The Wamdi Group, 1988; Tolman
et al., 2002), state-of-the-art wave models (third-generation wave models) have relatively good
performance for regional scale (Cui et al., 2012; Chen et al., 2013; Xu et al., 2017). Furthermore,
wave models assimilate satellite wave height and provide further reanalysis data for surface waves(Dee et al., 2011; Wang et al., 2016).
Recently, the results of long-term (1988 to 2002) simulations of surface waves in the northern
Pacific, using a third-generation wave model, supplied several maps of surface waves in the YECS(He and Xu, 2016; He et al., 2018a). As far as a buoy station is concerned, the comparison validates
the model results, where the relative errors of wave height keep below 11%, and relative errors ofwave period are better than –22%. Similarly, when we compare satellite and model data, the highcorrelation reveals the consistency between the two datasets on monthly mean significant wave
heights (Hs) (Fig. 1.2.4; He et al., 2018a).
The climatological results emphasize the seasonal variations of surface waves (Fig. 1.2.5). In winterand autumn, Hs in the ECS is generally greater than 1.5 m, and can reach more than 2.0 m on theeast side of Taiwan Island and in the northwestern Pacific while in spring and summer, Hs is reallyweak and less than 1.5 m. The seasonal variances of Hs are closely related to the monsoon. In winter,
the generation of high waves in the southeast of the ECS is not only the result of powerful winds
from the north but also due to the large fetch length (fetch length is defined as the effect forcing
distance between a local area with the coast along the direction of wind forcing). In summer, Hs isrelative weak due to the low wind speed. On the other hand, the dependence of Hs on fetch of wind
forcing also reveals the influence of topography, where the Hs pattern is similar to the bathymetry.
As for mean wave period (Tm), it also decreases from southeast to northwest and from open ocean tothe coastal area. There are some seasonal variations on Tm, in which Tm in autumn is longer than inother seasons (He and Xu, 2016; He et al., 2018a).

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Fig. 1.2.4 (a) Satellite passes of TOPEX/Poseidon altimeter (TP) and (b) time–latitude Hovmöller plotof monthly mean significant wave height along pass 138 of TP in the Yellow Sea. (c–d) are the same with(a–b), but for the East China Sea. In (c), bathymetry contours (units: m, blue), and the buoy location of theJapan Meteorology Agency (B22001, inverted red triangle) are also presented. In (d), data sources include
TP, ERA-Interim reanalysis (ERA) and wave modeling (WW3). (a–b) are from He and Xu (2016), and (c–d) are from He et al. (2018a). Reproduced with permission of Springer Nature.

Extreme value like N-year return Hs is an important index in ocean engineering. In the YECS,
satellite and modeling underestimate the yearly maximum Hs, and therefore, the N-year return Hs.
For instance, at a buoy station, the 10-year return Hs from buoy, satellite and WW3 are 13.49, 8.56
and 7.95 m, respectively (He et al., 2018a). In essence, we uncover the patterns of extreme Hs (Fig.
1.2.6), where the extreme value near the Northwest Pacific is generally larger than that in the innerpart of the ECS, and there is an obvious peak around west of Ryukyu Island. The peak is as high as
10 m, with an area covering 3.0 zonal degrees and 2.0 meridional degrees. The peak is closely
related to the wind forcing; nonetheless, extreme values of wind forcing have more complex
patterns than the Hs field (He et al., 2018a). As for Yellow Sea (YS), there are two weak peakextreme Hs in southeast of the YS (near 126.0°E, 32.0°N) and central Bohai Sea, which are also
explainable by the extreme wind forcing (He and Xu, 2016).

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Fig. 1.2.5 Climatological monthly mean significant wave heights (units: m) from WW3 modeling in(a) Yellow Sea and (b) East China Sea. (a) is from He and Xu (2016), and (b) from He et al. (2018a).
Reproduced with permission of Springer Nature.

Fig. 1.2.6 N-year return significant wave heights (units: m) from WW3 modeling in (a) Yellow Sea and(b) East China Sea. (a) is from He and Xu (2016), and (b) from He et al. (2018a). Reproduced withpermission of Springer Nature.

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1.2.4 Internal tides
Distribution and energetics of the 1st-mode semidiurnal (M2) internal tides (IT) around the shelfedge in the YECS were investigated using a three-dimensional numerical model (Niwa and Hibiya
2004; Liu et al., 2019). In spite of only few in-situ or satellite remote sensing observations in alimited space and time, the observational results along with few modeling works suggest 1) multiple
sources and local generations of ITs with complex topographic features, 2) multi- directional
propagations of ITs with a wavelength of 15 to 40 km and an amplitude of O(10 m), and dominantmode-1 ITs with potential significance of mode-2 ITs. One of the modeling results demonstrates theeffective generation of M2 ITs over topographic features such as the subsurface ridges in the Luzon
and Tokara straits, the ridges along the Ryuku Island chain, and shelf slope in the YECS (Fig. 1.2.7)
where the M2 barotropic tides are strong and bottom slopes are steep enough to form conditions
favorable for IW generation. The M2 ITs propagating away from these multiple source regionsinterfere to form a complicated pattern which agrees well with the satellite altimeter observations(Liu et al., 2019). Niwa and Hibiya (2004) estimated roughly 10% of the energy conversion from
the M2 barotropic to baroclinic tides with a conversion rate of 35 GW although about half of the M2
IT energy is subject to local dissipation in close proximity to the generation sites.

(a)

(b)

Fig. 1.2.7 (a) Amplitude (color, in cm) and phase (contour, in degrees) of the calculated surface
elevation of the M2 surface tide. (b) Model-predicted distribution of the depth-integrated kinetic energy of
the M2 internal tide (IT) averaged over the final 2 days of the calculation (Niwa and Hibiya, 2004).
Reproduced with permission of Wiley & Sons.

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Fig. 1.2.8 (Top) Locations of instrument. NAHA coastal tide gauge (red square) and historicalhydrocasts where acoustic travel time to 700 m is shorter than (blue dots) and longer than (green dots)
0.924 s. Bathymetry is in meters. (Bottom) Correlation coefficients between the instrument-derived watertemperature at 120 m and those at other pressure levels with acoustic travel time to 700 m shorter than
0.924 s in the ECS (Park et al., 2006). Reproduced with permission of Wiley & Sons.

Structures of various (particularly 2nd-mode) ITs in the YECS have been confirmed fromobservations using an underwater sliding vehicle (Kuroda and Mitsudera, 1995) and an array ofpressure-sensor-equipped inverted echo sounders (Park et al., 2006). Kuroda and Mitsudera (1995)
presented and classified a variety of detailed structures of the lowest two modes of ITs with internal
Froude numbers as a ratio between the speed of barotropic tidal current and the IW phase speed. In
contrast to the dominance of 1st-mode ITs in the southern ECS, 2nd-mode ITs are most strongly
generated at the shelf break in the central YECS and propagate onto the shelf in the north (Kuroda
and Mitsudera, 1995). Park et al. (2006) also confirmed strong generation of 2nd-mode ITs in the
Okinawa Trough region of the YECS from 2-year-long time series measurements during 2002–2004
and historical hydrocasts observations (Fig. 1.2.8), contrasting with no significant 2nd-mode ITs(1st-mode IT dominance) southeast of the Ryuku Islands. Vertical profiles of the correlation
between the instrument-derived water temperature at 120 m and those at other pressure levels withacoustic travel time to 700 m shorter than 0.924 s in the Okinawa Trough region reflect thesignificance of 2nd-mode ITs (Fig. 1.2.8) contrasting with those in the southeast of the Ryuku
Islands.

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1.2.5 Nonlinear internal waves
The ITs at M2 and other frequencies, and presumably near-inertial waves, in the YECS subsequently
accompany shorter internal waves (IWs) at frequencies close to the buoyancy frequency as in otherseas. They are also known as nonlinear IWs (NLIWs) or internal solitary waves due to theirnonlinearity of different degrees, and have often been understood in the context of the Korteweg
deVries (KdV) type models since Benjamin (1966), such as extended KdV or eKdV (e.g., see Cho etal., 2016 and references cited therein). NLIWs have been observed ubiquitously in stratified oceansof shelves and marginal seas though their characteristics and underlying dynamics still remain
unclear in many regions, including the ECS, due to the lack of observations at required resolutions(in space and time) under a variety of background conditions. The NLIWs in the ECS areconsidered complex in perspectives of multiple (unknown) source regions for generation (Hsu et al.,2000), multiple directions of propagation from multiple sources and consequent significance of their(wave-wave) interactions (Hsu et al., 2000), relatively small amplitude of O(10 m) and slow
propagation speed of O(10–1 m s–1), and multi-layered stratification implying a significant role ofhigh-order-mode IWs (Liu et al., 2009). These have been reported from only a few studies andfewer observations from in-situ field experiments and satellite imagery such as synthetic apertureradar (SAR).
The NLIWs were observed in the northwestern ECS (blue ellipse labeled (i) in Fig. 1.2.9 Top (a)) in
1996 August and 1997 July (Hsu et al., 2000), and more recently southwestern ECS in 2007 August((iv) in Fig. 1.2.9 Top (a), Li et al., 2008) and northern ECS in August 2005, August 1998, andAugust 2008 ((ii), (iii), and (v) in Fig. 1.2.9 Top (a), Lee et al., 2006; Teixeira et al., 2006; Smith,
2010; Lozovatsky et al., 2015). The amplitude, propagating speed, and characteristic widthcharacterized from the observations range from 2 to 15 m, from 0.6 to 1.1 m s–1, and from O(10 m)
to O(102 m), respectively. The propagating speed and characteristic width of 1st-mode NLIWs are
consistent with those predicted by the two-layered eKdV model for given amplitude andstratification where the latter varies in space (K1, K2, and K3 in Fig. 1.2.9), season (Fig. 1.2.9), andyear (Cho et al., 2016). Most recently, using satellite SAR images acquired in and around a shallow(i) Hsu et al. (2000), (ii) Lee et al. (2006), (iii) Teixeira et al, (2006), (iv) Li et al. (2008), (v) TAVEX (2008)/Smith (2010)

Fig. 1.2.9 (a) Five areas where internal wave (IW) observations were reported previously (blue circles,(i) to (v)) and three areas where historical hydrographic data were analyzed for characterizing 1st-mode
nonlinear IWs (orange circles, K1 to K3). Depth contours are in meters. (b) Close-up of the K2 region
around southern Jeju Island, Korea, corresponding to the black framed area in (a). The historicalhydrographic data were collected from stations marked with orange crosses in the hatched areas.

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sill named ‘IEODO’ in the ECS, along with in-situ observations and numerical models, various
forms (spiral, wedge, and arc-like curvature patterns, Fig. 1.2.10) of NLIW fronts were presented,
which are locally generated with wave energy radiating from the sill with a significant time lag
dependent on background conditions (Nam et al., 2018).

Fig. 1.2.9 Continued. Propagating speed for three areas (K1: green circles, K2: red triangles, and K3:
blue boxes) as a function of the IW amplitude (η0) based on the eKdV model: (a) May, (b) August, and(c) November. Symbols are sampled at 3 m intervals in amplitude starting from 1 m, and each pair ofdashed curves represents a range of one standard deviation. All plots are from Cho et al. (2016).
Reproduced with permission of Creative Commons.

Fig. 1.2.10 Synthetic aperture radar(SAR) images in the vicinity of a small
topographic sill known as IEODO in thenorthern ECS. The images taken on (a)21:45:30 UTC September 10, 2014, (b)09:43:54 UTC May 28, 2015, (c) 21:38:07
UTC June 3, 2015, (d) 21:31:17 UTC June
5, 2015, (e) 21:45:31 UTC June 12, 2015,
and (f) bottom topography around the sill(color scale in the top-left), superimposed with a surface manifestation of
IW fronts where the increased sea surface
roughness is found in association withconvergences of near-surface currents
induced by IWs (from ‘A’ to ‘H’ in 2014;
pink, from ‘I’ and ‘M’ in 2015; orange).
All plots are from Nam et al. (2018).
Reproduced with permission of Creative
Commons.

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The NLIWs play a significant role in transporting and redistributing heat, mass, energy, andmaterials, and they have been known to be important to the understanding of underwater acoustics(Williams et al., 2001), submarine navigation and offshore engineering (Osborne et al., 1978),marine biogeochemistry (Bogucki et al., 1997; Lucas et al., 2011), and the Earth’s climate (Alford,
2003; Ferrari and Wunsch, 2009; Melet et al., 2013). In spite of a few works on the NLIWs in the
ECS, our understanding of generation, propagation, evolution, and dissipation of NLIWs remains ina primitive state. Increasing the number of in-situ and satellite observations at a better resolution in
space and time will surely improve our understanding on these important processes in the ECS.

1.2.6 Future work
With the developing economy, human activities such as oceanic engineering and deep sea mining
are growing fast, especially in high seas. Tides and waves exist everywhere in the ocean although
the later presents mainly in the ocean surface layer. We need to study the role of tides and waves inbroader aspects, such as the mixing role of tides and internal tides in the pelagic ocean and themixing and resuspension role of tides in food-supply in the growing and breeding period of marine
life. The need to study the destructive force of the internal waves on marine infrastructures and inoceanic engineering is also growing. Both nonlinear theory study (especially for internal waves) andin-situ observations are needed for future investigation of tides and internal tides.
For multi-scale surface wave study, we need to deploy more surface buoys, and obtain long-termtime series. The surface model, where the sources terms need to be further improved into anear-perfect stage, is still under development. We should also pay attention to the data quality ofboundary conditions, where the data quality of wind forcing is not as good as operational demands
for extreme events like tropical cyclones. The model output of surface waves in the YECS calls forfurther validation, and the studies of surface waves in the YECS still leaves large room for future
improvement.

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Lucas, A.J., Franks, P.J.S. and Dupont, C.L. 2011. Horizontal internal-tide fluxes support elevatedphytoplankton productivity over the inner continental shelf. Limnol. Oceanogr. 1: 56–74,
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spirals in the northern East China Sea. Sci. Rep. 8: 3473, doi:10.1038/s41598-018-21461-3.
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1.3 Water Masses
Fei YuInstitute of Oceanology, Chinese Academy of Sciences, China

1.3.1 Introduction
The East China Sea (ECS) and Yellow Sea (YS) are located east of China on a broad continental
shelf, which is one of the widest shelves in the world ocean, and is influenced by the East Asianmonsoon system, with a strong northeast wind in winter and a weak southwest wind in summer.
Because of the influence of the East Asian Monsoon, the climate of the ECS and YS shows typicalseasonal characteristics. In winter, the climate of the YS is affected by the Siberian High pressure
system such that a north and northwest wind prevails with an average wind speed between 6 and10 m s–1. Seasonal changes in solar radiation and other climatic⁄meteorological factors are
responsible for the large seasonal variability of the ECS and YS hydrography. The major currents
in the ECS and YS are the Kuroshio, which is the western boundary current of the North Pacificsubtropical gyre, the Taiwan Strait Current (TSC) which transports the South China Sea (SCS)
water through the Taiwan Strait (TS), the Tsushima Warm Current in the outer continental shelfwith depths over 100 m, a branch of the Tsushima Warm Current–Yellow Sea Warm Current whichtransports high salinity water into the YS, the Taiwan Warm Current (TWC) in the middle
continental shelf, with depths of 50–100 m, the Min-Zhe Coastal Current (MZCC) along the coastsof Fujian and Zhejiang Provinces (Min-Zhe coasts) and the Yellow Sea Coastal Current (YSCC)
along Jiangsu Province in the inner continental shelf, shallower than 50 m (Fig. 1.3.1; Lie and Cho,
2002). The Changjiang River discharges a large volume of fresh water into the ECS. Obviously,
these currents affect the oceanographic conditions as well as the distribution and properties of water
masses in the ECS (Ichikawa and Chaen, 2000).

1.3.2 East China Sea
Current system background
The Kuroshio enters the ECS along east coast of Taiwan and the southernmost one of the Ryukyu
Islands chain. Under the constraint of the ECS continental slope, the mainstream of the Kuroshio in
the ECS runs stably along the 200-m isobath at a maximum velocity of 0.75 to 1.5 m s−1 (Nitanni,1972). Passing through the Okinawa Trough, the Kuroshio hugs the shelf break of the ECS until it
approaches the shoaling northern end of the trough, where it separates from the shelf and bends
east-southeastward. After it turns eastward around 30°29′N, 129°E (Qiu and Imasato, 1990), theKuroshio separates from the continental margin, and eventually flows into the Pacific Ocean
through the Tokara Strait. The Kuroshio intrudes into the ECS from the subsurface water ofKuroshio northeast of Taiwan, upwells northwestward gradually from 300 to 60 m, then turns to

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northeast in the region around 27.5°N, 122°E, and finally reaches 31°N off the mouth of the
Changjiang River along approximately the 60-m isobath, forming bottom saline water off the coastof Zhejiang Province (Yang et al., 2011). The Kuroshio has been known to have two branchcurrents entering the continental shelf of the ECS: the Taiwan Warm Current in the southwestern
ECS and the Tsushima Warm Current in the southeastern ECS (e.g., Guan and Mao, 1982). The two
branches have a strong influence on water circulation and water mass distribution not only in the
ECS, but also in the YS (e.g., Kondo, 1985).
The TWC refers to the northward movement of warm water off the Min-Zhe coast. It was firstobserved in the late 1950s (Guan and Chen, 1964). Originating from the Kuroshio, the TWC runs
through middle continental shelf and intrudes into the inner continental shelf outside the Changjiang
mouth, influencing the coastal ecological, chemical and physical environments by transporting
warm, saline and oligotrophic open ocean materials across the shelf into the ECS (e.g., Miao and Yu,
1991; Zhang et al., 2007). The TWC originates from two parts of waters (see Bai et al. (2004)): oneis the Taiwan Strait Current (TSC) from Taiwan Strait (Su et al., 1994) and the other is theKuroshio Branch Current to the northeast of Taiwan (KBCNT) (Kondo, 1985; Ichikawa andBeardsley, 2002; Yang et al., 2011; Lian et al., 2016). The high salinity lower TWC water isconsidered derived from the KBCNT, and the high temperature and less saline surface TWC wateroriginates both from the TSC and the KBCNT (Weng and Wang, 1984). As proposed by Su and Pan(1987), the TWC bifurcates into two branches near 28°N within the middle continental shelf: theinshore branch (TWCIB, this is the traditional TWC) which flows northward along approximately
the 50-m isobath off the Min-Zhe coast and turns to the northeast off the mouth of the ChangjiangRiver, and offshore branch (TWCOB) which first flows anticyclonically, then turns cyclonically andfinally joins the western flank of the Kuroshio. However, there is no agreement on where the northend of the TWCIB is because of a lack of long-term current observations.

Fig. 1.3.1 Schematic circulation patterns in the ECS. Left panel indicates the circulation in summer andright panel indicates the circulation in winter.

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The Tsushima Warm Current carries volume, heat, and salt from the ECS to the east and has a large
impact on the surface circulation. The scientific questions for the Tsushima Warm Current in the
ECS are its origin and volume. In general, there are two points of view on the origin of the
Tsushima Warm Current. The first one is that the Tsushima Warm Current is considered as anorthward branch of the Kuroshio when it separates southwest of Kyushu and that the Yellow Sea
Warm Current (YSWC), separated from the Tsushima Warm Current southeast of Cheju Island,
flows into the eastern Yellow Sea (Nitani, 1972). This point is supported by trajectories ofsatellite-tracked drifters (Lie and Cho, 1994). The second point of view is that the Tsushima Warm
Current originates mainly from the Taiwan Strait (Beardsley et al., 1985). This was supported bycurrent meter data over the continental shelf of the ECS (Fang et al., 1991). The volume transport of
the Tsushima Warm Current is estimated from ferryboat acoustic Doppler current profiler (ADCP)
data (Takikawa et al., 2005) and numerical models (Guo et al., 2006). The debates over the origin
and volume of the Tsushima Warm Current are induced by lack of long-term direct current
measurements.
The coastal currents in the ECS are complicated. Along the southern coast of the ECS, thenorthward flow through the Taiwan Strait is called the Taiwan Strait Warm Current (TSWC). The
TSWC is reported flow northward along the Fujian–Zhejiang coast all year round, downwind insummer and upwind in winter (Su and Wang, 1987; Guan and Fang, 2006). As the extension of theSouth China Sea Warm Current (SCSWC), the TSWC brings high temperature and salinity SCS
water into the ECS (Hu and Liu, 1992). Paralleling the TSWC on the coastal side, the MZCC exists
in winter, induced by the winter monsoon, while there is no evidence of the existence of the MZCC
in summer during the summer monsoon. Along the southern coast in the ECS, the YSCC moves
southward in winter, but there is a conflict in the direction (Liu and Hu, 2009) of the YSCC insummer because of a lack of direct current measurements. The YSCC plays an important role in theevolution of the ECS water masses. Specifically, it causes the intensification of the MZCC and theYSWC water, and blocks the northeastward dispersion of the TWC water (Li et al., 2006).
Freshwater output from the Changjiang River forms a large-scale plume which plays a key role inphysical processes in the adjacent ECS and YS. This water flux, known as Changjiang diluted water(CDW), is a prominent hydrographic feature in the ECS and YS. In summer, the CDW extends
northeastward and forms a surface plume, while in winter it flows south in a narrow band along theeastern Chinese coast. The Changjiang River carries an enormous volume of fresh water, sediments,
and nutrients to the ECS and YS, which significantly influences the water properties, circulation
structure, sediment deposition, and ecosystems of the adjacent seas (Beardsley et al., 1985; Zhu andShen, 1997). The influence of the Changjiang River on the water masses in the ECS is through the
spreading of riverine plumes. The spreading of the plumes is strongly influenced by river discharge(Le, 1984), the East Asia monsoon (Zhu and Shen, 1997; Chang and Isobe, 2003), tidal mixing (Wuet al., 2011), coastal currents (Zhu et al., 1998), and continental shelf circulation. From salinity data,
Mao et al. (1963) found that the CDW turns to the northeast in summer, but not to the south as
would be expected, driven by the Coriolis force. The southerly summer monsoon is the dominantfactor in the northeastward extension of the plume (Zhu and Shen, 1997; Chang and Isobe, 2003). In
summer, the far-field diluted Changjiang water joins the northeastward shelf–current system andflows out of the ECS and YS through the Tsushima Strait where the year-round freshwater outflow
is estimated to be at least 70% of the total Changjiang River discharge (Isobe et al., 2002; Changand Isobe, 2003). In winter the CDW riverine plume flows south in a narrow band confined to thecoast (e.g., Mao et al., 1963; Beardsley et al., 1985; Zhu and Shen, 1997).

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Water masses
Previous studies on the ECS focus on typical seasons such as summer and winter. Because current
observations were limited, most previous studies classified water masses according to theircharacteristic temperature and salinity, using different water mass analysis methods such as similarcoefficients (Weng and Wang, 1984) and clustering method (Su and Weng, 1994; Qi et al., 2014).
However, different methods and datasets give different opinions. As described by Qi et al. (2014),
there are eight water masses in summer and only five in winter in the ECS. Among these, Kuroshio
Surface Water (KSW), Kuroshio Intermediate Water (KIW), ECS Surface Water (ECSSW),
Continental Coastal Water (CCW), and Yellow Sea Surface Water (YSSW) exist throughout theyear. Kuroshio Subsurface Water (KSSW), ECS Deep Water (ECSDW), and Yellow Sea Bottom
Water (YSBW) are all seasonal water masses, occurring from May through October. The CCW,
ECSSW and KSW all have significant seasonal variations. In addition, the CCW is affected by river
runoff and ECSSW by the CCW and KSW (see Table 1.3.1).

Table 1.3.1 T-S characteristics of water masses in East China Sea.
Water mass

February

May

AugustNovemberContinental Coastal Water
(CCW)

T
S
D

7.0–15.5
30.4–32.2
0–30

13.4–23.5
28.5–32.4
0–20

22.0–28.0
27.0–31.0
0–20

15.5–21.5
29.0–32.3
0–30

East China Sea Surface Water(ECSSW)

T
S
D

11.5–20.5
33.5–34.4
0–200

14.5–24.5
33.2–34.2
0–50

23.0–28.5
33.0–34.0
0–75

17.0–25.0
33.4–34.2
0–200

East China Sea Deep Water(ECSDW)

T
S
D

13.5–21.5
33.5–34.4
50–20015.5–24.5
33.4–34.4
75–200Kuroshio Surface Water
(KSW)

T
S
D

19.0–27.8
34.3–34.8
0–60

23.7–30.2
34.0–34.6
0–75

Kuroshio Subsurface Water(KSSW)T
S
D

13.0–23.3
34.6–35.0
50–40013.0–25.0
34.5–35.0
60–400Kuroshio Intermediate Water
(KIW)

T
S
D

7.4–13.5
34.3–34.6>270

6.5–13.6
34.1–34.5>240

7.0–13.8
34.2–34.5>250

6.6–13.9
34.3–34.5>280

Yellow Sea Surface Water(YSSW)

T
S
D

7.5–13.5
32.5–33.6
0–50

11.5–15.5
32.0–33.2
0–20

18.3–22.4
31.5–32.4
10–20

13.0–16.5
32.0–33.4
0–50

Yellow Sea Bottom Water(YSBW)

T
S
D

12.5–14.0
32.5–33.5
20–50

13.5–17.0
32.0–33.5
20–50

14.2–24.4
34.5–34.9
0–450

15.0–26.0
34.2–34.8
0–400

Temperature, T (°C), Salinity, S, Depth, D (m)Some other works have been carried out based on remote sensing data (e.g., Yuan et al., 2008) andmodels (e.g., Yuan and Hsueh, 2010; Yang et al., 2011; 2012). Yuan et al. (2008) studied the
intrusion of the TWC into the Changjiang mouth and Subei using sea surface temperature (SST) andocean color in winter. Yang et al. (2011, 2012) proposed an intrusion pathway of the Taiwan Warm

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Current Inshore Branch (TWCIB) in summer. Using remote sensing data, we can only investigate
phenomena in the sea surface, which is strongly influenced by solar heating and Changjiang runoff.
It is difficult to study the phenomena in lower layers. Application of a local marginal sea model
depends strongly on the artificial boundary conditions along the open boundary, where the verticalstructures of currents and their temporal variations are difficult to set up (Guo et al., 2003, 2006).
The existing T-S diagram methods of analyzing water masses are useful in open ocean water mass
analysis, but it is difficult to determine the core of original water masses in a shallow sea area bythese methods because a large error may be introduced.

1.3.3 Yellow Sea
Current system background
The main water masses in the YS include the Yellow Sea Warm Current (YSWC) water mass,
Yellow Sea Cold Water Mass (YSCWM) and the coastal water mass (Fig. 1.3.2). The water masses
in the YS have distinct seasonal variation. The YSCWM appears mainly in summer and the YSWC
mainly in winter.
Using hydrology observation data and drift bottle data, Uda (1934) drew the distribution of thecurrent system of the Bohai Sea, YS and ECS. The YSWC is a unique current flowing against thewind direction in winter. The YSWC brings the open sea water into the YS and forms a hightemperature and high salinity tongue from the southwest of Cheju Island toward the interior of theYS. It has great impact on the zooplankton ecosystem and the water exchange between the YS and
ECS. Studies about the YSWC focus on its origin, formation mechanism, and path.

Fig. 1.3.2 Water mass distribution in the YS: Northern Yellow Sea Cold Water Mass (NYSCWM);
Southern Yellow Sea Cold Water Mass (SYSCWM); Qingdao Cold Water Mass (QDCWM); Yellow Sea
Middle Layer Cold Water (YSMLCW); Yellow Sea Warm Current (YSWC). Grey lines representbathymetry in meters.

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In the earlier times, the YSWC was thought to be a branch of the Tsushima Warm Current from thesouth of Cheju Island (Uda, 1934). This concept was supported by some scholars (Guan, 1962, 1994;
Nitani, 1972; Chen et al., 1979; Park, 1986; Lie and Cho, 1994). However, based on current meterdata obtained by Chinese researchers at day-night anchored stations, a new line of thought about theorigin was considered. Fang et al. (1991) believed there was a current system, called the
Taiwan-Tsushima-Tsugaru Warm Current System, placed on the ECS shelf and that the YSWC is abranch of this warm current system in the YS. Fang et al. (1997) proved this argument by numericalsimulation. Another result of research suggested that the YSWC water is formed by lateral mixingof the Tsushima Warm Current water with the mixed water of the ECS southwest of Cheju Island(Lie et al., 2000) and that the mixed water of the YS and the Korean coastal water are also involvedin the mixing process to some degree (Le and Mao, 1990; Le, 1992). In winter, the Tsushima Warm
Current water with high temperature and high salinity meets the shelf water of the ECS with low
temperature and low salinity and the mixing process forms a strong temperature and salinity front.
The YSWC comes from this front area so it has the characteristic of mixed water. The YSWC
matures in winter and disappears in summer (Beardsley et al., 1992; Lie, 1986; Park, 1986; Lie etal., 2000; Teague and Jacobs, 2000). However, the mature period of the TWC and Tsushima Warm
Current is in summer. Further, no drifters, which were put in the northeastern area of the ECS, could
get into the YS from the southwest of Cheju Island (Lie et al., 2000). These results show that theYSWC is mixed water other than a direct branch of the TWC or the Tsushima Warm Current. In
other words, forcing of the TWC or the Tsushima Warm Current is not the formation mechanism of
the YSWC.
Observation data and drifter trajectories show the YSWC has the characteristic of intermittent flowwhich may be related to the burst of the north wind in winter. Wind forcing in winter is widely
thought to be the main dynamic mechanism of the YSWC. In climatology, the mean north wind in
winter makes the coastal water of the YS flow toward the south. Because of mass conservation,
there will be a northward compensation current in the middle YS (Fang et al., 1997; Yuan et al.,1982). The YSWC could be seen in a stable mode under a stable north wind. This viewpoint is
consistent with the upwind circulation theory (Csanady, 1982). In meteorology, the forcing from astrong north wind makes the sea level at the south of the YS higher than that at the north of the YS.
When the north wind recedes, a northward pressure gradient force causes the YSWC (Hsueh, 1988;
Riedlinger and Jacobs, 2000; Teague and Jacobs, 2000). Strong winds produce the barotropic modeof the YSWC. Wan (2014) thought the YSWC is the superposition of the barotropic mode and thestable mode. The baroclinic effect on the YSWC is also considered (Lie, 1999; Lie et al., 2001,
2009, 2013). The coastal water and YSWC water form a strong front in winter, creating strong
difference in temperature and salinity between the YSWC and coastal water. A strong north windbreaks the front and allows the high temperature and salinity water of the YSWC to flow into the YS.
This mechanism results in the variation of the YSWC lagging behind that of the north wind (Zhao etal., 2011; Qu, 2014).
After formation, the YSWC is thought to flow to the north along the YS trough (Uda, 1932, 1933;
Nitani, 1972; Chen et al., 1979; Guan, 1994). However, some scientists have held a different
viewpoint: the YSWC axis is at the west side of the YS trough along the isobaths of 50 to 70 m
(Mao et al., 1965; Kondo, 1985; Fang et al., 1997; Tang et al., 2001; Xu et al., 2005). The YSWC
axis has seasonal and interannual variability and is located at the west side of the YS trough in mostyears (Tang et al., 1999, 2000, 2001). In some years, the YSWC axis shows a double-peak structure.
One warm tongue is at the middle YS trough and the other is at the west side of the trough (Le andMao, 1990; Ma et al., 2006). After the YSWC flows into the middle YS, a northwestward branch
toward Shandong peninsula and a northwestward branch toward the north YS are generated (Su,
1986; Zhao et al., 2011).

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The seasonal variation of the YSWC has a great impact on the characteristics of water masses. An
earlier study (Park, 1986) pointed out that the YSWC is strong in winter and weak in summer.
Analyzing CTD data from 1996 to 1999, Zou et al. (2000) found that the YSWC forms in winter,recedes in spring and disappears in the middle of spring. In early spring, the YSWC axis moves
eastward compared with that in winter (Tang et al., 2001). Further, numerical simulation results
show that the YSWC forms in December, matures in February, and recedes quickly in spring (Xu etal., 2005). Its axis moves to the west from December to February and reaches the 60- to 70-m
isobath area. The YSWC remains stable at the bottom layer during the winter (Xu et al., 2005).

Water masses
In the central deep region of the YS, there is a water mass characterized by lower temperature(<10°C) and high salinity, called the Yellow Sea Cold Water Mass (YSCWM; Table 1.3.2). It
occupies a third of the region above the deep and bottom layers of the YS in summer (Su and Weng,
1994) and exhibits a strong front in the YS. The YSCWM usually has three cold cores: one is theNorthern Yellow Sea Cold Water Mass (NYSCWM, Fig. 1.3.2) and the other two form an eastern
and western part of the Southern Yellow Sea Cold Water Mass (SYSCWM, Fig. 1.3.2) (Tang, 2005).
The YSCWM is an important influence on the catches and fishing grounds of demersal fishes andhas a great impact on the distribution of the biocenose and the acquisition of fishery resources (Cho,1982; Du et al., 1996; Wang et al., 2003; Zhang et al., 2007).
Uda (1934) first found the existence of the YSCWM in 1934. He et al. (1959) first studied theformation mechanism of the YSCWM and concluded that it forms locally during the previous winter.
This point of view is widely accepted. Further, Guan (1963) found that the temperature of theYSCWM has a relationship with the local air temperature during the previous winter and that theYSCWM shows cyclonic circulation pattern. Since then, quite a number of studies on the formation
mechanism of the YSCWM have been made. The formation of a seasonal thermocline (Miu et al.,
1991; Ren and Zhan, 2005), cooling at surface, strong mixing in winter (Hur et al., 1999), tidal
mixing (Zhao, 1986; Lee and Beardsley, 1999; Ren and Zhan, 2005) and topography heataccumulation effect (Takahashi and Yanagi, 1995; Xu et al., 2003) are all important mechanisms
during the formation of the YSCWM.
The YSCWM has clear seasonal variability based on in-situ observations and numerical simulations.
With the seasonal change of the thermocline, the YSCWM forms in spring (approximately in May),
matures in summer (approximately in August), recedes in autumn, and completely disappears in
winter. Many research results have shown the seasonal characteristics of the YSCWM (Guan, 1963;
Chu et al., 1997; Yu et al., 2006; Zhang et al., 2008; Bao et al., 2009; Yao et al., 2012). During thecooler season (from September to February), changes in the heat content of the YS are caused byvariation in latent and sensible heat at the surface layer, which is related to the East Asian WinterMonsoon and the Arctic Oscillation (Wei et al., 2013).
The long-term variation of the YSCWM is still unclear. Following the accumulation of historical
data, long-term variability of YSCWM has been analyzed. Weng et al. (1988) and Zhang and Yang
(1996) explored the distribution range, volume, low temperature center and thermohaline
characteristics of the YSCWM using the “similar coefficient” method and found that the YSCWM
has obvious interannual variability. The SST of the YS has an increasing trend which is consistent
with climate warming in northern China and the adjacent seas (Lin et al., 2005). Interannual
variability in temperature has been discussed in some studies regarding the NYSCWM (Jiang et al.,2007; Li et al., 2015), with NYSCWM temperature showing an increasing trend. Temperature at thethermocline has the greatest temporal variance that is caused mainly by surface heating (Hu andWang, 2004). Wei et al. (2010) reported that the summer T-S properties of the YSCWM correspond

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to the YSWC in winter. Interannual variability of eastern part of the SYSCWM also deserves
academic concern. Given the survey area, the study of the eastern part of the SYSCWM is mainly
taken by Korean scientists. Park et al. (2011) conducted empirical orthogonal functional (EOF) andsingular value decomposition (SVD) analyses on the Korea Oceanographic Data Center (KODC)
dataset and described the temperature variability of the eastern part of the SYSCWM. He attributed
the interannual temperature variation of the SYSCWM to winter forcing, including the SiberianHigh, Aleutian Low, Pacific Decadal Oscillation, East Asian Jet Stream, and Arctic Oscillation.
Summer forcing could also intensify the temperature anomaly of the SYSCWM, for example, theSST of the Kuroshio and sea level pressure over the Asian continent. Yang et al. (2014) used thesame data and simulation results to show that SST in the previous winter is associated with thesouthern limit of the SYSCWM in the following summer. Li (1991) pointed out that the YSCWM isin a warm phase in the year following a large meander of the Kuroshio, and in a cold phase when El
Niño events occur. However, some studies have concluded that the temperature of the SYSCWM is
not directly related to El Niño (Jia and Sun, 2002; Bai et al., 2004). Furthermore, winter processes
play an important role in the interannual variability of the YSCWM. Wei et al. (2013) found that theheat content of the deep region of the YS is mainly influenced by lateral heat transport controlled by
the YSWC, which also has a 4- to 7-year period of interannual variability (Wang et al., 2009).

Table 1.3.2 T-S characteristics of water masses in the Yellow Sea.
Water mass
Northern Yellow Sea Cold Water Mass
(NYSCWM)

TS
D

6.0–10.0
32.5–34.0
50–70Southern Yellow Sea Cold Water Mass
(SYSCWM)

T
S
D

6–10
32.5–34.0
50–70

Qingdao Cold Water Mass
(QDCWM)

T
S
D

6–10
31.7
40–60

Yellow Sea Middle Layer Cold Water(YSMLCW)

T
S
D

6–12
31.8–32.5
20–30

Yellow Sea Warm Current(YSWC)

TS
D

8–14
32.5–34
50–70

Temperature, T (°C), Salinity, S, Depth, D (m)Spring could be seen as a conversion period in which the YS changes from vertically homogeneous
YSWC mode to vertical stratified YSCWM mode. More attention is usually given to how the YS
changes in spring. In 1996, the Yellow Sea Middle Layer Cold Water (YSMLCW) and inversion
layer was found during a China and Korea joint ocean investigation (Tang et al., 1999; Zou et al.,2000). Zou et al. (2000) explained the formation mechanism of YSMLCW: The YSWC weakens inspring. The mixed water of the western YS, which has a lower temperature, expands toward the eastand south and spreads over the YSWC water. In the meantime, surface heating forms a thermocline.
Observation data (Zou et al., 2001) and simulation results (Xu et al., 2001) show that the YSWC is
one of main reasons for the formation of the YSMLCW. The Qingdao Cold Water Mass (QDCWM)
is also a classic phenomenon of the YS in spring, forming offshore southeast of Qingdao. The

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source of the QWCWM is coastal water that originates from the north YS (Zhang et al., 1996). The
QDCWM has lowest temperature in spring. It forms in March, matures in April, and blends into theYSCWM gradually from May (Yu et al., 2006). Yu et al. (2006) found that the Inchon Offshore
Cold Water Mass has the same seasonal characteristics as the QDCWM. The QDCWM has a great
impact on the fishing grounds in the YS (Zheng and Zhang, 1983) by upwelling nutrients to theupper layer water. Long-term variation in water properties analysis shows that the QDCWM became
stronger before the mid-1970s, and weakened after the mid-1970s. The interannual variability of the
QDCWM strength is related to the ENSO events in which the QDCWM weakens during El Niño
events (Zhang et al., 2004).

1.3.4 Future work
Influenced by the monsoon and currents system, the water masses of the ECS and YS are complex,
showing strong seasonal variation and complex vertical structure. Influenced by global warming,
the water masses of the ECS and YS also show a warming trend, with interannual or interdecadal
variations. A deep understanding of the variation of water masses in the ECS and YS could give us
better insight to ecological and environmental variations, and help us promote environmentally
sustainable management ability.
For the Yellow Sea, new observations show that the southern YSCWM and YSWC exist
concurrently in the southern Yellow Sea during spring and autumn and their co-existence time ismore than half a year. The mechanism for this co-existence and the influence of the biogeochemical
process in Yellow Sea needs to be studied further.
For the East China Sea, recent observations suggest that there exists some mesoscale water masses
between the Kuroshio and Taiwan Warm Current waters. The seasonal-to-interannual variation
characteristics of the mesoscale water masses should be analyzed and their influence on the ecologyof the East China Sea should be studied in the future.

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Zou, E.M., Guo, B.H. and Tang, Y.X. 2001. An analysis of summer hydrographic features and circulationin the southern Yellow Sea and the northern East China Sea. Oceanol. Limnol. Sin. 32: 340–348.

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1.4 Ocean Mixing
Takeshi Matsuno1, Hao Wei2, Jae Hak Lee3 and Chuanjie Wei4
1

Research Institute for Applied Mechanics, Kyushu University, Japan
School of Marine Science and Technology, Tianjin University, China
3
Korea Institute of Ocean Science and Technology, Korea
4
Institute of Oceanography, Chinese Academy of Sciences, China2

1.4.1 Introduction
Mixing processes in the ocean play an important role in dissipating energy and diffusing materials.
Mixing can be divided into two types, horizontal and vertical, the features of which are quite
different because of the ocean’s large aspect ratio and stratified structure. While horizontal andvertical mixing are similar in many ways to isopycnal and diapycnal mixing, respectively, they are
not identical because, for example, horizontal mixing should be diapycnal if there is a horizontal
density gradient in a frontal region. Horizontal or isopycnal mixing may be important in basin-scale
phenomena, such as the formation of North Pacific Intermediate Water. Very few reports haveconsidered horizontal mixing processes or related topics in the East China Sea (ECS). Various
aspects of vertical/diapycnal mixing in the ECS have, however, been reported, so mainly verticalmixing is considered in this section.
Vertical mixing plays a very important role in the stratification and transport of material in the ocean.
It is particularly important to have a quantitative understanding of this process in the shelf region
and coastal water because vertical mixing and stratification strongly control biogeochemical
processes and the biological environment. The ECS is well-known for its high biologicalproductivity, which is sustained by nutrients from land and sub-surface of the ocean, including thedeep ocean. Therefore, the mixing processes that supply the necessary nutrients for primary
production and control the transport of material need to be evaluated correctly. While the biologicalabundance and environmental conditions in the ECS have been studied extensively, there are veryfew quantitative surveys of the mixing processes. We therefore need to find out more about thephysical and biological aspects of the mixing processes, and enhance our understanding of themarine environment in the ECS. In this context, a summary of the information that is currently
available about vertical mixing in the ECS is essential.
Studies of turbulent mixing in the ECS can be divided into various phases. Some studies discussturbulent mixing as a physical process that changes the stratification and ocean structure. Others
consider turbulent mixing as a process that transports biogeochemical materials related to primary
production, such as nutrients. Other research has focused on measuring the microstructure, andimproving the accuracy of estimates of the turbulence intensity by parameterization.
From another perspective, the role that vertical mixing plays in physical and biological processes
may be divided into three separate cases based on the relative position of the turbulent mixing,
namely, the surface, intermediate, and bottom layers; this classification is particularly useful for the

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shelf region. It is generally thought that mixing processes in the surface layer are related to theformation and evolution of the surface mixed layer and contribute to air–sea interactions. Verticalmixing in the intermediate layer seems to be related to the activities of internal waves, includinginternal tides, near-inertial internal gravity waves, and high frequency internal waves. Turbulence in
the intermediate layer contributes to the evolution of stratification and the vertical flux of various
materials. Turbulence in the bottom boundary layer (BBL) makes an important contribution to theflux of materials just above the sea floor. As well as these main layers, we should also carefully
consider a sort of diffusive bottom layer (DBL), which, even though it is very thin, is very importantfor sediment–water exchange fluxes.
In this review, we summarize information about mixing processes in the different layers, including
the physical processes and their influence on various phenomena, such as air–sea interactions, thesupply of nutrients for primary production, and other marine environments.

1.4.2 Methods for estimating the turbulence intensity
Over recent decades, the most reliable estimates of the turbulence intensity have been based on
measurements of microstructure from which the turbulent energy dissipation rate (ε) can be
determined by integrating the shear spectrum within a specific wave number range, assuming
isotropy (e.g., Gregg, 1987). Although the assumption of isotropy may not be correct, particularly instrongly stratified situations, there is no standard method for estimating the intensity of anisotrophic
turbulence in the ocean.
Various instruments, such as the Turbulence Ocean Microstructure Acquisition Profiler (TurboMAP)(Wolk et al., 2002), Vertical Microstructure Profiler (VMP) (Lueck et al., 2013), Microstructure
profilers MSS (ISW Wassermesstechnik) and Micro-Structure-Turbulence (MST) profiler (Prandke
and Stips, 1998), use shear probes to detect small-scale velocity shear and can be used to measure
the microstructure. The velocity shear is measured at frequencies of between 512 and 1024 Hz when
the instrument is freely descending at a speed of between 0.5 and 0.6 m s−1. Turbulence in the ECS
has been measured mostly with microstructure profilers (e.g., Matsuno et al., 2005, 2006; Lee et al.,2006; Liu et al., 2009).
Acoustic Doppler velocimeters (ADV) or acoustic Doppler current profilers (ADCP) (e.g.,
Lozovatsky et al., 2008) have been used to make high resolution measurements of the turbulence
intensity, particularly in the BBL. These instruments give high frequency measurements of thecurrent velocity at a fixed point, and the shear produced is calculated from the fluctuations.
Instead of measuring the turbulence directly, vertical mixing processes can be evaluated in otherways, for example, by assessing temporal changes in the vertical structure, or by using chemical
tracers. In this review, we will mainly consider vertical mixing that is related to direct measurementsof turbulence.

1.4.3 Characteristics of mixing processes
Surface layer
Because of its strong relationship with the heat flux through the sea surface, the turbulence in thesurface mixed layer has been investigated thoroughly. Even so, some aspects remain unclear and,for example, it would be beneficial to quantitatively evaluate the interactions between the

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turbulence and the waves. The turbulence intensity in the surface layer controls the formation of thesurface mixed layer (SML), which then influences the sea surface temperature (SST). Therefore, theheat flux through the sea surface is controlled by the turbulence intensity. The turbulence intensity
in the SML may be determined by the buoyancy flux and wind stress at the sea surface. Because of
the contribution it makes to air–sea interactions, the turbulence intensity in the SML of oceans
worldwide has been studied extensively.
That said, the turbulence in the SML of the ECS has not been studied so intensively, perhaps
because of the large spatial and temporal variabilities. For example, Liu et al. (2009) analyzed
hourly measurements of the microstructure and found that diurnal variations in the turbulence
intensity in the surface layer of the Yellow Sea (YS) were associated with nighttime convection anddaytime stratification. However, this does not occur throughout the YS, and other studies reported
that the turbulence intensity varied over shorter time scales, probably because of variations in windeffects over shorter time scales.
While the assumptions of the dissipation method proposed by Osborn (1980) may not be applied
when describing the equilibrium of turbulent kinetic energy (TKE) in the ECS, we generally do still
use this assumption. Endoh et al. (2014) discussed the contribution of the transport term in theequilibrium equation for TKE, based on direct measurements of ε and indirect estimations of thebuoyancy flux and shear production. They assumed the temporal variation in the TKE was zero andshowed, with measurements collected with Lagrangian tracking surface drifters, that the TKE
transport term made a significant contribution to the equilibrium of the TKE.
Because of their influence on turbulent mixing in the surface layer in the ECS, typhoons are
discussed in relation to air–sea interactions and primary production. The passage of a typhoon cancause surface divergence because of the cyclonic wind field and strong stirring of the surface layer.
The former is caused by Ekman transport, in which the thickness of the Ekman layer depends on thevertical eddy viscosity, and the latter generates turbulent energy production. Both are related to theturbulence intensity. Hung et al. (2013) and Tsai et al. (2013) discussed how a typhoon influenced
the cold dome to the northeast of Taiwan along with the interaction between the Kuroshio andtopography. They showed that the passage of the typhoon resulted in upwelling of cold and highnutrient water by Ekman divergence, and also mentioned the contribution of vertical mixing.
Elsewhere, using Argo float and satellite data, Siswanto et al. (2008) showed that primary
production was enhanced when a typhoon passed. While they did not directly discuss verticalmixing in their study, they suggested that upwelling of high nutrient water driven by the typhoon
made a significant contribution to primary production in the ECS. Moon and Kwon (2012)suggested that, when typhoons passed over the Yellow Sea Bottom Cold Water regions in thenorthern parts of the ECS and the YS, the intensity of the typhoon decreased significantly becauseof cooling at the surface induced by the vertical mixing and upwelling.
As mentioned earlier, it is important to determine the depth of the SML and the SST. Numerous
researchers have examined the mixing processes in the surface layer with numerical models but few
of these studies have been presented concerning the ECS. Huang et al. (2008) suggested that the
influence of waves needed to be considered when determining the depth of the SML and showed
that, by including wave-induced mixing, the estimates of the SST were more accurate.

Sub-surface layer
Since there is no direct influence of wind or bottom stress, vertical mixing in the interior of theocean should be controlled by internal processes, such as the breaking of internal waves and/or
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relationship between enhanced turbulence and shear instability in the intermediate layer and in the
BML, and there were limited direct measurements of turbulence (Matsuno et al., 1997). After 2000,
there were more opportunities to obtain microstructure measurements. Matsuno et al. (2006) usedmicrostructure observations from July 2003 and July 2004 to show the structure of the turbulence
intensity in the outer shelf region of the ECS, and summarized the turbulence intensity in the upper,
intermediate, and bottom layers. As shown in Fig. 1.4.1, the range of the observed turbulence in theintermediate layer was wider than the ranges in the upper and bottom layers, which means thatvertical mixing was enhanced intermittently, and this intermittent strong turbulence may have been
related to the development of internal waves. They also observed that the turbulence was enhanced
along a characteristic ray of internal tides around the shelf break of the central ECS (Matsuno et al.,2005).

uu

Fig. 1.4.1 Histograms showing the magnitude of turbulent dissipation rate, ε (left) and the vertical eddy
diffusivity, Kz (right) in specified layers in the outer shelf region of the ECS in July 2004. The layers, u, b-2,
and b-1 refer to the layer between 15 and 30 m below the surface, between 10 and 20 m above the bottom,
and within 10 m of the bottom, respectively. From Matsuno et al. (2006). Reproduced with permission ofWiley & Sons.

Lee et al. (2006) observed short period nonlinear internal waves during a campaign on the shelfsouth of Jeju Island in August, 2005. While they were mainly interested in the evolution of internal
solitary waves, they also found, by analyzing microstructure measurements, that the turbulence wasintermittently intensified in the sub-surface. They found that the dissipation rate exceeded 10−7 W
kg−1 around the pycnocline (Fig. 1.4.2), where K–H instability sometimes occurred, possibly
associated with internal solitary waves. Liu et al. (2009) suggested that the internal waves
influenced the turbulence intensity just below the pycnocline in the YS. While their observations
showed that there was very small diffusivity within the pycnocline, the values of ε were relatively

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distance, as also reported by Lee et al. (2006). However, when the turbulence was enhanced aroundthe pycnocline, there was no consistent aspect in the detailed structure of the turbulence intensity,that is, the turbulence may have been enhanced within the pycnocline in one case but may havebeen enhanced just above or below the pycnocline in other cases.
Other researchers have reported weak turbulence around the pycnocline. Wei et al. (2014) estimatedthe TKE dissipation rate and diffusivity in three seasons between 2006 and 2007 in the south YS
and found that the vertical mixing tended to be more intensive in the near-surface and bottom layers
than in the middle layer. They also showed that the pycnocline played an important role inpreventing wind-induced mixing. They found that the mixing in summer was structured into twolayers, one above and one below the pycnocline, and that the stratification inhibited the occurrenceof shear instability and prevented vertical exchange across the pycnocline. The Yellow Sea Warm
Current is a stable stratified current in which the mixing is quite weak. Enhanced mixing only
occurs in the boundary of the warm current because of the shear instability. The depth of the upper
mixed layer can be limited by the current in winter.
Lozovatsky et al. (2015), from their observations in August 2006 southwest of Jeju Island, also
showed that the turbulence was intensified intermittently in the sub-surface layer in the shelf regionof the ECS. They suggested that the ε was either negatively or positively correlated with thestratification, depending on the situation. When the stratification was relatively weak and waswithin the range 2 < N2/N02 < 6.3, where N0 was the standard buoyancy frequency in the deep ocean
proposed by Garrett and Munk (1975) and N0 = 5 × 10−3 s−1, the relationship between ε and N2 wasclearly related with a power law ε ~ (N2)−3. This means that the turbulence was stronger when thestratification was weaker. However, when the stratification was stronger, internal waves tended todevelop and break, resulting in intensified turbulence, so the relationship between ε and N2 waspositive, as proposed by Mackinnon and Gregg (2005). However, internal waves are not always
generated under intensified stratification, and internal waves do not always break, which means thatthe relationship would only occasionally be positive, and it would be very difficult to give auniversal description of the relationship between ε and N2, at least in the shelf region.
Vertical mixing in the intermediate water changes the structure of the stratification and transports
materials. From a biogeochemical perspective, vertical transport of materials is a very important
process by which nutrients are supplied from dark lower layers to the upper euphotic zone. A
subsurface chlorophyll maximum generally forms in the lower part of the euphotic zone. With the
exception of the coastal region, nutrients are usually depleted in the surface layer of the ECS
because the light is sufficiently intense, and a sub-surface chlorophyll maximum (SCM) forms with
the nutrients supplied from the lower layer under dusky light conditions.

Fig. 1.4.2 Vertical distribution of ε (W kg–1) shown on a log-scale along the northeast–southwest section
south of Jeju Island. The gray-shaded plot of ε was overlain with contours of the specific potential density.
The southwest end of the section is on the left. From Lee et al. (2006). Reproduced with permission ofWiley & Sons.

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Fig. 1.4.3 Mean (marks) and 95% confidence interval (dotted line with two solid lines) of the verticaleddy diffusivity (Kz) (solid circle), primary production converted from the nitrate flux (solid triangle),
primary production converted from the phosphate flux (open triangle), and the ratio of the nutrient flux tointegrated primary production within the sub-surface chlorophyll maximum (open circle) for low, medium,and high Nitrogen/Phosphate ratios. From Lee et al. (2017). Reproduced with permission of Elsevier.

As mentioned above, nutrients may be supplied to the upper layer from the lower layer because of
upwelling generated by divergence in the surface layer. However, SCM visible even in calm
conditions suggests that there might be more frequent upward transport of nutrients. To evaluate thesupply of nutrients to the SCM, we need to have a quantitative understanding of the vertical mixing
around it. Using a relatively limited dataset of turbulence measurements from the eastern end of the
ECS, Shiozaki et al. (2011) discussed how vertical fluxes of nutrients contributed to primary
production. Recently, Lee et al. (2017) used cumulative data from the central part of the shelf region
in the ECS to evaluate how vertical mixing helped to supply nutrients to the SCM. They showedthat the nutrient supply to the SCM, because of vertical mixing, was notably large in some cases, but
not in others, and that the difference was caused mainly by the magnitude of the vertical eddy
diffusivity. As mentioned by Lozovatsky et al. (2015), large values for the eddy diffusivity aroundthe pycnocline occurred intermittently, depending on the activities of internal waves. Lee et al.
(2017) found that the large nutrient fluxes associated with intensified vertical mixing tended tocorrespond with low nitrogen/phosphorus (N/P) nutrient ratios around the SCM. When the N/P ratio
was low, primary production in the SCM was thought to be supported by nutrients supplied from thelower layer via vertical mixing (Fig. 1.4.3). The N/P ratio may be a useful indicator of the nutrient
sources, and low and high ratios may show whether the nutrients are from the lower layer or riverdischarge, respectively. The intensified turbulent mixing may transport nutrients from the lower
layer into the SCM, and may be associated with the development and breaking of internal waves.

Bottom boundary layer
In shelf regions such as those in the ECS, the vertically well-mixed bottom boundary layer (BBL)
commonly forms because of the force of strong tidal currents. Turbulence just above the sea floor
also has a role in the formation of the BBL. Of all the mixing processes in the ECS, turbulence in
the BBL has perhaps been studied most. In the shelf region of the ECS, noticeable periodicity
caused by tidal motions may mean that the mixing processes show regular variations influenced bytidally variable bottom stress. Using time series of current velocity taken with a bottom-mounted
ADV and ADCP on the inner shelf of the ECS, Lozovatsky et al. (2008) showed that there wereclear variations in ε associated with tidal motion. They found that the TKE and ε were related in

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relationship under influence of convection in the deeper area (Fig. 1.4.4). They discussed thefriction velocity that controls the mixing processes in the BBL and showed that, when thedissipation rate was scaled with the friction velocity, it was 50% larger for the rotating tidal currents
than for reversing tidal currents at shallower sites near the coast.
By analyzing hourly repeated measurements made with a microstructure profiler, Liu et al. (2009)
showed that there was temporal variation in the turbulence intensity in the BBL at shallow (38 m)
and relatively deep (73 m) sites in the YS. The turbulence in the BBL was enhanced because of
semi-diurnal tidal motions, and the well correlated relationship between the enhanced turbulence
and the smaller values of the gradient Richardson number was shown using data obtained with abottom-mounted ADCP at the shallow station. At the shallow station, the BBL was about 15 m thick
and was mostly homogeneous; at the deep station, the BBL was thinner than at the shallow station,
there was weak stratification, and turbulence seemed to be suppressed in the upper part of the BBL.
Lozovatsky et al. (2015) discussed turbulence around the pycnocline and in the BBL in the shelfregion south of Jeju Island. They suggested that ε decreased more quickly with the distance from thesea floor in a weakly stratified upper portion of the BBL compared with the homogeneous layer
close to the sea floor.
We can gain an improved understanding of the turbulence processes by examining the weakstratification in the BBL. Yoshikawa et al. (2010) compared the shear production and ε from a timeseries of repeated measurements of microstructure with Lagrangian tracking of a drifting buoy inthe shelf region of the ECS and showed that the production of buoyancy in the BBL was important.
They mentioned that the eddy viscosity profile could provide useful insights into how Ekman
dynamics changed in the BBL, and also showed that the turbulence intensity could affect the current
velocity profiles in the bottom Ekman layer. They also found that the vertical profiles of the bottom
Ekman spiral depended on the stratification, even when the BBL was weakly stratified.

Fig. 1.4.4 Relationship between the turbulent kinetic energy (etr) and the ε in the bottom boundary layer
at sites in (a) shallow and (b) relatively deep areas in the Yellow Sea, along with the relationship when the
mixing was influenced by convection with advected warm water (open marks) in the deeper site. From
Lozovatsky et al. (2008). Reproduced with permission of Elsevier.

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The stratified structure of the BBL may be influenced by mixing processes associated with both theinclination of the bottom slope and the current direction. Endoh et al. (2016) found evidence fortidal straining, which is usually found in coastal waters or estuaries, even on the outer shelf of the
ECS. They analyzed turbulence measurements and vertical profiles of the current velocity on theslightly inclined continental shelf and found that the stratification switched between unstable andstable with a semidiurnal period in the lower part of the BBL. This means that, where there wasshear-induced convection (Lorke et al., 2005), the turbulence intensity just above the bottom
depended on both the magnitude of the current and its direction. On the other hand, the turbulence
intensity in the upper part of the BBL appeared to be related to the small diurnal variation in thestratification, which was caused by horizontal advection. The variation range of the bottom Ekman
layer thickness was much larger for diurnal motion than for semidiurnal motion, which is why there
was diurnal variation in the horizontal advection in the upper part of BBL. The energy dissipation
rate was enhanced in the upper part of the BBL when the weakly stratified structure was advectedwith diurnal motion. The turbulence intensity around the BBL therefore strongly depends on
stratification, even if the stratification is weak.
Turbulence in the BBL strongly affects the bottom stress, as observed by Lozovatsky et al. (2008,2010). While the results of numerical models should be seriously influenced by the intensity of thebottom stress, most numerical models do not have enough vertical resolution to express the structureof the BBL in detail. From another perspective, the turbulence in the BBL is important to consider
the resuspension of sediment, particularly in shallow areas. Yuan et al. (2008) deployed abottom-mounted quadrapod equipped with an ADCP, ADV, and optical backscatter sensor (OBS)
over two semidiurnal tidal cycles along the western coast of the YS. They combined these data withmeasurements from shipboard Conductivity-Temperature-Depth (CTD) profiling and a submersible
laser-diffraction particle size analyzer (LISST-100), and constructed the temporal and spatial
distributions of the tidal currents, TKE, suspended sediment concentration (SSC), and particle size
distributions. The tidal-induced bottom shear stress was the main cause of stirring during theobservations. The friction velocity was clearly related to both the SSC and particle size, as shown in
Fig. 1.4.5.

Fig. 1.4.5 Temporal variations in the friction velocity (solid lines), concentration of suspended sedimentconcentration; SSC (upper dotted line), and median size (d50) (lower circles and line) of suspended
sediments (SS) in the bottom boundary layer in a shallow area of the Yellow Sea. The SSC was estimatedby calibrating the back-scattering intensity obtained from an Acoustic Doppler velocimeter set at 0.45 m
above the sea floor. From Yuan et al. (2008). Reproduced with permission of Elsevier.
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However, the weak tidal flow during the ebb phase was accompanied by two large- andmedian-sized SSC events. The interactions between seiche-induced oscillations and a weak ebb
flow induced multiple flow reversals and provided a source of turbulence production which, in turn,stripped up the benthic fluff layers (only several millimeters). They also found that sediment was
resuspended during the intermittent turbulent bursts and that the coherent structures were related tothe sediment resuspension (Yuan et al., 2009). They found that most of the turbulent sediment flux
was generated during ‘ejection’ (56%) and ‘sweep’ (42%) events, and that the contributions fromoutwards and inwards interactions were negligible. The wall turbulence containing coherent
structures vertically transported momentum more efficiently than suspended sediments. By
spectrally scaling the sediment concentration, they found that there were two types of suspended
sediment clouds with different sizes during the peak flood. The large-sized particle clouds containedcoarse sediments that were locally resuspended from the seabed, whereas the smaller particle clouds
consisted of silts and clays, and acted as washload advected from Jiaozhou Bay and the YS by tidal
flows. Finally, they found that wavelet transformations could provide a more intuitive way to study
and visualize the relationship between the burst cycle and sediment suspension, and the results
clearly showed that the intermittent Reynolds stress generation was accompanied by intense
sediment resuspension.

Fig. 1.4.6 (Top) Temporal variations in the current speed (Um) and turbulent dissipation rate (εm)
estimated with the friction velocity at 0.24 m above the seafloor obtained with Acoustic Doppler
velocimeters set in a shallow area, 6.5 m deep, in the Bohai Bay. (Bottom) Vertical profiles of dissolved
oxygen measured within very thin layers of water and sediments close to the seafloor (depth = 0 in thevertical axis), where the vertical resolution is 50 µm. From Wang et al. (2013). Reproduced withpermission of Wiley & Sons.

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Bian et al. (2013) surveyed the distribution of SSC over the extent of the YS and the ECS throughfour seasons and showed that the water in the surface layer in winter and spring was turbid and had
a high SSC, reflecting the resuspension of sediments by intensified vertical mixing. The SSC was
low in the surface layer during summer because resuspension of sediments was suppressed bystratification. The turbulence in the BBL therefore influenced the surface layer in the convective
season, but not in the stratified season. Bian et al. (2013) also discussed the suspension of thesediment, and considered that wind-waves drove the sediment suspension in the coastal region, andthat tidal motions were more important in the offshore region. In the BBL, SSC is mainly controlled
by tidal motions and seasonal patterns are not so clear; further, upward movement of water withhigh SSC water is suppressed by stratification.
Wang et al. (2012, 2013, 2016) took in situ measurements of the DBL and BBL under different
environmental and oxygen conditions at three sites in the coastal region of the ECS. As well as
recording the usual suite of BBL variables, they measured oxygen concentration profiles with aUnisense Mini Profiler MP4 that was equipped with a Clark-type oxygen microsensor (OX25,
Unisense A/S; Müller et al., 2002). Each oxygen profile had a vertical resolution of 50 µm, as
shown in Fig. 1.4.6. Scaling methods for the DBL thickness (δDBL) rooted in the Batchelor lengthscale gave consistent dimensions for the three stations. They found that the method that represented
the Batchelor length scale as a function of flow speed (U) was the most appropriate for scaling theδDBL when the law of the wall applied. The diffusive flux was controlled by the dynamic-forced δDBL
and the difference in the oxygen concentration over the DBL (DC). Values of DC were scaled using
the oxygen concentration of the BBL (CBBL) and the normalized benthic temperature. Theydeveloped a useful method for scaling the diffusive flux from measurements of the benthic
temperature, salinity, U, CBBL, and estimates of the bottom roughness.

1.4.4 Concluding remarks
Researchers have made considerable progress in the area of vertical mixing in the YS and ECS sincethe 2000s. Studies have shown that vertical mixing is generally enhanced in the surface and bottom
layers because of wind effects and tidal motions, but that the turbulence intensity is not always
enhanced in the sub-surface layer. Various studies have suggested that the turbulence is enhanced
intermittently around the pycnocline (Matsuno et al., 2006; Lozovatsky et al., 2015), perhaps
because of the activities of internal waves. Shear instability associated with the breaking of internal
waves might occur intermittently. Liu et al. (2009) found that there were large vertical variations in
the eddy diffusivity Kz around the pycnocline, that is, the Kz was very small within, but was large
just below, the pycnocline. It is very important to note that stratification is strongly related to the
distribution of the turbulence intensity, and this occurs not only around the pycnocline, but also
under weak stratification in the SML and BBL.
The relationship between the turbulence intensity and stratification is difficult to understand because
stratification not only contributes to, but is also a result of, turbulent mixing. Given that the shear
instability may cause enhanced turbulence, strong mixing might occur when the gradientRichardson number is small, which means the turbulence intensity should be negatively correlatedwith the stratification. However, as mentioned by Lozovatsky et al. (2015), even in the ECS, theturbulence intensity was positively correlated with the stratification in some cases, which suggeststhat it follows the parameterization of Mackinnon and Gregg (2005). The fact that the internal wave
fields in the ECS were not fully developed, as expressed by the model of Garrett and Munk (1975),
probably reflects various complicated boundary conditions. It would therefore be difficult topropose a universally applicable parameterization; instead, some specialized parameterizations thatare specific to individual conditions would need to be developed so that the mixing processes in the
ECS can be quantitatively evaluated.

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1.4.5 Future work
We may have two issues for future research on turbulent mixing in the ECS. One is the relationshipbetween the stratification and turbulence intensity, and the other is the contribution of surface waves.
The former is strongly related to internal waves. The most mysterious and challenging issue relevant
to mixing processes in the ECS would be the relationship between turbulence intensity and internal
waves. As mentioned above, turbulence intensity in the subsurface has large variability, where asignificant part of the large variability would be caused by the intermittent occurrence of internal
waves. However, the behavior of internal waves has not been fully determined in this region,
particularly their dissipation processes, which should be connected to mixing processes. Therefore,a full understanding of the internal wave activities, including internal tides and shorter time scaleinternal waves, would be one of the important issues for future work.
Another issue for future research is understanding the role of vertical mixing in the thermohaline
fronts, especially in the northern ECS where the lateral intrusive structure in salinity or temperatureappears frequently in the depth below the thermocline. Lateral intrusion in the front itself isindicative of the mixing process but no (repeated) observation has been conducted yet. A
quantitative understanding of the frontal mixing is important to examine the material exchange
across the front in the region.
Measurements of Reynolds stress could be important to evaluate the turbulence intensity because it
determines the turbulence production. However, high frequency current velocity fluctuations
contains not only turbulence but also (surface) waves, and turbulence intensity could depend partly
on waves. It is an important issue for future work in identifying the contribution of waves after
distinguishing wave motion from direct measurements of high frequency current velocity fluctuations.
Quantitative understanding of the contribution of internal and surface waves to the mixing processes
must improve the parameterization for the vertical mixing, which should be formulated, dependingon various situations, as mentioned above. The parameterization to shelf mixing under different
conditions or different regions should be one of the most important future works, while linkages totheoretical and numerical studies are necessary.
Furthermore, as mentioned earlier, we did not discuss horizontal mixing in this review, mainly
because horizontal mixing processes have rarely been observed in situ in the ECS and YS. However,
we have frequently observed horizontal intrusion of low salinity and low temperature water into thesub-surface in the shelf region of the ECS, which probably affects the formation of the water masses
and the biological environment in this region. Horizontal mixing processes should be quantitatively
evaluated, and future studies should consider both horizontal and vertical mixing processes.

Acknowledgements
We thank Deborah Ballantine, PhD, from Edanz Group (www.edanzediting.com/ac) for editing a
draft of this manuscript.

1.4.6 References
Bian, C., Jiang, W., Quan, Q., Wang, T., Greatbatch, R. and Li, W. 2013. Distributions of suspended
sediment concentration in the Yellow Sea and the East China Sea based on field surveys during thefour seasons of 2011. J. Mar. Syst. 121–122: 24–35, doi:10.1016/j.jmarsys.2013.03.013.

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1.5 Atmosphere–Ocean Interaction
Atsuhiko Isobe
Research Institute for Applied Mechanics, Kyushu University, Japan

1.5.1 Introduction
The “atmosphere–ocean interaction (air–sea interaction)” has been defined as a group of two-way
coupling processes. Oceanic processes such as sea surface temperature (SST) field modify
atmospheric processes such as sea surface winds and pressure fields, while the modified atmosphereagain modifies the ocean in a feedback loop. However, unlike the ENSO event on a planetary scale,spatio-temporal scales are quite different between atmospheric and oceanic processes on theregional scale. Thereby, it seems unlikely to accomplish the two-way processes in a conventionalmanner over the Yellow and East China Sea shelves. Nonetheless, oceanic processes over shallow
shelves are always vulnerable to atmospheric processes via momentum and/or heat fluxes throughthe sea surface, and thus, two-way coupling processes may occur if oceanic processes on the shelvesmodify the atmosphere. In this section, we start with the discussion on whether or not the Yellow
and East China seas can modify extratropical cyclones on a sub-weekly timescale, one of the mostenergetic atmospheric processes over these shelves (subsection 1.5.2). Thereafter, two-way coupling
processes longer than on a sub-weekly timescale are discussed in the subsection 1.5.3.

1.5.2 Two-way coupling process related to extratropical
cyclones
The development of winter extratropical cyclones over warm oceans in a relatively short period(typically 2 to 3 hPa/6 h of surface pressure falls; Gulev et al., 2001) has been an important scientifictopic in both meteorology and oceanography from a viewpoint of atmosphere–ocean interaction. In
particular, many meteorologists have quantitatively estimated characteristics of the cyclone life
cycle, including deepening rate. Warm ocean currents make conditions favorable for the formationof storm tracks in winter (Hoskins and Valdes, 1990). Thus, 50% of rapidly developing (faster than
6 hPa/6 h) cyclones are generated over the Gulf Stream or the Kuroshio in the Northern Hemisphere(Gulev et al., 2001). In fact, synoptic and sub-weekly-scale variability with a period of 2 to 6 days is
prominent over these western boundary currents, in both standard deviation maps of 500 hPa
geopotential height (Gulev et al., 2002) and sea surface heat flux (Zolina and Gulev, 2003). One
may also refer to composite maps of intensive sea surface heat flux within an individual
extratropical cyclone in Rudeva and Gulev (2011). The above statistics of extratropical cyclone
deepening may have uncertainties because of various reanalysis datasets used in the studies.
Nevertheless, recent intercomparison (Allen et al., 2010; Hodges et al., 2011) shows the similarityof cyclone occurrence in multiple reanalysis datasets, at least in the Northern Hemisphere.

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Many publications indicate that winter extratropical cyclones deepen explosively over East Asia
(e.g., Chen et al., 1991) as well as the Gulf Stream region. Rapid deepening of extratropical
cyclones is reasonable because sub-weekly-scale baroclinic wave activity, with intra-seasonal,
interannual, and decadal variations, is energetic over East Asia (Nakamura, 1992; Nakamura et al.,2002), and because warm SST is sustained by intense horizontal heat transport by warm ocean
currents such as the Kuroshio and Tsushima Current (Chen et al., 1992). In fact, Yamamoto andHirose (2007) used a numerical model approach with a high resolution SST field at the lower
boundary to show that cyclogenesis is significantly enhanced north of Japan’s islands. They asserted
the primary importance of lower-level baroclinicity, enhanced by a sharp horizontal SST gradient
for activation of winter extratropical cyclones. Furthermore, based on a long-term reanalysis dataset(JRA-25, Onogi et al., 2007), Yoshiike and Kawamura (2009) found that the enhanced baroclinicity
in the lower troposphere indeed intensifies winter extratropical cyclones north of Japan, especiallyduring strong monsoon periods.
The oceanic influence of the Yellow and East China seas on the deepening of winter extratropical
cyclones may be comparable with that in the area north of Japan, because both areas are at
mid-latitudes between 30° and 50°N, with zonal width O(1000 km) sufficient for extratropical
cyclone development. In addition to this geographical similarity, relatively high SST with sharp
horizontal gradient is maintained in both areas, even in midwinter. Figure 1.5.1 shows a winter SST
map averaged using 5-km resolution cloudless SST images (Isobe and Kako, 2012), in which
infrared and microwave observations from multiple satellites are synthesized by the New
Generation Sea Surface Temperature (NGSST) research group at Tohoku University (Guan and
Kawamura, 2004).

Fig. 1.5.1 Study area. Regional atmospheric model domain of this study is represented by the areasurrounded by the bold line. The contour map denotes SST averaged over winters (December–March)from 2002 to 2008 in the area. Contour interval is 1°C. Dotted line is 200-m isobaths for indicating theshelf-break locations. Redrawn from Isobe and Kako (2012). ©American Meteorological Society. Used
with permission.

Meteorological studies suggest, however, that the activity of winter extratropical cyclones over the
Yellow and East China seas may be very different from that north and south of Japan, although thisdifference has received little attention. For instance, a well-known map of winter cyclone frequency
within 5° boxes over the North Pacific (Figure 1 of Gyakum et al. (1989)) shows that surface
cyclones are less common over the Yellow and East China seas in comparison with other76

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extratropical Pacific regions. Figure 1(b) in Yoshida and Asuma (2004) indicates that positions
where extratropical cyclones develop faster than 1 bergeron are sparse in the Yellow and East Chinaseas, suggesting that extratropical cyclones are prevented from developing in these areas. Based on
5° resolution surface pressure maps, however, Hanson and Long (1985) mentioned that wintercyclogenesis tends to be active above the Kuroshio in the East China Sea. Likewise, Xie et al.
(2002) used a regional atmospheric numerical model to demonstrate that an extratropicalsynoptic-scale cyclone developed rapidly in February 2001 along the East China Sea shelf break,where lower-level baroclinicity is enhanced above the Kuroshio (see Figure 5 in Yoshiike andKawamura (2009)).
Are the Yellow and East China seas favorable for extratropical cyclone development in winter? Boththe objectively analyzed and reanalysis datasets are no doubt powerful tools for investigating
extratropical mesoscale cyclones over the western North Pacific (e.g., Yoshida and Asuma, 2004;
Yoshiike and Kawamura, 2009). Nevertheless, horizontal resolution larger than 1° in both latitude
and longitude (e.g., 1.25° in the case of JRA-25) might still be too coarse for distinguishing oceanic
influences of each East Asian marginal sea on cyclone activity. This is because their zonal scales areO(1000) km, and because horizontal scales of SST fronts and eddies are an order of magnitude
smaller. In fact, Yamamoto and Hirose (2007) used the Pennsylvania State University/National
Center for Atmospheric Research mesoscale model (MM5V3; Grell et al., 1994) with 10-km(~1/12°) grid cells to investigate the dependency of cyclogenesis on the sharpness of SST fronts inthe model domain.
Isobe and Kako (2012) used Quick Scatterometer (QuikSCAT) data to investigate lower-tropospherevorticity fields with 0.25° resolution over the study area. Furthermore, using the NGSST datasetwith SST condition at the finest resolution, they conducted numerical model experiments using
MM5V3 at 12-km resolution to explore atmospheric and/or oceanic conditions favorable forextratropical cyclone development over the Yellow and East China seas. Standard deviations of thehigh-pass-filtered vorticity derived from the QuikSCAT data were computed at each grid cell over
six winters (2002–2008) by removing low-frequency fluctuations with a 7-day box-car filter in each
winter (Fig. 1.5.2).

Fig. 1.5.2 Standard deviation map of sub-weekly-scale vorticity fluctuations computed from
QuikSCAT-derived surface winds. Standard deviation was computed using vorticity high-pass filtered by
a 7-day box filter over six winters from 2002 to 2008. Contour interval is 0.5 hPa. From Isobe and Kako(2012). ©American Meteorological Society. Used with permission.

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This satellite-derived vorticity field demonstrates that, on average, extratropical cyclone activity ismoderate over the warm Yellow and East China seas in winter. This is because enhanced lower-levelbaroclinicity (measured by the modeled gradient of equivalent potential temperature (EPT) at 925
hPa height) over these ocean areas is transferred as far as the shelf beak of the East China Sea bystrong northwesterly surface winds (Fig. 1.5.3a). It seems that the disappearance of the areas withhigh baroclinicity over the Yellow and East China seas results from prevailing spatiallyhomogeneous northwesterlies because of the absence of high mountains surrounding these areas.
However, based on the numerical model results, it is found that the lower-level baroclinicity isenhanced over the Yellow and East China seas only when northwesterly surface winds weaken(Fig. 1.5.3b). Moderate winds are therefore required in the Yellow and East China seas formaintaining an oceanic influence on the atmospheric planetary boundary layer, and for enhancing
cyclone activity in winter.

Fig. 1.5.3 Horizontal distribution of modeled equivalent potential temperature (EPT) at 925 hPa height(solid curves) along with its horizontal gradient depicted by shading. The two panels are shown in the (a)positive and (b) negative phases from the temporal mean of the EPT time series averaged over a box (30–36°N, 122–126°E) in the Yellow Sea. Contour interval of EPT is 5°C, and scale of shading is at the top of
figures. Also shown are wind vectors at every tenth grid in each phase. All modeled data used here were
low-pass-filtered by a 7-day box filter. From Isobe and Kako (2012). ©American Meteorological Society.
Used with permission.

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As well as winter cyclones, a two-way coupling process is accomplished in spring in the East China
Sea. Kueh and Lin (2015) found that the springtime development of the Taiwan low depends on thepreconditioning caused by the passage of a cold front and related cold-air outbreak. Using a regional
atmosphere–ocean coupled model, they demonstrated that the strong wind during a cold-air
outbreak results in SST lowered around the East China Sea shelf break owing to vertical mixing andretreat of the Kuroshio, and that the Taiwan low becomes stagnant over the upper ocean with the
reduced heat content.

1.5.3 Two-way coupling processes longer than on a sub-weekly
timescale
As mentioned above, lower-level baroclinicity (and hence, cyclone development) is activated owing
to the SST gradient over the winter in the Yellow and East China seas, while the areas with highbaroclinicity depend strongly on the northwesterly winter monsoon. In spring, the Taiwan low
becomes stagnant because the SST is lowered by the cold-air outbreak. Although they are indeed an
atmosphere–ocean interaction, their two-way processes are significant only during the passage ofextratropical cyclones over the seas. We next examine atmosphere–ocean interaction maintained in
the Yellow and East China seas, at least, over the intraseasonal timescale.
It is frequently found that surface winds are positively correlated with both SST and its gradient.
This is partly because of vertical momentum mixing within the atmospheric boundary layer(Wallace et al., 1989), and partly because of the adjustment of the sea level pressure (SLP) above
sharp oceanic fronts (Lindzen and Nigam, 1987), although a negative correlation was transiently
observed at the onset of a cold-air outbreak over the warm Kuroshio in the East China Sea (Kasamo
et al., 2014). Recent advances in satellite observations have revealed how ocean winds are modified
above oceanic fronts on horizontal scales much smaller than typical atmospheric processes (e.g.,
Chelton et al., 2001; Nonaka and Xie 2003; Vecchi et al., 2004). For instance, Chelton et al. (2001)
developed a quantitative procedure to examine the vertical mixing mechanism of winds modified byoceanic tropical instability waves, and showed that the perturbed wind stress curl (divergence) islinearly proportional to the crosswind (downwind) gradient of perturbed SST. Based on analyses ofsatellite microwave measurements over the North Pacific, Nonaka and Xie (2003) found a positive
correlation between SST and wind speeds over the intense Kuroshio and Kuroshio Extension,
whereas the correlation that is usually observed between these two properties is negative, especiallyabove weak currents south of the Aleutian low. Furthermore, Vecchi et al. (2004) showed that low(high) wind velocities are observed over cold (warm) SST areas in the western Arabian Sea. As
described above, a positive SST–wind correlation, which suggests an active influence of the
mid-latitude ocean on the atmosphere, has been detected by satellite in various regions of the world.
In the Yellow and East China seas during winter, oceanic influences on atmospheric processes arelikely to occur because, even in mid-winter, relatively warm water originating from the Kuroshio
spreads over the sea surface, and because vertical momentum/heat transfer is enhanced by the cool,dry northerly monsoon winds prevailing over these warm seas at this time. In fact, Xie et al. (2002)
showed a positive correlation between surface winds and SST over the winter in the Yellow andEast China seas using wind speeds observed by the QuikSCAT/Seawinds and SST observed by the
Tropical Rain Measuring Mission satellite’s microwave imager.
Meanwhile, in both conventional oceanography and meteorology, it has been considered that SST is
passively influenced by the atmosphere via surface heat flux. In addition, the surface momentum
flux, which drives ocean currents, also alters SST fields by the horizontal advection of heat. In fact,
these processes of atmospheric forcing have a significant impact on the oceanic condition over the

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Yellow Sea and East China Sea shelves because most of the area is shallower than 100 m. Xie et al.
(2002) demonstrated that isotherms appear to lie parallel with isobaths over the Yellow Sea and East
China Sea shelves in winter because shallow areas become much cooler owing to the smaller heat
content compared with deeper areas. In addition, Hsueh (1988) and Isobe (2008) showed that strong
northerly (southward) wind events over the Yellow Sea are followed by the occurrence of northward
counter-wind ocean currents 1 to 2 days later. Thereafter, this northward ocean current alters the
SST field in the Yellow Sea, as shown by Lie et al. (2001).
As mentioned above, the relatively warm Yellow and East China seas beneath the cool and dry air
might play an active role in altering the wind field during winter, whereas the SST field must be
altered passively due to the intense northerly winter monsoon winds prevailing over the seas.
Different from the tropics, where a well-known interaction such as Bjerknes feedback exists on the
ENSO timescale, the difficulty in accomplishing two-way processes in mid-latitude shelf seas stems
from the considerably different spatio-temporal scales between oceanic and atmospheric processes.
Note that the most energetic signal in the SST field in shallow shelf seas is the seasonal (regional)
signal, whereas sub-weekly (synoptic) and monsoonal (planetary) signals are the more energetic in
the wind field.
The most reliable method to find two-way oceanic and atmospheric processes longer than on a
sub-weekly timescale is probably the regional atmosphere–ocean coupled model approach,corroborated by observed datasets. For instance, a regional coupled model developed by Fang et al.
(2010) demonstrated that SST bias was revealed between the coupled and uncoupled (ocean generalcirculation) model owing to the mismatch of the surface heat flux. Also, in a recent study done by
Iwasaki et al. (2014), both coupled and uncoupled models were built in order to compare theirresults with synthesized satellite-derived SSTs, and to evaluate how effective the coupled model wasin reproducing the SST variability actually observed in the Yellow and East China seas in winter.
Results of their study showed negative feedback between monsoon winds and SST in coastal waters
during winter.
A regional atmosphere–ocean coupled model (Fig. 1.5.4) was developed by Iwasaki et al. (2014),
based on the MM5V3 in conjunction with the Princeton Ocean Model (POM; Mellor, 2003), toinvestigate atmosphere–ocean interaction that might occur over the Yellow Sea and East China Sea
shelves in winter. To examine how the coupled processes actually work in the ocean, SST fields
computed in both coupled and uncoupled models were compared with those synthesized frommultiple satellite observations. As mentioned above, the shallow areas, especially around theChinese coasts, become much cooler owing to the smaller heat content compared with deeper areas.
Their results, however, indicated that the coupled model significantly improves the negative SST
bias revealed around the Chinese coast in the uncoupled model (Fig. 1.5.5).
Figure 1.5.6 shows a schematic of the negative feedback process accomplished in the shallow
coastal waters of the Yellow and East China seas in winter. Northerly monsoonal winds prevail overthe Yellow and East China seas in winter because, in general, the horizontal sea level pressure (SLP)
gradient is sharp owing to the large landmass–ocean heat contrast (Fig. 1.5.6a). Enhanced cool anddry winds from the Asian landmass lower the SST, especially in shallow coastal waters, due to theintensive upward heat loss (Fig. 1.5.6b). Thereafter, the horizontal SLP gradient around the Chinese
coast becomes moderate because the land–ocean heat contrast weakens due to the lowered SST incoastal areas (Fig. 7.5.6b). As a result, the wind speed is reduced in line with the moderated
horizontal SLP gradient around the coast (Fig. 1.5.6b). Moreover, surface air temperature andspecific humidity becomes high because cool and dry air is reduced due to the weakening of thenortherly winds over the coastal waters. Hence, the upward heat flux in the coastal waters is reduced
because of the weakening of the northerly winds and the decrease of cool and dry air and thus, thisnegative feedback process can suppress excessive cooling along the Chinese coast during winter.

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The Yellow and East China seas meet two prerequisites favorable for the above mentioned negative
feedback process to occur during winter. One is warm and shallow shelf seas neighboring a huge
Asian landmass to maintain the sharp gradient in heat content (hence, SLP). The other is theoffshore winds that carry cool and dry air over the warm ocean lowering the SST in coastal waters.

Fig. 1.5.4 Flow chart showing the coupling procedure between the atmospheric and oceanic models. The
properties computed in these two models are exchanged in the order of numerals within the boxes. The
Princeton Ocean Model (POM) gives SST to the Penn State Mesoscale Model (MM5V3), while theMM5V3 gives the momentum flux computed using wind speed at 10 m height (U10, V10), heat flux, andfreshwater flux to POM. The exchange of the modeled properties are conducted every 6 hours. From
Iwasaki et al. (2014). ©American Meteorological Society. Used with permission.

Fig. 1.5.5 (a) Model domain and bottom topography. Rectangles in panel (a) indicate the MM5V3 andPOM domains. Letters A to C and B to D, placed beside the broken lines, indicate the locations of inflow,
outflow, and sidewall boundaries. Isobaths are in meters. The small rectangle along the Chinese coast is
used for panel (b), where temporal variations of daily SST averaged over the rectangle are depicted during
winter of 2009 to 2010. Different line types in the upper right-hand corner are used for temperaturesderived from MURSST (Multi-sensor Ultra-high Resolution SST), the coupled model, and uncoupledmodel. Abscissa indicates days elapsed from December 1. Redrawn from Iwasaki et al. (2014).
©American Meteorological Society. Used with permission.

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Fig. 1.5.6 Schematic view of the atmosphere–ocean coupled process over the coastal waters of the
Yellow Sea and East China Sea in winter. The variation in sea level pressure before and after the negative
feedback process is expressed by changing the size of the red circle with the letter “L”. The variation inwind speeds in the coupled process is expressed by changing the thickness and length of the arrows. From
Iwasaki et al. (2014). ©American Meteorological Society. Used with permission.

Besides winter processes mentioned above, one of the most remarkable intraseasonal atmosphericfeatures in East Asia is the Baiu rainband in early summer. The oceanic (especially, Kuroshio)
influence on the Baiu frontal zone has been pointed out by recent studies such as Sasaki et al.
(2012), Kunoki et al. (2015), and Sato et al. (2016). The Kuroshio anchors the ascent motion of theBaiu frontal zone and large diabatic heating, and thus, the Kuroshio influence results in a deep
atmospheric response extending to the upper troposphere (Sasaki et al., 2012). The two-way
processes may occur if the modified Baiu frontal zone (hence, surface momentum and heat fluxes)
again changes the ocean, although the accomplishment of such two-way processes should be further
explored in terms of atmosphere–ocean interaction studies.

1.5.4 Future work
In this short review, our attention is focused only on physical processes. However, as suggested by
Isobe et al. (2014), a coupling of physics and ocean biology is potentially capable of improving a
regional atmospheric and oceanic model, which will be a challenging topic valuable to understanding
the regional climate.

Acknowledgements
The critical and careful reading by Dr. Hanna Na is greatly appreciated.

1.5.5 References
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Chen, S.-J., Kuo, Y.-H., Zhang, P.-Z. and Bai, Q.-F. 1991. Synoptic climatology of cyclogenesis overEast Asia, 1958-1987. Mon. Wea. Rev. 119: 1407–1418,
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the Pacific storm track activity and East Asian winter monsoon. J. Climate 15: 1855–1874,
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1.6 Long-Term Variations of Physical Properties
Sang-Wook Yeh1 and Jae-Hak Lee2
1
2

Hanyang University, ERICA, Korea
Korea Institute of Ocean Science and Technology, Korea

1.6.1 Introduction
There exist previous studies which reported that there is faster ocean warming around marginal seasthan in other areas within ocean basins (Hansen et al., 2010; Burrows et al., 2011; Lima and Wethey,2012). Such warming in marginal seas could be due to the increase of greenhouse gas forcing,natural variability, or the combined influence of both. In particular, it has been reported that thestrongest warming in the globe has been observed in the East Asian marginal seas (Belkin, 2009;
Yeh et al., 2010; Bao and Ren, 2014; Wu et al., 2017; Kim et al., 2018). Therefore, it is useful tounderstand the long-term variations of physical properties in the East Asian marginal seas. Amongthem is the Yellow Sea/East China Sea (hereafter, YECS), which is a shallow, semi-closed basinwith depths < 100 m located in the northwestern Pacific Ocean (Fig. 1.6.1). The YECS is bounded

Fig. 1.6.1 The Yellow and East China seas (YECS). Continuous lines denote isobaths in meters. The
dashed line connecting the Changjiang River mouth and Jeju-do indicates the geographical boundary
between the Yellow Sea and East China Sea. From Lie and Cho (2016).

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by the Korean Peninsula, the Ryukyu islands (East), China (West), Japan (North), and Taiwan Island(South) (Chu et al., 2005; Lie and Cho, 2016). The YECS is connected not only to the Pacific but
also to two neighboring marginal seas through the Taiwan Strait to the southwest and the Korea–
Tsushima Straits to the northeast. Therefore, the YECS plays a role as a shallow but large reservoir
for water and materials originating from both land and ocean; it also serves as link supplier for thenorthwestern Pacific and the deep marginal seas. In addition, it is known that the social andeconomic conditions around the coast of East Asia marginal seas are largely influenced by the East
Asian monsoon system, which is associated with the state of YECS sea surface temperature (SST)(Li et al., 2010). The SST condition in the YECS under global warming is able to regulate thegrowth of extratropical storms, which significantly modulates weather and climate conditions inEast Asia (Inatsu et al., 2013). This review paper contains a brief summary of the physicalproperties of the YECS in terms of long-term variations of SST, sea level pressure and theirassociated atmospheric circulation.

1.6.2 Basic features of physical properties in the Yellow Sea
and East China Sea
The physical properties of the YECS are relatively well known in terms of its hydrography, watermass features based on observations and high-resolution numerical modeling (Lie et al., 1998;
Tseng et al., 2000; Lie et al., 2001; Ichikawa and Beardsley, 2002; Lie and Cho, 2002; Chu et al.,2005; Lie and Cho, 2016). The most important element of the YECS circulation system is theKuroshio which flows northeastward along the continental slope of the ECS (Zheng and Klemas,
1982; Hickox et al., 2000; Park and Chu, 2008; Chen, 2009; Lee et al., 2014; Lie and Cho, 2016).
The Kuroshio, which is a main driving forcing of the YECS, carries a large amount of heat, salt andoceanic materials from the tropical and subtropical ocean to the YECS. On the other hand, coastal
currents in the YECS also carry a large amount of freshwater and materials from land to the offshore
shelf. This results in the formation of various oceanic fronts between the Kuroshio region with ahigh salinity and coastal waters with a low salinity (Zheng and Klemas, 1982; Hickox et al., 2000;
Park and Chu, 2008; Chen, 2009; Lee et al., 2014).
On the other hand, the variability of SST in the YECS is usually associated with both local andnon-local forcings. Local forcings include solar radiation, tide flow variations and atmosphere
intraseasonal variability (Weng et al., 1993, Zeng et al., 2006). In general, the high frequency
variability of SST in the YECS is mainly associated with local forcings (Weng et al., 1993). For
example, the SST variability in the YECS on seasonal and sub-seasonal timescales is dominated bymonsoons, oceanic advection, vertical mixing, bathymetry and so on (see, e.g., Tang et al., 1989;
Bao et al., 2002; Furey and Bower, 2005; Liu et al., 2005). According to a recent study (Wu et al.,2016) in particular, over 90% of the temporal variability of SST in the YECS can be explained by
the annual harmonic whose amplitude is one order larger than that of the semiannual harmonic.
Similarly, Huang et al. (2012) argued that the SST in the YECS over the last 141 years (1870–2010)
can be partitioned into an annual component (i.e., seasonal signal with zero mean) and an annual
mean component (i.e., inter-annual, decadal and long-term trend). They further argued that theannual sinusoidal signal dominates the semi-annual signal by contributing greater than 94% to theannual range in general; the semi-annual signal contributes more than 10% to the annual range inthe eastern Yellow Sea, particularly to the area south of the Korean Peninsula. The annual cycle of
SST reaches its peak from mid-July in the south to early August in the north and central YECS.
In contrast, non-local forcing, including El Niño–Southern Oscillation (ENSO), Pacific Decadal
Oscillation (PDO) and North Pacific Oscillation (NPO) is associated with the interannual SST
variability in the YECS through ocean and atmospheric teleconnections (see, e.g., Park, 2000; Wu et

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Physical OceanographyLong-Term Variationsal., 2005). Using a fine resolution SST dataset, analysis indicates that a significant peak around 3.3
years exists in the YECS and such interannual variability of SST is linked with the ENSO
teleconnection through which anomalous surface heat flux warms or cools the YECS during El Niño
or La Niña events (Wu et al., 2016). Therefore, SST variations over the YECS exhibit multiple
timescales and spatial patterns with different origins (Zhang et al., 2010).

1.6.3 Long-term variation of SST variability and sea level
The low-frequency variability of the YECS climate system has been extensively studied in terms of
SST (e.g., Belkin et al., 2009; Huang et al., 2010; Yeh and Kim, 2010; Zhang et al., 2010; Kim etal., 2011; Park et al., 2011; Oey et al., 2013; Wang et al., 2013; Bao and Ren, 2014; Park et al.,2015, Wu et al., 2017, Kim et al., 2018). Previous studies have shown that SST over the coastal
China seas demonstrates the strongest warming trend over the global ocean (Chen and Ho, 2002;
Wu et al., 2012). Using a 38-year dataset collected from six costal stations, Lin et al. (2001)
identified a warming trend in the Bohai Sea. The warming trends of the marginal seas of China for
1870–2011 are generally larger than the global and hemispheric averages, with the largest warmingin the ECS (Bao and Ren, 2014). Yeh and Kim (2010) also examined the characteristics in the SST
variability in the YECS during boreal winter for 1950–2008. They found that the mean SST in the
YECS gradually increases until the late 2000s. A warming trend of a basin-scale SST is significant
in most of the regions in the YECS. A broadly accepted conclusion from these studies indicates arobust and persistent SST increase in the YECS over recent decades, with the rates of 2 to 4 times
faster than the global averaged trend of 0.13°C per decade (Trenberth et al., 2007).
In addition to the warming trend in the YECS, low-frequency SST variability is also dominant (Parkand Ho, 2000). For example, Zhang et al. (2010) examined the mechanisms of SST low-frequency
variations in the YECS using the HadISST (Rayner et al., 2006) for 1900–2006. They suggested
that there are two distinct low-frequency modes in the YECS: a basin mode and a north–south
dipole mode (Fig. 1.6.2). The former is characterized by a uniform warming over the entire coastal
oceans, while the latter is characterized by a seesaw pattern over the shelf seas and the South China
Sea. The basin mode is associated with global warming, but it is primarily attributed to oceanic
advection. The dipole mode varies coherently with the PDO (Mantua et al., 1997). In contrast to thebasin mode, the dipole mode is broadly associated with the surface heat flux, with oceanic advection
acting to dampen SST in the shelf seas but sustaining SST in the South China Sea. Zhang et al.
(2010) further argued that the basin mode and the dipole mode are dominant in boreal winter(December–January–February) and summer (June–July–August) despite some differences in thegoverning processes. Yeh and Kim (2010) argued that the NPO-like sea level pressure plays animportant role to warm the YECS. The southern lobe of NPO-like sea level pressure over the North
Pacific causes a weakening of northerly mean winds over the YECS during winter, which
contributes to an increase in the SST in the YECS through the changes in the latent heat andsensible heat fluxes.
Figures 1.6.3a and b display the 11-year average of SST anomalies in the East Asian marginal seas,
including the YECS in boreal winter and summer for 1980–2015, respectively. As suggested inprevious studies, the decadal variations of SST anomalies in the YECS are evident in both boreal
winter and summer. In particular, the SST in the YECS was characterized by a warming trend until
the late 2000s. However, such a warming trend is not seen in the 21st century. The SST anomalies in
the YECS for 2000–2015 are cooler than the climatology (1980–2015) in both winter and summer,
respectively, indicating that the warming trend over the YECS has become weaker in the past
decade.

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Fig. 1.6.2 Empirical orthogonal
function (EOF) modes of lowpass-filtered (>7 years) sea surface
temperature (SST) from theHadley Centre Sea Ice and Sea
Surface Temperature data overthe coastal China seas: (a) first
EOF, (b) second EOF, (c) firstprinciple component (PC), and (d)second PC. Units in (a) and (b) are
in °C. PCs have been normalizedwith a unit variance. From Zhang
et al. (2010). Reproduced withpermission of Wiley & Sons.

Recently, Bao and Ren (2014) showed that the warming in the marginal seas of China for 1998–
2011 undergoes a slowdown in warming and such a slowdown is more evident than the global andnorthern hemispheric averages. Similarly, Wu et al. (2017) found that the most significant warming
occurred in boreal winter during 1982–1998, based on four different SST datasets although thewarming trends derived from these datasets differ in magnitude. Wu et al. (2017) argued that the
rapid warming for 1982–1998 is a result of the asymmetry in the ENSO teleconnection, throughwhich El Niño events induce significant warming over the eastern China Coastal Waters at its peak,whereas La Niña events fail to do the opposite that would completely reverse the trends. That is,more frequent occurrence of El Niño than La Niña events for 1982–1998 leads to the warming trend
in the eastern China Coastal Waters. Since late 1998, however, a La Niña event occurs more
frequently than the El Niño event. This also may contribute to the slowdown of the warming trend
in the East Asian marginal seas.
Consistent with the result in Figure 1.6.3, Kim et al. (2018) also argued that a basin-scale cooling in
the YECS followed after the warming of the sea surface reached a peak in the late 1990s (Fig. 1.6.4).
They reasoned that this is closely associated with the variation in the Siberian High along with its
intensification after the late 1990s. A contrasting relationship between the YECS SST and theSiberian High intensity is observed before and after the late 1990s, i.e., the two variables (YECS
SST and Siberian High intensity) are barely correlated for 1982−1997, while their out-of-phaserelationship becomes significant in the recent years (1998−2014) when the intensity of the SiberianHigh becomes stronger. This suggests that the Siberian High has played an increasing role indetermining the state of the YECS since the late 1990s. On the other hand, there is a possibility thata low-frequency variability of YECS SST is associated with the changes in the relationship between
the YECS SST variability and the PDO. While the YECS SST has a close correlation with the PDO
index before the late 1990s, it weakens after the late-1990s, when the North Pacific regime shift
occurred. In other words, the atmospheric–oceanic signals associated with the PDO were regionallyconfined to the eastern basin of the North Pacific after the late-1990s, and concurrently resulted in
the decoupling of the YECS climate from the PDO.

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Fig. 1.6.3 The 11-year average of sea surface temperature (SST) anomalies in the East Asian marginal seas, including the Yellow Sea and East China Sea (YECS) in (a) boreal
winter (December–January–February) and (b) summer (June–July–August) for 1980–2015. The year above in each figure indicates a center of year for the 11-year period. For
example, 1985 denotes the SST anomalies averaged in 1980–1991.

(a)

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Fig. 1.6.3(b)Long-Term Variations

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Fig. 1.6.4 (a) Time series of the monthly sea surface temperature (SST) anomaly averaged in the Yellow
and East China seas (black); thick green and red lines indicate the 8-year low-pass filtered SST anomaly
and the trends for the periods 1982−1997 and 1998−2014, respectively. The spatial distributions of thelinear SST trends are illustrated for the periods (b) 1982−1997 and (c) 1998−2014. Gray dots show that thetrends are insignificant trends at the 95% confidence level. From Kim et al. (2018). Reproduced withpermission of Elsevier.

Sea level changes may affect regional tidal dynamics in shelf seas such as the YECS (Pickering etal., 2012; Ward et al., 2012). Therefore, coastal ecosystems and their communities in the YECS areclosely associated with sea level variations (Chen et al., 2014). There are a number of studies
investigating sea level variability in the YECS, including the coastline of China (Kang et al., 1995;
Han et al, 2008; Pelling et al., 2013; Chen et al., 2014). It has been reported that the altimetric sea
level rise rate is 0.64 cm yr–1 for 1992 to 2002, which is much larger than the rate of 0.24 cm yr–1
for 1980 to 2002 (Han et al., 2008). For the period of 1992 to 2002, in details, the altimetric annual
harmonic has a magnitude of 10 to 30 cm in amplitude and is highest in summer in the ECS (Han etal., 2008). In particular, the interannual and longer-term sea level variability in the altimetric data
are negatively correlated with the PDO, attributed in part to steric height change in the ECS (Han etal., 2008).
The tidal regime in the Bohai Sea has recently experienced rapid coastline changes due to naturaldevelopments of the Yellow River delta and to large-scale anthropogenic land reclamation (Pelling
et al., 2013). It is reported that there have, indeed, been significant changes in the tidal regime in the
Bohai Sea over the last 35 years (1976–2002), with M2 amplitudes changing up to 20 cm in someparts. On the other hand, a significant increase in sea level extremes is found with trends in therange between 2.0 and 14.1 mm yr–1 adjacent to the Chinese coast for 1954–2012 (Feng andTsimplis, 2014). Hourly sea level records at 20 tide gauges at and adjacent to the Chinese coasts
show that tides and tropical cyclones determine the spatial distribution of sea level maxima. Theinterannual variability in the sea level extremes is also influenced by the changes in median sea
level at the north and by the 18.6 year nodal cycle at the South China Sea.

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1.6.4 Summary
Previous studies have shown that SST over the East Asian marginal seas is characterized by a strong
warming trend. In addition, global warming has been thought to be a key mechanism leading tosuch a warming trend in the YECS. However, such a warming reached a peak in the late 1990s;
subsequently, a basin-scale cooling has been observed in the recent past decade. Recent studies
argue that a low frequency variability of YECS SST, which is associated with both atmosphericcirculation and oceanic condition, is responsible for such a cooling trend in the YECS.
Because the variability of the YECS SST is closely connected to the Pacific Ocean SST on
interannual-to-decadal timescales, including ENSO and PDO, in particular, it is important toexamine the changes in the relationship of such phenomena with YECS SST variability. Recent
studies have shown that the Pacific Ocean experienced an abrupt, systematic change, which can becharacterized by: 1) the central Pacific El Niño having occurred more frequently than theconventional El Niño (Yeh et al., 2009); 2) the North Pacific Gyre Oscillation (NPGO), defined as
the second sea surface height mode in the Eastern North Pacific (Di Lorenzo et al., 2008, 2010),
becoming dominant compared to the PDO; 3) the low-frequency PDO index entering a negative
regime from a positive one (Peterson and Schwing, 2003; Overland et al., 2008; Jo et al., 2015;
Newman et al, 2016). It has been found that the SST in the YECS varied closely with the PDO
before the late 1990s, indicating that the warming trend in the YECS SST was closely linked to the
PDO. However, the recent SST in the YECS is largely influenced by the intensified Siberian Highpressure system via enhanced heat losses in terms of latent and sensible heat fluxes. This indicatesthat the low frequency SST variability in the YECS might be determined largely by the relative roleof the atmosphere and ocean system.
A number of studies have indicated that the sea level in the YECS, including the coastline of China,
is rising, which is closely associated with regional tidal dynamics. In addition, the interannual
variability of sea level variations is associated with a large scale of SST variations in the Pacificsuch as the PDO. In particular, a regional ocean general circulation model simulation showed that at
the end of the 21st century, the sea level in the YECS will rise about 0.12 to 0.20 m, which is caused
mainly by ocean mass redistribution due to the ocean dynamic change of the Pacific Ocean (Chen etal, 2014). These results indicate that it is essential to examine the relationship of the YECS and thesurrounding Ocean to understand the SST and sea level variations in the YECS from the present tofuture.

1.6.5 Future work
Considering global warming due to the increase of greenhouse gas concentrations, it is necessary to
examine more details on the impact of YECS SST by analyzing various observational datasets andmulti-model datasets. In particular, it is essential to correctly project YECS SST to gain anunderstanding of anticipated changes in the marine ecosystem as well as climate variability within
East Asia in a future climate. To do so, first of all, it is crucial to understand the details of physicalprocesses associated with atmosphere–ocean coupled interactions in the YECS because of its
shallow depth. On the other hand, it is also necessary to understand the inter-model diversity
simulating YECS SST during the near future in climate models. Recently, Yi and Yeh (2019)
examined the inter-model diversity when simulating East Asian marginal seas SST in the period2020 to 2049 under four different Representative Concentration Pathway runs. They argued that theinter-model diversity of simulated tropical Pacific SST is associated with the diversity of East Asian
marginal seas SST and is related to the wind speed associated with atmospheric teleconnections
from the tropics to the western-to-central North Pacific (see Fig. 1.6.5). This indicates that it is92

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necessary to understand the details of physical connections of the YECS and its surrounding ocean
basins, including the North Pacific and the tropical Pacific in both the observations and climate
models.

Fig. 1.6.5 Scatter plot of annual mean central-to-eastern tropical Pacific (170°W–120°W, 10°S–10°N)
SST (°C) with western-to-central North Pacific (150°E–175°W, 35°N–50°N), wind speed (m s–1) at 850
hPa for 11 climate models in the near future (2020–2049) in the RCP2.6 scenario. The simultaneous
correlation coefficient between the two variables is 0.56, which is statistically significant at the 90%
confidence level. From Yi and Yeh (2019). Reproduced with permission of Wiley & Sons.

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Preface
The water in the Yellow Sea and the East China Sea originates mainly from the Kuroshio Current.
The water resides for about 5 years in the Yellow Sea, which is one of the largest continental shelvesin the world. This region receives disproportionally large amounts of anthropogenic materials fromthe continents via the atmosphere, rivers, and groundwater inputs. Owing to such oceanographic andenvironmental settings, vigorous biogeochemical alterations of natural and anthropogenic chemical
constituents take place in this region. Thus, the observational results in this region may provide key
information on the biogeochemical processes of human impacts in the ocean. This chapter aims at
understanding the inputs of chemical constituents from rivers, the atmosphere, and groundwater, thebiogeochemical reactions in seawater and sediments, and the fluxes in the Yellow Sea and the East
China Sea to the Pacific Ocean.
The chapter includes six sections: (1) dissolved methane and nitrous oxide – distribution, controlling
factors, and air–sea exchange; (2) nutrient biogeochemistry – spatial and temporal variations,distribution patterns, sources and sinks, and budgets; (3) hypoxia – hypoxia events, current features,organic matter decomposition, and impact of hypoxia; (4) biogeochemical tracers – trace elements
and radioisotopes in seawater, sediments, and depositional fluxes; (5) persistent organic pollutants(POPs) and heavy metal pollution – OCPs, PCBs, SCCPs, Brominated POPs, Fluorinated POPs, andheavy metals; (6) atmospheric deposition – dusts, trace elements, and nutrients.
Although there has been remarkable progress in the accurate analyses of various chemical
constituents in this region during the last few decades, the measurements are limited to specificareas in specific seasons. Therefore, more comprehensive studies covering the entire Yellow Sea andthe East China Sea over different seasons are necessary to better understand biogeochemical
characteristics and cycling. Since this region is included in the Exclusive Economic Zones of three
countries, extensive collaboration is necessary for cooperative research cruises, sampling exchanges,
and data exchanges. In order to compile the data from different laboratories and regions,inter-comparison of experiments and data validation steps are also necessary. All these cooperative
activities can be performed more efficiently through international platforms such as PICES.
Jing Zhang, Su Mei Liu, Guebuem Kim

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Dissolved Methane and Nitrous Oxide

2.1 Dissolved Methane and Nitrous Oxide
Gui-Ling Zhang and Wang-Wang Ye
Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, Ocean University of
China, China

2.1.1 Introduction
Methane (CH4), the simplest organic matter, is the most abundant hydrocarbon in the atmosphere.
Despite its relatively low concentrations, short-lived CH4 in the atmosphere is approximately 25
times more effective on a per molecule basis than carbon dioxide (CO2) in trapping heat. Thus CH4
has a significant influence on global climate change, the oxidative capacity of the atmosphere, andthe chemistry of the stratospheric ozone. The global atmospheric CH4 has increased significantly
since the industrial revolution; the concentration was reported as 1803 ± 2 ppb in 2011, which isabout 2.5-fold of that (722 ± 25 ppb) in 1750 (IPCC, 2013). The continuing rise of atmospheric CH4
indicates an imbalance of sources and sinks. The ocean is one of the natural sources of atmospheric
CH4 (11–18 Tg CH4 yr–1, Bange et al., 1994). However, CH4 distributions and emissions in marine
ecosystems have large spatial and temporal variations (Bange et al., 1994; Zhang et al., 2004,
2008a). The continental shelf and its margins are potentially the most significant sources of CH4 to
the oceans (Bange et al., 1994; Rehder and Suess, 2001) and show great dynamics in CH4
distributions and emissions due to complicated influencing factors.
Nitrous oxide (N2O) is a potent greenhouse gas in the atmosphere, and has a global warming
potential of about 300 times that of carbon dioxide and a long atmospheric lifetime of 118 to 131
years (IPCC, 2013). Despite its low atmospheric mixing ratio, N2O contributes about 6% to theanthropogenic greenhouse effect (IPCC, 2013). It also has the ability to destruct stratospheric ozone(Ravishankara et al., 2009). The N2O atmospheric mixing ratio has increased up to 20% above
pre-industrial levels, from 271 ppb in 1750 to 324 ppb in 2011 (IPCC, 2013), which indicates thatsources are larger than sinks. The ocean is the second largest natural source of atmospheric N2O, withmajor contributions from the open ocean (Nevison et al., 1995), but disproportionally highcontributions from coastal waters (Bange et al., 1996). N2O in oceanic environments, including
estuaries, coastal seas, and the open ocean is produced by microbial processes (Freing et al., 2012). It
is mainly formed as a byproduct during nitriﬁcation (NH4+ → NH2OH → NO2− → NO3−) and as
an intermediate during denitriﬁcation (NO3− → NO2− → NO → N2O) (Bange, 2006). In bothprocesses, the yield of N2O strongly depends on the concentration of dissolved oxygen. Marine N2O
production and emission show large spatial and temporal variability and may increase substantially asa result of eutrophication and expansion of marine hypoxic regions (Codispoti, 2010; Naqvi et al.,2010). However, measurement of oceanic N2O is still sparse, and the derived emission estimate haslarge uncertainties (Nevison et al., 1995; Bange et al., 1996; Seitzinger et al., 2000; Bange, 2006).
The quantification of the oceanic N2O emissions and the identification of the marine pathways ofnitrous oxide formation and consumption have received increasing attention during the last few
decades.

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2.1.2 CH4 distribution in the East China Sea and Yellow Sea
and the controlling factors
CH4 concentrations in the East China Sea (ECS) and Yellow Sea (YS) have obvious spatial andseasonal variability due to the complex mixing of different water masses and other variables.
Maximum CH4 concentrations, sea–air and sediment–water fluxes all occur during summer. CH4
concentrations decrease gradually from the coastal area to the open sea, and high levels of CH4
generally appear near the Changjiang River Estuary and outside Hangzhou Bay (Zhang et al., 2004,
2008a,b; Sun et al., 2018). In early spring and winter, CH4 in the shelf region has a uniform
distribution from the surface to the bottom, while it increases gradually with depth in other seasons.
Published CH4 data for the ECS are summarized in Table 2.1.1. Horizontally, CH4 in the surface
water (usually 2–5 nM) shows a gradient from onshore to offshore, where the Changjiang River
enters the ECS with abundant CH4 and the Kuroshio intrudes onto the shelf with poor CH4
concentrations (Zhang et al., 2004, 2008a,b; Sun et al., 2018). Vertically, three patterns can begenerally summarized to differentiate the depth profile of CH4 in the ECS, i.e., with and without thethermocline at the shelf region and deep water at the slope (Fig. 2.1.1). The first two patterns areusually present in the shallow shelf regions near the coast, where the wind and land-source inputs
strongly influence the shelf. In summer and autumn, the water column is well stratified, resulting in
CH4 accumulation in the bottom water due to sedimentary release. In winter and early spring,
however, the water column is well mixed and the absence of the thermocline at most regions of the
ECS supports the diffusion of CH4 from bottom to surface water and finally its release to the air.
Hence CH4 distribution is vertically uniform in the continental shelf zone in spring and winter. The
third pattern is usually present in the deep waters, such as the edge of the shelf or the slope. It issimilar to the vertical distribution of CH4 in the pelagic zone because those areas are far from land
and are mainly influenced by the Kuroshio. Specifically, CH4 in the first 100 m depth is uniform butreaches a subsurface maximum in the next 100 m. Below the peak, CH4 decreases with depth andstabilizes at the lowest level (~1.0–1.5 nM). Research has shown that high CH4 is also found at theedge of the continental shelf, where organic matter flows from the coastal region to the oceanic zone
and accumulates in the break areas. The YS is a semi-enclosed shelf sea and has shallow water. The
horizontal distribution of CH4 in the YS shows an onshore-to-offshore gradient with the CH4
concentrations, decreasing with distance off the coast. The vertical profile of CH4 in the YS issimilar to the first two patterns of the ECS due to the shallow water depth (Zhang et al., 2004).

Fig. 2.1.1 Typical depth profiles of CH4 in the ECS. Modified from Zang (1998) and Sun et al. (2018).
Reproduced with permission of Elsevier.

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Table 2.1.1 Summary of CH4 measurements in surface waters of the ECS during 2011.
RegionCoastalarea(S≤32)

Open
sea(S>32)Monthn

CH4 con.
(nM)

CH4 sat.
(%)

U10(m s–1)

FW2014(CH4)(μmol m–2·d–1)

FN2000(CH4)(μmol m–2 d–1)

F′ (CH4)(μmol m–2 d–1)

March

5

5.05~29.7(11.6)

185~1063(413)

0.2~12.
6 (5.7)0.05~53.7(14.3)

0.33~50.7(13.9)

0.85

May

26

2.45~26.4(9.09)

99~1107(386)

1.2~14.
8 (7.0)–0.03~140(23.7)–0.03~131(23.3)

4.46

August29

3.71~33.6(8.84)

182~1644(433)

1.9~8.9(5.8)

1.75~119(18.0)

2.11~121(18.7)

3.85

October

44.33~13.5(7.00)

195~589(344)

5.5~10.
9 (8.4)

3.99~51.8(26.7)

4.29~51.3(26.3)

1.85

December2

5.60~12.1(8.85)

193~439(316)

4.7~7.5(6.1)

2.69~25.5(14.1)

3.00~25.8(14.4)

0.57

March

31

2.39~4.36(3.55)

96~165(133)

0.3~13.
5 (8.2)–0.81~14.0(3.62)–0.77~13.2(3.52)

3.36

May

29

1.88~5.41(3.14)

91~258(148)

5.0~23.
6 (11.5)–2.63~86.9(10.0)–2.45~77.9(9.35)

7.84

August8

4.03~10.7(6.54)

206~557(342)

5.1~8.4(6.6)

5.65~25.3(14.9)

5.92~26.4(15.4)

12.0

October

32

2.82~4.37(3.80)

142~215(190)

7.2~11.
8 (9.1)7.98~11.2(9.11)

7.59~11.1(9.01)

8.43

December34

3.12~6.67(4.34)

135~280(186)

3.7~14.
1 (8.8)1.36~26.9(10.1)

1.61~25.6(9.84)

9.57

Number in brackets is the mean value of each item.
CH4con. and CH4sat. denote CH4 concentration and saturation rate, respectively.
U10 is the wind speed at 10 m above the sea surface.
FW2014(CH4) and FN2000(CH4) represent the sea–air flux density calculated by the W2014 and N2000 models,
respectively.
F′ (CH4) indicates the area-weighted mean sea–air CH4 flux in each sub-area.

CH4 distribution in the ECS is significantly influenced by complex mixing of various water masses,
such as Changjiang diluted water, Taiwan Warm Current Water and Kuroshio (Zhang et al., 2004;
Sun et al., 2018). The high CH4 content (hundreds of nM) in the Changjiang River is an importantsource that impacts the CH4 concentration along the coast, although most of CH4 is removed via
air–sea exchange and aerobic methane oxidation during the mixing in the estuary. The Kuroshio,
however, with low CH4 content, is an important sink of shelf CH4. Export to the open sea via the
Kuroshio accounts for 60% of total riverine input (Zhang et al., 2004). Sediment release is anotherimportant source of dissolved CH4 in the water column of the ECS and YS. CH4 can be produced
via bacterial degradation of organic matter in the sediments of the shelf and slope regions. The
rivers carry high loads of nutrients, suspended particles and organic carbon into the ECS, which
provide favorable conditions for methanogenesis. The annual average CH4 release rate fromsediments is about 1.11 μmol m–2 d–1 on the continental shelf of the ECS (Sun et al., 2018). The
maximum CH4 concentration and sediment–water CH4 flux both occur in summer, which might berelated to the occurrence of hypoxia in the bottom water.
Temperature can affect the gas solubility and microbial production rates of CH4, which then leads tothe seasonal variation of CH4 in the ECS. Correlation between temperature and CH4 has been foundin the ECS, indicating that temperature is an important factor in regulating the distribution andemission of CH4 (Sun et al., 2018). Oxygen is another potential factor that regulates CH4
distribution. CH4 is traditionally thought to be produced in strictly anoxic environments, while it canbe consumed in aerobic seawater by bacterial oxidation. High CH4 concentrations (>13 nM) within
hypoxic waters (DO < 2 mg L–1) were observed at the bottom water and weak negative correlation

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between CH4 and oxygen was observed in the ECS during the seasonal hypoxia off the Yangtze
River Estuary (Ye et al., 2016). Except for traditional production in the anoxic environment (i.e.,
sediment, suspended particles), CH4 can also be formed in the oxygenated water via degradation of
methyl compounds such as dimethylsulfoniopropionate (DMSP) and methylphosphonate (MPn),and these aerobic CH4 production mechanisms have also been proved in the ECS and YS (Ye et al.,2020, 2021). In the coastal seawaters of the YS, CH4 was observed to accumulate after MPn
enrichment with high MPn-to-CH4-conversion efficiency (~60%). Moreover, several Vibrio isolates
isolated from the coastal waters were found to produce CH4 while growing in culture using MPn as
the sole phosphorus (P) source, thus indicating that Vibrio spp. are capable of cleaving MPn for P
acquisition and might be the major contributors to MPn-dependent CH4 production (Ye et al., 2020).
Results from both laboratory and field incubations showed an increase in CH4 after the addition of
DMSP to the seawater from the ECS, indicating that the DMSP-dependent CH4 production occurs
in the coastal seawaters (Ye et al., 2021). For CH4 oxidation, Tsurushima et al. (1996) estimated that
the CH4 turnover time in the ECS is longer than one year, which has a minor impact on CH4
distribution, but might be shorter for coastal waters which contain a large amount of CH4.

2.1.3 Distribution of N2O in the East China Sea and Yellow Sea
and the influencing factors
Previously published N2O data are summarized in Table 2.1.2 which shows seasonal and spatial
variations in the East China Sea and the Yellow Sea. N2O concentrations showed an offshore
decreasing trend in surface distribution, and a surface-to-bottom increasing gradient in the watercolumn (Zhang et al., 2008a; Wang et al., 2016). The surface N2O concentration was highest insummer, followed by fall, and the lowest N2O usually was found in early spring.

Table 2.1.2 Summary of N2O measurements in the surface waters of the Yellow Sea and East China Sea.
Study area

Date

Surface N2O(nmol L–1)

N2O saturation
(%)

Flux
(μmol m–2 d–1)

North Yellow Sea

2005.3(11.94 ± 2.88)(90 ± 22)

Yang et al. (2009)2006.8(11.68 ± 3.34)(163 ± 45)

2007.10(11.90 ± 1.62)(149 ± 20)(–1.3 ± 4.0)a(–1.9 ± 6.2)b(6.5 ± 8.3)a(11.3 ± 13.4)b(7.9 ± 8.2)a(13.8 ± 14.3)b

2003.95.75–24.1(11.5 ± 4.5)
6.31–11.88(9.13 ± 1.45)
6.33–11.07(8.47 ± 0.85)

94–382(191 ± 74)
93–139(119 ± 10)
106–168(140 ± 13)(9.8 ± 16.0)a(17.1 ± 26.4)b(4.96 ± 6.12)a(10.25 ± 17.18)b
0.6–42.0b(12.2 ± 10.8)b
0.8–31.9d(9.8 ± 8.2)dZhang et al. (2008a)

East China Sea

2011.52013.8Reference

Ma et al. (2014)
Wang et al. (2016)

Numbers in brackets are average values.
akw was estimated by the LM86 equation; b kw was estimated by the W92 equation; d kw was estimated by
the N00 equation.

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The distribution of N2O in the ECS was found to be influenced mainly by water temperature,mixing of water masses, and biological production. N2O concentrations showed a negative
correlation with temperature, but a positive one with suspended particulate matter concentration.
There was no specific overall correlation observed between salinity and N2O concentration, but acoupled increasing trend within the salinity range of 0–2 and a decreasing trend within the salinity
range of 2–30 was found at the Changjiang River Estuary (Wang et al., 2016). Nitrification wasconsidered to be the main process that controls the N2O production. Prominent bottom water
hypoxia occurred in the northern ECS in summer, where the nitrification and denitrification
processes and the production of N2O were expected to be enhanced, but only a slight increase ofN2O concentrations was found. The frequent vertical mixing of the water column prevented theaccumulation of N2O in the bottom waters to significantly high levels, and may lead to more
emission of N2O to the atmosphere (Wang et al., 2016).

2.1.4 Sea-to-air fluxes of CH4 and N2O in the East China Sea
and Yellow Sea
The ECS and YS are net sources of atmospheric CH4 and N2O. CH4 in the surface water of the ECS
is usually at supersaturated levels (140–480%) (Zhang et al., 2004; Sun et al., 2018). The calculated
sea-to-air fluxes of CH4 ranged from 2.8–36.3 μmol m–2 d–1, and CH4 emissions from surface waterin summer and autumn are higher than those in spring and winter. Sun et al. (2018) estimated theannual mean area-weighted sea–air flux density of CH4 in the ECS to be about 10.7 μmol m–2 d–1 in
2011. Accordingly, an area-weighted, seasonally adjusted annual rate of CH4 efflux from the ECS to
the atmosphere was estimated to be 2.98 × 109 mol yr–1 (~0.05 Tg CH4 yr–1). Hence the ECS is anactive site for CH4 emissions to the atmosphere and contributes about 0.55% of total emissions fromthe global ocean. In the YS, the sea-to-air fluxes of CH4 were 1.3 μmol m–2 d–1 in spring and 21.1μmol m–2 d–1 in summer, respectively (Yang et al., 2010). The seasonal variability of the fluxes ismainly due to the variations in CH4 surface concentration, temperature, and wind speed. Due to thelarge variations during different observations, CH4 emissions from the YS and the ECS still havelarge uncertainties and are possibly underestimated.
Surface N2O saturations in the ECS ranged from 90% to 660% (Zhang et al., 2008a; Wang et al.,2016). Wang et al. (2016) estimated the sea-to-air fluxes of N2O as 30.6 ± 59.1 μmol m–2 d–1 fromthe Changjiang River Estuary, 9.8 ± 8.8 μmol m–2 d–1 from the coastal region and shelf, and 21.0 ±12.7 μmol m–2 d–1 from the continental slope using the Wanninkhof (1992) equation. Annual N2O
emission from the ECS was estimated to be about 8.2 × 10–2 to 16.0 × 10–2 Tg N2O yr–1. Although
the ECS only accounts for 0.2% of the world’s oceanic area, it accounts for about 0.8% of global
oceanic N2O emission; hence the ECS is an active region for N2O production and emission.
The saturation of dissolved N2O in the North Yellow Sea showed obvious seasonal variations withhigher values occurring in summer and autumn and lower values in spring, and the dissolved N2O ofthe surface water changed from undersaturated to oversaturated with the water temperature,gradually increasing during three cruises in spring (Yang et al., 2009). Correlation analysis showedthat temperature was the main factor influencing N2O saturations. The estimated average N2O fluxes
ranged between 0.6 and 13.8 μmol m–2 d–1 for spring, summer, and autumn, which showed obvious
seasonal variations, with the N2O fluxes of summer and autumn higher than those of spring. Basedon the average annual N2O flux and the area of the North Yellow Sea, the annual N2O emission
from the studied area was estimated to be 5.3 × 10–3 to 9.2 × 10–3 Tg yr–1. Hence, the North Yellow
Sea is a net source of atmospheric N2O.

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2.1.5 Future perspective
Although previous research has given us a glimpse of CH4 and N2O cycling in the ECS and YS, we
are still far from understanding the seasonal variations of CH4 and N2O distribution and emission,and quantification of CH4 and N2O sources and sinks in these areas. Most studies were based on
discrete sampling and were conducted at large sampling intervals with coarse spatial resolution. In
order to obtain high-quality data to evaluate the role of the ECS and YS in the global ocean,continuous measurements with higher temporal and spatial resolution are needed. Another prospect
for future studies is the biological production processes of CH4 and N2O in seawater. The budget of
CH4 and N2O in the ECS remains a puzzle because the contribution of microbial processes is still
ambiguous. The explanation of “the oceanic methane paradox” is a hot topic, and aerobic CH4
production in oxygenated water has been gradually revealed by recent studies. For the marginal seasof China, which pathway is the dominant one: the traditional one, the new one, or both? Is the CH4
production mechanism the same as that in the open ocean, or does CH4 in the ECS have a specificbiogeochemical cycle due to the complicated conditions? The relative importance of the microbial
processes (i.e., nitrification, denitrification, and nitrifier denitrification) leading to the production
and consumption of N2O in the ECS and YS remains unclear. The isotopic composition of N2O has
been used to characterize local production and consumption mechanisms of dissolved N2O inmarine environments. Hopefully, new techniques (such as isotopes) may help us make progress on
this in the future. Thus more studies on CH4 and N2O in the coastal and shelf waters of the ECS andYS are still needed to further enrich the global oceanic CH4 and N2O database, and to understandthe biogeochemical cycles of CH4 and N2O in the shelf areas and their regional contribution to
global oceanic CH4 and N2O emission.

2.1.6 References
Bange, H.W. 2006. Nitrous oxide and methane in European coastal waters. Estuar Coast. Shelf Sci. 70:
361–374, doi:10.1016/j.ecss.2006.05.042.
Bange, H.W., Bartell, U.H., Rapsomanikis, S. and Andreae, M.O. 1994. Methane in the Baltic and North
Seas and a reassessment of the marine emissions of methane. Global Biogeochem. Cycles 8: 465–480,
doi:10.1029/94GB02181.
Bange, H.W., Rapsomanikis, S. and Andreae, M.O. 1996. Nitrous oxide in coastal waters. Global
Biogeochem. Cycles 10: 197–207, doi:10.1029/95GB03834.
Codispoti, L.A. 2010. Interesting times for marine N2O. Science 327: 1339–1340,
doi:10.1126/science.1184945.
Freing, A., Wallace, D.W.R. and Bange, H.W. 2012. Global oceanic production of nitrous oxide. Philos.
Trans. R. Soc. London Ser. B 367: 1245–1255, doi:10.1098/rstb.2011.0360.
IPCC (Intergovernmental Panel on Climate Change). 2013. Climate Change 2013: the physical sciencebasis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change edited by T.F. Stocker, D. Qin, G.K. Plattner, M. Tignor, S.K. Allen, J.
Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley, Cambridge University Press, Cambridge.
Ma, X., Zhang, G.L., Cao, X.P., Song, G.D., Wang, L. and Liu, S.M. 2014. Distribution and air-sea
fluxes of dissolved nitrous oxide in the East China Sea in spring. Mar. Sci. 38: 21–28 (in Chinese).
Naqvi, S.W.A., Bange, H.W., Farias, L., Monteiro, P.M.S., Scranton, M.I. and Zhang, J. 2010. Coastal
hypoxia/anoxia as a source of CH4 and N2O. Biogeosciences 7: 2159–2190,
doi:10.5194/bg-7-2159-2010, 2010.

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Nevison, C.D., Weiss, R.F. and Erickson, D.J. 1995. Global oceanic emissions of nitrous oxide. J.
Geophys. Res. Oceans 100: 15,809–15,820, doi:10.1029/95JC00684.
Nevison, C.D., Lueker, T.J. and Weiss, R.F. 2004. Quantifying the nitrous oxide source from coastal
upwelling. Gobal Biogeochem. Cycles 18: doi:10.1029/2003GB002110.
Ravishankara, A.R., Daniel, J.S. and Portmann, R.W. 2009. Nitrous oxide (N2O): The dominantozone-depleting substance emitted in the 21st century. Science 326: 123–125,
doi:10.1126/science.1176985.
Rehder, G. and Suess, E. 2001. Methane and pCO2 in the Kuroshio and the South China Sea during
maximum summer surface temperatures. Mar. Chem. 75: 89–108,doi:10.1016/S0304-4203(01)00026-3.
Seitzinger, S.P., Kroeze, C. and Styles, R.V. 2000. Global distribution of N2O emissions from aquatic
systems: natural emissions and anthropogenic effects. Chemosphere Global Change Sci. 2: 267–279,
doi:10.1016/S1465-9972(00)00015-5.
Sun, M.S., Zhang, G.L., Ma, X., Cao, X.P., Mao, X.Y., Li, J., Ye, W.W. and Liu, S.M. 2018. Dissolved
methane in the East China Sea: Distribution, seasonal variation and emission. Mar. Chem. 202: 12–26,
doi:10.1016/j.marchem.2018.03.001.
Tsurushima, N., Watanabe, S. and Tsunogai, S. 1996. Methane in the East China Sea Water. J. Oceanogr.
52: 221–233, doi:10.1007/BF02235671.
Wang, L., Zhang, G., Zhu, Z., Li, J., Liu, S., Ye, W. and Han, Y. 2016. Distribution and sea-to-air flux ofnitrous oxide in the East China Sea during the summer of 2013. Cont. Shelf Res. 123: 99–110,
doi:10.1016/j.csr.2016.05.001.
Wanninkhof, R. 1992. Relationship between wind speed and gas exchange over the ocean. J. Geophys.
Res. Oceans 97: 7373–7382, doi:10.1029/92JC00188.
Yang, J., Zhang, G.-L., Zheng, L.-X. and Zhang, F. 2009. Seasonal variation of fluxes and distributionsof dissolved nitrous oxide in the North Yellow Sea. Environ. Sci. 30: 656–662 (in Chinese).
Yang, J., Zhang, G.-L., Zheng, L.-X., Zhang, F. and Zhao, J. 2010. Seasonal variation of fluxes anddistributions of dissolved methane in the North Yellow Sea. Cont. Shelf Res. 30: 187–192, doi:
10.1016/j.csr.2009.10.016.
Ye, W., Zhang, G., Zhu, Z., Huang, D., Han, Y., Wang, L. and Sun, M. 2016. Methane distribution andsea-to-air flux in the East China Sea during the summer of 2013: Impact of hypoxia. Deep Sea Res. II
124(Suppl. C): 74–83, doi:10.1016/j.dsr2.2015.01.008.
Ye, W.-W., Wang, X.-L., Zhang, X.-H. and Zhang, G.-L. 2020. Methane production in oxic seawater ofthe western North Pacific and its marginal seas. Limnol. Oceanogr. 65: 2352–2365, doi:10.1002/lno.11457.
Ye, W.-W., Du, G.-X., Zhang, H.-H. and Zhang, G.-L. 2021. Methane in the Yellow Sea and East China
Sea: dynamics, distribution and production. J. Oceanol. Limnol. doi:10.1007/s00343-021-1010-4.
Zang, J. 1998. Dissolved methane in sea water of the East China Sea. Acta Oceanol. Sin. 20: 52–59 (inChinese).
Zhang, G.L., Zhang, J., Kang, Y.B. and Liu, S.M. 2004. Distributions and fluxes of methane in the East
China Sea and the Yellow Sea in spring. J. Geophys. Res. Oceans 109: doi:10.1029/2004JC002268.
Zhang, G., Zhang, J., Ren, J., Li, J. and Liu, S. 2008a. Distributions and sea-to-air fluxes of methane andnitrous oxide in the North East China Sea in summer. Mar. Chem. 110: 42–55,
doi:10.1016/j.marchem.2008.02.005.
Zhang, G., Zhang, J., Liu, S., Ren, J., Xu, J. and Zhang, F. 2008b. Methane in the Changjiang (Yangtze
River) Estuary and its adjacent marine area: Riverine input, sediment release and atmospheric fluxes.
Biogeochemistry 91: 71–84, doi: 10.1007/s10533-008-9259-7.

PICES Scientific Report No. 62105

Nutrients Biogeochemistry

Chemical Oceanography

2.2 Nutrients Biogeochemistry
Su Mei Liu1,2 and Yu Umezawa3
1

Frontiers Science Center for Deep Ocean Multispheres and Earth System, and Key Laboratory of Marine
Chemistry Theory and Technology, Ministry of Education, Ocean University of China, China
2
Qingdao National Laboratory for Marine Science and Technology, China
3
Tokyo University of Agriculture and Technology, Japan

2.2.1 Introduction
Nutrient dynamics has been greatly altered by human disturbances and global change over the lastcentury. Excessive nutrient discharges and changes in relative nutrient concentrations caused byland use transformation and anthropogenic emissions have been known to result in eutrophication,
hence modifying aquatic food webs and causing severe hypoxic events in coastal environments.
Water exchange between coastal and offshore waters can also be a very important factor affecting
coastal ecosystems
Many rivers, such as the Yalujiang and Changjiang (Yangtze River) transport large amounts of waterand suspended matter from the continent into the Yellow Sea and East China Sea (YECS, Fig. 2.2.1).
Dust and soil particles from Northeast Asia are transported to these seas via the atmosphere and aredeposited on the sea surface. The rainfall is 1000–2000 mm in the East China Sea (ECS) and 600–
800 mm in the Yellow Sea (YS). Circulation, upwelling, riverine transport, and atmosphericdeposition carry nutrients to the surface water of the YECS. The nutrient budgets for the YECS have

Fig. 2.2.1 Map of the Yellow Sea andEast China Sea, showing the location oflarge rivers dealt with in this study. The
circulation regimes and main water masses
are also indicated, including the Yellow
Sea Warm Current (YSWC), Yellow Sea
Coastal Current (YSCC), Korea Coastal
Current (KCC), Taiwan Warm Current(TWC), East China Sea Coastal Current(ECSCC), and Kuroshio.

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been evaluated for the relative importance of external nutrient inputs versus physical transports andinternal biogeochemical processes (Chen and Wang, 1999; Liu et al., 2003a; Zhang et al., 2007).
Historical observation of nutrients in this region started in the late 1950s, including several national
and international cooperative programs in the 1980s. Most of these research activities focused on
the distribution and variation of nutrients in the water column. The YECS is the area receiving themost extensive marine research, which forms a strong data basis for our knowledge of nutrient
biogeochemistry in this region. However, there is almost no review that is internationally available.

2.2.2 Spatial and temporal variations of nutrients
Nutrients biogeochemistry is highly dynamic in the YECS, with different characteristics for various
parts from the north to the south (Zhang and Su, 2006).

Nutrients in the Yellow Sea
High surface concentrations of nutrients are found in the coastal areas in summer, 5- to 10-foldhigher than those in the Central YS, reflecting the effects of the Changjiang effluent plume, surface
runoff in the west and east coasts and the circulation in the YS (Chung et al., 1999; Liu et al., 2003a;
Zhang and Su, 2006). In winter, high concentrations of nutrients are found south of the YS,
reflecting the influence of the YS warm current water originating from the Tushima warm current
extending northwestward to enter the YS, but nutrients are deficient in the northwestern YS (Wanget al., 2003). In addition, nutrient concentrations in the central part of the YS are generally higher
than those in coastal areas, owing to strong vertical mixing by northerly winds and reduction ofland-source input (e.g., river) in winter (Zhang and Su, 2006). Moreover, the concentrations of
nutrients in near-bottom waters are significantly higher in the Central YS (i.e., Yellow Sea Cold
Water Mass: YSCWM) than those in the coastal region (Chung et al., 1999; Liu et al., 2003a). The
high concentrations of nitrogen compounds and Si(OH)4 in the Jiangsu coastal zone and southwest
results most likely from the convergence of the Yellow Sea Coastal Current and the Changjiang
effluent (Wu et al., 2011). Elevated suspended particulate matter in the Jiangsu coastal zone andsouthwest of the YS is responsible for the high concentration of nutrients, particularly particulate
nitrogen (N) and particulate phosphorus (P), where the sediment mainly comes from the Changjiang
Delta and the abandoned Huanghe Delta. The high concentration of PO43– in the southern YS,
especially the southeast part, indicates intrusion of waters from ECS and terrigenous inputs in theeast and west sides (Liu et al., 2003a). In spring, high concentrations of nutrients cover the coastal
areas affected by Changjiang Diluted Water (CDW) and the prevailing southerly winds. In contrast,
the concentration of nutrients in the northern and eastern YS are very low. In autumn, highconcentrations of nutrients are found in the southwestern and eastern coastal waters of the YS which
are related to the low freshwater discharge and the prevailing northeasterly wind (Wang et al.,2003).
For total P (TP), dissolved organic phosphorus (DOP) is the dominant species, representing 50% of
the TP, PO43– and particulate P (PP) accounts for 30% and 20% of the TP, respectively. For nitrogencompounds, dissolved organic nitrogen (DON) is dominant in the upper 10 m water column,
accounting for 70% of the total dissolved nitrogen (TDN), but only 30% of the TDN in near-bottom
waters; particulate nitrogen (PN) represents 25% of the total nitrogen (TN). The high concentrationsof DON and DOP should be considered in models of nutrients cycling, which could help to explain
observations of nutrient limitation in ecosystems of the YS (Liu et al., 2003a).

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The spring phytoplankton bloom is an important phenomenon in the YS. Jin et al. (2013) addressed
the coupling between the nutrient condition and phytoplankton blooms in the YS. They indicate thatabundant nutrients and the fitting nutrients ratios in the central YS provide favorable conditions forthe onset of phytoplankton blooms. The upward nutrient fluxes from deep water to the euphotic
zone by means of diffusion and turbulent entrainment are important nutrient sources to sustain the
bloom, and they appear to represent 56 to 69% of the nutrients requested for phytoplankton growth(Jin et al., 2013).

Nutrients in the East China Sea
The ECS receives a tremendous riverine supply of fresh water, suspended sediment and nutrients,
notably from China. The salt marsh in the Changjiang Estuary plays an important ecological role innutrient transport from the river to offshore areas, and increases P limitation (Liu et al., 2016).
Eutrophication has become an overwhelming phenomenon in the coastal environment off the
Changjiang Estuary; high-level nutrients from land-sources are constrained to the inner and middle
shelf region with P as a limiting element for phytoplankton growth (Zhang et al., 2007; Gao et al.,2015), red-tide events happen frequently and hypoxia exists in near-bottom waters (Zhu et al., 2011,2016, 2017; Liu et al., 2015), while the open ECS shelf and slope waters remain oligotrophic (Zhang
et al., 2007). Across the ECS shelf, nutrients in surface waters gradually decrease from eutrophic
coastal to oligotrophic open shelf waters, and the influence of land-source nutrients can be seen at thesurface over a distance of up to 250–300 km from the coast (Wu et al., 2003; Ren et al., 2006).
Taiwan Warm Current Water and Kuroshio Surface Water are devoid of nutrients. The Kuroshio
Subsurface Water is rich in nutrients, and extensive exchange of water and nutrients between the
ECS and Kuroshio occurs across the shelf break region through upwelling and frontal processes andspreads over a broad part of the shelf, reaching water depth of 50–100 m at mid-shelf (Chen et al.,
1995; Chen, 1996; Gong et al., 1996; Liu et al., 2000; Zhang et al., 2007). The exchange across theshelf edge with the Kuroshio is of great importance for productive fisheries and the nutrient budget(Chen and Wang, 1999; Zhang and Su, 2006).
Nutrients in the Changjiang Estuary and the ECS show seasonal variations in terms of riverine input,
biological activities, and current regime (Zhang et al., 2007; Chen, 2008; Liu et al., 2016). In
summer, affected by riverine input, upwelling, and monsoons, high concentrations of nutrientscover the Changjiang Estuary and the river plume which extends towards Cheju Island, in the area
southwest of Korea, and in an upwelling area northeast of Taiwan (Zhang et al., 2007; Chen, 2008).
In winter, cooling and strong vertical mixing results in the concentrations of nutrients generally
exceeding those in summer, especially in the surface layer. Affected by the strong northeasterly
monsoon, high concentrations of nutrients derive from the Changjiang flow southwestward along
the coast of southeastern China (Zhang et al., 2007; Chen, 2008). While the concentration ofnutrients is deficient in the offshore area of the ECS, which is related to the influence of theKuroshio and the Taiwan Warm Current intruding into the saline warm surface waters of the ECS,
the Subei coastal current extends southeastward to enter the northern ECS, with low discharge and aprevailing northeasterly wind (Wang et al., 2003; Chen, 2008). In spring and fall, spatial variationsof nutrients are subject to transitions between summer and winter.

2.2.3 Main sources and sinks of nutrients
Riverine input
The Chinese rivers are characterized by high dissolved inorganic nitrogen (DIN) and low PO43–
concentrations with high DIN/PO43– ratios. The dissolved silicate levels, in general, are higher in the
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warm and high-flow South China rivers than those in the cold and low-flow North China rivers, due
to differences in climate, rock type, and physical and chemical weathering (Zhang, 1996). Nutrient
levels in the Chinese rivers are higher than those from the large and less-disturbed rivers in theworld such as the Amazon and the Congo, but comparable to the values for the European and North
American polluted and eutrophic rivers such as the Loire, Po, Rhine, and Seine (Zhang, 2002). The
concentrations and fluxes of nutrients in the rivers that empty into the YECS are provided in Tables
2.2.1 and 2.2.2.
Table 2.2.1 Concentrations (µM) of nutrients in the major Chinese and Korean rivers discharging into the
Yellow Sea and East China Sea. Data from Liu, S.M. et al. (2009) and references therein.
NO3–

NO2–

NH4+

PO43–

Si(OH)4

Nutrient concentrations from rivers to the Yellow Sea
HuaiheDec 2004–Feb 2005
76.3
Yalujiang
August 1992, 1994
268
Yalujiang
May 1996
76.3
Han
June 1992
57.1
Han
June 2000
97.6

1.71
0.37
0.17
2.0
12.7

16.5
2.38
5.78
75.8
217.5

4.02
0.30
0.09
3.5
–

38.1
127
146
36.9
–

Keum
Yeongsan

1.5
–

26.5
37.1

1.4
3.9

85
72.2

0.61
0.07
1.54
11.1
–
0.44
–
–
–
–

16.0
4.64
0.70
–
0.59
3.21
4.13
3.61
10.7
19.6

0.49
0.34
1.00
3.2
2.21
0.30
1.45
0.50
0.61
0.91

112.0
100.2
128
183
162
150
208
156
261
279.4

River

Season1997–1998
1998–2000

160–

Nutrient concentrations from rivers to the East China Sea
Changjiang*
January 1997–2001
62.7
Changjiang*
July 1997–2001
70.2
Qiantangjiang
August 2004
101
Yongjiang
May 2002
119
Jiaojiang
October 1994
123
Oujiang
August 2004
72.3
Minjiang
October 1986
65
MinjiangJune 1986
45
Jiulongjiang
Summer
36.2
Jiulongjiang
Winter
36.2* Note: Part of the Changjiang freshwater discharge empties into the Yellow Sea (Liu et al., 2003a).

Nitrogen and phosphorus originate from both agricultural and domestic wastes, and nitrogen ispreferentially leached from the drainage basin compared to reactive phosphorus. The longer
residence time in the estuaries allows for greater utilization of nutrients by phytoplankton than in therivers (Liu, S.M. et al., 2009).
Almost 30 rivers empty into the YS, including the Yalujiang, Huaihe, Han, Keum, and Yeongsan(Liu et al., 2003a). The Changjiang drains directly into the ECS, but the discharge of the
Changjiang can be carried into the YS (Fig. 2.2.1), especially in summer when the Changjiang
effluent may reach Cheju Island (Le, 1984). Twelve to 27% of the Changjiang discharge empties
into the YS from June to October, with the highest discharge in August. About 10% of the
Changjiang discharge empties into the YS in November and December while less than 5% of the
Changjiang discharge empties into the YS from January to May. The annual water discharge to the
YS is 14.1% (130 × 109 m3) of the Changjiang discharge (Liu et al., 2003a). The riverine input
fluxes of NO3–, NH4+, PO43–, and Si(OH)4 to the YS are 24 ± 12, 2.7 ± 1.4, 0.42 ± 0.21, and 23.2 ±11.6 × 109 mol yr–1, respectively. Compared to riverine nutrient inputs to the YS from China and

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Korea, the NO3– inputs were mainly by Yalujiang (~50%) and Changjiang (~40%), the NH4+ and
PO43– inputs were mainly by Han River (~50%) and Changjiang (20–30%), and the Si(OH)4 inputs
were mainly by Changjiang (57%) and Yalujiang (~30%). This indicates that riverine inputs of
NO3– and Si(OH)4 are mainly from Chinese rivers (Changjiang and Yalujiang), while inputs ofNH4+ and PO43– are mainly by Korean rivers (e.g., Han River), followed by a Chinese river(Changjiang).
Table 2.2.2 Nutrient fluxes (× 106 mol d–1) from rivers to the Yellow Sea and East China Sea. The data arefrom Liu, S.M. et al. (2009) and references therein.
NO2–

NH4+

PO43–

Nutrient fluxes transport from rivers to the Yellow Sea
HuaiheDec 2004–Feb 2005
5.52

0.12

1.19

0.29

2.76

Yalujiang

August 1992, 1994

29.2

0.040

0.26

0.033

13.8

Yalujiang

May 1996

4.81

0.011

0.37

0.006

9.21

Han

June 1992, 2000

4.43

0.42

8.40

0.20

2.11

Keum

1997–1998

2.54

0.024

0.42

0.022

1.35

Yeongsan

1998–2000

–

–

0.21

0.022

0.42

Nutrient fluxes transport from rivers to the East China Sea
Changjiang*
January 1997–2001
65.9
0.64

16.8

0.51

117.7

Changjiang*

July 1997–2001

QiantangjiangRiver

SeasonNO3–

Si(OH)4

277.4

0.28

18.3

1.34

395.9

August 2004

9.46

0.14

0.066

0.094

12.0

Yongjiang

May 2002

1.12

0.11

–

0.030

1.73

Jiaojiang

October 1994

2.24

–

0.011

0.040

2.96

Oujiang

August 2004

2.89

–

0.13

0.012

6.00

Minjiang

Winter

4.48

0.11

0.28

0.10

14.3

Minjiang

Summer

12.5

0.23

1.00

0.14

43.2

Jiulongjiang

Summer

2.6

–

0.78

0.04

19.1

Jiulongjiang

Winter

0.5

–

0.30

0.01

4.2

* Note: Part of the Changjiang freshwater discharge empties into the Yellow Sea (Liu et al., 2003a).

This is due to the fact that Chinese rivers appear to be largely contaminated by agricultural practices
and Korean rivers are largely contaminated by municipal sewage (Liu et al., 2003a). A number ofrivers empty into the ECS, including the Changjiang, Jiaojiang, Oujiang, Minjiang, Jiulongjiang. In
total, riverine input to the ECS is 63.2 ± 19.8 × 109 mol yr–1 of NO3–, 6.01 ± 2.21 × 109 mol yr–1 of
NH4+, 0.41 ± 0.10 × 109 mol yr-1 of PO43–, and 103.5 ± 29.2 × 109 mol yr–1 of Si(OH)4 (Table 2.2.2).

Nutrients recycling in sediments
Although sediments of the coastal environment play an important role in nutrients recycling, there
are limited studies in the YECS. Based on incubation experiments on a boat, the fluxes of NO3‒,
NH4+, PO43–, and Si(OH)4 at the sediment–water interface were 185 ± 14.3, –1.81 ± 0.24, 0.035 ±0.012, and 138 ± 35.4 × 109 mol yr–1, respectively, in the YS in May 1998 (Liu et al., 2003a). The
primary production of the YS sampled during three cruises in spring (April 1996, May 1995 and1997) revealed values of 68–5303 mgC m–2 d–1 with an average of 2066 mgC m–2 d–1 (Yang et al.,1999). Based on Redfield ratios (C: N: P = 106:16:1), 0.86–66.7 mmol m–2 d–1 for nitrogen and

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Nutrients Biogeochemistry0.053–4.17 mmol m‒2 d‒1 for phosphorus can be estimated to support phytoplankton growth.
Comparison with the benthic fluxes of nutrients reveals that sediment–water exchange could match
2 to 150% of the N requirement for phytoplankton, but could only match <1% of the P requirement
for phytoplankton, which is indicative of either a rapid PO43‒ recycling in the water column orimportant riverine and atmospheric inputs. These calculations indicate a smaller role for sediments
in supplying nutrients for phytoplankton than for some other systems, for example, Chesapeake Bay
and Mobile Bay (Cowan and Boynton, 1996; Cowan et al., 1996).
The fluxes of NH4+, NO3‒, and Si(OH)4 at the sediment–water interface were −2.6–3.4, −1.4–3.2,
0.13–13.2 mmol m–2 d–1, respectively, in the Changjiang Estuary in June 1980, and August andNovember–December 1981 (Aller et al., 1985). This was the first study on benthic nutrient
regeneration in Chinese seas. In spring 2001 and 2002, sediment-core incubations indicated thatnutrient exchange fluxes in the ECS were −0.385–0.143 mmol m‒2 d‒1 for NO3‒, −1.42–0.548 mmolm‒2d‒1 for NH4+, −0.078–0.006 mmol m‒2 d‒1 for PO43‒, and −0.673–2.63 mmol m‒2 d‒1 for Si(OH)4,
respectively, with a DIN:DIP ratio of 15–60 and DIN:Si ratio of 0.4–0.5 (Qi et al., 2003). Over theentire ECS shelf, nutrient exchange fluxes provided 3.6 × 109 mol yr‒1 for NO3‒, 32 × 109 mol yr‒1
for NH4+, 0.8 × 109 mol yr‒1 for PO43‒, and 79 × 109 mol yr‒1 for Si(OH)4 from sediment to water
based on in situ incubations and pore-water profiles (Zhang et al., 2007).
Nitrogen transformation processes in sediments of the ECS are addressed based on the 15N isotope
pairing technique and by developing a mathematical model to correct the effects of DNRA
(Dissimilatory Nitrate Reduction to Ammonium). N transformation processes are very complicatedin the ECS sediments, including denitrification, anammox, and DNRA, as well as intracellular
nitrate release (Song et al., 2013). The nitrate reduction rate was 16.2 ± 7.4 and 7.0 ± 3.7 nmol N
cm–3 h–1 in June and October 2010, respectively, in which denitrification, anammox, and DNRA
represented 46 ± 6% and 8.5 ± 0.1%, 20 ± 12% and 49% ± 20%, and 35% ± 10 and 43% ± 19%,
respectively. The genuine N2 production rate through denitrification was 0.26 ± 0.08 nmol N cm–3
h–1 in October 2010 (Song et al., 2013, 2016). Because nitrogen cycles in the sediment affectnutrient concentrations in pore-water, clarifying these processes are effective to evaluate nutrient
fluxes from sediment into the water column of the ECS.

2.2.4 Nutrient budgets
Nutrient budget models for the YS (excepting the Bohai Sea) were developed based on LOICZ
Biogeochemical Modelling Guidelines suggested by Gordon et al. (1996). For the model, the waterand salt budgets were calculated first and then the mass balance of nutrients were built (Fig. 2.2.2).
In calculation, we ignored the contribution from submarine groundwater and waste drainage, as thedata were not available and had large uncertainties. The atmospheric inputs are becoming important
relative to the riverine pathway with regard to the nutrient inputs. Moreover, advection dominates
the nutrient budgetary issues; the active sink/source terms account for approximately 60 to 70% of
annual input/export flux in the region (Liu et al., 2003a; Zhang and Su, 2006).
Nutrient dynamics are more complicated in the ECS than in the YS. The nutrient budget calculation
for the ECS differs from previous LOICZ procedures (Gordon et al., 1996) because the limited data
sets from the ECS do not allow specification of the ‘‘mixing terms’’ for water channels/straits
and/or at the open boundary (Zhang et al., 2007). Nutrient budgets were first built for the ECS by
Chen and Wang (1999). The revised budgets take into account monitoring and field observation
data for river inputs, atmospheric dry and wet depositions, and sediment–water interface exchange
fluxes, and separated nitrate and ammonium (Fig. 2.2.3). The results of the revised budgets suggestthat the ECS shelf does not likely export substantial amounts of dissolved nutrients to the open

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Northwest Pacific Ocean (Zhang et al., 2007). Further study should focus on nutrienttransformation processes, such as NO3− assimilation and nitrification, which play an important rolein the nitrogen cycle on the ECS shelf (Liu et al., 2020).

River
-

N O 3 :24+

N H 4 :2.7
Atmosphere3-

PO 4 :0.42-

N O 3 :12.4

2-

SiO 3 :23.2+

N H 4 :37
3-

PO 4 :0.89
2-

SiO 3 :1.1

Bohai

East C hina Sea
Yellow Sea-

N O 3 :22.25-

N O 3 :0.088+

N H 4 :1.20+

N H 4 :0.082
3-

PO 4 :0.073∆N O 3 :−55.4-

PO 4 :−2.18
3-∆N H 4 :−40.3+∆PO 4 :0.97
3-∆SiO 3 :−56.6
2-

Fig. 2.2.2 Box model showing the nutrient budgets of the Yellow Sea, excluding the Bohai Sea. The
negative and positive values in nutrient exchange and sink/source terms indicate nutrient loss from andinput to the system, respectively (× 109 mol yr‒1). Redrawn from Liu et al. (2003a). Reproduced withpermission of Elsevier.

Yellow Sea
NO3-: 0.027 (0.053)
NH4+: 0.008 (0.013)
PO43-: 0.004 (0.006)
SiO32-: 0.107 (0.111)

River
NO3-: 2.76 (0.765)
NH4+: 0.182 (0.195)
PO43-: 0.013 (0.006)
SiO32-: 3.94 (1.37)

AtmosphereNO3-: 0.37 (0.71)
NH4+: 0.80 (1.27)
PO43-: 0.002 (0.003)
SiO32-: 0.047 (0.017)

East China Sea Shelf

Taiwan Strait
NO3-: 7.10 (5.73)
NH4+: 1.35 (0.69)
PO43-: 0.359 (0.46)
SiO32-: 14.7 (17.9)

Outflow
NO3-: 11.8 (17.1)
NH4+: 2.38 (0.98)
PO43-: 0.57 (1.34)
SiO32-: 20.1 (33.6)

KuroshioNO3-: 5.43 (12.6)
NH4+: 0.47 (0.97)
PO43-: 0.384 (0.92)
SiO32-: 6.20 (23.7)

Fig. 2.2.3 Box model showing the nutrient budgets of the East China Sea Shelf, including the nutrient
fluxes (kmol s‒1) in summer and winter (in brackets), respectively. Redrawn from Zhang et al. (2007).

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2.2.5 Long-term changes of nutrients
Long-term environmental changes have been observed in the YS due to human activities andclimate change: the concentrations of NO3‒ and dissolved inorganic nitrogen (DIN) in the southern
YS have been continuously increasing since the 1980s, with a most rapid increase after the
mid-1990s (Lin et al., 2005; Li et al., 2015; Wei et al., 2015). While the concentrations of Si(OH)4and PO43‒ generally exhibited a decreasing trend before the mid-1990s, they then gradually
increased (Wei et al., 2015). Nutrient composition also changed: the N:P ratio has been continuously
increasing since the 1980s and reached a level of >16 (Redfield ratio) at the end of the last century;
the Si:N ratio decreased rapidly from the mid-1980s to the mid-1990s and then maintained a low
level of ~1; the Si:P ratio decreased from the 1970s to the mid-1990s and then increased gradually.
In the southern YS there was an evolution from N limitation to P and Si limitation (Lin et al., 2005;
Wei et al., 2015). The variations in the concentrations of nutrients were related to the variations of
the riverine and atmospheric inputs.
Over the ECS shelf from the river to the shelf slope, nutrient concentrations and composition havechanged differently. Based on monitoring observations in the lower reaches of Changjiang River, Liet al. (2007) indicated that concentrations and fluxes of DIN and PO43‒ had increased greatly sincethe 1950s, while annual average concentration and flux of dissolved silicate had an opposite trend,
showing a sharp decrease since the 1950s. They stated that fertilizer application peaking after the
1980s could be responsible for the high DIN and DIP levels, and construction of dams could belinked to the decrease of dissolved silica. Based on historical summertime data from 1959 to 2009,
Jiang et al. (2014) reported similar results for the concentrations of NO3–, NO2–, NH4+, and PO43–
and ratios of N:P and N:Si in the freshwater and saline sections of the Changjiang Estuary which all
increased because of persistent riverine loading, but Si(OH)4 levels remained constant like previous
studies (Liu et al., 2003b, Liu, S.M. et al., 2009). Further studies have indicated that theconcentrations of PO43‒ increased more rapidly than those of DIN and DIN:DIP molar ratio
decreased by 60 to 70% over the period from 1995 to 2016 (Zhang et al., 2021). Moreover, Si(OH)4
remained most likely stable, with a possible weak increase over the last 10 years due to erosion in
the lower reach of the Changjiang (Wang et al., 2018; Ding et al., 2019; Zhang et al., 2021). As aresult, microalgal biomass showed a dramatic increase with a decreasing diatom–dinoflagellate ratio
and exacerbated harmful algal blooms, which is closely related to an increase in anthropogenic
activities and climatic change (Jiang et al., 2014). Using historical in situ data sets from 1971 to
2001, an increase in DIN at summer surface water was also observed in the northern ECS, probably
due to the combination of changes in Changjiang discharge and increase of nitrogen fertilizer use in
China (Siswanto et al., 2008a). Although scientists are paying much attention to the response of
ECS ecosystem to changes in the nutrient supply arising from the Three Gorges Dam project, it isstill not clear if there are any changes because of the lack of time-series data and the complex
interactions in the Changjiang Estuary (Gong et al., 2006; Jiang et al., 2014).
Environmental change in the ECS slope and Kuroshio region is also not clear due to a limited data
set. Based on in situ data from 1987 to 2009 in the ECS outer shelf/slope region, nitrateconcentrations and nitrate fluxes in the Kuroshio were examined; Guo et al. (2012) indicated that
although the nitrate concentration in the middle and bottom layers across the Kuroshio was found toincrease significantly over the 23-year period especially after 2004, nutrient transport did notincrease significantly due to decreased current velocity.

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2.2.6 Isotopic applications in nutrient biogeochemistry studies
Complicated spatial and temporal variations and limited shipboard sampling lead to uncertainty inthe extrapolation of direct measurements of nutrient transports at regional and global scales.
Isotopes, however, can solve these constraints to some extent.

Phosphorus
Using natural activities of produced radioisotopes 32P and 33P in total dissolved phosphorus (TDP),
Zhang et al. (2004) provided the first direct estimates of residence times of dissolved and biological
P pools within the upper water column of the ECS. They indicated that the dissolved P turnoverrates varied with residence times of 3 to 4 days for TDP in coastal waters, which suggested that low
P concentrations could support relatively high levels of primary production. The short residence
times of TDP suggest that studies of dissolved nutrient concentrations alone are insufficient for
determining either nutrient limitation or maximal primary production.

SiliconZhang et al. (2015) measured the dissolved silicate concentration and silicon isotopic composition(δ30Si) in the surface water of the Changjiang estuary in summer and winter to study the behavior of
dissolved silicate fluvial inputs into the ECS. The dissolved silicate concentration decreased awayfrom the estuary and had a negative linear relationship with salinity affected by mixing between
river water and seawater, similar to previous studies (Zhang et al., 2007). Measured δ30Si in the
Changjiang estuary ranged from +1.48‰ to +2.35‰ in summer, and from +1.54‰ to +1.95‰ inwinter. As a result of low light levels and abundant dissolved silicate riverine inputs, dissolved
silicate and the isotope remain relatively unaffected by biological utilization and fractionation in thenear-shore region. Beyond a salinity level of 20, an obvious increase in δ30Si was observed,
indicating a significant increase in biological utilization and fractionation of dissolved silicate inhigh salinity waters in summer. Lower water temperatures and light levels that prevail over the
winter lead to the reduced fractionation of dissolved silicate compared with that in summer.

Nitrogen
In comparison to phosphorus and silicon, there are many more works that apply nitrogen isotopes toaddress nitrogen biogeochemistry in the YECS. The dual isotopes (δ15N and δ18O) of nitrate andnitrite and other nitrogen isotopes (e.g., δ15N in NH4+, DON and PON) are powerful approaches toseparate nitrogen flux processes in the ocean that are difficult to obtain through more traditional
measurements of solute concentration or direct rate determination (Liu and Kaplan, 1989; Sigman etal., 2005). The earliest work in this region was done by Kon-Kee Liu, who reported the NO3––δ15N
value of +4.7 ± 0.6‰ in Kuroshio upwelling water northeast of Taiwan measured 20 years ago (Liuet al., 1996), which was comparable to the δ15N values for NO3– in the Kuroshio Subsurface Water
observed recently (Umezawa et al., 2014; Liu et al., 2020). Based on isotope balance in the watercolumn, Liu et al. (1996) also estimated the nitrogen fixation flux to be 0.5 mg m–2 day–1 N in the
Okinawa Trough.
The seasonal variations of δ15N–NO3‒ values were measured in the Changjiang Estuary in 2006 and2010 and the effects of nitrogen transform processes (nitrate assimilation, nitrification, anddenitrification) and human activities (fertilizer, sewage, and manure) were discussed (Liu, X. et al.,2009; Yu et al., 2015; Liu et al., 2020). Chen et al. (2013) measured δ15N and δ18O values of NO3‒

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in the Changjiang Estuary in spring and studied the influence of nitrification, nitrate assimilation,
and water stratification. Wang et al. (2016) measured nitrate dual isotopes in the southern ECS andthe east of Taiwan in spring 2014, and indicated nitrate assimilation happened in the euphotic layer,
causing 5‰ isotope fractionation, and nitrification occurred in the bottom water when Kuroshio
Subsurface Water intruded into the ECS and oxygen consumption concomitantly. On the shelf ofsouthern Cheju Island, the δ15N and δ18O of NO3‒ were useful for clarifying the sources of NO3‒
(i.e., CDW, YSCWM and Kuroshio Subsurface Water-derived NO3‒), which were actuallyassimilated by phytoplankton. Although isotopic fractionation during NO3‒ uptake by phytoplankton
seemed to drastically increase δ15N and δ18O in summer, the relatively light nitrate with δ15N lower
than expected from this fractionation effect might be explained by contribution of atmosphericnitrogen and/or nitrification to NO3‒ dynamics in the surface and subsurface layers (Umezawa et al.,2014). Off the Changjiang Estuary to the ECS continental slope in summer, phytoplankton
assimilation leads to high δ15N and δ18O values for NO3− in the upper water layers; organic matter
remineralization, nitrification, and coupled sedimentary nitrification and denitrification results in
low NO3− isotope values in the lower water layers; assimilation, nitrification, and N2 fixation resultsin high δ15N and δ18O values and low Δ(15, 18) values in the upper water layers of the ECS
continental slope. Moreover, NO2− in the ECS is dominated by NH4+ oxidation, and NO2− oxidation
plays an important role in depleting NO2− in δ15N values (Liu et al., 2020).
Compared to the ECS, studies on nitrate isotopes in the YS are limited. Liu et al. (2017) reported
that the δ15N and δ18O values for NO3– in the YS in spring varied among the different water masses.
The high level of δ18O relative to δ15N in NO3– in the YS was related to the decomposition oforganic matter and subsequent nitrification, as well as atmospheric deposition. Moreover, nutrient
regeneration from sediments was an important source of water column NO3–.

2.2.7 Regeneration of nutrients
The relatively lower f-ratio (ratio of nitrate-based new production to total primary production) in the
shelf water and Kuroshio region compared with that in the upwelling area and Changjianginfluenced coastal waters (Chen et al., 2001; Chen and Chen, 2003) suggested that nutrients
re-mineralized from organic matter are also non-negligible nutrient sources to support primary
production in the shelf mixed water. Although NH4+ is primarily remineralized DIN and ispreferentially incorporated into phytoplankton, NH4+ is easily converted to nitrate throughnitrification at oxic conditions. Therefore, the extent of nitrification in the euphotic layer may imply
the importance of regenerated nutrients.
Nitrification rate and nitrate assimilation rate have been examined using 15N incubation technique in
the Changjiang Estuary and the ECS. Hsiao et al. (2014) determined the nitrification rate in the
Changjiang plume in August 2011. They reported the nitrification rate of bulk water (NRb) was
positively correlated with NH4+ concentration that ranged from undetectable up to 4.6 μmol L−1 d−1,
peaking at a salinity of ~29. In the river mouth and the inner plume, NRb was much higher than thatat particle-free water (particle >3 μm eliminated), indicating that the nitrifying microorganism ismainly particle associated. Shiozaki et al. (2011) determined the nitrate assimilation rate anddiapycnal nitrate flux in the northeast of the ECS and simultaneously examined the potential
distribution of nitrification activity. Active nitrate assimilation larger than diapycnal nitrate flux was
observed when the surface nitrate was depleted (<0.1 µM), with mean rates of 1400 and 96 µmol N
m−2 d−1 in the ECS and the Kuroshio Current, respectively. Archaeal amoA was observed at shallow
light depths at or above 10% light depth, suggesting that nitrification occurred within the euphotic
zone in the ECS. Shiozaki et al. (2011) concluded that nitrification within the euphotic zone would
be attributed to at least, in part, the observed discrepancy between nitrate assimilation and vertical

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flux. In the ECS in the period from 1993 to 1996, Kanda et al. (2003) reported that the nitrate
uptake rate integrated over the euphotic zone was higher on the ECS shelf (56 mg N m−2 d−1) than in
the shelf-break and slope region (21 mg N m−2 d−1). Both seasonal and spatial variations were larger
on the shelf. Uptake rates were low in summer due to low nitrate concentration when the watercolumns were stratified and were also low in well-mixed water columns in winter and spring when
the water temperature was low. High rates of uptake were observed in the spring bloom and in
autumn. These findings suggest that nutrient fluxes based on the budget model alone do notnecessarily indicate the contribution of each nutrient source to primary production because NO3–
uptake rate by phytoplankton is affected by many environmental and physiological factors.

2.2.8 Other nutrient sources
In addition to remineralized nutrients, there are several nutrient sources to support primary
production in the nutrient-depleted surface water of the ECS, especially during summer.
Shiozaki et al. (2010) reported that there was a patch of higher abundance of N2-fixing filamentous
diazotrophs, Trichodesmium spp., in the surface water of the ECS and Kuroshio region, and the
median N2 fixation activity at the observed stations was about 3.0 nmol N L−1 d−1, with an order ofhigher values at several locations (i.e., 14 or 28 nmol N L−1 d−1). Unicellular diazotrophic
cyanobacteria such as UCYN-A are also major agents to carry out N2 fixation, and their activity in
the Kuroshio-influenced ECS area is equal to or greater than those of Trichodesmium and diatom–
diazotroph associations containing the cyanobiont (Wu et al., 2018). Because Trichodesmium iscapable of hydrolyzing DOP compounds as phosphorus sources (Mulholland et al., 2002) as well as
many of the phytoplankton species, the bloom of these N2-fixing cyanobacteria can become
additional nutrient (DIN and DIP) sources for other microorganisms through biological decomposition.
In the ECS, the DOP concentration generally exceeded the DIP concentration in the surface layer(<100 m) of the outer shelf, while DIP is the major form of dissolved P, accounting for more than
70% of the TDP pool in inner and middle shelf waters (Fang, 2004). Because excess nitrogen
supplies to surface water of the ECS have occurred due to increasing atmospheric deposition (Kimet al., 2011) and terrestrial input (Liu et al., 2016), the availability of DOP is an essential factor toconsider nutrient supplies supporting primary production at offshore waters. Because one of the
DOP components, phosphomonoesters, is hydrolyzed into phosphate by an extracellular enzyme,
alkaline phosphatase (AP), its activity (APA) has been used to check whether microphytoplankton
experience phosphorus stress (Dyhrman and Ruttenberg, 2006). APA is very low at the upwellingzone where DIP is supplied from deeper layers, while APA is enhanced at DIP-depleted surface
Kuroshio waters (Liu et al., 2010), suggesting the potential use of DOP as a nutrient supply.
Typhoon-induced mixing is an important physical factor to lift up nutrients from the subsurface
layer to surface waters. Typhoon passages crossing shelf-slope push the Kuroshio current axis
shelfward and lead to the upwelling and vertical mixing of subsurface Kuroshio water with plenty ofnutrients, in addition to enhancing input of nutrient-replete terrestrial waters. Hung et al. (2013)
reported that nitrate and phosphate supplies to the water column in the southern ECS after thetyphoon’s passage were 5.6 × 1011 g-N d–1 and 7.8 × 1010 g-P d–1, respectively, which weresignificantly higher than those before the typhoon occurred (nitrate supply = 1 × 109 g-N d–1,
phosphate supply = 1.6 × 108 g-P d–1), which account for approximately 86% and 87% of summer
nitrate and phosphate supplies, respectively, to the southern ECS. Enhanced phytoplankton
chlorophyll-a and primary productivity (Siswanto et al., 2008b, 2009; Hung et al., 2013) andsinking particulate organic carbon (POC) fluxes (Hung et al., 2010) were observed after the typhoon
passages.

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2.2.9 Future perspective
Extensive marine research on nutrient biogeochemistry has been carried out in the YECS, including
spatial and temporal variations of nutrients, and the main sources of nutrients (such as riverine
inputs and atmospheric deposition). However, more studies should focus on the following aspects:(1) sources derived from nutrients regeneration in the water column and sediments; (2) nutrientsexchange between the YECS with offshore seawaters, such as the effects of the Yellow Sea Warm
Water input into the YS and water exchange between the ECS and open sea; (3) quantifying
transformation rates between nutrient species, such as nitrification rate and nitrate assimilation;(4) the effects of dissolved organic nutrients; (5) POM dynamics including export of PON/P as
sinking matter from the shelf to deeper ocean, Okinawa Trough, by the Continental Shelf Pump
(Tsunogai, 2002); (6) response of the YECS ecosystem to the change of nutrient dynamics.

Acknowledgments
The authors are very grateful to the Advisory Panel for a Circulation Study of East Asian MarginalSeas (AP-CREAMS) of North Pacific Marine Science Organization (PICES) for organization anddiscussion a Scientific Report; Zhaomeng Xu for his contribution to preparing Figure 1; and Dr.
Dongseon Kim for constructive comments and suggestions. This study was funded by the Natural
Sciences Foundation of China (NSFC: 41776087, 42176040), the Taishan Scholars Programme of
Shandong Province, Aoshan Talents Program supported by Qingdao National Laboratory for
Marine Science and Technology (No. 2015ASTP-OS08).

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Hypoxia

2.3 Hypoxia
Zhuoyi Zhu
School of Oceanography, Shanghai Jiao Tong University, China

2.3.1 The occurrence of hypoxia
Hypoxia means dissolved oxygen (DO) content in the water column is lower than a certain value.
The most commonly accepted threshold value is 2 mg/L (or 62.5 μM). Namely, when DO content in
the water column is lower than 2 mg/L, the water is termed hypoxic.
Due to biological respiration and chemical redox reaction, DO in the water column is being
consumed all the time. On the other hand, the processes of primary production (and hence
regeneration of DO), exchange with air via the water–atmosphere interface, and DO-rich watersupply (e.g., lateral advection) tend to increase DO content. When balanced with air, DO in thesurface water is around 7.5 mg/L (or 233 μM) at 20°C and salinity of 33. Hypoxia takes place when
the DO consumption rate in the water column exceeds the DO generation rate, and this imbalance islong enough in time to drop the DO concentration to a value lower than the threshold (i.e., 2 mg/L).
The basic mechanism for the occurrence of hypoxia is very clear, namely stratification and excessorganic matter decay in the near-bottom waters are the key factors that induce hypoxia (Diaz, 2001).
Therefore, at regions where hypoxia take place, the water column profiles are usually similar: lower
salinity and/or higher temperature is found in the surface waters with higher salinity and/or lower
temperature in the near-bottom waters; in the meantime, dissolved nutrients are very low in thesurface waters, and a clear increase can be found in the near-bottom waters (see Fig. 2.3.2).
Stratification is common in estuarine and coastal zones, but under the eutrophication trend
worldwide, an excess of organic matter that is input into the near-bottom waters is basically due toanthropogenic activities, and it usually becomes the main reason for the coastal hypoxia whencompared with past times (Diaz, 2001). The source of this decaying organic matter is interesting aswell as important, especially in the assessment of hypoxia and further environmental policy
amendments. There are two possible sources of organic matter. One is the organic matter that is
produced in the overlying water column due to intensive primary production (which is promoted byexcess nutrients input due to anthropogenic activities), and the other is from terrestrial sources (e.g.,
from soil or sewage). In that case, a case by case study may be needed to distinguish which organic
matter is the main source of decaying organic matter that causes the bottom hypoxia.
Organic matter decay is usually suggested as the main process that induces oxygen depletion; insome cases, however, nitrification is also found to be very important. In the Pearl River estuary, forexample, hypoxic water is reported in the surface waters, and in this case, nitrification is suggested
to be one of the key reasons (He et al., 2014).

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2.3.2 Yellow Sea and East China Sea background
The Yellow Sea (YS) and the East China Sea (ECS) are strongly impacted by strong monsoons.
Summer is the wet season when southerly winds prevail, and stratification becomes very common inestuarine and coastal zones. However, there are usually several cyclones (typhoons) passing by inthis region (on average, six) in summer, which cause strong vertical water column mixing and cantemporarily interrupt the water column stratification. Further, carried by riverine water, moreterrestrial materials (e.g., nutrients) are input into the coastal zone, which usually causes elevatedprimary production. Hence an abundance of organic matter is produced and settles down beneaththe pycnocline, where decomposition, which then consumes DO in the near-bottom waters, takes
place. In winter, strong northerly winds prevail which usually mix the water column quite well, andriver discharge is also usually much smaller when compared to that in summer.
The YS and ECS are bordered by coastlines having among the highest population densities in theworld. Industrialization and urbanization are very predominant and fast-growing in this region. Also,
there are several big rivers that empty into the YS and ECS, with the Changjiang River (also known
as the Yangtze River) being one of the biggest rivers in the world. All of these aspects make the YS
and ECS prone to very strong anthropogenic activities. Nutrients from many of China’s rivers thatempty into the Yellow Sea and/or the East China Sea have increased a lot in the past few decades.
For example, the Changjiang River dissolved inorganic nitrogen concentration increased from 0.1
mg/L in the 1960s to 1.5 mg/L in the 1990s (Yan and Zhang, 2003). Smaller Chinese rivers also
show an increase in nutrient concentrations under strong anthropogenic activities (Zhang, 1996).
For the estuaries and adjacent coastal zones, like the vast area close to the Changjiang River mouth,
it is clear that eutrophication has become an overwhelming phenomenon. Harmful algal blooms
have increased a lot in the YS and ECS from the 1980s to the 21st century (Wang, 2006). In addition
to the era with available historical and/or instrumental data, in an even longer time scale, like fromthe 1850s to now, there is still likely to be a mild eutrophication trend (Zhu et al., 2014).

2.3.3 Overview of hypoxia in the Yellow Sea and East China
Sea
Hypoxia takes place widely in the shallow coastal zones, namely from the coasts of Korea, Japan,
and China. For example, hypoxia has been reported in Chinhae Bay, the Youngsan River estuarinebay, Chonsu Bay, and Kamak Bay (Lim et al., 2006 and ref. therein), Jinzhou Bay, Liaohe Estuary,
Laizhou Bay, (Zhu, 2007 and ref. therein) and the Changjiang Estuary (Zhu et al., 2011).
Due to the monsoon season, hypoxia in the YS and the ECS usually occurs in summer (usually inAugust), whereas in winter, oxygen depletion in the near-bottom waters is usually alleviated (Lim etal., 2006; Tishchenko et al., 2013; Zhu et al., 2017). All of the hypoxic areas are located in theestuarine and coastal region of the YS and ECS (Fig. 2.3.1). These regions are zones where thewater column is strongly stratified in summer, organic matter is abundant, and there is strong
eutrophication.
Of all the reported hypoxic zones in the area, most of the hypoxic zones in the YS and ECS are on
the order of a few hundred square kilometers (or even less). The biggest hypoxic zone is located in
the region off the Changjiang Estuary adjacent to the ECS. In 2006 it was found to cover an area aslarge as 15,400 km2 (Zhu et al., 2011).

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HypoxiaFig. 2.3.1 Schematic of the hypoxia areas of concern (stars) in the pan-area of the Yellow Sea and East
China Sea.

In addition to hypoxia in the estuarine and coastal zones, and lagoons, oxygen depletion also takes
place in the deep Kuroshio waters where water depth can be over thousands of meters. However, no
hypoxia has been found in these regions, and the DO minimum in the deep Okinawa Trough in thesouthern ECS can be as low as 2.37 mg/L (at 1450 m depth, our unpublished data), and higher than
2 mg/L.

2.3.4 The biggest hypoxia event in the Yellow Sea and East
China Sea
The hypoxia event and its current features
Hypoxia off the Changjiang Estuary and adjacent ECS is likely the biggest hypoxia area in the YS
and ECS. It was first revealed in 1959, located at around 31.5°N, 123°E with a DO minimum of
0.49 mg/L, according to the Office of Integrated Oceanographic Survey of China (1961). In thatevent, the hypoxic zonal area was calculated as 1900 km2 (Zhu et al., 2011). In a dissolved oxygen
maximum study (Gu, 1980), a DO minimum (< 2 mg/L) zone was found off the Changjiang Estuary,
though the area is hard to quantify. After the 1980s, DO depletion and further hypoxia were more
frequently reported ( Limeburner et al., 1983; Chen et al., 1988). In 1988, a further hypoxia event
was discovered by Tian and his colleagues, which was located at around 30.83°N, 123°E (Tian et al.,1993). In the 1990s, Li and his colleagues revealed a large hypoxia event, with an area up to
13,700 km2 (Li et al., 2002). In the 21st century, additional hypoxia events were found and studied(e.g., Chen et al., 2007; Wei et al., 2007; Rabouille et al., 2008; Wang et al., 2012; Wei et al., 2017).
Oxygen depletion in the water column is also recorded in the sediment core via redox sensitiveelements (e.g., Mo, V, Fe Mn and U). Based on cores of tens of years time scale, upcore increasing
trends of Mo, V, and U in an E3 core match well with a general lowering trend of bottom water DO
minima since the mid-1980s (Wu et al., 2020). Further, a less DO-depleted period 1991–1997 isalso suggested in the identical research (Wu et al., 2020). Based on benthic foraminifera andelemental geochemistry, hypoxia in an even longer time scale (3000 years) is further reconstructed

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from a core named YD0902 (Ren et al., 2019). Based on their reconstructed hypoxia history overthe past 3000 years, it is interesting to find that hypoxia likely occurred during a warm climate but
lessened in a cold climate (Ren et al., 2019).
Though hypoxic areas can be traced back as early as in the 1950s, large ones (i.e., >10,000 km2)
were all found after 1999. Hence it is generally accepted that oxygen depletion is becoming more
severe in recent decades (Chen et al., 2007; Zhu et al., 2011; Wang et al., 2012). Different fromother large coastal hypoxia zones like the Gulf of Mexico, the northern part of the Black Sea, andthe central Baltic Sea, where perennial hypoxia events are found, hypoxia off the Changjiang
Estuary and adjacent ECS is recurrent and unstable in both time and space. Temporally, hypoxia
events were found in summer (usually in August), and quickly disappeared in autumn (Zhu et al.,2011). Continuous measurement data from a bottom-mounted system further revealed that hypoxia
can develop within 17–18 days and last for half a month, and is finally interrupted by strong wind
mixing (Ni et al., 2016). In winter, no hypoxia was found in the ECS (Zhu et al., 2017). In space,
the hypoxia zone can be sporadically distributed (Wei et al., 2007; Zhu et al., 2011). In August 2013,
three hypoxic zones were found during a survey off the Changjiang Estuary and adjacent ECS, witha total area of 11,150 km2 (Zhu et al., 2017). Despite the sporadic distribution pattern, the hypoxia
zones were all found between 30°N and 32°N before the 2000s. In August 2006, hypoxia was foundat 33°N (Zhu et al., 2011) and further confirmed in August 2013 (Zhu et al., 2017; Fig. 2.3.2a).
Whether or not the hypoxia zone is moving northwards requires more field observation to confirm.
The area of the hypoxic zone was first reported as 1900 km2 (Office of Integrated Oceanographic
Survey of China, 1961). In the 1980s, it was hard to derive its area, which was conservatively
estimated as smaller than the area found in the 1950s (Fig. 2.3.2b). As shown in Figure 2.3.2b, after
the 1990s the hypoxia area became much larger. In 1999, it was found to be 13,700 km2 (Li et al.,2002), 15,400 km2 in 2006 (Zhu et al., 2011), and 11,150 km2 in 2013 (Zhu et al., 2017). These
three hypoxia events are, so far, the largest events that were ever found. In the three big hypoxia
events (i.e., hypoxia in 1999, 2006, and 2013, the same hereafter), oxygen depletion was estimatedas 4.7, 7.2, and 5.1 million tons, respectively (Zhu et al., 2017). The methods used in the publishedliterature basically include the traditional Winkler titration method and DO probe (via DO meter or
DO probe on a CTD). Different methods give different precisions/detection limits, and hence overthe past a few decades, it remains difficult for us to determine the DO minimum trend before theprecision and detection limit of every DO data set was clear. However, in the three big hypoxia
events, DO minimum was found to show a descending trend, from 30.1 μM (1999), to 29.3 μM(2006) and finally 22.7 μM (2013), with a comparable DO measurement precision as discussed (Zhu
et al., 2017).

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Hypoxia34a201308
20130832°
20130819590820030930°
199908

28

°

121

°

°
123

°
127

10

b
20

area
DO min.
8
15.4

15

13.7

6
11.2

10
4
5

2.6

2

DO minimum (mg L-1)

Area of hypoxia zone ( 103 km2)

°
125

1.9

0

?
a

0.6

0.55
c

0.5
e

g
i
Aug.1988
Aug.1959
Aug.1999
Aug.2005
Aug.2013
Aug., 1959
Aug., 1981
Aug.,
1988
Aug., 1998
Aug.,
1999
Aug., 2002
Aug.,
2005
Aug., 2006
Aug.,
2013
d
f
h
b
Aug.1998
Aug.2002
Aug.2006
Aug.1981

0Time
Fig. 2.3.2 Pooled key hypoxia zone of its a) locations and b) areas in the region off the Changjiang
Estuary and adjacent ECS. Plots after Zhu et al. (2011); a: Office of Integrated Oceanographic Survey ofChina (1961), b: Limeburner et al. (1983), c: Tian et al. (1993), d: Wang and Wang (2007), e: Li et al.
(2002), f: Wang (2009) and ref. therein, g: our unpublished data, h: Zhu et al. (2011), i: Zhu et al. (2017).

The occurrence of the hypoxia: Basic physical and biochemical factors
Two conditions are required before oxygen depletion can be observed and further hypoxia develops,
namely physical background (stratification of the water column) and further, a biochemical processthat consumes DO (organic matter decay in the bottom layers) (Diaz, 2001). Physically, strong
terrestrial freshwater input in summer (basically from the Changjiang River) enhances strong
stratification of the water column. In the upper layer, the brackish Changjiang Diluted Water is themain water mass, whereas in the bottom layer, the marine water prevails. A low DO (< 3 mg/L)
distribution pattern was found to be similar to the area of the pycnocline (> 2.0 kg/m4) (Zhu, J.R. etal., 2016), suggesting the stratification as the basic background in hypoxia occurrence. A Q
quantitative relation was further presented (r = 0.66, p < 0.001, n = 86) between bottom water AOU

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Chemical Oceanography(apparent oxygen utilization) and delta σ (bottom water density minus surface water density) (Zhu etal., 2011). When delta σ is bigger than 5 kg/m3, bottom AOU does not show a positive relation withdelta σ, and instead of the correlation changes from positive to constant (Zhu et al., 2011). In
addition to the stratification, the tide is another important physical background control on thebottom DO distribution, which is fundamental in estuarine and coastal regions. Tide is usuallyconsidered as a factor that alleviates the bottom DO, as it commonly generates currents that enhancevertical exchange in the water column (Luo et al., 2009; Tyler et al., 2009). Indeed, hypoxia ismostly found in August, when the estuarine region shows the smallest residual tidal currents (Zhu,
J.R. et al., 2016). Also, numerical model application suggests that the hypoxia area may be reducedby the tide, especially destroying the bottom hypoxia in the region where stratification would be
weakened due to tide (Chen et al., 2015). In addition to the numerical models, field data from dielobservations (24 hours continuous observation at 3-h intervals), however, indicates that at least at
some sites, the tide may cause the bottom waters to move back and forth, while the stratification of
the water column is not interfered with. As a result, horizontally hypoxic water undergoes back andforth movement, whereas, at a given point (station), bottom DO concentration would show a tidal
pattern over diel observations (Zhu et al., 2017). A sinusoidal model has been further applied to thediel observation bottom DO data, which yields 12 hours (r2 = 0.6) for the bottom DO data (Zhu etal., 2017). This is consistent with the tidal period in this region (~13 hours). Given that tide causes
the hypoxic waters to move back and forth in the bottom layer (at least at over a tidal time cycle
time scale), a more accurate assessment of the hypoxia and its further environmental impact
becomes more complicated. Almost certainly the region that undergoes hypoxia will be different
from what we suggest now, as these results are all based on grid station data, in which no tide isconsidered when carrying out the field investigation. The numerical model further implies that the
tidal excursion (i.e., the distance over which a water particle travels up and down the estuary withthe flood and ebb tides) is larger in the northern Changjiang Estuary (~15 km) when compared tothat in the southern Estuary (< 5 km). Given that hypoxia is happening more often in the northern
Estuary (Fig. 2.3.2a), it is hence expected to influence a larger area under the tidal impact, whencompared to hypoxic zones that have happened in the southern Estuary (Zhu et al., 2017). The wind
is found to play an important role in determination of the water column physical structure. Strong
winds not only cause vertical mixing, which alleviates bottom oxygen depletion (Ni et al., 2016),
but also proper winds favor the near-bottom waters to dwell and develop hypoxia (Zhang et al.,2019).
With respect to the second factor, namely the biogeochemical process that consumes DO in thewaters due to strong anthropogenic activity, the Changjiang River nutrient flux has been found to be
increasing in the past decades (Zhang et al., 1999) and eutrophication is hence suggested to play amore prominent role in the estuarine region and adjacent ECS (Wang, 2006). In addition to the trend
in eutrophication in recent decades, sediment core studies further imply that the marginal sea has
been under mild eutrophication over the past 100 years (Zhu et al., 2014). Elevated terrestrial
materials input from the Changjiang River (and a minor contribution from those small rivers along
Zhejiang province) into the estuarine zone and adjacent ECS promote the in situ production. Indeed,blooms become common from May to August in the region off the Changjiang Estuary and adjacent
ECS (Zhu et al., 1997). The vigorous primary production in the upper layer generates excessorganic matter, part of which sinks to the bottom layer, and finally decays there. In the vicinity of
the Changjiang River mouth, terrestrial organic matter may be an important source of decayingorganic matter in the bottom waters that consumes bottom DO. However, we think the majority ofthe decaying organic matter in this region (i.e., where hypoxia is frequently found) should be ofmarine origin. Taking the whole area where hypoxia was most frequently reported into
consideration (i.e., the black dashed line area in Figure 2.3.2a), it is estimated to be 180,100 km2,
and this area is also most likely to be receiving the Changjiang export material in a 100% manner.
On one hand, the Changjiang annual particulate organic carbon (POC) flux is 1.8–4.2 × 106 tons

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Hypoxia(Zhang et al., 1999), or more recently 1.3 × 106 tons (Gao et al., 2012). On the other hand, the insitu primary production within this area is likely to be 115 g C m–2 yr-1 (Gong et al., 2003) or anannual value of 21 × 106 tons (multiplied by the area 180,100 km2). Given the f-ratio of 0.4 (Chen,
2003), the new production would yield an annual POC amount of 8.4 × 106 tons. As we emphasize
the organic matter produced above the thermocline (not concerning export out of the shelf), hence
there is not an overestimate of the f-ratio here (Chen, 2003). A more precise calculation in summer(i.e., August) gives a monthly POC flux of 0.2 × 106 tons for the Changjiang River (Gao et al.,2012), and a monthly new production rate of 5 × 106 tons (Gong et al., 2003) or 11 × 106 tons (Chenet al., 2001). So the ratio between the two sources (i.e., terrestrial and marine) within the area isannually as 1.3:8.4 (or 15%), or monthly (i.e., August) as 0.2:11–0.2:5 (i.e., 1.8–4%). As theestuarine filter effect and the decomposition process before the terrestrial organic matter finallyreaches the bottom layer is not yet considered, hence the actual percentage is likely to be evensmaller. In other words, terrestrial organic matter would play an even smaller role. The main
uncertainty in the above estimate approach lies in the primary production and further new
production value, which may show a large spatial distribution gradient and hence making theestimate of in situ produced organic carbon involves great error. Furthermore, from the aspect ofchemical lability, in situ produced organic matter is more labile and easy to decompose, whereas theterrestrial organic matter that finally escapes from the estuarine filtration effect is expected to be
more refractory. Recently, Wang and her colleagues used a mixing model to discuss the decayingorganic matter in the bottom waters and identified a surface diatom bloom as the major source of theorganic carbon (Wang et al., 2017). Further, carbon isotope study provided direct evidence that the
decaying organic matter in the bottom layer is of marine origin (Wang et al., 2016).

Fig. 2.3.3 Comparison of the factors that form hypoxia in the three big hypoxia events in 1999, 2006 and2013: a) stratification as indicated by water density difference Δσ (in kg/m3) and b) organic matter decay as
indicated by POC (particulate organic carbon) content (in %) in the total suspended matter in the bottom
layers. (Data in August 2011 are also shown as a comparison, when no hypoxia was found; data in 1999 arederived from Li et al. (2002), data in 2006 are derived from Zhu et al. (2011) and data in 2011/2013 arederived from Zhu et al. (2017).

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In the three big hypoxia events in 1999, 2006 and 2013, the physical (e.g., stratification) andbiochemical DO-consumption process (e.g., organic matter decay) differed from each other (Fig.
2.3.3). In 1999, stratification was most severe (Fig. 2.3.3a), whereas the organic matter decay seems
to be most thorough in 2006 and 2013 (Fig. 2.3.3b). Though stratification in 2011 was also verystrong (Fig. 2.3.3a), POC content in the total suspended matter (TSM) of the bottom layer remained
very high (Fig. 2.3.3b), which implies that the organic matter decaying process may not occur
thoroughly when compared to the other three years (i.e., 1.6% in 2011 vs. 0.9 in 1999, 0.7 in 2006,
and 0.7 in 2013; Fig. 2.3.3b). As a result, no hypoxia was found in 2011, though a large area of low
DO zone was detected (i.e., 2 mg/L < bottom DO < 3 mg/L) (Zhu et al., 2017).

The occurrence of the hypoxia: Its instabilities
The entire ECS lies over a broad continental shelf (over 500 km), which extends from the Chinese
coast to the Okinawa Trough, and the water column of the vast ECS is hence usually very shallow.
In the region off the Changjiang Estuary, it can be as shallow as 50 m when it is already over
100 km away from the mainland. The hypoxia usually occurs in the region with water depth < 50 m
(Fig. 2.3.2a). In addition to occasional northerly wind events, there are usually three to six typhoons
in the summer season in the ECS (Wei et al., 2007). Given the shallow water depth of the hypoxia
zone, the stratification of the water column can be easily interrupted during these weather eventsand hence can terminate the hypoxia. Therefore, different from other regions where hypoxia isperennial (like the Gulf of Mexico, the northern part of the Black Sea, the central part of the BalticSea), hypoxia off the Changjiang Estuary and adjacent ECS is recurrent and unstable. Ni and hiscolleagues (2016) found that hypoxia developed within 17–18 days, lasted for around two weeks,
and was then quickly destroyed due to a strong wind event. The interesting part is that after thestrong vertical mixing, the water column stabilized again, and it took another 17–18 days to develop
another hypoxia event (Ni et al., 2016). The repeated occurrence of hypoxia within the summer
season (or even within the same month) alleviates the bottom oxygen-deficient environment, as DO
would be occasionally supplied due to vertical mixing. The corresponding nutrient regeneration,
however, would also be repeated (and hence enhanced), as each hypoxia event means organic matterdecay and corresponding nutrient regeneration.
When discussing the recurrent nature of the hypoxia off the Changjiang Estuary and in the adjacent
ECS, which is quite different from other similar regions like the Gulf of Mexico, one would
naturally consider the annual variability of the Changjiang River impact (e.g., annual discharge andnutrient export variability), as well as annual weather differences (e.g., occurrence of typhoons
differs from year to year). However, we argue that the marine background may also be essential.
The water residence time of the ECS is on the order of one year (Li, 1994). Hence, oxygen depletion
impact in the previous summer can hardly be delivered to the next summer as the waters are almost
totally renewed in the following year. Against this background, we need to keep an eye on the natureof the source water as it impacts the summer hypoxia annually (Qian et al., 2017).

Respiration rate estimates and comparison with sedimentary contribution
Organic matter decay is the key process that drops DO concentration, and hence its rate, and further,
the plankton community respiration rate is essential in hypoxia studies. In the Changjiang Estuary
and adjacent ECS, direct studies about the bottom plankton community respiration rate are limited(Wang et al., 2017). Within the euphotic layer, the plankton community respiration rate wasmeasured previously and is summarized in another section of this report (see subsection 3.1.9).
Briefly, the plankton community respiration rate in the region off the Changjiang Estuary andadjacent ECS (i.e., stations 18, 19, 20, 29) ranged from 0.04 to 0.10 mg O2/L/day in autumn

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Hypoxia(October/November; Chen et al., 2003), and ranged from 0.03 to 0.26 mg O2/L/day in summer(August; Chen et al., 2006). The rate in summer is higher when compared to that in autumn, and ismuch higher when compared to that in the northern YS (~0.03 mg O2/L/day; Zhai et al., 2014). By
measuring dissolved inorganic carbon and further calculations, Wang and her colleagues suggested
that the bottom water should be characterized by a community respiration rate of 2.4 g O2/m2/day(Wang et al., 2017). Assuming the bottom water depth as 20 m, a respiration rate of 2.4 g O2/m2/day(Wang et al., 2017) then equals 0.12 mg O2/L/day, and this value is comparable to the mean value(i.e., (0.03 + 0.26)/2 = 0.145 mg O2/L/day) reported by Chen et al. (2006), though which focused in
the upper water column. It should be noted that when the DO decreasing rate is constant with time(linear decreasing of organic matter at a given oxygen level), it means the kinetics has a formal
zero-order with regard to organic matter, whereas when the rate of oxygen decreasing is notconstant with time and depends on oxygen concentration, kinetics has at least first-order regarding
oxygen. Using time-serial observations of oxygen depletion near the bottom in Amursky Bay,
kinetics first-order was successfully used for the modeling process (Tishchenko et al., 2016).
Therefore, the aforementioned oxygen consumption rates from different scientific groups should beviewed and compared with care.
With respect to the organic matter decay rate, an in situ incubation experiment based on bottom
waters indicated that decay of POC over the entire incubation period (8 days) roughly followed alinear decrease, and the rate averaged 0.3 mmol C/m3/day in the low DO condition (DO < 50%)
whereas the mean rate was 1.5 mmol C/m3/day in the high DO (DO > 95%) condition (Zhu et al.,2013). For dissolved organic carbon, its decay follows the exponential pattern, and the decaying
constant k ranged from 0.12– 0.29/hour (Gao et al., 2015).
Based on 224Ra/228Th disequilibrium in the sediment, Cai and his colleagues suggested that the
sediment process contributes 6 to 61% bottom AOU (Cai et al., 2014). Direct in situ DO
consumption incubation experiments suggested that the sedimentary respiration rate ranged from
7.6 to 10.2 mmol O2/m2/day (Song et al., 2016); a corresponding bottom water respiration rate canbe derived from the aforementioned POC decay rate, and phytoplankton further drives thecommunity respiration rate (Zhu, Z.Y. et al., 2016), which ranges from 27 to 300 mmol O2/m2/day.
Together, sedimentary respiration and the bottom water respiration rate give sediment and bottom
water contribution of bottom AOU as 14.5% and 85.5%, respectively.
More recently, a δ18O technique was applied in the hypoxia study off the Changjiang Estuary and in
the ECS (Zhou et al., 2021), which can simultaneously quantify the contribution of oxygen
depletion from both water respiration and sedimentary respiration. The rationale behind this is thatthe oxygen isotope fractionation during water column respiration and sedimentary respiration differs
greatly. The interesting point is that at a given site, the water column respiration and sedimentary
respiration contribution to oxygen depletion is not stationary and instead, changes along withsurface water production, or along with how much organic matter is sinking into the near-bottom
waters. From the viewpoint of observations, the lower the bottom water DO, the greater thecontribution from water column respiration. Generally, hypoxia off the Changjiang Estuary and in
the adjacent ECS is caused by water column respiration (contributing over 70% of oxygen depletion)and the rest is contributed by sedimentary respiration (Zhou et al., 2021).

2.3.5 Impact of hypoxia
The most prominent and well-known impact of hypoxia is its negative influence on the ecosystemand fishery resources. In the Changjiang Estuary and adjacent ECS, the heterotrophic bacteria to
autotrophic species ratio increased, fish biomass was reduced, and there was a change in

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biodiversity and dominance of economic species (Zhang, J. et al., 2010). Overfishing may be themain reason, but environmental quality retreat, including hypoxia, worsens the problem.
Quantitative assessment of the impact of hypoxia on fishery resources requires further study.
Also, hypoxia changes the microenvironment; hence the bacteria community structure underhypoxic conditions may be different from that in normal DO level conditions (Liu et al., 2012; Ye etal., 2016). Microbial activities accordingly enhance processes like anaerobic ammonium oxidation,
denitrification, and/or dissimilatory nitrate reduction to ammonium, which generates notablegreenhouse gas effects like N2O and CH4 (Zhang G.L. et al., 2008, 2010;). N2O and CH4 gas
emission rates were reported as 22 and 38 μmol/m2/day in the Changjiang Estuary and adjacent ECS,
respectively, where a large area of hypoxia was found (Zhang et al., 2008). When compared to otherlarge hypoxia zones, the greenhouse emission rate from the region off the Changjiang Estuary andadjacent ECS is among the highest (Zhang, J. et al., 2010). Also, bottom hypoxia and consequential
hydrogen sulfide (H2S) release from sediment in eutrophic estuaries is another major global
environmental issue (Rathnayake et al., 2017).
Further, hypoxia plays an important role in the cycling of materials, and nutrients generated frombottom hypoxia and oxygen depletion is as efficient as the Changjiang River flux (Zhu et al., 2017).
A conservative estimate to nutrient amounts that were regenerated in the three big hypoxia events
off the Changjiang Estuary and adjacent ECS suggested that the N (nitrate) and P (phosphate)regeneration amount can be as high as 30,000 and 4100 tons, respectively (Zhu et al., 2017). Given
that hypoxia can develop within 17–18 days (Ni et al., 2016), or even within 6 days (Wang et al.,2017), the nutrient regeneration rate is similar to that of the Changjiang River nutrient discharge (as17–18 days) or even higher if 6 days of development into hypoxia would be considered (Zhu et al.,2017).

2.3.6 Uncertainties, unknowns, and perspectives
The YS and ECS is a region under extremely strong anthropogenic activity, including eutrophication
and big hydrology projects (like the Three Gorges Dam project in the Changjiang River). Whileeutrophication promotes vigorous phytoplankton growth, the Three Gorges Dam interferes with thelarge river suspended load discharge pattern, and modifies the stoichiometry of riverine nutrientsthat are being finally emptied into the ECS. It has been found that suspended load in the ChangjiangRiver water has been decreasing since the Three Gorges Dam project (Yang et al., 2007). Less totalsuspended matter in the estuarine surface water means the alleviation of light limitation for
phytoplankton growth. Also, silicate, nitrate, and phosphate concentrations have been found tochange in the past few decades. The ratio between these nutrients hence also changes, which may
cause a phytoplankton structure shift in the estuary. A shift from diatom to dinoflagellates blooms in
the Changjiang Estuary and adjacent ECS has been reported (Tang et al., 2006), and further
evidence has also been found in sediment cores (Zhu et al., 2014). Anthropogenic activities,
contributing to ecosystem changes, induce additional challenges to the YS and ECS environment,
and hence further the development of bottom hypoxia.
Overall, hypoxia in the YS and ECS is a subject of strong scientific interest and attention, but we arestill unaware of many key details when compared to other large coastal hypoxia regions like thecentral Baltic Sea and the Gulf of Mexico. For example, hypoxia and its feedback to the ecosystem
via nutrient modification remain largely unknown, while in the Baltic Sea, it is clear that hypoxia
alleviates P limitation, promotes further phytoplankton blooms and hence enhances further hypoxia
occurrence (Zilius et al., 2014). Also, whether the hypoxia is highly related to terrestrial output
seems more complex when compared to the case in the Gulf of Mexico, where a simple relation was

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found between terrestrial N flux and the following bottom hypoxia (Turner et al., 2012). Anotherimportant puzzle is the role of sub-ground marine water discharge in bottom hypoxia; yet we know
very little in this aspect.
In addition to these unknowns, the numerical study of hypoxia remains very limited (Chen et al.,2015). This is fundamental when making quantitative predictions to the public and government. To
pave the road for a pertinent numerical study, the key parameters should be quantitatively confirmedby field observations or in situ incubation experiments, although this corresponding work is still
limited (Zhu, Z.Y. et al., 2016).

2.3.7 ReferencesCai, P.H., Shi, X.M., Moore, W.S., Peng, S.Y., Wang, G.Z. and Dai, M.H. 2014. 224Ra:228Th
disequilibrium in coastal sediments: Implications for solute transfer across the sediment–waterinterface. Geochim. Cosmochim. Acta 125: 68–84.
Chen, C.C., Shiah, F.K., Gong, G.C. and Chiang, K.P. 2003. Planktonic community respiration in the
East China Sea: importance of microbial consumption of organic carbon. Deep Sea Res. II 50: 1311–
1325, doi:10.1016/S0967-0645(03)00025-0.
Chen, C.-C., Chiang, K.-P., Gong, G.-C., Shiah, F.-K., Tseng, C.-M. and Liu, K.-K. 2006. Importance ofplanktonic community respiration on the carbon balance of the East China Sea in summer. Global
Biogeochem. Cycles 20: GB4001, doi:10.1029/2005GB002647.
Chen, C.-C., Gong, G.-C. and Shiah, F.-K. 2007. Hypoxia in the East China Sea: One of the largest
coastal low-oxygen areas in the world. Mar. Environ. Res. 64: 399–408,
doi:10.1016/j.marenvres.2007.01.007.
Chen, C.T.A. 2003. New vs. export production on the continental shelf. Deep-Sea Res. II 50: 1327–1333,
doi:10.1016/S0967-0645(03)00026-2.
Chen, J.Y., Chen, X.L. and Yang, S.L. 1988. Report of the survey to the Shanghai coastal zone and tidal
flat resources. Shanghai Scientific and Technical Publishers, Shanghai.
Chen, X.F., Shen, Z.Y., Li, Y.Y. and Yang, Y. 2015. Tidal modulation of the hypoxia adjacent to the
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2.4 Biogeochemical Tracers
Jing Zhang1 and Guebuem Kim2
1
2

University of Toyama, Japan
Seoul National University, Korea

2.4.1 Introduction
Trace elements and their isotopes (TEls) are key parameters of the GEOTRACES program(https://www.geotraces.org/). Many trace elements (e.g., Fe, Co, Cu) are micronutrients that play
critical roles in the ocean ecosystem, and therefore control the structure and productivity of marine
ecosystems as well as carbon cycling. Other trace elements and isotopes can be used to assess ocean
processes. For example, Al, Mn, rare earth elements (REEs; Zhang et al., 2019), 143Nd/144Nd, andRa isotopes have been used to identify and quantify mixing of various water masses, including
riverine input and submarine groundwater discharge. Given the crucial importance of TEls andisotopes that serve as biogeochemical tracers, knowledge on their processes and fluxes which
control their distribution and internal cycling is required.

2.4.2 Water column
Distribution of TEls in the Changjiang Estuary and East China Sea
Aluminium
The distribution of dissolved Aluminum (Al) in the Changjiang Estuary and East China Sea (ECS)
was reported as early as 1984 by Mackin and Aller (1984). Dissolved Al in the Changjiang Estuary
varies widely (18–200 nmol L–1) at very low salinity, but seems to be constant when the salinity is
between 1 and 30 (Ren et al., 2006). The concentration of dissolved Al generally decreases
gradually from the Changjiang Estuary to the central shelf, and then decreases sharply at the shelf
break. Dissolved Al ranges from 2 to 113 nmol L–1 on the ECS shelf (Minakawa and Watanabe,
1998; Ren et al., 2006, 2015). The average residence time of dissolved Al is 339 ± 118 days (Ren etal., 2006). Ren et al. (2015) calculated the cross-shelf transport of Al flux at the subsurface layer
and found that the coastal enriched Al water was mainly exported to the marginal sea north of Japanand the northwestern Pacific Ocean. The presence of an Al plume indicates cross-shelf transport
over the ECS shelf which is evident at the isopycnal layers at Sq = 23.5 ± 0.5 kg m–3. The possible
transport passage originates from the near-bottom layer of the Min-Zhe coast, extends
northeastward and then shifts southeastward through the ECS shelf to the Kuroshio water at 30° N(Wang et al., 2018).

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Manganese
The dissolved Manganese (Mn) concentrations in the ECS range from 2 to 141 nmol L–1, with anaverage of 8 nmol L–1. Seasonal variations of dissolved Mn in the ECS are significant, with thehighest concentrations occurring in summer and the maximum concentrations (141 nmol L–1)
occurring in near-bottom waters of the suboxic zone. Extensive consumption of oxygen results in
the regeneration of dissolved Mn in near-bottom waters of the suboxic zone (Wang et al., 2016).
Dissolved Mn in the Changjiang Estuary behaves nonconservatively and is removed significantly by
net sorption onto suspended particles. The concentration of dissolved Mn decreases across the ECS
with distance from the coast. Mn-rich ECS shelf waters are exported to the Kuroshio waters andmay have the potential to influence the northwestern Pacific Ocean as well as the marginal sea northof Japan. The estimated average residence time for dissolved Mn is 76 to 350 days in the ECS(Wang et al., 2016).

Iron
The average concentrations of total dissolved iron (D-Fe) in the surface water of the Changjiang
Estuary in spring and autumn are 40 ± 27 nmol L–1 and 21 ± 11 nmol L–1, respectively. The averagebottom D-Fe concentrations in the estuary in spring and autumn are 76 ± 59 nmol L–1 and 79 ± 39nmol L–1, respectively. The average D-Fe concentration in both surface and bottom waters in springis higher than that in autumn. The average surface concentrations of Fe organic ligands (Lt) in theestuary in spring are higher than those in autumn (40 ± 27 vs 23 ± 8 nmol L–1). Both D-Fe and Lt areelevated in the Changjiang Estuary and Hangzhou Bay but are depleted with distance away in theoffshore of the ECS (Su et al., 2015). The dissolved Fe is significantly correlated with Lt, and thelimitation of total dissolved phosphate (TDP) decreases the uptake of Fe (Su et al., 2017).

Other trace elements
In the coastal waters of the ECS, the average concentrations of dissolved Zinc (Zn), Copper (Cu),
Cobalt (Co), and Vanadium (V) are 14 ± 9.0, 23 ± 5, 0.9 ± 0.2, and 20 ± 6 nmol L–1, respectively(Su et al., 2017). TDP is associated with dissolved Fe, Cu, Zn, and Co, but not with V. Su et al.
(2017) highlighted that the limitation of TDP decreased the uptake of Fe.

Distributions of trace elements in the Yellow Sea and Bohai Sea
The studies on dissolved Al in the Yellow Sea (YS) are very limited; the concentration of dissolved
Al ranges from 8 to 71 nmol L–1 (Ren et al., 2006), but was observed to be as high as 197 nmol L–1
due to the influence of dust storms (Ren et al., 2011). Dissolved Al in the southern YS is high in the
coastal area and decreases towards the central area, and is regulated by the mixing of different water
masses, including the Yellow Sea Coastal Current, Korea Coastal Current and Yellow Sea Warm
Current (Ren et al., 2011). The residence time of dissolved Al in the YS is 148 ± 32 days.
In general, the average metal (Cu, Co, Cd, and Ni) concentrations found in the southern YS are
lower than those in the Bohai Sea (BS; Li et al., 2015, 2017). The average concentrations of Cu, Co,
Cd, and Ni are 8, 0.1, 0.4, and 7 nmol kg–1, respectively in the southern YS, while they are 19, 0.3,
0.8, and 13 nmol kg–1, respectively in the Central BS (Li et al., 2017). These trace elements areaffected by river discharge and biological uptake.
Overall, current studies are mainly focused on the distributions of trace elements in the watercolumn; studies on the processes (desorption/adsorption, generation) and fluxes of biogeochemical
tracers are still very limited. Lee et al. (2014) quantified the fraction of Changjiang Diluted Water

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Chemical Oceanography(CDW) using 223Ra and 228Ra. They found that CDW contributes >20% to the northern ECS. This
result is comparable with the estimate from Che and Zhang (2018). Furthermore, the contribution of
Kuroshio Subsurface Water to the Changjiang Estuary is >80% using 143Nd/144Nd (Che and Zhang,2018); Kuroshio water was found to be the major source, at 98–99%, in the bottom water (100 m
depth) of the ECS central shelf using REEs in combination with salinity and potential temperature(Zhang et al., 2018). The water contributions from the ECS to the northwestern Pacific and to themarginal sea north of Japan are ~6% and 10%, respectively. Ren et al. (2015) evaluated Al export
from the ESC along the 100 m isobath to be 1.67 × 1010 g yr–1. Those studies suggested that the
ECS might be an important material source to the northwestern Pacific Ocean.

Distribution of radionuclides in the East China Sea and Yellow Sea
Ra isotopes
Radium (Ra) isotopes are very useful for tracing complicated water mixing and transport since they
are soluble in seawater and have different half-lives: 228Ra (half-life: 5.75 years), 226Ra (half-life:
1,602 years), 223Ra (half-life: 11.4 days), and 224Ra (half-life: 3.66 days). In the ECS, the activitiesof 226Ra and 228Ra have ranged from 5 to 26 dpm 100L–1 with a mean value 14 dpm 100L–1 and from
1 to 73 dpm 100L–1 with a mean value 34 dpm 100L–1, respectively (Nozaki et al., 1991; Inoue etal., 2012; Su et al., 2013; Men and Liu, 2015). The highest concentrations of long-lived 226Ra wereobserved in the river plume area (Fig. 2.4.1), associated with the efficient desorption of Ra from
fresh riverine sediments. In contrast, the highest concentration of 228Ra were observed in the central
YS water. This trend is due to much larger inputs of 228Ra from bottom sediments, ingrown from232
Th in sediments, although there are significant inputs of 228Ra from riverine sediments(Fig. 2.4.1).

Fig. 2.4.1 Locations of sampling sites and the distributions of (a) 226Ra and (b) 228Ra activities in thesurface water of the East China Sea obtained in previous studies (Okubo, 1980; Nozaki et al., 1991; Yeh
and Chung, 1997; Kim et al., 2005; Zhang et al., 2007; Inoue et al., 2010, 2012; Su et al., 2013; Lee etal., 2014; Kim et al., 2015; Men and Liu, 2015).

The activities of 226Ra and 228Ra have been successfully utilized to evaluate the mixing of multiple
water masses (Zhang et al., 2007; Lee et al., 2014) and water circulation patterns on the surface(Nozaki et al., 1991). For example, Lee et al. (2014) concluded that CDW contributes >20% to thenorthern ECS. In addition, the decay of 228Ra relative to 226Ra in the YS has been used to estimate
the water residence time (Nozaki et al., 1991; Kim et al., 2005). These Ra isotopes were also used

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to estimate Submarine Groundwater Discharge in this region (Kim et al., 2005; Kim et al., 2015).
Su et al. (2013) used 226Ra and 228Ra to estimate the diffusivity and advection velocities in thisregion.
In the ECS, the distributions of short-lived 224Ra and 223Ra were also utilized successfully for
tracing water-mass transport rates from the river plume to the upstream and vertical water mixing inthe shelf region after a typhoon (Lee et al., 2014). As such, Gu et al. (2012) used these isotopes toestimate the water residence time and the contribution of Submarine Groundwater Discharge in the
Changjiang effluent plume.
210

Po and 210Pb

Of the naturally occurring radionuclides, the Polonium isotope 210Po (half-life: 138 days) is
produced by 210Pb (half-life: 22.3 years) decay, and 210Pb is produced by 226Ra decay, via 222Rn.
Both Pb and Po are particle reactive. Especially, Po is known to be more preferentially taken up by
biota (Kim, 2001). Due to their slightly different chemical properties, the ratios of 210Po/210Pb in theocean can vary in the water column.
In the ECS and YS, total 210Pb activities range from 2 to 45 dpm 100 L–1 (with a mean value of 15 ±9 dpm100 L–1) and total 210Po activities range from 1 to 30 dpm100 L–1 (with a mean value of 8 ± 7
dpm 100L–1) in the surface ocean. In general, both nuclides show considerably large seasonal andregional variations in the surface water layer, depending on atmospheric depositions, marine
productivity, and sediment amounts. The activities of 210Pb and 210Po in the shelf water are
considerably lower than those in the Kuroshio Current (Fig. 2.4.2), perhaps due to their particle
reactive characteristics.

Fig. 2.4.2 Maps showing the locations of the data acquired and the distributions of 210Pb and 210Po inprevious studies. Data from Nozaki et al. (1990, 1991), Hong et al. (1999) and Su et al. (2017).

The activities of both nuclides are higher in the vicinity of the Changjiang Estuary and thesouthwest region of Korea, perhaps due to higher suspended sediment loadings. Similarly, excess
dissolved 210Po activities have been observed in the western and eastern shallow shelf areas in themouth of the YS, due to high suspended particle content (Hong et al., 1999). Based on a massbalance model, the residence time of 210Pb was calculated to be approximately two months on the

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shelf, and seven months in the Kuroshio Current (Nozaki et al., 1991). Nozaki et al. (1990) found alarge 210Po deficiency relative to 210Pb in the upper 1000 m water column of the ECS and thePhilippine Sea.
137

Cs and 90Sr

Among artificial radionuclides, the Cesium isotope 137Cs (half-life: 30.17 years) and Strontium
isotope 90Sr (half-life: 28.7 years) have received particular attention due to their long half-lives in
the ocean. The main source of both nuclides is atmospheric fallout from global weapon testing
during the 1960s in the western Pacific. Thus, in general, the distribution of water-soluble 137Cs
depends on ocean currents, radioactive decay, water mixing, and inputs through the remobilizationof terrestrial sources, while the distribution of 90Sr is influenced significantly by local
biogeochemistry and physical mixing of different water masses.
The activity of 137Cs in surface seawater in the ECS ranges from 0.4 to 1.8 Bq m–3 with a meanvalue of 1.0 ± 0.1 Bq m–3 in 2011 (Wu, 2018; Zhao et al., 2018; Fig. 2.4.3). It is similar to the range
from the South China Sea (1.1–1.4 Bq m–3 with a mean value of 1.3 ± 0.1 Bq m–3, Wu et al., 2013).
In the YS and the southern sea off Korea, 137Cs activity ranges from 1.5 to 2.3 Bq m–3 and from 1.0
to 2.0 Bq m–3, respectively (KINS, 2012) (Fig. 2.4.3). The ranges of 137Cs activity show similarvalues for the period 2007–2011 (1.0–3.0 Bq m–3, KINS, 2012). The higher activities of 137Cs in thecenter of the YS seem to be associated with the accumulation of terrestrial 137Cs over a water
residence time of about five years.
The 90Sr activity in surface seawater of the ECS ranged from 2.0 to 3.0 Bq m–3 in 1994 (Fig. 2.4.3,
Hong et al., 2006). In the YS, the activity of 90Sr ranged from 1.0 to 4.0 Bq m–3 for the period 1994–
2000 and 2012 (Fig. 2.4.3, Hong et al., 2006; KINS, 2012). Hong et al. (2006) suggested that riverwaters are the main source of 90Sr in the YS based on a correlation between 90Sr activity and salinity.
As such, 90Sr activities were higher towards the river mouth and the YS.

Fig. 2.4.3 The spatial distribution of 137Cs activities in surface water of the East China Sea, the Yellow
Sea, and the southern sea off Korea. Data from KINS (2012), Wu (2018) and Zhao et al. (2018). The
spatial distribution of 90Sr activities in surface water of the East China Sea and the Yellow Sea. Data from
Hong et al. (2006) and KINS (2012).

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2.4.3 Sediment
As one of major GEOTRACES ocean interfaces, the biogeochemical reaction between sediments
and the overlying water column influences the net sources and sinks for dissolved TEIs in seawater
as well as the internal cycling of TEIs in the ocean (GEOTRACES Science Plan, 2006). However,as the largest marginal shelf adjacent to the northwestern Pacific Ocean, little is known about net
supplies of TEIs from sediments or about the key processes and key areas of the supplies in the ECS,
YS, and BS.
Meanwhile, the signal from land changes and anthropogenic activities with the injection of
Changjiang and Yellow River water, and the signal from ocean processes have been recorded
synthetically in the sediments. However, there are few systematic studies that focus on these signals.
Some trace metals play a key role in controlling biogeochemical processes in the ocean, such as Fe,
Cu, Zn, Co, Cd, and Mn. Meanwhile, some trace metals are harmful to the natural and humanenvironment due to their toxicity and/or radioactivity, such as Lead (Pb), Mercury (Hg), Cadmium(Cd), Arsenic (As), Zinc (Zn). Studies (Huang et al., 2014; Hu et al., 2015) in recent years haveadvanced our understanding of the distribution of these micronutrients and pollutant trace metals in
the continental shelves of the ECS, YS, and BS.

East China Sea
Higher heavy metal (Cu, Pb, Zn, Cd, As, Hg) concentrations are found at the entrance to the
Changjiang, decreasing toward the northeast. The vertical distribution of contamination levels andits ecological risk can be divided into four periods based on the down core variation in heavy
metals: pre-1940s, 1940s–1970s, 1970s–1990s, and late 1990s to the present (Hu et al., 2015).
Higher concentrations of metals (Cu, Mn, Ni, Zn, Pb, Fe) are generally found in the inner ECS shelfand decreasing seaward. The sequences of the enrichment factor (EF) of the studied metals are Cu >Mn > Ni > Zn > Pb > Fe. The metal contamination in the middle and outer shelves of the ECS is
still minor (Fang et al., 2009). Total concentrations for As, Hg, Selenium (Se), Antimony (Sb),Tellerium (Te), Tin (Sn), Bismuth (Bi), and Germanium (Ge) ae 4.6–30, 0.01–0.40, 0.04–0.38,
0.36–1.48, 0.02–0.10, 0.48–6.58, 0.13–0.64 and 0.83–2.43 mg/g, respectively, in the Changjiang
Estuary and Hangzhou Bay. As, Hg and Sn are at low risk, whereas Bi, Se, Sb, Te and Ge are at
medium risk (Duan et al., 2014).
The results of sequential extraction, the geoaccumulation index (Igeo) and principal component
analysis indicate that Se and Sn come mainly from natural inputs (crustal and biological inputs)
while As, Sb, Hg, Bi and Te come from both crustal and anthropogenic inputs via the atmosphereand rivers, whereas Ge possibly come from natural (crustal and biological inputs) andanthropogenic inputs (Duan et al., 2014). Distributions of enrichment factors (EFs) and labile
fractions reveal that the input of anthropogenic Cd and Cu is mainly through the Changjiang River,
Pb is delivered both through the Changjiang River and the atmosphere, while Zn pollution is carried
from land through the Changjiang River and Hangzhou Bay (Yu et al., 2013).
The annual sedimentation fluxes of trace metals in the ECS are: Fe, 4–107 t yr–1; Mn, 9–105 t yr–1;
Zn, 1–105 t yr–1; Ni, 4–104 t y–1; Pb, 4–104 t yr–1 and Cu, 3–104 t yr–1, respectively, andapproximately 55–70% and 10–17% of the sedimentation fluxes of trace metals are deposited to theinner shelf and the Changjiang Estuarine zone, respectively (Fang et al., 2009). The budget
calculation shows that the Changjiang contributes 82–90% of potentially harmful elements (PHEs,
V, Cr, Co, Ni, Cu, Zn, Mo, Cd and Pb) influxes, while 38–77% of PHEs are buried in sediment,
mainly along the inner shelf (Yu et al., 2013).

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Yellow Sea
Higher concentrations of heavy metals (Cu, Mn, Cd, Hg, Pb, Zn, Fe, Al) are generally found in thecentral part of the southern YS, and according to the calculated EF of the studied metals, Cd in thesediments poses a high risk to local environments, while Mn, Hg, Pb, and Zn are at moderate risk
levels (Yuan et al., 2012).
The values of the EF values suggest that Pb, Cd and Cu in the northwestern YS region originate
from both anthropogenic and natural inputs, while Pb, Cd, and Cu in the eastern and northern YS aswell as Zn, Cr, Ni, and Co in the YS mainly come from natural sources, including terrestrial
weathering delivered to the YS through rivers and atmospheric deposition (Huang et al., 2014).
The annual sedimentation fluxes of heavy metals in the west part of YS are Fe, 1–106 t yr–1; Mn, 2–
104 t yr–1; Cu, 9–102 t yr–1; Zn, 3–103 t yr–1; Pb, 1–103 t yr–1; Cd, 4.9 t yr–1; Cr, 1–104 t yr–1; Ni, 1–
103 t yr–1 and Co, 5–102 t yr–1 (Huang et al., 2014).

Bohai Sea
High concentrations of Cu, Co, Ni, Zn, and Cr are found in the sediments off the coast of the Bohai
Bay and in the central Bohai Sea mud, while high concentrations of Cd and Pb are found in thesediments in the whole Bohai Sea. The sediments in the Bohai Sea are unpolluted with respect tothe heavy metals of Co, Ni, and Cr and unpolluted to moderately polluted with respect to Cu, Zn,
Cd, and Pb (Liu et al., 2016).

2.4.4 Atmosphere
The influence of trace elements on the surface ocean
It has been recently recognized that atmospheric inputs of particles (dry deposition) and rainwater(wet deposition) result in significant fluxes of REEs and other trace elements (TE) to the oceans.
These inputs are frequent and even more significant than riverine inputs (e.g., Duce et al., 1991;
Sholknovitz et al., 1993; Greaves et al., 1994; Spokes et al., 2001).Transition metals such as Fe, Mn,
Cu, and Zn are all essential nutrients for marine phytoplankton. Iron, with low solubility in seawater,
has become an important factor limiting primary productivity in most areas of the sea, especially inthe high nutrient and low chlorophyll regions (Martin et al., 1994; Boyd et al., 2000; Tsuda et al.,2011). The lack of Mn and Zn sometimes will be the factor that limits the growth of phytoplankton,
while some other TE such as Cu and As will have a toxic effect on phytoplankton (Bruland et al.,
1989; de Baar et al., 1990; Paytan et al., 2009; Szivák, 2009).

Sources of trace elements in the atmosphere
There is a lot of evidence showing that the concentration of heavy metals in the environment has
significantly increased since the industrial revolution, and the concentration continues to grow
(Huisman et al., 1997; Couture et al., 2008). The origins of TE in atmospheric wet deposition are
extremely complex owing to the mixing impacts from multiple anthropogenic activities and natural
sources (Vuai and Tokuyama, 2011; Montoya Mayor et al., 2013). Except for Al and Fe, which are
absolute crust-derived elements, the elements Mn, Co, Zn, Se, Pb, Cd, and Cr are all derived fromhuman activities, based on a one-year study on dissolved TE in the atmospheric wet deposition at
Jiaozhou Bay (Xing et al., 2017). Studies on the trend of metal elements in atmospheric aerosols in
Beijing showed that Cu, Zn, Cd, and Pb were all affected by coal-burning emissions (Okuda et al.,144

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2008). In addition, transportation means and materials, such as exhaust emissions, tire wear, road
paint and brake pads, are important sources of Cu, Zn and Cr (Manalis et al., 2005). The
concentrations of Cd, Cr, Co, Ni, As, and Zn are also affected by the burning of garbage, whichsuggests that waste incineration is another important source of heavy metals in urban aerosols (Huet al., 2003).

Depositional fluxes of trace elements
The atmospheric deposition of TE has a profound influence on marine biogeochemical processes;
thus they have attracted increasing attention from the academic community (Song, 2010), withstudies being carried out on dissolved TE and dry deposition velocities of marine aerosol (Voutsa
and Samara, 2002; Cizmecioglu and Muezzinoglu, 2008; Buck et al., 2010, 2013). The research inthis area is mainly focused on total concentrations and less focused on TE dissolved in aerosols(Ben et al., 2015), which has seriously restricted the understanding of the effects of bioavailability
and geochemical migration on atmospheric deposition. The dry deposition flux of dissolved TE in
the ECS aerosol is lower than that at Tokyo and higher than that in the Pacific (Sakata andMarumoto, 2004; Hsu et al., 2010; Buck et al., 2013). Xing et al. (2017) observed that the input of
Zn, Cd, and Cr by the atmospheric wet deposition accounted for 24%, 0.07%, and 1.4%,
respectively, compared with the riverine input in Jiaozhou Bay. The deposition fluxes of atmosphericdissolved TEls in the ECS and YS are listed in Table 2.4.1.
Table 2.4.1 Deposition fluxes of atmospheric dissolved trace elements in the ECS and YS (× 107 g yr–1).
Al

Fe

Mn

Co

Zn

Se

Pb

Cd

Cr

Reference

Wet deposition

3400

1600

270

10

2800

83

250

15.0

77

Xing et al., 2017

Dry deposition

1700

1600

280

2.9

800

11

100

7.9

9.7

Hsu et al., 2010

Depositional fluxes of rare earth elements
Heaton et al. (1990) and Sholknovitz et al. (1993) investigated the concentration of REEs inrainwater samples. However, the concentrations of REEs in the rainwater were below the
instrumental detection limit by neutron activation analysis (INAA) method. Freydier et al. (1998)
determined the total REE concentration in precipitation samples from Lamto (Niger) and Kollo(Ivory Coast), but they did not separate the dissolved phase from the particulate phase. Ryu et al.
(2007) carried out a systematic research on REE concentrations in rain, river waters, and totalsuspended particles (TSP) in air in Korea. One possible reason for the high REE concentrations in
the Han River might be the seasonal contribution of atmospheric input. For REE concentrations
during the Asian dust period, TSP were much higher than the average concentrations, suggestingthat atmospheric input from China (the Yellow sand) could affect the REE concentrations on the
Korean Peninsula. The dissolved REE concentrations in rainwater collected from Guiyang in theacid rain impacted zone of southwest China were negatively correlated with pH. Speciation
calculations predicted that the free REE metal ions, sulfate, oxalate and fluoride complexes areimportant forms of dissolved REEs (Zhu et al., 2016). The ECS and the YS, as a linking passage
between the Asian continent and the northwestern Pacific Ocean, plays an important role inatmospheric transportation. However, few studies have focused on the deposition flux of REEs inthis area since the work of Zhang and Liu (2004). They first determined REEs together with major
ion composition in rainwater samples collected from the marginal sea north of Japan and the ECS.
The results showed large differences in major ion compositions and REE patterns of the samples
which reflected mixing of oceanic, crustal, and anthropogenic sources. The wet deposition fluxes of

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atmospheric dissolved REEs in the ECS and YS calculated by the data of Zhang and Liu (2004) arelisted Table 2.4.2.

Table 2.4.2 Wet deposition fluxes of atmospheric dissolved rare earth elements in the ECS and the YS(× 103 mol yr–1).
Y

La

Ce

Pr

Nd

Sm

Eu

Gd

Tb

Dy

Ho

Er

Tm

Yb

Lu

37

44

140

10

33

3.9

0.83

3.2

0.47

2.7

0.65

2.1

0.33

2.49

0.36

Y = Yttrium, La = Lanthanum, Ce = Cerium, Pr = Praseodymium, Nd = Neodymium, Sm = Samarium, Eu= Europium, Gd = Gadolinium, Tb = Terbium, Dy = Dysprosium, Ho = Holmium, Er = Erbium, Tm =Thulium, Yb = Ytterbium, Lu = Lutetium

The sources of rare earth elements in the atmosphere
Four main types of shale-normalized patterns are identified from the data obtained for the dissolved
REEs in rainwater: (middle) MREE-enrichment, flat REE, (heavy) HREE-enrichment, and (light)
LREE enrichment (Sholkovitz et al., 1993; Aubert et al., 2002; Zhang and Liu, 2004; Ryu et al.,2007; Iwashita et al., 2011; Soyol-Erdene et al., 2011). The REEs have also been found to be apotential indicator of environmental pollution, since anthropogenic inputs show characteristic
signatures of REE composition (Olmez and Gordon, 1985; Olmez et al., 1991; Chiarenzelli et al.,
2001; Zhang et al., 2001). In addition, large anomalies of La, Eu, and Gd were also noted inrainwater. The La anomaly might be derived from automobile emissions and REE-fertilizers (Zhu etal., 2016). The Eu anomaly in rainwater is geologically inherited through chemical weathering ofrocks and minerals (Sholkovitz et al., 1993). The Gd-DTPA used as a Magnetic Resonance Imaging(MRI) contrasting agent may be the main anthropogenic source for Gd in rainwater (Kulaksız andBau, 2013).

2.4.5 Future perspectives
Taken together, biogeochemical tracers can provide direct evidence for understanding the oceanic
processes of physics and biology as well as their controlling factors. Field observation data will help
the setup, improvement, and validation of simulations; meanwhile, models will provide aframework to design and evaluate the observations. The synergy of numerical simulations andobservation data will facilitate the establishment of methods of interdisciplinary cooperation. Finally,
the international GEOTRACES program, working on the studies of marginal seas, will improve theframework for regional cooperation by the co-observation of cross-over stations. Future work needs
to be addressed as follows: (1) key mechanisms and biogeochemical processes on long-term scales
are still not clear in the ECS and its adjacent marginal seas and (2) speciation of biogeochemicalelements and their implications for climate change and the ocean ecosystem are largely unknown.

Acknowledgements
We are grateful to Qian Liu, Huijun He, Qian He, Wenkai Guan, Hojong Seo, Jun Hyung Seo,
Yongjin Han and Ji Hyun Kim in organizing the data tables, figures, and references.

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2.5 Persistent Organic Pollutants and Heavy Metal
Pollution
Xianguo Li1,2 and Yanbin Li1,2
1
2

College of Chemistry and Chemical Engineering, Ocean University of China, China
Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, Ocean Universityof China, China

2.5.1 Introduction
Over the past few decades rapid population growth and intensive anthropogenic activities, as well as
unprecedented industrialization and urbanization, have led to large amounts of heavy metals andpersistent organic pollutants (POPs) being emitted into the environment. Due to their environmental
persistence, bioaccumulation and biomagnification via the food chain, toxicity to humans andwildlife, and long-range transport, they have aroused increasing public concerns in recent years. The
Stockholm Convention, aimed to protect humans and the environment from exposure to hazardousand toxic POPs, initially listed twelve “legacy” POPs in 2001, including eight types oforganochlorine pesticides (OCPs), two kinds of industrial chemicals (polychlorinated biphenyls(PCBs) and hexachlorobenzene), and two types of byproducts (polychlorinated dibenzo-p-dioxins
and polychlorinated dibenzofurans) (UNEP, 2001). Meanwhile, polycyclic aromatic hydrocarbons(PAHs) were also classified as POPs under the Aarhus Protocol (UNECE, 1998). In 2009, nine newchemicals of emerging environmental concern (“emerging” POPs), with potential POPs-likebehavior, were added to Stockholm Convention (UNEP, 2009), among which polybrominateddiphenyl ethers (PBDEs) and perfluoroalkyl substances (PFASs) were of the most concern.
Additionally, hexabromocyclododecane and short-chain chlorinated paraffins (SCCPs) were listed
in Annex A of the Stockholm Convention on POPs in 2013 (UNEP, 2013a). In 2013, the Minimata
Convention, aimed to protect human health and the environment from the adverse effects ofmercury, was lanched by the UNEP (UNEP, 2013b). Mercury is among the heavy metals drawing
the most public concern world-wide.
POPs are hydrophobic with octanol-water partition coefficients (log Kow) ranging from 3.0 to 7.0(Jones and De Voogt, 1999; Meng et al., 2017). Thus, POPs are prone to being trapped in marine
sediments after entering marine systems. Marine sediments are then generally regarded as one of the
major sinks of POPs. Moreover, the solubility of POPs decreases significantly in seawater, due togreater ionic strength, compared to that in freshwater (Turner and Millward, 2002). When POPs aretransported from rivers to seas, the adsorption of POPs onto suspended particles and accumulationin sediments increases in the estuaries and coastal areas. Meanwhile, POPs in sediments can bere-released to aquatic environments, leading to secondary pollution of water systems (Zhen et al.,2016). Aquatic (especially benthic) organisms can ingest these POPs from the water phase andsediments, and thus introduce them to the food chain, posing great risks to the marine ecosystemand even to humans (Li et al., 2007; Jiao et al., 2016). Consequently, sediments play an importantrole in evaluating the overall environmental quality of aquatic systems.

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The rapid growth of the economy, coupled with intensively increasing anthropogenic activities, has
accelerated the environmental deterioration in the Yellow Sea (YS) and the East China Sea (ECS).
Additionally, about 10% of the worldwide riverine sediments, predominantly from the Yellow Riverand Yangtze (Changjiang) River, are delivered into the YS and ECS (Milliman and Meade, 1983).
As a result, rivers play an important role in controlling the physical and biogeochemical features ofthe sediments as well as the associated POPs and heavy metals in the adjacent estuaries and coastal
margins of the YS and ECS (Gao et al., 2008; Wang, 2008; Liu et al., 2009b; Hu et al., 2011; Ci etal., 2014; NBO, 2016). Besides, the YS and ECS are in the pathway of Asian dust in spring andwinter, thus receiving large amounts of atmospheric particles and associated POPs and heavy metals(Liu et al., 2005; Guo et al., 2006; Lammel et al., 2006; Hu et al., 2011). The YS and ECS havebecome one of the most heavily polluted sea areas and thus act as a sink and possible long-termsources of POPs and heavy metals, causing increasing concerns among researchers and the generalpublic. This review summarizes the distributions, potential sources and fates of a few legacy andemerging POPs (OCPs, PCBs, SCCPs, PBDEs and PFASs) commonly detected in sediments fromthe YS and ECS, as well as the distribution of heavy metals in sediment, water and biota, and thecycling of heavy metals in both marine systems. Discrepancies in environmental conditions between
the YS and ECS are also documented in the present review.

2.5.2 Legacy and emerging persistent organic pollutants insediments of the Yellow Sea and East China Sea
Chlorinated POPs
Organochlorine pesticides
Organochlorine pesticides (OCPs), one of the typical legacy POPs, continue to attract considerable
scientific and regulatory interest because of their historically high production volumes, persistence,
bioaccumulation and potential adverse impacts on non-target organisms (Yang et al., 2005a). OCPs
can be transported from pollution sources (mainly pesticide plants and related non-point sources) tomarine systems through pathways such as atmospheric transport and deposition, direct and indirect
local discharge, riverine inputs and surface runoff of contaminated soils (Zhou et al., 2014). Due totheir hydrophobicity, OCPs tend to strongly partition into particulate matter and settle down throughthe water column to coastal sediments, and then are transported towards offshore areas (Chen et al.,2002). On the other hand, resuspension in estuarine and coastal areas may act as a source of OCPs
to the overlying water column and offshore sediments (Eggleton and Thomas, 2004).
Among OCPs, dichloro-diphenyl-trichloroethane (DDT) and hexachlorocyclohexane (HCH) were
widely used in China from the 1950s until their officially banning in 1983 (Gong et al., 2007). To
monitor the pollution status, the occurrences of DDTs and HCHs were extensively investigated inestuarine, coastal and oceanic sediments from the YS and ECS (Fig. 2.5.1). As shown in Figure
2.5.1, concentrations of DDTs and HCHs vary greatly among these areas. In the YS, the highest
level of DDTs was detected in Jiaozhou Bay, with a mean value of 10.3 ng g–1 dry weight (d.w.)(Liu et al., 2013). The concentrations of HCHs in sediments were distinctly lower than those of
DDTs, with the highest level of 8.08 ng g–1 d.w. in the Qingdao coastal area (Wang, J. et al., 2010),
for which the higher potential of degradation for HCHs and the widespread usage of technical DDTs
over HCHs in China (Jones and De Voogt, 1999; Peng et al., 2015) might be responsible. To
evaluate the ecotoxicological effects of DDTs and HCHs, the concentration data in this review werecompared with quality guideline values. HCHs from most of the studied areas were higher thanprobable effect level (PEL: 0.99 ng g–1 d.w.; Feng et al., 2011), indicating potentially adverse

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effects to the benthic environment and wildlife. However, DDTs were overall lower than PEL (46.1
ng g–1 d.w.; Feng et al., 2011), suggesting that the ecotoxicological risks of DDTs in the YS might
be negligible. Over the entire studied areas in the YS, OCP concentrations in sediments from rivers,
estuaries and bays were overall higher than those from the open seas (Fig. 2.5.1). Moreover, Hu etal. (2011) found that OCP levels showed a significantly decreasing trend with increasing distancefrom shoreline in the northern YS, indicating land-based sources and intensive land–sea migration.
In the ECS and adjacent estuaries, the average OCP concentrations ranged from 1.2 to 15.3 ng g–1
d.w., with the highest level in the Yangtze River Estuary for DDTs and Minjiang Estuary for HCHs
(Fig. 2.5.1). Compared with the YS, the OCP concentrations were overall higher in the ECS. This
discrepancy could be attributed to the variations in environmental conditions between the YS and
ECS, as well as the high urbanization and industrialization in the Yangtze River Delta and adjacentcoastal regions (An et al., 2009; Hu et al., 2011). In addition, the Yangtze River Delta is an
important production base for cereals, and has a record of the highest pesticide application in China(Li et al., 2001; Zhang, H. et al., 2009). Over the inner shelf area of the ECS, OCP levels showed adecreasing trend from the Yangtze River Estuary toward the south, indicating the important role the
Yangtze River plays on OCP pollution in the ECS (Yang et al., 2005b; Li et al., 2014; Zhou et al.,2014). Similar to the YS, the levels of DDTs were overall higher than those of HCHs, indicating theextensive usage of technical DDTs in China. Compared with the quality guidelines, the HCHs in the
Yangtze River Estuary and Minjiang Estuary were distinctly higher than PEL, while DDTs werelower than PEL in all the studied areas. Considering the high inputs of the Yangtze River to the ECS,
more attention should also be paid to DDTs and HCHs in this aquatic environment.

Fig. 2.5.1 Average concentrations (ng g–1 d.w.) of HCHs and DDTs in surface sediments from nearshore
and offshore regions of the Yellow Sea and East China Sea. Data were collected from Chen et al. (1996),
Zhang and Lin (2004), Yang et al. (2005b), Chen et al. (2010), Wang, G.L. et al. (2010), Wang, J. et al.
(2010b), Hu et al. (2011), Liu et al. (2013), Li et al. (2014) and Zhou et al. (2014).

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Polychlorinated biphenyls
Up to about 50 years ago polychlorinated biphenyls (PCBs) were widely used in transformers,
capacitors, paints and printing ink, as well as in some other industrial applications (Erickson and
Kaley, 2011). Concerns about their toxicity and bioaccumulation led to their banning throughout theworld in the late 1970s (Erickson and Kaley, 2011; Duan et al., 2013a). In China, approximately
10,000 tons of PCBs were produced from 1965 to 1974, with 9000 tons for trichlorobiphenyls and1000 tons for pentachlorobiphenyls (Mai et al., 2005b). A series of studies were conducted acrossthe whole area covered in this review (Fig. 2.5.2). In general, PCB concentrations in sediments weredistinctly higher than OCPs in the YS and ECS (Figs. 2.5.1 and 2.5.2). PCB concentrations in thestudied areas varied greatly. In the YS, the highest concentration of PCBs was detected in Jiaozhou
Bay (an average of 10.3 ng g–1 d.w.; Guo et al., 2011), which was 3- to 10-fold higher than that inother areas. In the ECS, Yueqing Bay had the highest PCB level, with a mean value of 14.1 ng g–1
d.w. (Yang, H. et al., 2011). Over the entire studied areas, PCBs in sediments generally followed
the order of bays > rivers > coastal areas > open seas (Fig. 2.5.2) in both the YS and ECS, indicatingland-based sources and intensive transport from source to sink. In addition, a decreasing trend of
PCBs could be observed from 2004 to 2011 in the YS (Zhang et al., 2007; Duan et al., 2013b) andfrom 2007 to 2011 (Yang et al., 2012; Duan et al., 2013a) in the ECS. This was also confirmed bydata from sediment cores in the area (Zhang, P. et al., 2009; Duan et al., 2013a), attributed to theban on production and usage of PCBs as well as to the regulatory strategies implemented in China.
However, Zhang, P. et al. (2009) and Duan et al. (2013a) reported relatively high proportions ofheavier homologs (6–8Cl PCBs) in recent years. According to previous reports, PCBs produced
unintentionally by industrial processes (Aries et al., 2006; Liu, G. et al., 2009), waste incineration(Shibamoto et al., 2007) and vehicle exhaust (Broz et al., 2000) were dominated by heavier
congeners (predominated by 7–8Cl PCBs). Consequently, the elevated proportions of heavier
homologs in recent years might be attributed to the intensive influence of local anthropogenic
activities mentioned above.

Fig. 2.5.2 Average concentrations (ng g–1 d.w.) of PCBs in surface sediments from nearshore andoffshore regions of the Yellow Sea and East China Sea. Data were collected from He et al. (2006), Zhang
et al. (2007), An et al. (2009), Yang et al. (2009), Pan et al. (2010), Yang, H. et al. (2011), Duan et al.
(2013a,b), Liu et al. (2013), Yang et al. (2012) and Zhang et al. (2014).

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To have an insight into the sources of PCBs in the YS, Duan et al. (2013b) applied partial
least-squares regression (PLSR) to analyze PCBs in marine sediments from the central mud area inthe southern YS. The results showed that PCBs produced unintentionally by industrial and otherprocesses appeared to be the major sources in sediments, accounting for 60.5%. Industriallysynthesized PCBs with 3Cl and 5Cl accounted for 15.5% and 24.0%, respectively. Among these
PCBs, atmospheric deposition was the main pathway (84.5%), followed by continental runoff(15.5%). In the ECS, Duan et al. (2013a) observed that lower chlorinated congeners were mainly
introduced by the Yangtze River, to be finally buried in sediments of the estuarine regions andvicinity. However, higher chlorinated congeners were mainly imported via direct emission fromlocal sources, to be finally buried in sediments far away from the estuary. Of the PCBs in surface
sediments, 40.1% came from the Yangtze River, 37.9% from local sources and the rest of 22.0%
from atmospheric deposition. Additionally, Fan et al. (2014) used the lipid biomarker (∑ C27 + C29+ C31 n-alkanes) of terrestrial organic matter to identify the regional sources of PCBs in marine
sediments from the ECS. The results indicated that the sedimentary PCBs in the mud area southwestof Cheju Island, northern inner shelf and southern inner shelf of 29°N were mainly fromatmospheric deposition, riverine input of the Yangtze River, and local coastal sources via
atmospheric deposition, respectively.

Short-chain chlorinated paraffins
As a kind of emerging POPs, chlorinated paraffins (CPs) are highly complex technical mixtures ofalkanes containing thousands of different isomers, enantiomers and diastereomers (Bayen et al.,2006). They have a general formula of CxH(2x + 2)-yCly with a degree of chlorination between 30%
and 70%. According to the carbon chain length, CPs can be subdivided into short-chain (SCCPs,
C10–C13), medium-chain (MCCPs, C14–C17) and long-chain CPs (LCCP, C>17; Tomy et al., 1998).
Because of their high chemical and thermal stabilities, CPs are widely used as flame retardants,
plasticizers and additives in paints, sealants and metal working fluids (Stejnarová et al., 2005).
Compared to the other two groups, SCCPs have caused growing environmental concerns worldwide,
due to their persistence, greater potential of long-range atmospheric transport, bioaccumulation infood webs and higher toxicity to aquatic organisms (Reth et al., 2005). To date, the quantification of
SCCPs in environmental samples is challenging, particularly because of the lack of suitable
standards, their low response in various detection systems and their high complex nature, leading togreat difficulties in chromatographic separation among CP congeners and between SCCPs and otherorganohalogen compounds (Mourik et al., 2015). In China, information on SCCPs in theenvironment is still scare, and the investigations were mainly implemented in the Bohai Sea (BS)and vicinity. Thus, the pollution status of SCCP in the BS was also considered in this review. To
understand the transport mechanisms and fates of SCCPs from terrestrial sources to marine
environments, data of SCCPs in nearshore and offshore sediments from the BS, YS and ECS weresummarized in the present review (Fig. 2.5.3). The SCCP levels in Dalian Bay (with a mean valueof 4040 ng g-1 d.w.) were significantly higher than in Jinzhou Bay (with a mean value of 667 ng g–1
d.w.; Yu, 2012), reflecting the influence of heavier industrial activities along Dalian Bay.
Considerable amounts of SCCPs were detected in sediments from nearshore regions of the BS(97.6–1760 ng g–1 d.w.; Ma et al., 2014a), Liaohe Estuary (64.9–1680 ng g–1 d.w.; Chen et al.,2014), Liaodong Bay (65.0–541 ng g–1 d.w.; Ma et al., 2014b), Laizhou Bay (1.0–1200 ng g–1 d.w. ;Pan, 2010), the mouth of Daliao River (64.9–407 ng g–1 d.w.; Gao et al., 2010), and coastal
estuaries of China (242–1450 ng g–1 d.w.; Yu, 2012). Tang and Yao (2005) reported that SCCP
sources around the BS and vicinity might come primarily from the usage of technical CP-42, CP-52
and CP-72 because their production volumes around the BS were estimated at 18, 39 and 5.5 kt,
respectively (Tang and Yao, 2005).

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Compared with nearshore sediments, approximately 1 to 2 orders of magnitude lower levels were
found in offshore sediments (Fig. 2.5.3), suggesting that only a minor fraction of SCCPs wastransported to offshore areas. According to a large-scale research project on the occurrence of
SCCPs in the BS and YS, SCCPs decreased in concentration (14.5–85.2 ng g–1 d.w.), but therelative abundances of C10 with Cl5 congeners increased with increasing distance from coast to theopen sea (Zeng et al., 2013). The spatial distribution and correlation analysis implied that SCCPs
originated mainly from riverine inputs via ocean currents and partly from atmospheric deposition by
East Asian monsoons. Similarly, SCCP levels in the ECS sediments (5.8–64.8 ng g-1 d.w.) also
showed a decreasing gradient, and the proportion of lighter SCCP congeners increased from innershelf to the open sea (Zeng et al., 2012; Zhao et al., 2013a). As shown in Figure 2.5.3, the variationin congener profiles, between sites proximal and distal to the coastline, indicated a combinatorial
influence of emission sources and the fractionation during long-range atmospheric and/or oceanic
transports, which was consistent with observations in air (Wang, 2012), water (Ma et al., 2014b)
and soil (Wang et al., 2013). Especially, a sharp increase in abundance of C10 in offshore sediments(Fig. 2.5.3) further verified the fact that lighter SCCP congeners were subjected to greater
long-range transport due to their high volatility and solubility compared with heavier congeners. In
addition to the spatial distribution of SCCPs, investigations have also been carried out in sedimentcores from the YS and ECS to explore the temporal trends and potential degradation of SCCPs(Zeng et al., 2012; Zeng et al., 2013). In both YS and ECS, extremely high concentrations of
SCCPs were detected in the upper layers, which was consistent with the rapidly increasing
production and consumption of technical SCCPs in recent years in China. Thereafter, SCCP levels
overall decreased exponentially with increasing depth. Additionally, the trend of increasing
proportions of short-chain and lower chlorinated congeners with increasing depth suggests theoccurrence of microbially mediated transformation of SCCPs and/or more down-core migration oflighter congeners (Chen et al., 2011; Zeng et al., 2014).

Fig. 2.5.3 Average concentrations (ng g–1 d.w.) and composition profiles (%) of short-chain chlorinatedparaffins (SCCPs) in surface sediments from nearshore and offshore regions of the Bohai Sea, Yellow Sea
and East China Sea. Data were collected from Gao et al. (2010, 2012), Yu (2012), Zeng et al. (2013), Zhao
et al. (2013a), Chen et al. (2014) and Ma et al. (2014a,b).

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Brominated POPs
As typical emerging brominated POPs, polybrominated diphenyl ethers (PBDEs) have been
extensively used as flame retardants in commercial and industrial products, such as plastics, textiles,
furnishing foams, electronic equipment and building materials (Jiang et al., 2010). Primarily, there
have been three PBDE commercial formulae in use, i.e., Penta- (over 70% of BDE-47 and BDE-99),
Octa- (over 40% of BDE-183), and Deca- (over 98% of BDE-209) BDEs (Alaee et al., 2003). In
China, the domestic production of PBDEs was up to 10,000 tons in 2000, with Deca-BDE as thedominant products (Mai et al., 2005a). With the rapid development of electrical and electronic
industries in recent decades, China has become one of the largest consumers of PBDE flame
retardants, with an estimated annual growth rate of 7% in 2005 (Wang et al., 2016b). PBDEs areeasily released into the general environment during the production, application and disposal
processes because they are added into products without chemical bonding (Rahman et al., 2001).
To date, several studies have been conducted on PBDEs in sediments from areas along the YS andECS (Fig. 2.5.4). As shown in Figure 2.5.4, relatively high concentrations were observed in Dagu
River (0.65 ng g–1 d.w. for ∑PBDEs (the sum of PBDEs without BDE-209) and 3.27 ng g–1 d.w. for
BDE-209; Zhen et al., 2016), Yellow River Estuary (0.69 ng g–1 d.w. for ∑PBDEs and 2.79 ng g–1
d.w. for BDE-209; Yuan et al., 2016) and Sanggou Bay (0.52 ng g–1 d.w. for ∑PBDEs and 3.66 ng
g–1 d.w. for BDE-209; Wang et al., 2017) in the YS, where the levels of PBDEs were 1 to 2 ordersof magnitude higher than those in other regions. Elevated levels in Dagu River and Yellow River
Estuary indicated the intensive influence of anthropogenic activities along the drainage basins.
However, the concentrations of ∑PBDEs and BDE-209 in Sanggou Bay were surprisingly higher
than in other bays, such as Taozi Bay, Sishili Bay and Jiaozhou Bay in the vicinity of the YS(Fig. 2.5.4). Wang et al. (2017) reported that Gu River is the main entrance to Sanggou Bay, towhich large quantities of household and industrial wastewater are transported, accounting for about
99% of the pollutants in the bay. In addition, traffic and shipping activities in a large multifunction
harbor, located in the northwest of Sanggou Bay, may serve as another source of PBDEs.
Undoubtedly, low levels of PBDEs were detected in offshore marine sediments from the southern
YS (0.24 ng g–1 d.w. for ∑PBDEs and 0.65 ng g–1 d.w. for BDE-209; Wang et al., 2016a),
indicating the land–sea migration of PBDEs. Principal component analysis (PCA) revealed that
PBDEs in the SYS were mainly from continental runoff (69.0%) and atmospheric deposition(31.0%) (Wang et al., 2016a).
In the ECS and adjacent areas, relatively higher levels were detected in the mouth of Qiantang River(0.55 ng g–1 d.w. for ∑PBDEs and 4.57 ng g–1 d.w. for BDE-209), followed by Yangtze River
Estuary (0.37 ng g–1 d.w. for ∑PBDEs and 3.17 ng g–1 d.w. for BDE-209) and Hangzhou Bay (0.16
ng g–1 d.w. for ∑PBDEs and 2.29 ng g–1 d.w. for BDE-209) (Chen et al., 2006). This is anindication that Qiantang River might be one of the sources of PBDEs in Hangzhou Bay. PBDE
concentrations dramatically decreased in offshore marine sediments of the ECS, with mean valuesof 0.12 and 0.54 ng g–1 d.w. for ∑PBDEs and BDE-209, respectively (Wang et al., 2016b). Input of
the Yangtze River significantly influenced the distribution of higher brominated congeners in the
ECS inner shelf north of 28°N (Wang et al., 2016b). PLSR revealed that PBDEs in the coastal ECS
were mainly from direct discharge of local anthropogenic activities (80.7%), followed by surface
runoff of contaminated soils (15.1%), microbial degradation after sedimentation (2.6%) andphotodegradation during atmospheric transportation (1.6%). Moreover, the potential cancer risk, via
fish consumption, of human exposure to BDE-209 at 95% confidence level was 3.09 × 10–7, 1.67 ×
10–7 and 8.86 × 10–7 for children, teens and adults, respectively, significantly lower than thethreshold level (10–6). Hazard index (HI) calculated for non-cancer risk was also far less than 1 forthe three groups, suggesting negligible non-cancer risk. The ecological risk of PBDEs in marine
sediments of the ECS was also evaluated.

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Fig. 2.5.4 Average concentrations (ng g–1 d.w.) of (a) ∑PBDEs and (b) BDE-209 in surface sedimentsfrom nearshore and offshore regions of the Yellow Sea and East China Sea. Data were collected from Chenet al. (2006), Chen et al. (2012), Wang et al. (2016a,b), Yuan et al. (2016), Zhen et al. (2016) and Wang etal. (2017).

Li et al. (2016) conducted a large-scale investigation on temporal trends of sedimentary PBDEs in
Chinese marginal seas, including the YS and ECS. The results showed that there was a persistentincrease from the 1970s and almost no clear sign of decline of PBDEs until recently in China. More
importantly, there was a faster increase of PBDEs from the 1990s in China, while in developed
countries, a gradual leveling off or even declining trend was usually observed in this period (Zegers
et al., 2003; Zhu and Hites, 2005). Wang et al. (2016b) reported a similar increasing trend for
PBDEs from the 1990s by analyzing sediment cores collected from the ECS. The persistently
increasing trend of PBDEs in China since the 1990s could be largely attributed to the booming of
the manufacturing industry and unexpected transfer of PBDEs pollution from developed countries
to China (Schwarzer et al., 2005; Chen et al., 2009). Besides, relatively high concentrations andfluxes of PBDEs in a core from coastal ECS located near a well-known electrical/electronic waste(e-waste) recycling area suggested a significant contribution of PBDE emissions from e-waste
recycling in China (Han et al., 2009; Luo et al., 2009; Wang, Y. et al., 2011). Although enormous
efforts in monitoring of PBDE contamination have been paid over the last decade, China is still
lagging behind in terms of nationwide regulation of PBDE usage and disposal of PBDE-containing
products.

Fluorinated POPs
Per- and polyfluoroalkyl substances (PFASs), a typical emerging fluorinated POP, have been widelyused in the past six decades as additives in surface coating of carpets, leather, papers and textiles, aswell as in firefighting foams, pesticides and paints (Gao, Y. et al., 2014). Among these PFASs,
perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA) are the most frequently
detected compounds in the environment (Kannan, 2011). In China, large-scale production of PFASs
started in 2003, and the total output of PFOS is about 230 t yr–1 in the past several years (Xie et al.,160

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POPs and Heavy Metals2013). However, data on PFASs in marine environments are very limited in China, andinvestigations have been conducted mainly in the BS and adjacent areas. Thus, PFASs data in the
BS were also included in the present review. As shown in Figure 2.5.5, concentrations of PFOS andPFOA in sediments from the northern BS coastal areas were non-detectable to 1.97 ng g–1 d.w. andn.d.– 0.54 ng g–1 d.w., respectively (Wang, T. et al., 2011), while those in the southern BS coastal
areas were 0.03–0.44 and 0.005–29.02 ng g–1 d.w. (Zhu et al., 2014), respectively. The highest
concentration was observed in Xiaoqing River, where PFOA was dominant and its concentrations
were between 3.86 and 456.20 ng g–1 d.w., likely because of the presence of local fluorine chemicalindustries (Sun et al., 2016). Pollution of PFASs in water and sediment of Liaodong Bay was also
investigated, and the results showed that the pollution status in the Daling River were relatively
severe (PFOA: 0.18–18 ng g–1 d.w.) (Chen et al., 2015). In addition, studies in Liaohe, Haihe andYellow River were also carried out (Fig. 2.5.5). Since there was no obvious production of PFASs
and related products, PFOS and PFOA in sediments were less than 10 ng g–1 d.w. in these rivers.
Occurrences and fates of PFASs in marine sediments from the BS, YS and ECS were also reported(Fig. 2.5.5). In the BS, concentrations of PFOS and PFOA were n.d.–0.15 ng g–1 d.w. and 0.06–2.70
ng g–1 d.w., respectively, among which relatively high concentrations were found near Liaodong
Bay (Gao, Y. et al., 2014). In the YS, concentrations of PFOS and PFOA were n.d.–0.40 ng g–1 d.w.
and n.d.–1.52 ng g–1 d.w., respectively (Gao, Y. et al., 2014), which was due to fluorine chemicalplants in the Daling and Xiaoqing river basins (Bao et al., 2010). However, among the three seas,
the lowest levels of PFOS (n.d.–0.89 ng g-1 d.w.) and PFOA (n.d.–0.87 ng g–1 d.w.) were detected in
the ECS, which was attributed to no fluorine industrial plants in adjacent areas of the ECS (Gao, Y.
et al., 2014). Over the entire studied areas in this review, it was noted that concentrations of PFOA
were generally higher than those of PFOS, indicating the wide usage and production of PFOA in
China. Moreover, both of PFOS and PFOA concentrations did not exceed water quality guidelines,
avian wildlife values (AWV) or criteria continuous concentration (CCC) (Giesy et al., 2010) in
sediments around the BS, YS and ECS, suggesting that they have no adverse effects to aquaticorganisms and humans at present.

Fig. 2.5.5 Ranges of (a) perfluorooctane sulfonate (PFOS) and (b) perfluorooctanoic acid (PFOA)
concentrations (ng g–1 d.w.) in surface sediments from nearshore and offshore regions of the Bohai Sea,
Yellow Sea and East China Sea. Data were collected from Li et al. (2011), Pan et al. (2011), Wang, T. et al.
(2011), Yang, L et al. (2011), Wang et al. (2012), Zhao et al. (2013b), Gao, Y. et al. (2014), Chen et al.
(2015), Meng et al. (2015) and Sun et al. (2016).

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2.5.3 Heavy metals in the Yellow Sea and East China Sea
Sources of heavy metals in the Yellow Sea and East China Sea
Heavy metals in marine systems originate from both naturally occurring and anthropogenic processes.
Compared with natural sources, the increase in anthropogenic discharge is deemed to be the major
cause of intensified heavy metal pollution in coastal environments (Kennish, 1997) in the past
several decades. Heavy metals released from these anthropogenic and natural sources then enter theocean via a variety of pathways, e.g., river input, atmospheric deposition, waste water discharge,
coastal erosion, etc.
Geological activities, such as structure activity, volcanic eruption, rock weathering, coastal erosion
and geothermal emissions are the major natural sources of heavy metals. These processes have agreat effect on the natural abundance (background values) and distribution of heavy metals in ocean.
Limited studies have been conducted to determine the background values of heavy metals in the YS
and ECS ( Chen et al., 2017; Xiao et al., 2017). The background values of As, Cd, Cr, Cu, Ni, Pb,
Zn, Hg and Mg in the YS were determined to be 6.26, 0.032, 57.77, 15.34, 31.48, 13.43, 69.12,
0.019 and 13327 mg/kg, respectively (Xiao et al., 2017), while the background values of Cr, Cu, Ni,
Pb, Zn were reported to be 59.9, 29.7, 53.9, 26.0 and 84.9 mg/kg in the Yangtze River Estuary andadjacent areas (Chen et al., 2017).
The atmosphere is an important pool of toxic metals and it receives metals from both natural
sources, e.g., volcanic emissions, geysers, thermal springs, forest fires, and anthropogenic sources,
e.g., coal combustion, fossil fuels combustion, and volatilization of metals such as Hg and Se fromore deposits. Heavy metals in atmospheric gases, aerosols and particulates can be subsequently
deposited into marine ecosystems by wet and dry deposition. It has been estimated thatapproximately 7000 and 6000 tons of heavy metals are deposited into the YS and ECS annually(Liu et al., 2005; Lammel et al., 2006).
Terrestrial runoff is among the most import routes for heavy metals entering the coastal marine
environment. Agricultural fertilizers, mining tailings, and industrial and municipal discharges are
the main sources of metals released into rivers, and they are eventually carried into the coastal
environments. The Yangtze River (the third largest river in the world), Yalu River, Qiantang Riverand dozens of other rivers flow directly into the YS and ECS. The Yangtze River, Qiantang Riverand Yalu River are estimated to discharge approximately 14,000 tons of metals into coastal area,
accounting for > 60% of riverine input of heavy metals from Chinese terrestrial areas into the ocean(Gao et al., 2008; Wang, 2008; Ci et al., 2014; NBO, 2016). The riverine discharge of some heavy
metals into the YS and ECS has been reported to have increased dramatically since the 1980s(Wang, 2008; Liu et al., 2016). Industrial wastewater and domestic sewage water that are directly
discharged into coastal seas are another important source of heavy metals in the YS and ECS.
However, there is still a lack of sufficient data for the discharge of heavy metals into the YS andECS from this source.

Heavy metals in the waters of the Yellow Sea and East China Sea
A number of previous studies were conducted to investigate the heavy metal levels in the seawaterof the YS and ECS (Tian et al., 2009; Shen, 2013), most of which were in coastal bays and river
estuaries, such as Jiaozhou Bay (Ling, 2010), Hangzhou Bay (Li et al., 2013), Yangtze River
Estuary (Sun et al., 2009), and Yalu River Estuary (Li et al., 2017). As shown in the Table 2.5.1,
concentrations of Cd, Cu, Hg, Pb and Zn were determined to be 0.14, 0.8, 0.0016, 0.35 and 3.8 μg
L–1 in the north YS (Tian et al., 2009; Ci et al., 2011). Concentration of As in the south YS was162

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reported to be 2.33 μg L–1 (He et al., 2009). In comparison to the YS, concentrations of heavy
metals in the ECS were relatively lower (Cd, 0.02 μg L–1; Hg, 0.0015 μg L–1; Pb, 0.13 μg L–1; Zn,
1.2 μg L–1) except Cu (1.4 μg L–1) (Wang, 2008). Concentrations of heavy metals in the offshore
areas of YS and ECS water were lower than the Criterion Continuous Concentration (chronic effects)
in saltwater (US EPA, 2004), suggesting that both ecosystems may be only slightly polluted byheavy metals. In contrast, heavy metal concentrations in the coastal bays and river estuaries of bothseas were relatively higher (Table 2.5.1). For example, concentrations of Zn (48.93 μg L–1) and Pb(22.72 μg L–1) in Jiaozhou Bay seawater were approximately 10 and 20 times higher than that in the
Yellow Sea (Ling, 2010). Mercury in the water collected in the Yangtze River Estuary was muchhigher than that in the ECS (Sun et al., 2009). A decreasing trend was observed for Cu, Pb, Zn andCr from inshore to offshore in the north YS surface water (Tian et al., 2009), suggesting the effectof anthropogenic discharge. In addition, heavy metals were found to decrease dramatically once
discharged into the ocean (Gao, X.L. et al., 2014), possibly due to their rapid settlement at estuaries(Pan and Wang, 2012). This could explain the inconsistency between the relatively low level ofmost heavy metals in the offshore water and the large amount of heavy metals discharged into bothseas from various sources. By using a single factor contaminant index method, Pb was assessed tobe the heavy metal posing great risks in the YS (Tian et al., 2009), while Cu and Pb were the ones
with potential risks in the ECS using Seawater Environmental Quality Index (SEQI) method (Shen,2013). Heavy metals in the water are directly associated with the toxicity and risk of them to pelagicorganisms. Instead of a single factor index method, ecosystem-based methods, e.g., species
sensitivity distributions (SSDs, non-mechanistic) (Newman et al., 2000) and food web models(mechanistic) (Hernout et al., 2015), have been widely used to evaluate the risk of contaminants topelagic organisms in recent years. More work should be conducted in the YS and BS to determine
the pollution impact of heavy metals in YS and BS water, especially the offshore areas, and evaluate
the risks of heavy metals to pelagic organisms at the ecosystem level.

Heavy metals in the sediments of Yellow Sea and East China Sea
Sediment is an important sink of heavy metals, and heavy metals in sediments come from aqueous
phase deposition via physical, chemical or biological ways. Heavy metals in sediments are good
indicators for both historical and present environmental pollution. More studies were conducted oninvestigating the pollution of toxic metals in the sediment of YS and ECS (Table 2.5.2) incomparison to that in the water column. Arsenic, Cd, Cr, Cu, Hg, Ni, Pb and Zn were measured to
be in the range of 1.46–32.31, 0.07–0.455, 14.28–73.00, 3.08–34.87, 0.015–0.362, 4.48–45.59,
6.36–30.61 and 16.43–117.57 mg kg–1, respectively, in YS sediments collected in 2013–2014 (Xiaoet al., 2017). Mean concentrations of Cd, Cr, Cu, Hg, Ni, Pb and Zn in ECS sediments were
measured to be 0.2, 71.4, 15, 0.09, 26, 27 and 60 mg kg–1 respectively (Fang et al., 2009; Duan etal., 2015). Compared with the offshore areas, concentrations of heavy metals were generally higher
in bays and estuaries, indicating the effects of anthropogenic discharges of heavy metals. For
instance, concentrations of Cu in the Yalu River Estuary were several times higher than those in the
YS and ECS (Gao et al., 2008).
Heavy metal concentrations in the sediment can be used to assess the ecological risk of heavy
metals, and several methods have been commonly used, e.g., the potential ecological risk index(PERI) (Hakanson, 1980) and sediment quality guidelines (SQGs) (Long et al., 1995; Macdonald etal., 1996). Sediment-based risk assessment methods have been proven to be a useful tool for
identifying toxic metals with potential risks in aquatic environments. By analyzing the potential
risks of eight heavy metals in the YS using the PERI and SQGs, Hg, As and Cd were identified tobe listed as the priority contaminant metals in BS and YS sediments (Xiao et al., 2017). Guo (2011)
reported that the potential risks of heavy metals showed an order of Cd > Hg > Cu >Pb> Cr > Zn at
the Yangtze River Estuary and adjacent seas. Xu et al. (2015) reported that Pb in the ECS was themost polluted metal and had higher potential ecological risk.

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164

Lianyungang
Jiaozhou Bay
Yalu River Estuary
Yangtze River Estuary
Luoyuan Bay
Hangzhou Bay
East China Sea

nd = no datandnd
1.60–2.05
3.6
9.31
1.9
nd

Yellow River Estuary

As

nd
2.33
0.43–1.40

North Yellow Sea
South Yellow Sea

System

0.131
1.13
0.83–1.33
0.387
2.50
0.13
0.02

0.14
0.078
0.10–3.22

Cd

nd
nd
0.113-0.14
nd
nd
0.52
nd

nd
nd
nd

Cr

1.93
3.48
1.80–4.70
1.99
5.58
1.76
1.40

0.8
1.41
0.10–4.46

Cu

nd
0.028
0.006–0.049
0.172
0.04
0.042
0.00150.00170.00360.004–0.028

Hg

nd
nd
nd
nd
nd
nd
nd

nd
nd
nd

Ni

Pb

0.21
22.72
0.40–1.80
0.9
0.16
nd
0.13

0.35
0.37
0.22–1.33

Table 2.5.1 Summary of heavy metal concentrations (μg L–1) in the waters of the Yellow Sea and East China Sea.

12.9
48.93
9.20–19.60
6.1
4.99
nd
1.2

3.8
6.21
12.00–81.84

Zn

Ge et al., 2010
Ling, 2010
Li et al., 2017
Sun et al., 2009
Qu et al., 2009
Li et al., 2013
Wang, 2008; Wang, C.J. et al, 2016

Tian et al., 2009; Ci et al., 2011
He et al., 2009
Tang et al., 2010

ReferencePOPs and Heavy Metals
Chemical Oceanography

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Haihe Estuary
Jiaozhou Bay
Qingdao coastal
Yalu River Estuary
Lianyungang coastal
Yangtze River Estuary
Luoyuan Bay
Hangzhou Bay
East China Sea

nd = no datand
2.04–20.1
8.4
7.1
8.4
nd
9.28
8.27
nd

Yellow River EstuaryAs

1.46–32.31
ndnd
6.5–12.6

Yellow Sea
Yellow Sea
South Yellow Sea

System

0.4–2.1
0–1
0.1
0.24
0.1
0.261
0.075
0.112
0.2

0.07–0.455
0–8.12
0.06–1.54
0.11–0.2

Cd

63.2–1550.714–130
55.61
52.3
nd
78.9
nd
nd
71.4

14.28–73.00
nd
nd
nd

Cr

21–246
4.45–211
22
93.15
36
30.7
21.2
23.2
15

3.08–34.87
4.95–26.1
6.00–32.9
21–38

Cu

nd
0.005–4.1
0.05
0.07
0.05
0.12
0.041
0.080
0.09

0.015–0.362
0.007–0.398
0.006–0.069
0.03–0.05

Hg

nd
nd
nd
21.2
nd
31.8
nd
nd
26

4.48–45.59
8.31–32.4nd
16.8–23.5

Ni

21.7–78.9
12.3–109
27.6
29.6
14.8
27.3
29.5
26.3
27

6.36–30.61
4.31–21.9
6.20–39.3
11.6–15.0

Pb

Table 2.5.2 Summary of heavy metals concentrations (mg kg-1) in the sediments of the Yellow Sea and East China Sea.

nd
10–384
58
108.4
nd
94.3
90.86
78.4
60

16.43–117.57
18.8–96.2
24.6–244
21–38

Zn

Liu et al., 2006
He et al., 2013
Ye et al., 2007
Gao et al., 2008
He et al., 2008
Zhang, W.G. et al., 2009
Qu et al., 2009; Chen, 2011
Cai, 2015
Fang et al., 2009; Duan et al., 2015

Xiao et al., 2017
Jiang et al., 2014; Meng et al., 2014
Yuan et al., 2012
Wu et al., 2007

Reference

Chemical Oceanography
POPs and Heavy Metals

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In addition to the total amounts, the speciation of heavy metals plays an important role in thecycling and risk of heavy metals in marine sediments. Heavy metals are present in several major
forms in the sediment, including acid soluble, reducible, oxidizable and residual (Gao and Li, 2012),
which have different bioavailabilities and toxicities. By using a BCR (Community Bureau ofReference) method, Chen et al. (2012) investigated the fraction of eight heavy metals in Rongcheng
Bay (in Yellow Sea) sediments. The residual form (78.16% and 65.89%) was found to be the major
species of Zn and Cr in the sediment, while more Cu and Pb were present in more active form(bound to Fe and Mn oxides). Most Cu, Zn, Cd, Pb and Hg were found to be present in the residual
fraction in the ECS (Yu et al., 2013; Duan et al., 2015), while organic matter fraction was found tobe the main component of their labile fractions (Duan et al., 2015). As the toxicity of heavy metals
depends on not only the total amounts but also the speciation, the speciation of heavy metals should
be taken into account when evaluating the risks of heavy metals in the YS and ECS.

Heavy metals in the organisms of Yellow Sea and East China Sea
Marine organisms can absorb heavy metals from seawater, suspended particles and sediments, andeventually are bioaccumulated via the food chain to high concentrations in high trophic level
organisms. Heavy metals in organisms draw the most public concerns due to their threat to humanhealth. Most investigations on heavy metal concentrations and their potential risks in marineorganisms have been conducted in coastal cities, bays and estuaries of the YS and ECS (Sun andYang, 1992; Yan, 1993; Fang et al., 2004; Asante et al., 2008; Liu et al., 2008; Qu et al., 2009; Luoet al., 2012, 2013; Yi and Zhang, 2012; Zhang et al., 2012; Li et al., 2013; Xia et al., 2013; Jiang etal., 2015; Liu et al., 2017) (Table 2.5.3). In general, organisms in the YS and ECS are not heavily
polluted by heavy metals. Their concentrations are comparable to or even lower than those reported
in the other marine systems (Asante et al., 2008). Among the eight common heavy metals, Pb in
shrimp and crab (Zhang et al., 2012) and Cd in fish (Xia et al., 2013) have been detected to be over
the Codex Alimentarius Commission (CAC) and European Commission Regulation in the YS.
Arsenic in sea cucumber was assessed to be over maximum residue limits (MRLs) in the YS (Jiang
et al., 2015). Mercy and Zn over the Criterion Continuous Concentration and Limit of the Food andAgriculture Organization of the United Nations (Luo et al., 2012) were detected in crabs along thecoast of the northern YS. High Zn and Cu concentrations (exceeding the maximum limits in China)
were detected in oysters collected from the coast of Zhejiang province (Li et al., 2013; Lu et al.,2017). Trophic level is among the most important factors controlling the levels of heavy metals in
the organisms. Mercury in fishes was observed to increase significantly with increasing trophic
level (Xia et al., 2013). The consumption of marine organisms is one of the primary pathways of
exposure to Hg for humans. By using the Target Hazard Quotient (THQ) method, shellfish
consumption was estimated to result in the low risk of Hg exposure to residents near Jiaozhou Bay(Zhang and Zhang, 2015).

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Zhejiang coastal

Yalu River Estuary
Lianyungang coastal
Shanghai coastal

Qingdao coastal

Yantai coastal
Jiaozhou Bay

Dalian coastal

Northern Yellow Sea
coastalYellow Sea

Systemmusselfish
shellfish
shrimp0.372

sea cucumber
fish
sea cucumber
musselshrimpfish
molluscs
shellfish
clam
fish
musselsea cucumber
fish
fish

0.161

0.54
0.07
0.22–3.50
0.29

Cd

0.108

nd
0.50
nd
nd

Cr

0.179

nd
0.51
0.48–2.30
nd

Cu
0.012
0.13
nd
0.68

Hg

0.034
0.023
0.368
0.079
0.118
0.204
0.037
13.50
2.30
2.18
1.83
0.028
14.11–23.65
nd
1.55–5.24 73.13–163.84
nd
ndnd
0.10–2.35
0.13–1.02
nd
ndnd
0.20–1.67 1.12–14.46
nd
nd
nd
1.22
1.45–7.57
0.001–1.096
8.32–14.33 0.39–0.68 0.96–4.04
7.70–9.14
0.53–1.42
0.023
12.93
1.27
8.33
22.08
0.27
0.40
0.18
0.24
0.26
0.062
nd
0.01–0.36 0.02–0.99
0.37–0.71
0.05–0.32
nd
0–0.090–0.04
2.31–6.72
0.03–0.11
0.024
24.55
2.81
6.07
3.32
0.058
<DL–7.6 0.001–0.131
nd
0.10–1.95
0.002–0.032
0.7–2.4
0.067–13
nd
0.87–135
0.009–0.044
1.1–9.2
0.046–1.12nd
1.43–23.4
0.004–0.028

0.52
nd
nd
nd

As

bivalvefishmolluscs
crab

Organism

nd
0.021
nd
nd
nd
nd
nd
nd
nd
0.014
nd
nd
nd
nd
0.014
nd
nd
nd
nd

nd
nd
nd
0.55

MeHg

Table 2.5.3 Summary of heavy metals in the organisms (mg kg–1) of the Yellow Sea and East China Sea.

0.065

0.23
0.60
0.05–0.81
nd

Pb

2.634

nd
nd
7.20–89.8
44Zn

Liu et al., 2008
Yan, 1993
He, 1996
Luo et al., 2012

Reference

Luo et al., 2013
Xia et al., 2013
nd
0.085
2.987
Jiang et al., 2015
1.50
0.51
76.89
Fung et al., 2004
5.77–23.13 1.82–4.95 74.6–97.09 Zhang et al., 2012nd
0.095–1.81 1.42–10.59 Sun and Yang, 1992nd
0.18–2.13 10.60–23.79 Sun and Yang, 1992nd
0.36–0.80 11.58–12.86 Zhang and Zhang, 2015nd
0.53–1.42 62.20–87.11 Ma et al., 2009
Xia et al., 2013
3.31
0.90
107.6
Fung et al., 2004
nd
0.15
3.32
Jiang et al., 2015nd
0–0.43nd
Yan, 1993nd
0.31–0.61
ndYan, 1993
Xia et al., 2013
2.91
0.71
141.3
Fung et al., 2004
nd
0.008–0.142 3.6–42.4 Fang et al., 2004
nd
0.029–0.348 5.42–244 Fang et al., 2004
nd
0.006–0.244 5.49–15.4 Fang et al., 2004

nd

nd
nd
nd
nd

Ni

Chemical Oceanography
POPs and Heavy Metals

167

168

Organismfish

nd = no data, DL = detection limit

East China Sea

fishmusselYangtze River Estuary fish
Luoyuan Bay
fishshrimpshellfishHangzhou Bay
oysterNantong coastal
fish

Zhoushan coastal

SystemTable 2.5.3 Continued.

1.3–200

17.7
nd
0.45
0.5
0.7
1.2
0.46–14.31

As

0.04–168

4.96
0.091
0.01
nd
1.28
1.70
0–3.84Cd

0.028–2.4

5.94
0.93
nd
nd
nd
0.35
0.17–3.42

Cr

1.29–1040

22.58
0.17
0.2
3.1
39.53
123.05
0.06–30.7

Cu<0.05–2.1

0.019
0.037
0.012
0.032
0.023
0.081
0.032
0.02–0.18

Hg

2.44
nd
nd
nd
nd
nd
0.07–0.75

Ni

0.67
0.52
0.19
0.04
0.06
1.59
0.01–0.41

Pb

0.003–0.057 0.004–0.041 0.034–36.5

nd
nd
nd
nd
nd
nd

0.012

MeHg

29.8–334

128.1
4.75
12.44
9.07
15.88
nd
0–68.65

Zn

Asante et al., 2008

Xia et al., 2013
Fung et al., 2004
Yi and Zhang, 2012
Qu et al., 2009
Qu et al., 2009
Li et al., 2013
Li et al., 2013
Liu et al., 2017

ReferencePOPs and Heavy Metals
Chemical Oceanography

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Cycling of heavy metals in the Yellow Sea and East China Sea
After entering the marine system, heavy metals undergo a variety of biogeochemical processes
before they are sequestered in the deep sediments or accumulated in organisms. These processes
include adsorption/desorption, transformations of heavy metals, diffusion from sediment to thewater column, sedimentation/re-suspension, and exchange with nearby oceans. Limited studies(Lin et al., 2000; Ren et al., 2010; Ci et al., 2016) have been conducted on investigating the cyclingof heavy metals in a limited part of the YS and ECS, most of which are only focused on thesedimentation of heavy metals in both ecosystems. Sedimentation has been deemed to be adominant sink of heavy metals in the coastal sea and most reactive heavy metals discharged throughrivers from the continent to the ECS continental shelf were estimated to deposit on the bottom
sediments prior to transporting to the open ocean (Zhang, 1995; Pan and Wang, 2012; Liu et al.,2016). Fang et al. (2009) calculated that the annual sedimentation fluxes of trace metals in the ECS
were 1.08 × 105, 4.48 × 104, 4.32 × 104 and 3.10 × 104 t per year for Zn, Ni, Pb and Cu, respectively.
Approximately 55–70% and 10–17% of the sedimentation fluxes of trace metals were deposited in
the inner shelf and the Yangtze River Estuary zone (Pan and Wang, 2012). Since the fate,bioavailability and toxicity of heavy metals depend highly on the transport and transformations ofheavy metals in the marine system, there is an urgent need to elucidate the major processes
controlling their concentrations and speciation in the YS and ECS.

2.5.4 Conclusions and recommendations
The YS and ECS receive a large quantity of pollutants from anthropogenic sources, including
legacy and emerging POPs and heavy metals from riverine input, sewage discharge and atmosphericdeposition. The total input fluxes of pollutants into both seas have increased dramatically since the1980s, resulting in POP and heavy metal contamination in the water, sediment and organisms. In thepresent review, spatial distributions and potential risks of several POPs and heavy metals in the YS
and ECS were summarized. Results of data review suggest that sources of different POPs wereindependent, and some hotspot areas with greater production of certain chemicals were identified.
Mercury, Cd and Pb were identified to be the major heavy metals posing risks to human andecosystem health in the YS and ECS.
However, it should be noted that studies on emerging POPs such as SCCPs, PBDEs and PFASs in
the marine system are still very limited. In addition, environmental quality guidelines of POPs forprotection of the coastal ecosystem have not been established in China. Given their ubiquity,persistence, long-range atmospheric transport, bioaccumulation and biomagnification through foodwebs, as well as their potential adverse risks to the environment and human health, actions are
required to bridge the knowledge gaps in the environmental occurrence, and to evaluate the risks toorganisms and humans.
We propose the following research directions: (1) Particularly in light of the continued andlarge-scale production and usage of emerging POPs, future monitoring studies should be designed
according to the knowledge gaps in terms of geographical distributions and environmental matrices;(2) Despite of some efforts to study the environmental behaviors of POPs, more information isurgently needed to explore the migration and transformation processes and mechanisms of POPs
after they enter the seas. For example, how does the hydrodynamic sorting of sediments affect the
distribution and environmental fates of POPs in the seas? How do the complex biochemical
processes affect the migration and transformation of POPs in the seas? How do their
physicochemical properties (such as chemical structure, solubility, and partitioning coefficients)
affect these processes and their environmental fates? (3) In aquatic environments, sedimentary total

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169POPs and Heavy Metals

Chemical Oceanographyorganic carbon is commonly considered to be a key factor controlling the distribution andaccumulation of hydrophobic organic pollutants due to their high affinity. However, very limited
information is available about the different effects of terrestrial (TOM) and marine organic matters(MOM) on the environmental behavior of organic pollutants in the seas. The molecular mechanism
for interaction between POPs and organic matter still remains unclear; (4) Further work is needed to
study the biomagnification of POPs in the food web and related influencing factors (such as theintake rate, degradation, and transformation), as well as the mechanisms of toxicity of POPs, andthus to evaluate the exposure pathways and ecological and health risks of POPs to organisms andhumans; (5) Additional biomonitoring data are needed to understand the routes of POPs uptake inthe human body and the levels present in tissues, and more efforts are necessary to conduct acomprehensive risk assessment and evaluate the relative importance of the various exposure routes(such as dietary intake, inhalation, dust ingestion, and dermal contact) for different populations.
Besides these previous studies on heavy metals in the YS and ECS, more effort should be made on
several aspects in this field. First, most studies on the sources of heavy metals were done on the
discharge from the major rivers. A comprehensive study that covers all the rivers and other sources(atmospheric deposition, sewage discharge, coastal erosion, and submarine ground water discharge)
should be conducted to elucidate these sources. Second, the risks of heavy metals were assessed bycomparing their criteria in water, sediments and organisms. An integrated method with theconsideration of heavy metals in all these matrices should be developed to evaluate the risks ofthese heavy metals in the YS and ECS. In addition, since the toxicity and risks of heavy metals
depend highly on their species, these risk assessment methods should take the speciation of heavy
metals into account. Third, more efforts should be made on identifying the controlling
biogeochemical processes and the mechanisms underlining these important processes to better
understand the cycling and fate of heavy metals in the YS and ECS.

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2.6 Atmospheric Deposition
Sai-Chun Tan1,2 and Chun-Ok Jo3
1

Institute of Atmospheric Physics, Chinese Academy of Sciences, China
Nanjing University of Information Science and Technology, China
3
Reseach Institute of Oceanography, Anyang University, Korea
2

2.6.1 Introduction
The impacts of atmospheric deposition, especially dust aerosols, on oceans have received muchattention since the “iron (Fe) hypothesis” (Martin and Fitzwater, 1988; Martin and Gordon, 1988).
The iron hypothesis postulated that Fe deficiency limits phytoplankton growth in high nutrient low
chlorophyll (HNLC) waters (such as the Northeast subarctic Pacific and the southern oceans) andthus the enhanced supply of Fe to the ocean from the atmosphere stimulates photosynthesis, whichleads to the reduction in atmospheric CO2 concentrations during glacial maxima (Martin and
Fitzwater, 1988). Biological productivity in some marine ecosystems may be limited by nitrogen(N), phosphorus (P), or Fe, or co-limited by those nutrients (Wu et al., 2000; Zou et al., 2000, 2001;
Liu et al., 2003; Wang et al., 2003; Shi et al., 2012; Mahowald et al., 2017). For example, based onfield observation data, high N:P ratio was found in the south and southwest of the Yellow Sea, theinshore area of the East China Sea, and the area east of the Changjiang estuary, which implies thatprimary production in a considerable portion of the Yellow Sea and East China Sea may be limitedby P (Liu et al., 2003; Wang et al., 2003) while there are potential N limitations off the southwestern
Korean Peninsula and west of Jeju Island in the Yellow Sea (Liu et al., 2003). In situ incubation
experiments show that N and Fe could obviously stimulate phytoplankton growth even in the P
limiting area of the Yellow Sea (Zou et al., 2000; 2001). Productivity in the ocean would respond to
the enhanced supply of these nutrients from aeolian dust or anthropogenic nitrogen deposition,affecting ocean biogeochemistry and hence having feedback effects on climate (Jickells et al., 2005;
Duce et al., 2008). Thus, the scientific issues on atmospheric aerosols, atmospheric deposition, themarine ecosystem, marine biogeochemical cycles, and their interconnections were focused by many
international projects, such as the Integrated Marine Biosphere Research project (IMBeR), theInternational Study of Marine Biogeochemical Cycles of Trace Elements and their Isotopes(GEOTRACES), the International Global Atmospheric Chemistry (IGAC), and particularly the
Surface Ocean Lower Atmosphere Study (SOLAS; Brévière et al., 2016).
The deserts in Mongolia, western China and northern China are three major sources of Asian dust
aerosols, accounting for 70% of the total dust production (Zhang et al., 2003; Wang et al., 2005).
When dust is transported to the ocean, it provides macronutrients (such as N and P) andmicronutrients (such as Fe) to surface ecosystems of the oceans (Jickells et al., 2005; Duce et al.,2008; Furutani et al., 2010; Qi et al., 2013; Shi et al., 2013; Tan et al., 2017). Bishop et al. (2002)
observed a doubling of biomass in the mixed layer in the subarctic North Pacific near Station PAPA
(145°W, 50°N) after the passage of a dust storm in April 2001, and provided evidence of biotic
response to natural iron fertilization caused by the dust particles. After that, many studies found thatthe deposition of nutrients associated with the intrusion of Asian dust in the marginal seas of the

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Asian continent and the Pacific Ocean could enhance phytoplankton growth (Yuan and Zhang, 2006;
Jo et al., 2007; Han et al., 2011; Tan et al., 2011, 2016; Shi et al., 2012; Tan and Shi, 2012b; Wanget al., 2012; Tan and Wang, 2014; Yoon et al., 2017; Luo et al., 2020; Wang et al., 2021).

2.6.2 Atmospheric deposition in the Yellow Sea
Since the 1990s, studies have investigated the possibility that atmospheric aerosol deposition may
play an important role in the ecosystem in the Yellow Sea, as one of the downwind seas influenced
by Asian dust and anthropogenic aerosols. Table 2.6.1 shows the deposition of dust or nutrients into
the Yellow Sea and the comparison with its riverine influx or sediment. Previous studies reported
great variability in the dust deposition flux into the Yellow Sea (0.13–0.9 g m−2 d−1, 9–76 g m−2 a−1),
and the flux is very large, being one order of magnitude higher than the open Pacific Ocean(Table 2.6.1). Gao et al. (1992b) suggested that the total suspended particulate load during one eventin a high dust year is nearly 18 times higher than the highest concentration observed in a low dust
year. Even in a low dust year, the estimated atmospheric deposition of mineral aerosols to the
Yellow Sea based on the representative crustal elements is 9–76 g m−2 a−1, and it can account for
20–70% of the total input of mineral material to the Yellow Sea (Gao et al., 1992a). It was estimatedthat annual dust flux was 53.7 g m−2 a−1 from a two-year observation in the northern Yellow Sea
during the period from December 1989 to May 1991, which is one order of magnitude higher thanthat over the central North Pacific Ocean (Zhang et al., 1993).
Analyses of rainwater and total atmospheric deposition samples at Qingdao from October 1988 to
May 1990 suggest that the concentrations of Fe and Mn from atmospheric deposition and marine
sediment in the southern Yellow Sea are close to each other, while the concentrations of atmosphericdeposition of Co, Pb, Zn, Cu, Ni and Cr is 2 to 25 times that in marine sediment, thereby showingthat atmospheric deposition may have an important role in trace elements input to the surface ocean
in the Yellow Sea (Liu et al., 1991). In addition, the dissolved trace elements in rainwater collected
at Qingdao is about one to three orders of magnitude higher than that in the open ocean of thetropical Pacific Ocean (Enewetak Atoll, 11°N, 162°E), suggesting the important effects of citypollutants on atmospheric trace elements (Liu et al., 1991). Based on observations at two land-basestations in the Yellow Sea, Zhang and Liu (1994) indicated that atmospheric deposition of nutrient
elements (e.g., nitrate, phosphate, etc.) may be equal to or even more important than input fromrivers. This has been supported by recent model simulations and field measurements. Model
simulations indicate that annual deposition of atmospheric ammonia nitrogen (NH4+–N) to the
Yellow Sea (171 GgN a−1) can account for 87% of the total external input (terrestrial + atmospheric),
which is 16 times the riverine input (10.6 GgN a−1) (Zhang et al., 2010, Table 2.6.1). Recently,
analyses of rainwater samples collected at Qianliyan Island in the Yellow Sea from 2009 to 2010
showed that atmospheric deposition of NH4+–N and phosphate phosphorus (PO43—P) is 6.6 times
and 1.1 times the riverine influx, respectively; nitrate nitrite (NO3– + NO2–) from atmosphericdeposition accounted for 47% of total terrestrial input (i.e., atmospheric deposition and riverine
influx), which is slightly less than and comparable to that from riverine influx (Han et al., 2013). In
addition, Qi et al. (2013) indicated that annual atmospheric deposition of total dissolved nitrogen(TDN) in the coastal region of the Yellow Sea (494 GgN a−1) was greater than that from riverine
input (88.0 GgN a−1) — the former is 5.6 times the latter (Table 2.6.1).

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Table 2.6.1 The deposition of dust or nutrients into the Yellow Sea and comparison with its riverine influx
or sediment.
Atmosphericdeposition

Flux / Concentration / TotalamountTimes the riverine influx or
sediment

Reference

Dust aerosol

360–3300 × 1010 g a−1(9–76 g m−2 a−1)

0.24–2.2 times the input ofsandy sediment from all rivers

Dust aerosol

53.7 g m−2 a−1

one order of magnitude higher Zhang et al. (1993)
than that over the central North
Pacific Ocean

Dust aerosol

9.27 g m−2 a−1

Liu and Zhou (1999)

Dust aerosol

13.3 g m−2 a−1

Dust aerosol

0.44 g m−2 d−1 (5.05±2.49μg m−2 s−1)

Yang et al. (2010)

Dust aerosol

0.56 g m−2 d−1

Tan and Shi (2012b)

Dust aerosol

0.33 g m−2 d−1 (30 g m−2 in spring)Tan et al. (2012)−2

one order of magnitude higher
than the Northwest Pacific(east to 165°E, 0–30°N)−1

Gao et al. (1992a)

Uematsu et al. (2003)

Dust aerosol

0.13 g m d for annual
0.2 g m−2 d−1 in spring

Zhang and Gao (2007)

Dust aerosol
Soluble Fe

0.9 g m−2 d−1
0.48 ± 0.29 mg m−2 d−1

Shi et al. (2013)

Atmospheric dry
deposition of Fe

31,582.9 μg/g

Atmospheric wet
deposition of Fe

49,641.2 μg/g

Ammonium (NH4+and NH3)

0.99 times the concentrationin sediment in the southern
Yellow Sea (31,900 μg/g)
1.6 times the concentrationin sediment

Liu et al. (1991)

171 GgN a−1

16 times

Zhang et al. (2010

NH4+–NPO43––P
SiO32––Si
NO3– + NO2–

29.3 mol a−1
0.35 mol a−1
0.66 mol a−1
23.4 mol a−1

6.6 times
1.1 times
0.03 times
0.9 times

Han et al. (2013)

TDN

494 GgN a−1

5.6 times

Qi et al. (2013)

TDN = total dissolved nitrogen

2.6.3 Atmospheric deposition in the East China Sea
Besides the Yellow Sea, atmospheric aerosols may exert an important role in the ecosystem in the
East China Sea. Previous studies showed that atmospheric deposition flux of aerosol into the East
China Sea ranged from several to hundred mg m−2 d−1 for different dust events (Table 2.6.2),
suggesting considerable variability. Hsu et al. (2010) showed that the fluxes were 39 ± 50
μg m−2 d−1 for soluble Fe and 41 ± 74 μg m−2 d−1 for the combination of some bioavailable elements(e.g., Zn, Cu, Mn, Ba, Co, V, Ni, and Cd) during the northeasterly monsoons of 2005–2007. Tan etal. (2016) estimated a similar mean soluble Fe of 37.5 μg m−2 d−1 for spring dust storms in 2010.
Although the deposition flux of aerosol in the East China Sea is smaller than that in the Yellow Sea,
the atmospheric deposition of nutrient species is still important compared with the riverine input inthe East China Sea. Based on observations at Shengsi Archipelago in the East China Sea, theestimated deposition flux of NO3– + NO2– and NH4+ was 28,690 and 39,720 μmol m−2 a−1,
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respectively (Table 2.6.2). The total inputs of dissolved inorganic nitrogen, DIN (= NO3– + NO2– +NH4+) to the East China Sea were 52.7 mol a−1, which is comparable to the riverine input (58.6
mol a−1). Organic nitrogen, which is also adsorbed into fine and coarse particles, is reported toaccount for 10–25% of total nitrogen in aerosols over the East China Sea (Nakamura et al., 2005).
Atmospheric PO43– input to the East China Sea contributes 11.8% of the total of atmospheric andriverine inputs. The atmospheric deposition of SiO32– has less importance compared with riverine
input (Zhang et al., 2007). Model results show that the atmospheric deposition of NH4+ and NH3 tothe East China Sea is up to 166 GgN a−1, which is 1.1 times of the riverine input to the East China
Sea (Zhang et al., 2010).

Table 2.6.2 The deposition of dust or nutrients into the East China Sea.
Atmospheric deposition
PM10
Dust deposition

Flux / Concentration / Total amount
8–43.6 mg m−2 d−1−2

8–178 mg m d−2

Yan et al. (2012)−1−1

Reference

Hsu et al. (2013)−2

Dust aerosol

76 mg m d (7 g m for the whole spring)Tan et al. (2012)

Dust aerosol
Soluble Fe

13.0–145.6 (mean 64.7) mg m−2 d−1
7.5–84.3 (mean 37.5) μg m−2 d−1

Tan et al. (2016)

Soluble Fe
Other elements (e.g., Zn, Cu,
Mn, Ba, Co, V, Ni, and Cd)

39 ± 50 μg m−2 d−1
41 ± 74 μg m−2 d−1

Hsu et al. (2010)

NO3– + NO2–
NH4+
PO43–
SiO32–

28,690 μmol m−2 a−1
39,720 μmol m−2 a−1
114 μmol m−2 a−1
1973 μmol m−2 a−1

Zhang et al. (2007)

Ammonium (NH4+ + NH3)166 GgN a−1

Zhang et al. (2010)

In summary, in the Yellow Sea, atmospheric ammonium input is significantly greater than riverine
input, atmospheric phosphate input is slightly higher than riverine input, and atmospheric nitrate
input is slightly less than and comparable to riverine input. In the East China Sea, atmosphericammonium input is slightly higher than the riverine input, the inputs of DIN (NO3– + NO2– + NH4+)
are comparable to the riverine input, while atmospheric phosphate and silicate inputs are less than
the riverine input. Based on observations of rain and aerosols at Qianliyan Island in the Yellow Sea
and Shengsi Archipelago in the East China Sea over the 1999–2003 period, nutrient species in both
wet and dry deposition events show clear seasonal signatures, with high values during the dry
season and low values during the rainy season. Nutrient concentrations are higher at Qianliyan
Island than from Shengsi because of relatively low rainfall and a greater influence of continental
dust storms at Qianliyan Island (Zhang et al., 2007). In the Yellow Sea and East China Sea wet
depositions are higher than dry depositions for all nutrient species. Moreover, the atmosphericdeposition of trace elements is very important in both the Yellow Sea and the East China Sea.

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2.6.4 Relationship between atmospheric deposition andbiological production in the Yellow Sea andthe East China Sea
During the transport pathway to the ocean, atmospheric aerosol particles sometimes react or mix
with atmospheric anthropogenic pollutants when they cross over industrial cities, altering thebioavailability of Fe and adding more nutrients, such as N and P. Zhuang et al. (1992) reported thata significant portion of the iron in remote marine aerosols is in the form of Fe(II) (soluble) and they
may come from the coupling and feedback between Fe and S in the atmosphere and in the ocean.
Zhuang et al. (2003) further detected a considerable amount of Fe(II) in the aerosols collectedduring dust storms in Beijing and found that Fe and S concentrations in the dust storm aerosol
samples represent a very positive relationship. During the transport pathway of dust from East Asia
to the North Pacific Ocean, acid mobilization is thought as one of the possible mechanisms for thesolubilization of Fe in aeolian dust, and SO2 pollutant emissions are thought as a potential source ofacidity (Meskhidze et al., 2003, 2005). Field measurements over the South China Sea, the Yellow
Sea and at a coastal site in Qingdao in spring 2005 and 2006 indicated that during dust storm events,
the concentration of particulate water soluble organic nitrogen in the coarse particles (>2.1 μm)
increased, which led to an increase of that in total suspended particulates (Shi et al., 2010).

south Yellow Sea

1060

Chl
OPP

980

East China Sea

1.0
0.9

1140

900
580
540

Chl
OPP

0.8

500
460

98 99 00 01 02 03 04 05 06 07 08
Year

Bloom frequency
Averaged days with AI>=2
120
100

20
15

80
10

60
40

5

20
0

98 99 00 01 02 03 04 05 06 07 08

Averaged days wtih AI>=2

Dust frequency (time)

Bloom frequency (time)

12
9
6
3
0
2.8
2.6
2.4
2.2
2.0
1.8
1.1

Primary production (mgC m-2 d-1)

Chlorophyll a concentration (mg m-3)

Recently, some studies proposed a link between aerosol deposition and phytoplankton growth in the
Yellow Sea. They not only suggested that a statistical relationship existed between biologicalproductivity in the Yellow Sea and the inputs of dust aerosols, but also implied that dust deposition
could promote the phytoplankton bloom. Tan et al. (2011) showed that a significant positive
correlation occurred between annual average chlorophyll a concentration/primary production in thesouthern Yellow Sea and the East China Sea and the frequencies of severe and very severe dust
storms (FDS) in China during 1998–2008 (Fig. 2.6.1), suggesting that biological productivity in thethose seas might be associated with dust storms. Figure 2.6.1 also shows that the algae bloom
frequencies are significantly correlated with the annually averaged occurrence days with the aerosol
index AI ≥ 2 in the coastal seas of China, including the Bohai Sea, Yellow Sea and East China Sea(correlation coefficient = 0.62), indicating the occurrence of algae blooms might be associated with

0

Year

Fig. 2.6.1 (Left panel) Yearly variations of frequencies of dust storms (FDS) in China during 1998–2008,
and annual average chlorophyll a concentration and primary production in the southern Yellow Sea and theEast China Sea. From Tan et al. (2011); (right panel) Annual variations of bloom frequency and averagedays with aerosol index (AI) ≥ 2. From Tan et al. (2011). Reproduced with permission of Wiley & Sons.

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atmospheric aerosol deposition, especially dust storms. Besides the annual correlation, the possible
link between dust storms at different Asian dust source areas and phytoplankton growth in the
Yellow Sea was further investigated. Based on dust storm observations at 222 meteorologicalstations in China during 1997–2007, significant correlation (Fig. 2.6.2) was found between dust
events and chlorophyll a concentration in the Yellow Sea (Tan and Shi, 2012a). High correlation
regions included the southeastern Xinjiang–Qinghai–Xizang region, the Hexi Corridor–western
Inner Mongolia–Hetao regions, and the Hunshandake Desert.

54
48

0.42
0.35

E2

F1

36

F3

30

0.41

D2

0.44

Ye
Se llow
a

42

24
18
90

110

0.4

0.3

120

130

0.4

0.2

0.2

100
0.3

80

Fig. 2.6.2 Correlation coefficient between monthly occurrence frequency of dust storms (days month−1)
at 222 meteorological stations and monthly area-averaged chlorophyll a concentration (mg m−3) in the
Yellow Sea between September 1997 and December 2007. Minimum contour is 0.2. D2, E2, F1, and F3
are the stations of Dingbian, Damaoqi, Mangya, and Xinghai. Rectangles show the location of the
Yellow Sea. Isobaths in the sea are 20 m. From Tan and Shi (2012a).

The importance of nutrient-containing aerosol deposition, especially dust aerosol, on the Yellow Sea
was confirmed by recent satellite remote sensing analyses, aerosol field measurements, and onboard
incubation experiments. Satellite observation analyses showed that algae blooms were detected inabout 73% dust storm events in the southern central Yellow Sea (Tan et al., 2011). Satellite remotesensing (Fig. 2.6.3) further revealed that the initial timing of the spring bloom in dust years in thesouthern central Yellow Sea was about 11–18 days earlier than that in 2005 (non-dust year) andpeak chlorophyll a concentration in dust years was about 1.4–2.6 times that in 2005 (Tan and Shi,
2012b). Cruise measurements in the southern Yellow Sea during the period from March 31 toApril 24, 2007 indicate that the calculated dust-derived N and Fe supplies can satisfy thephytoplankton needs at the initiation of the bloom (Shi et al., 2012, 2013). Results of onboard
incubation experiments performed in the southern Yellow Sea in the spring of 2011 showed that theaddition of a large amount of Asian dust (20 mg L−1) could increase both the maximum
concentration of chlorophyll a and the conversion efficiency index of N into chlorophyll aconcentration by ~40% and ~30%, respectively, which is indicative of promoting the growth ofphytoplankton (Liu et al., 2013). The dry and wet nitrogen deposition estimated by Qi et al. (2013)
could be converted to a new primary biological productivity of 1.5–30.0 gC m−2 a−1, which accounts
for 0.3% to 6.7% of the new productivity in the Yellow Sea.
Recent studies also show the contribution of nitrogen, phosphorus, and iron to phytoplankton bloomin the East China Sea. Tan et al. (2016) estimated that iron deposition from five dust storm events inthe East China Sea in 2010 accounted for 20% to 227% of the new springtime production and 5% to115% of the mean increase in chlorophyll a concentration in spring 2010. The total inorganic
nitrogen deposition from five dust storm events may have supported 0.4% to 4.0% of the new

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springtime production. Zhang et al. (2010) estimated that annual total nitrogen deposited to theeastern China seas can support 1.1% to 3.9% of the new production in the East China Sea. The
contribution ratio of total inorganic nitrogen and soluble phosphorus supported by the five dust
storms to the mean increase in chlorophyll a concentration varied from 0.1% to 2.0% and 0.01% to
0.2%, respectively (Tan et al., 2016). As a result, the atmospheric deposition of iron is seen to be
the most important factor in phytoplankton growth in the East China Sea; nitrogen also plays animportant part, whereas phosphorus has less impact.

3
2

a Dust year 2007
31 March, dust day

1

3

c Non-dust year 2005

2
1

0
0
77
87
97 107 117 127 137
7787
97 107 117 127 137
1212
d Non-dust year 2005b Dust year 2007
10
10
8
8
6
6
4
bloom level
4
2
2
0
0
77
87
97 107 117 127 137
7787
97 107 117 127 137
Julian Day of Year
Mar.18May 20 Mar.18
May 20Julian Day of Year1212
e Dust year 2004f Dust year 2006
10
10
8
8
7,17,19,23-24,30 April, dust day
2 April, dust day
6
6
bloom level
4
4
2
2
0
0
77
87
97 107 117 127 137
7787
97 107 117 127 137
Julian Day of Year
May 20May
19
Mar.18Mar.17
Julian Day of Yearaerosol index

o

-2

-1

PAR/5 (Ein m d )SST/2 ( C)-3daily chlorophyll a concentration (mg m )Fig. 2.6.3 Variations in daily aerosol index, daily chlorophyll a concentration, and 8-day surface
photosynthetically available radiation (PAR) and sea surface temperature (SST) in the central southern
Yellow Sea from March 17 through May 20 in 2007, 2005, 2004, and 2006. Green dashed line shows the
bloom level defined as chlorophyll a concentration is 3 mg m−3 (twice as large as winter average). From
Tan and Shi (2012b).

Note that not all dust depositions could stimulate phytoplankton blooms. Case studies illustrated
that 6 out of 22 dust episodes did not induce any algae blooms in the southern central Yellow Sea(Tan et al., 2011). Onboard incubation experiments performed in the southern Yellow Sea in thespring of 2011 found no promotion in phytoplankton growth when a small amount of Asian dust
(2 mg L−1) was added (Liu et al., 2013). Incubation experiments in the northern Red Sea showedthat toxic effects were observed with some aerosols, where the toxicity affected picoeukaryotes andSynechococcus but not Prochlorococcus (Paytan et al., 2009). The toxic effects could be due tohigher copper concentrations in these aerosols, and/or other elements present in the aerosols or
unknown synergistic effects between these elements (Paytan et al., 2009). In situ mesocosm

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experiments at Huaniao island in the East China Sea found that phytoplankton growth was
suppressed under high aerosol copper but was increased when high copper was accompanied byhigh iron in aerosols, suggesting the combined effects of iron and copper from atmospheric dry
deposition (Wang et al., 2017). Those studies suggest the complex effect of atmospheric deposition
on marine productivity.

2.6.5 Future perspective
The complexity of the impacts of atmospheric aerosols on marine ecosystems is due to the change
in physical and chemical properties of aerosols during the transport pathway from the source
regions to the oceans, the uncertainty in the estimate of atmospheric deposition flux, thebioavailability affected by atmospheric processing and environmental condition in surface water, thevariation of N and P ratios, and the complicated effects of deposition on the ocean ecosystem. For
example, global climate change and human activities could lead to more atmospheric pollution
events, which will influence both the supply and impact of atmospheric material deposited into thesurface ocean (Baker and Croot, 2010; Boyd et al., 2010; Li et al., 2012). In addition, atmosphericdeposition may change the nutrient structure in the ocean. Kim et al. (2011) found that theabundance of N relative to P in northeastern Asian marginal seas has increased since 1980 due toatmospheric N deposition. Anthropogenic nitrogen deposition has even resulted in surplus of N in
the marginal seas around China, Korea, Japan and Russia, commencing in the mid-1990s (Kim et al.,2011). Kodama et al. (2016) showed that phosphate concentrations has declined in the surface
layers in summer and winter over the last 40 years (1964–2010), while no significant linear trend
was observed for nitrate concentrations in the Tsushima Current area. They further concluded thatthe declining trend in the phosphate concentrations is caused by horizontal advection of East China
Sea water. The increase in N:P due to larger increases in N than P in many regions of the globe cancause shifts from N to P limitation (Moore et al., 2013; Mahowald et al., 2017).
More systematic and accurate measurements of atmospheric chemistry, atmospheric deposition,
nutrients, and ecosystem response are required to resolve variability in both atmospheric depositionand ecosystem response, particularly in regions where atmospheric supply plays an important role,
including the Yellow Sea and the East China Sea.
Linkage between atmospheric deposition and marine ecosystem is related to atmospheric pollution,
marine health, marine fertilization, fishery and carbon sequestration, etc., and is one of the hot
topics in atmospheric and marine sciences. Thus, understanding how atmospheric aerosols impact
the marine ecosystem in the Yellow Sea and the East China Sea is worth further investigation.

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195Biological Oceanography

PrefaceThis chapter summarizes the general understanding of biological oceanography in the Bohai, Yellow
and East China seas based on published literature. The chapter consists of five sections: the pelagic
microbial food web, phytoplankton, zooplankton, macrobenthos, and fish and invertebrate fisheries.
There is a large amount of observations and studies in this area; however, the information is still
scattered, and was not easy to fully synthesize. The authors of each section have tried hard tosynthesize within their limitations. Section 3.1 covers studies of abundances of virioplankton,
heterotrophic bacteria, picophyoplankton, flagellates, ciliates, and tintinnids, as well as theirinteraction and community respiration. Section 3.2 deals with studies of chlorophyll-a and primary
production from satellite observation, new production and nitrogen fixation, and pigment-based
community structure, as well as the interannual variation caused by the Three Gorge Dam andtyphoons. Section 3.3 considers studies of adaptation to distinct habitats, responses to climatechange and anthropogenic impacts focusing on physical–biological coupling. Section 3.4
describes studies of macrobenthos taxonomic groups, dominant species, community structure,abundance, biomass and diversity, as well as secondary productivity for the Yellow Sea and East
China Sea separately. Section 3.5 reports on the fish community, invertebrate community and 10
key species, mostly in the Yangtze River estuary.
At present, the ecosystem of this region is facing serious multiple pressures based on climate change,
as well as anthropogenic stresses due to global warming, acidification, pollution, and fishing activity.
However, it is noted that information on organisms for the whole area during the entire season isstill limited. Understanding of ecosystem dynamics, including the end-to-end food web with energy
transfer and material cycling coupled with physical and chemical processes, is required.
Development of ecological models to understand and predict future conditions is also necessary toprotect the natural resources and to recover ecological services. Long-term observations of large
spatial coverage is necessary, and the development of molecular methods as well as other new
technologies are expected. Finally, international collaboration between the countries surrounding
these seas under an international body such as PICES is very important.
Joji Ishizaka

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Microbial Food Web

3.1 Pelagic Microbial Food Web
Wuchang Zhang1, Chung Yeon Hwang2, Hiroshi Koshikawa3, Jae Hoon Noh4 and Yuan Zhao1
1

Institute of Oceanology, Chinese Academy of Sciences, China
Seoul National University, Korea
3
National Institute for Environmental Studies, Japan
4
Korea Institute of Ocean Science and Technology, Korea
2

3.1.1 Introduction
The microbial food web includes small organisms such as viruses, heterotrophic bacteria andautotrophic bacteria (Synechococcus and Prochlorococcus), picoeukaryotes, and heterotrophic
flagellates and ciliates (Fig. 3.1.1). In the 1990s, researchers in Asia began to pay attention to thoseorganisms due to the notion of the “microbial loop” (Azam et al., 1983) and “microbial food web”.
Methodological developments and field studies on marine viruses and bacteria were conducted inJapan during that same period (Hara et al., 1991; Fukuda et al., 1998). Korean scientists tookbacteria and bacterivores into consideration in understanding microbial ecology (Cho et al., 1994;
Shim et al., 1995), and later added viruses as a member of the microbial loop in domestic studies(Hwang and Cho, 2002a,b). Microbial food web research was initiated in the scope of Chinese
GLOBEC project in the Bohai Sea, Yellow Sea and East China Sea (Tang et al., 1996).

Fig. 3.1.1

Microbial food web and classical food chain. Adapted from Stenseth et al., (2004).

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Biological Oceanography

There are studies on the abundance and biomass distribution, growth or grazing rates of microbialorganisms in the Yellow Sea and East China Sea, but seasonal or annual variation has been scarcely
reported. In this section, we present a synoptic review on these topics. It is hard to synthesize thegeneral characteristics of microbial distributions and the dominant factors influencing them. Making
comparisons between the different seas is almost impossible because we do not even know theseasonal variation in each area. As a result, we could not identity whether the difference is a spatialdifference or simply a result of phenology.

3.1.2 Virioplankton
In Bohai Sea, the virioplankton abundance was investigated by flow cytometry. The abundance ofvirioplankton ranged from 6.40 × 105 to 3.59 × 107 particles/mL in the four seasons underinvestigation (Wang et al., 2013). In summer, the abundance of virioplankton was significantly
higher than in the other seasons. In summer, there was a significant abundance difference between
the surface and bottom layer. However, no vertical abundance difference was found in the otherthree seasons. Diel abundance fluctuation was obvious in summer. Virioplankton abundance wasclosely correlated with heterotrophic bacteria, picophytoplankton, and temperature.
In the northern Yellow Sea, the distribution and daily variation of virioplankton was determined by
epifluorescence microscopy in summer (Bai et al., 2008). The abundance of virioplankton ranged
from 1.58 × 106 to 1.38 × 107 particles/mL, with a mean abundance of 5.86 × 106 particles/mL.
Virioplankton was more abundant in the offshore than in other areas (Fig. 3.1.2). High virioplankton
abundance was found at the surface, 30 m and bottom layers near Liaodong Peninsula. Vertically,
virioplankton abundance was higher at surface than at the 30 m layer and bottom. The dielabundance fluctuation was more significant at surface layer than at the 10 m and 30 m layers.
According to multi-correlation analysis, virioplankton abundance was negatively correlated withdepth and salinity. No significant correlation was found between virioplankton abundance and watertemperature.

Fig. 3.1.2 Virioplankton abundance (×106 particles/mL) in summer in the northern Yellow Sea;a. surface layer; b. 5 m layer; c. 10 m layer; d. 30 m layer; e. bottom layer. From Bai et al. (2008).

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In the northwestern part of the Yellow Sea, the abundance of virioplankton was (0.67 ± 0.95) × 106
particles/mL, ranging from 0.03 to 4.41 × 106 particles/mL in spring. In autumn, the abundance ofvirioplankton was (0.31 ± 0.28) × 106 particles/mL, ranging from 0.02 to 1.58 × 106 particles/mL(Shi et al., 2009). Virioplankton was more abundant inshore and gradually declined toward theoffshore. In both spring and autumn, the abundance of virioplankton was higher at the surface layer
than at the bottom. In spring, virioplankton abundance was negatively correlated with salinity. No
correlation was found between virioplankton abundance and environmental factors in autumn.
Viral production and bacterial mortality due to viral lysis (BMVL) are important parameters inunderstanding biogeochemical cycles in marine microbial ecology (Fuhrman, 1999). Viruses are
generally produced by rupturing host cells, which leads to the conversion of particulate organic
matter into dissolved organic matter. However, information on viral activity is still rare in the
Yellow Sea. Measurements of BMVL for surface samples obtained from a coastal site on the Korean
Peninsula indicate that approximately 30% of bacterial populations in summer are subjected to virallysis during a turnover time of the bacterial populations (Hwang and Cho, 2008). Spatial andseasonal changes of viral impacts on bacterial mortality need to be investigated further to enhance
an understanding of the ecological roles of viruses.
In the Changjiang (Yangtze River) Estuary and adjacent coastal waters, virioplankton abundance
was investigated with flow cytometry (Jiao et al., 2006; Pan et al., 2007; Li and Li, 2012) andepifluorescence microscopy (Bai et al., 2007; Liu et al., 2011) in different seasons. The dynamics ofviruses in the Changjiang Estuary is characterized by distinct seasonal and spatial variations due tonatural forcing and anthropogenic impacts (Jiao et al., 2006). Total viral abundance over various
investigations fluctuated between 0.49 to 99.7 × 106 particles/mL. Seasonally, the abundance peakvalues were recorded in the summer and winter. The summer viral abundance peak was coupledwith the development of bacterial hosts which were controlled largely by temperature year-roundand by trophic state occasionally. The high winter viral abundance was mostly contributed to byhigh virus-containing freshwater discharge in winter due to a higher proportion of anthropogenic
sewage relative to low natural flooding in winter run-off (Jiao et al., 2006).
In the East China Sea, the abundance of virioplankton was also investigated along the Kuroshio
Current and adjacent area. The abundance of virioplankton ranged from 0.34 to 2.3 × 106 particles/mL (Yang and Jiao, 2002). In the Kuroshio Current water, virioplankton exhibited lower abundance
than in the shelf water. Virioplankton abundance was positively correlated to heterotrophic bacteria,
Synechococcus, and picoeukaryotes, but negatively correlated with Prochlorococcus. There was no
significant correlation with chlorophyll a (Chl-a) concentration.

3.1.3 Heterotrophic bacteria
In the Bohai Sea, the abundance of heterotrophic bacteria enumerated with DAPI
(4',6-diamidino-2-phenylindole) staining and epifluorescence microscopy varied from 5.57 × 103
cells/mL to 1.78 × 105 cells/mL from April to May. The abundance of bacteria was higher on thebottom than at the surface, with average abundance of 5.54 × 104 cells/mL and 3.47 × 104 cells/mL,
respectively (Bai et al., 2003). Enumerated with flow cytometry, the abundance of bacteria ranged
from 3.51 × 105 to 5.15 × 106 cells/mL in winter, and from 5.09 × 105 to 3.44 × 107 cells/mL insummer. The average bacteria abundance was markedly lower in winter (1.84 × 106 cells/mL) thanin summer (5.05 × 106 cells/mL). High abundance of bacteria was observed around the Yellow
River Estuary, and decreased towards the Bohai Strait (Bai et al., 2003). Vertically, bacteria
abundance was slightly higher (~1.17 times) in the bottom than in the surface waters in winter.
Conversely, in summer the surface bacteria abundance was higher (~1.14 times) than in the bottom
layer (Fig. 3.1.3).

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Fig. 3.1.3 Horizontal distribution of heterotrophic bacteria abundance (cells/mL) in the Bohai Strait inwinter 2013 and summer 2014. (A) surface, winter; (B) bottom, winter; (C) surface, summer; (D) bottom,
summer. From Wang et al. (2016). Reproduced with permission of Oxford University Press.

In the Bohai Strait, the abundance of bacteria was closely correlated with the concentration ofdissolved organic carbon (DOC), water temperature and dissolved oxygen. In the bottom layer,
bacteria abundance was also closely correlated with the concentrations of particulate organic carbon(POC). The main limiting factors to the number of bacterioplankton were the contents of DOC and
POC, as well as the water temperature in the Bohai Strait in spring (Bai et al., 2003). Bacterial
production (BP) is higher in autumn [125.5 mg/(m2·d)] than spring [115 mg/(m2·d)]. In autumn, thevertical distribution of BP was higher in the bottom layer, while in spring BP was greater in thesurface layer (Xiao and Wang, 2003).
In the northern Yellow Sea, the abundance of bacteria ranged from 1.91 × 105 cells/mL to 5.33 ×106
cells/mL, with average abundance of 1.64 ± 0.32 ×106 cells/mL in summer. High abundance ofbacteria was observed in the near-shore regions off Liaodong Peninsula. Vertically, high abundanceof bacteria mostly occurred in the 10 m layer, and the minimum values occurred mainly at thebottom (Bai et al., 2012). In southern Yellow Sea, bacteria abundance was at lower level in winter,with minimum average abundance of 4.1 × 105 cells/mL. From spring to summer, the abundance ofbacteria increased along with the water temperature. The highest bacteria abundance was recorded
in autumn, with average abundance of 8.9 × 105 cells/mL. High abundance of bacteria was observed
around the central area of the southern Yellow Sea (Fig. 3.1.4). Seasonally, high concentrations ofheterotrophic bacteria (>7 × 105 cells/mL) were found from late spring (May) to autumn (September,
October). During the cold season (from winter to mid-spring, i.e., December to April), when thewater temperature was lower, relatively few heterotrophic bacteria were present in the water column(Fig. 3.1.5). A positive relationship between average sea surface temperature and log averagebacteria abundance was found in the southern Yellow Sea, suggesting a positive influence oftemperature on the bacteria abundance (Zhao et al., 2011).

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A prilO ctoberFig. 3.1.4 Integrated cell abundance for heterotrophic bacteria (×1013 cells/m2) in April and October
2006 in the southern Yellow Sea. From Zhao et al. (2011). Reproduced with permission of Springer
Nature.

Fig. 3.1.5 Average abundance of heterotrophic bacteria (×105 cells/mL) in different seasons in thesouthern Yellow Sea. From Zhao et al. (2011). Reproduced with permission of Springer Nature.

The Yellow Sea Cold Water Mass (YSCWM) forms locally during the previous winter owing tosurface cooling and strong vertical mixing. The distribution of heterotrophic bacteria is correlatedwith temperature and salinity, and the minimum distribution of heterotrophic bacteria occurs in theYSCWM waters (Li et al., 2006; Zhao et al., 2011; Bai et al., 2012). A tidal front is a significant
physical phenomenon in the coastal waters of the Yellow Sea. Seasonally thermal stratification
forms at the end of spring. The maximal abundance of bacteria occurs in both stratified and mixed
zone. High abundance of bacteria does not necessarily couple with the front- associated Chl-a
maximum, suggesting that the bacteria depend mainly on particles and dissolved organic matterbrought into the sea by water run-off from land (Li et al., 2007).
Another study in the tidal front region in the Yellow Sea found notably that physical properties suchas turbulent mixing and water temperature are important in regulating major energy pathways
associated with bacteria and phytoplankton (Cho et al., 2001). The ratios of BP to primary
production (PP), a parameter for estimating energy pathways on microbial food webs, revealed asignificantly positive relationship with sea surface temperature (SST), but a significantly negative
relationship with f-ratio (i.e., a fraction of total PP fueled by nitrate). These relationships are
attributed to different hydrographic regimes in a euphotic zone, subsequently changing adependency of the nitrogen source for phytoplankton. The new finding that SST may largely explain

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the variations of BP/PP ratios is not a unique phenomenon in a local sea, but also was confirmed inan ocean-scale study in the Atlantic Ocean (Hoppe et al., 2002). In addition, the microbial
ecological relationship between SST and BP/PP ratio has been successfully applied to evaluate
environmental disturbance in dumping areas in the east of Korea (Choi et al., 2005).
During phytoplankton blooms in the southern Yellow Sea, enhanced bacterial abundance, biomass,
production, and turnover rates were observed in the bloom area. The variation of abundance
followed that of Chl-a concentration, but not with temperature (Hyun and Kim, 2003; Zhao, Y. etal., 2013). These results indicate that resource supply from phytoplankton primarily stimulates
bacterial growth. Higher bacterial growth but smaller increases in bacterial biomass indicates thatbacterial growth and biomass are independently controlled during the spring bloom in the central
Yellow Sea, in which bacterial growth is primarily stimulated by organic material produced from thephytoplankton bloom, but the enhanced biomass is more tightly controlled by grazing (Hyun and
Kim, 2003). In another study, diatom blooms were observed in the mouth of the Yellow Sea. The
blooms occurred in the inner bay and in waters outside of a tidally induced turbidity maximum.
Bacterial and phytoplankton variables were not correlated significantly with each other. The
uncoupling appeared to be unrelated to phytoplankton community structure. Strong tidal mixing
seemed to cause low substrates level and unbalanced growth for bacteria in the euphotic zone andthereby the observed uncoupling of bacteria from phytoplankton (Cho et al., 1994).
In the East China Sea, heterotrophic bacteria were ubiquitous, with average abundances of about
1.00–1.39 × 106 cells/mL (Pan et al., 2007a; Li and Li, 2012). The abundance and biomass ofbacteria decreased distinctly in offshore directions (Fig. 3.1.6). Bacteria were most abundant in theplume area where nutrients were replete and DOC resources were rich although light availability
was low, with depth-weighted average cell abundance reaching a level of 1.2 × 106 cells/mL (Jiao etal., 2002). The biomass of bacteria was positively correlated with the sum of that of Synechococcus
and picoeukaryotes in the study area out of the estuary (Jiao et al., 2002; Li and Li 2012), while it
was negatively correlated with that of Prochlorococcus (Jiao et al., 2002). The inverse relationshipbetween the two seems to be a feature of the transitional zones between oligotrophic and eutrophic
regimes (Jiao et al., 2002).
In an East China Sea survey, the cruises crossed three major water masses: the coastal water mass
(CWM), the mixed water mass (MWM), which forms on the continental shelf, and the Kuroshio
water mass (KWM). Depth-averaged integrated abundance of heterotrophic bacteria exhibited asmall seasonal variation with respect to water mass (Zhao, L. et al., 2013). In autumn, the maximum
abundance of heterotrophic bacteria in surface waters and the maximum concentration of surface
Chl-a were observed in the MWM, but they did not overlap. In winter, both distributions showedmaximum levels that overlap close to the central region of the study area (encompassing the
intrusion of the CWM inside MWM) and the KWM, which expanded westward. The difference in
non-overlapping and overlapping situations could be linked to differences in phytoplankton
development dynamics; a lower rate in winter would help bacteria development catch up withphytoplankton development (Zhao, L. et al., 2013).
In the southern East China Sea during spring and fall, integrated bacterial biomass (IBB; 350–1200
mgC/m2), integrated bacterial production [IBP; 28–329 mgC/(m2·d)] and averaged turnover rates(µ = IBP/IBB; 0.09–0.22 /d) in the coastal and upwelling areas were at least 2-fold of those in the
Kuroshio waters. IBB, IBP and µ were positively correlated with PP and POC, but showed anegative trend with temperature in the outer shelf area. The spatial patterns of IBB, IBP and µ weremore likely to be controlled by substrate supply and temperature interactively. Under the assumptionof a growth efficiency of 20%, shelf bacteria might consume carbon equivalent to almost 100% of
the in situ daily particulate primary production. DOC from non-phytoplankton and allochthonous
sources might play a significant role in supporting bacterial carbon demand in the shelf area of theEast China Sea (Shiah et al., 2000).

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Fig. 3.1.6 Vertical distribution of heterotrophic bacteria (Bact, cells/mL) along transects in the East
China Sea. From Jiao et al. (2002). Reproduced with permission of Inter-Research Science.

The bacteria distribution in the East China Sea was investigated during bloom (Chl-a concentrationof >10 µg/L) events occurring in spring (June) and summer (August) 2011. In spring, there was no
signiﬁcant difference in bacteria abundances between areas where bloom conditions were or were
not established. In the bloom area, bacteria exhibited higher abundance than in the non-bloom area
at only some stations. Among the picoplankton components, heterotrophic prokaryotes represented
the highest carbon biomass. Factors that most inﬂuenced bacteria distribution under bloom
conditions varied with season. In spring, ciliates tended to be the main factor, whereas in summer,
this role was played by Chl-a concentration as revealed by multiple stepwise regression analyses(Zhao et al., 2016).

3.1.4 Picophytoplankton
Cyanobacteria Synechococcus in the Bohai Sea were measured by epifluorescence microscopy inautumn (September to October 1998) and spring (April to May 1999). Synechococcus biomass washigher in autumn than in spring, with average biomass of 3.27 mgC/m3 and 0.13 mgC/m3,
respectively. In spring, high Synechococcus biomass was found near Laizhou Bay and Dalian City,and the vertical distribution patterns were 10 m > surface > bottom. In autumn, Synechococcus
biomass was minimum in the central Bohai Sea. Sea water temperature was an important factor

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affecting the seasonal variations of Synechococcus. The cyanobacterial population was grazed by
microzooplankton (Xiao and Wang, 2002).
From July 2006 to October 2007, the picophytoplankton (0.2–2 μm) abundance were enumerated
seasonally in coastal area of the Bohai Sea. The average abundance of cyanobacteria Synechococcus
was 4.46 × 103 cells/mL (0.79–16.19 × 103 cells/mL); the average biomass was 1.31 mgC/m3 (0.84–
17.47 mgC/m3). The seasonal variation of Synechococcus was autumn > winter > summer > spring.
Picoeukaryotes abundance ranged from 0.84–17.47 × 102 cells/mL (on average 4.43 × 102 cells/mL),and biomass was 0.21–4.37 mgC/m3 (on average 1.11 mgC/m3). The seasonal variation ofpicoeukaryotes was autumn > winter > spring > summer. The contribution of picophytplankton tototal phytoplankton biomass was from 1.84–8.91% (average 5.32%), which was lower than inoffshore and oligotrophic water. In the colder season, low temperature was an important factor
limiting the growth and distribution of picophytoplankton (Li et al., 2012).
In the northern Yellow Sea, flow cytometry analysis revealed spatial heterogeneity inpicophytoplankton populations in the offshore and near-shore waters during the summer. Totalabundance of picophytoplankton varied greatly among stations, which ranged from 1.03 × 103 to
2.95 × 105 cells/mL, with a mean of 5.59 ± 1.04 × 104 cells/mL. Synechococcus abundance 49.4 ±14.3 × 103 cells/mL was about one order of magnitude higher than picoeukaryotes abundance 6.49 ±1.46 × 103 cells/mL, which accounted for the overwhelming majority of picophytoplankton insummer in the northern Yellow Sea (Fig. 3.1.7). The abundance of Synechococcus in surface waters
was high in the offshore water and low in the near-shore water (Fig. 3.1.7). In contrast, highnumbers of picoeukaryotes were found in the near-shore water, but they were less abundant in theoffshore water (Fig. 3.1.7; Bai et al., 2012).

Fig. 3.1.7 Picophytoplankton abundance (×103 cells/mL) in the surface water of the northern Yellow Sea
in summer. (A) Synechococcus; (B) Picoeukaryotes. From Bai et al. (2012). Reproduced with permissionof Springer Nature.

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In the southern Yellow Sea, Synechococcus abundance was minimum in spring, with an average of
4.0 × 103 cells/mL (range 0.3–25.2 × 103 cells/mL) in April. From summer to autumn,
Synechococcus had high average abundance of 32.9–54.2 × 103 cells/mL, with a maximum value
occurring in October. In winter, Synechococcus abundance dropped to 9.2 × 103 cells/mL. The
seasonal variation of picoeukaryotes was quite different from Synechococcus. Picoeukaryotes
average abundance was high in spring (2.9–8.0 × 103 cells/mL), with maximum abundance
occurring in May. From summer to winter, picoeukaryotes abundance was at a low level (1.4–2.7 ×
103 cells/mL), with little seasonal variation (Fig. 3.1.8). Spatially, a high value of Synechococcus
was observed in the northeastern area, while in coastal waters near the Chinese mainland, relatively
few Synechococcus were present in the water column. Picoeukaryotes were homogeneously
distributed over the entire study area except in a small frontal zone in the coastal area on the western(in spring) and central Yellow Sea (in autumn) (Fig. 3.1.9). A positive relationship between average
SST and log average Synechococcus abundance was found, suggesting a positive influence oftemperature. However, population abundance of picoeukaryotes was negatively affected bytemperature, indicating a different control mechanism (Zhao et al., 2011).

Fig. 3.1.8 Averaged cell abundance for Synechococcus and picoeukaryotes (×103 cells/mL) in different
seasons. From Zhao et al. (2011). Reproduced with permission of Springer Nature.

Fig. 3.1.9 Integrated cell abundance (×1011 cells/m2) for Synechococcus and picoeukaryotes in April andOctober 2006. From Zhao et al. (2011). Reproduced with permission of Springer Nature.

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In the offshore waters of the northern Yellow Sea, Synechococcus and picoeukaryote abundance inthe upper layer was significantly higher than in the 30 m and bottom layers. It decreased sharply (upto one to two orders of magnitude) in the water columns deeper than 30 m at the offshore stations,which were located in the YSCWM. In contrast, in near-shore waters, picophytoplankton abundance
showed no vertical variation (Bai et al., 2012). In the southern Yellow Sea, along the verticaldirection, the main distribution order of Synechococcus abundance was surface > mid-layer >bottom zone, whether inside or out of the YSCWM. Picoeukaryote biomass was also lower in theYSCWM, but the vertical distribution pattern was mid-layer > surface > bottom zone (Zhao et al.,2011).These results showed picophytoplankton were sensitive to environmental changes. In the
Yellow Sea, tidal fronts have steep horizontal gradients of density caused by turbulent mixing driven
by tidal currents over shallow areas. High Synechococcus abundance distributed more often in thefrontal area and middle-surface layer of a stratified zone (Li et al., 2007).
Temporal variation of Synechococcus and picoeukaryote abundance and depth-integrated biomass
during three spring blooms in 2007 and 2009 were investigated in the Yellow Sea. Synechococcus
and picoeukaryotes responded differently to different types and courses of spring blooms. During
the diatom blooms of 2007 and at Station B20 (35.9°N, 123.0°E) in 2009, Synechococcus andpicoeukaryote abundance decreased sharply during the bloom period. However, during a mixed
dinoﬂagellate and diatom bloom at Station B23 (35.5°N, 124.0°E) in 2009, Synechococcus andpicoeukaryotes increased in abundance and biomass with the development of the bloom. Ciliates
and heterotrophic nanoﬂagellates grazing could be responsible for the Synechococcus andpicoeukaryote abundance and biomass decrease during bloom periods (Zhao, Y. et al., 2013).
Prochlorococcus distributed ubiquitously from the surface to the bottom of the euphotic zone in theocean between 40°N and 40°S. Prochlorococcus is present in most of the East China Sea. The
coastward distribution boundary in the East China Sea is around the 50-m isobath in summer (Fig.
3.1.10, dashed line a), but back to the Kuroshio Current and adjacent waters in winter (Fig. 3.1.10,
dashed line b). Abundance of Prochlorococcus changed significantly in different regions of the East
China Sea with a general pattern of increasing in the offshore directions. In the shelf waters of theEast China Sea, vertical distribution of Prochlorococcus could reach 50–100 m depth, whereas in
the Kuroshio Current, Prochlorococcus was observed down to 150 m (Jiao and Yang, 2002).

Fig. 3.1.10 Distribution of Prochlorococcus in the China seas (averaged depth-integrated cellabundance). Red dashed line a, summer distribution boundary; blue dashed line b, winter distribution
boundary. Adapted from Jiao and Yang (2002).

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In the northern East China Sea, temporal and spatial distribution of picophytoplankton abundance
was investigated using flow cytometry with environmental factors in all seasons of May, August,and November 2010 and February 2012 (Fig 3.1.11; Lee et al., 2014). Synechococcus was the
predominant group in the picophytoplankton abundance in all seasons, indicating its ecologicalimportance as a primary producer in the northern East China Sea. In winter, Synechococcus
abundance was low at 2.4 ± 1.0 × 103 cells/mL, but high in summer at 209.4 ± 163.9 × 103 cells/mL(Fig. 3.1.12a). Picoeukaryote abundance was relatively lower than Synechococcus abundance,
ranging from 0.4 ± 0.4 × 103cells/mL in summer to 7.6 ± 8.9 × 103 cells/mL in spring (Fig. 3.1.12b).
However, picoeukaryotes contributed significantly to the total picophytoplankton carbon biomass inspring and winter due to their larger size among picophytoplankton groups. Prochlorococcus was
detected in the eastern side of the study area during autumn and winter but not during spring andsummer, indicating the possible effect of the low Kuroshio intrusion on the northern East China Sea
in late spring and intrusion of Changjiang diluted water in summer (Fig. 12c). This study showedthat the distributions of these picophytoplanktons are strongly influenced by the seasonal
fluctuations of the Changjiang discharges and Kuroshio intrusion in the northern East China Sea.

at tude ( )

36

Korea

34

32

Yangtze
River
30
120

122

124

126
Longitude (E)

128

130

Fig. 3.1.11 Sampling stations in the East China Sea ( stations from May, August, and February cruises, stations from November cruise). From Lee et al. (2014).

(a)

(b)

1e+6
1e+5
1e+4
1e+3
1e+2
1e+1

Prochlorococcus

Picoeukaryotes

Abundance (cells/ml)

Abundance (cells/ml)

Abundance (cells/ml)

(c)
1e+6

1e+6

Synechococcus
1e+5
1e+4
1e+3
1e+2

May

Aug.

Nov.

Month

1e+4
1e+3
1e+2
1e+1

1e+1
Feb.

1e+5

Feb.

May

Aug.

Month

Nov.

Feb.

n.d.

n.d.

May

Aug.

Nov.

Month

Fig. 3.1.12 Seasonal variation of picophytoplankton abundances. (a) Synechococcus; (b) Picoeukaryote;(c) Prochlorococcus. Figures were drawn from the data in Lee et al. (2014) provided by the main author.
Note that stations in November are different from stations in other months. n.d. denotes no data (no studies
in May and August).

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In the continental shelf region of the northern East China Sea (125.10°E, 32.07°N), Synechococcus
abundance varied by up to three orders of magnitude across seasons (Fig 3.1.13a). In February,
Synechococcus abundance was extremely low, 0.07 × 104 cells/mL; the abundance then showed asharp increase and peaked between late spring and autumn. Synechococcus diversity also showedseasonal variations. The coastal and cold water-adapted Synechococcus Clades 5.1-I and 5.1-IV
were dominant during winter and spring, whereas the warm water-adapted Synechococcus Clade
5.1-II was dominant during summer and autumn (Fig. 3.1.13b). Prochlorococcus was not detectedin this region, suggesting that their growth appears to be inhibited in shallow shelf waters and/or
inhibited by the low salinity effect of Changjiang diluted waters.
Temperature is a good predictor for the relative seasonal change in Synechococcus abundance (Tsaiet al., 2013b). The horizontal distribution of Synechococcus abundance showed pronounced
seasonal changes with the effect of different water masses. From winter to early spring whentemperature is low, few Synechococcus (1–5 × 1011 cells/m2) existed in the water column, and the
population distributed homogeneously over the entire continental shelf, except for a high front
located to the north side of East China Sea in winter (Fig. 3.1.14A), which may have been driven by
intrusions of the YSCWM. Synechococcus abundance increased in late spring (Fig. 3.1.14B). Whenthe average temperature was over 20°C, more than 10 ×1011 cells/m2 were observed at the Shelf
Mixing Water (Fig. 3.1.14C). This represents an approximately 10-fold increase over the winter low
abundance. In summer, Synechococcus abundance increased further to over 25 ×1011 cells/m2, witha high-density area (>25 × 1011 cells/m2) in the center of the East China Sea and lower densities(<10 ×1011 cells/m2) in the coastal and Kuroshio waters. In autumn, Synechococcus abundance
remained high, with a maximum of 70 × 1011 cells/m2, and increased from the inner shelf to theouter shelf (Fig. 3.1.14E). The average carbon biomass of Synechococcus in the East China Sea
ranged from 0.09 (early spring) to 0.90 C/m2 (autumn). Synechococcus is a major contributor to theseasonal variation of phytoplankton in the East China Sea, accounting for less than 25% of the total
photosynthetic plankton in the cold season and about 50% in the warm season (Chiang et al., 2002).

5.1-I
5.1-II
5.1-IV
5.3-I

(a)

(b)
Percentages of major clads(%)

1e+6Abundance (cells/ml)(Surface)
1e+5

1e+4

1e+3

1e+2
Feb.

Apr.

Jun.

Aug.

Month

Sep.

Dec.

100
80
60
40
20
0
Feb.

Apr.

Jun.

Aug.

Sep.

Dec.

Month

Fig. 3.1.13 (a) Seasonal variation of Synechococcus abundance at the surface and (b) relative percentage
of clades in each sample. Figures redrawn using the data from station H6 from Tables 1 and 2 from Choi etal. (2013). Data are from 6 research cruises conducted between 2008–2011.

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Fig. 3.1.14 Horizontal distribution of integrated Synechococcus abundance (×1011 cells/m2) in the East
China Sea. (A) winter (December 1997); (B) early spring (March 1998); (C) late spring (May 1996); (D)summer (June 1998) and (E) autumn (October 1998). From Chiang et al. (2002). Reproduced withpermission of Elsevier.

The influences of sudden environmental changes on Synechococcus assemblage composition werealso investigated. The injection of abundant anthropogenic pollutants, and the enhancement oftransparency within the Changjiang River diluted water expansion area after flooding appear to alter
the ecology of Synechococcus. During the non-flooding summer of 2009, phycoerythrin-rich(PE-rich) Synechococcus thrived near the outer boundary of the Changjiang River diluted water
coverage, while phycocyanin-rich (PC-rich) Synechococcus predominated inside the turbid diluted
water with a transparency of <80%. During the 2010 summer, flooding expanded the ChangjiangRiver diluted water coverage area to over half of the East China Sea. PE-rich cells showed ahomogeneous distribution and a decline in abundance, while the spatial pattern of the PC-rich
Synechococcus resembled the pattern from 2009 (Chung et al., 2014).
In summer and winter, the abundance of picoeukaryotes was about 4 ×103 cells/mL. Thoughpicoeukaryotes were less abundant compared with Prochlorococcus and Synechococcus, they still

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contributed signiﬁcantly to picophytoplankton biomass. Picoeukaryotes exhibited very different
distribution patterns in different seasons. In summer, the average picoeukaryote abundance
decreased from the plume region to the transitional and Kuroshio regions, showing a tendency to
distribute in regions with lower salinity and higher nutrient and Chl-a concentrations. In winter,average picoeukaryote abundance increased dramatically from the plume region to the transitional
and Kuroshio region, which was positively correlated with temperature, salinity and Chl-a
concentrations, but negatively correlated with nutrient concentrations. The highest abundances ofpicoeukaryotes occurred at around 10 to 20 m depths in the inshore region and 40 m depth in theoffshore the region in summer, while they were highest in the surface in winter (Guo et al., 2014).
Picophytoplankton distribution in the East China Sea was investigated during bloom eventsoccurring in spring (June) and summer (August) 2011. In spring, Synechococcus and picoeukaryote
abundance resolved by ﬂow cytometry exhibited similar vertical distribution patterns along thestudy transect. No signiﬁcant difference in Synechococcus and picoeukaryote abundance was foundbetween the non-bloom (<10 µg/L) and bloom stations (>10 µg/L). However, during the summer
bloom, abundances of Synechococcus and picoeukaryotes were signiﬁcantly higher under bloom
than non-bloom conditions. Ciliate grazing and salinity could be the controlling factors of
picophytoplankton population variation in spring. In summer, temperature was the most importantvariable that controlled the abundances and distribution of picophytoplankton (Zhao et al., 2016).

3.1.5 Flagellates
In early summer, flagellate abundance in surface waters was higher in the coastal area than in thecentral part in the Yellow Sea (Huang et al., 2003; Ding and Xu, 2012). The vertical distribution
pattern varied in different studies. Flagellate abundance in the YSCWM was very high in earlysummer (Huang et al., 2003). However, this phenomenon was not found in October (Lin et al.,2014). High abundance of flagellates was also found to occur in both surface and bottom waters inspring and summer (Ding and Xu, 2012). As to the seasonal change, there was a higher percentageof heterotrophic flagellates in summer than in spring (Ding and Xu, 2012).
Abundance, biomass, cell size composition and trophic structure of flagellates dramatically changed
with the succession of the spring bloom. Compared with that in the pre-bloom phase, the cellabundance and biomass of flagellates in the water column and deep Chl-a maximum increased morethan 2-fold in the bloom phase and the post-bloom phase. Flagellates were composed mainly of 2to 5-µm pigmented nanoflagellates (PNF) whose rapid growth in response to the bloom outbreakcharacterized the flagellate dynamics in the bloom phase. Heterotrophic nanoflagellates (HNF)
showed a rather mild response during the bloom. The 2- to 5-µm fraction decreased during thebloom despite a significant increase of both pigmented and heterotrophic flagellates. In the
post-bloom phase, HNF increased while PNF declined. Larger flagellates (5–20 µm) constituted<20% of flagellate abundance in most of the samples. However, their biomass occupied a larger
percentage in total biomass (Lin et al., 2013).
Water mass had a significant impact on the distribution of flagellates. Either in autumn or in winter,the highest abundance and biomass of flagellates were recorded in the East China Sea Shelf Mixing
Water (ECSSMW), and the lowest in the Kuroshio Subsurface Water (KSSW). While in the East
China Sea Coastal Water (ECSCW), the abundance and biomass of both HNF and PNF were only
slightly higher than that in Taiwan Strait Water (TSW) and Kuroshio Surface Water (KSW). In
respect to the seasonal variation, the abundance and biomass of nanoflagellates in TSW declined in
winter, while in other four water masses they showed an increasing trend from autumn to winter,mainly due to the decrease (in TSW) or increase (in ECSCW, ECSSMW, KSW and KSSW) of HNF.

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The distribution pattern of abundance- or biomass-based PNF/HNF ratio was found to be correlated
to the nutrient level of the water mass. Results of Pearson correlation analysis and principal
component analysis indicates that PNF was mainly constrained by nutrient supply, and HNF wascontrolled by food availability (Lin et al., 2016).
Changjiang Diluted Water (CDW) could also affect the nanoflagellates distribution. Tsai et al. (2010)
divided CDW into two different periods: large plume period and small plume period. Nanoflagellate
abundance was higher in the large plume period than in the small plume period. During large plumeperiod, high total nanoflagellates abundance occurred in the CDW. The abundance variation wasclosely related to changes in salinity.

3.1.6 Ciliates
Planktonic ciliate data in the Bohai Sea is very scarce. Typhoons have a significant impact onplanktonic ciliates. In the period from 23 September to 7 October, 1998, planktonic ciliates were
sampled during two grid station investigations which were six days apart during which time atyphoon swept through the Bohai Sea between the two cruises. Surface ciliate biomass decreased
from 0.2–12.3 mg C/m3 in the first cruise to 0.02–2.8 mg C/m3 in the second cruise. Water column
ciliate biomass also decreased from 2–136 mg C/m2 to 0.01–47 mg C/m2. The ratio between ciliate
biomass to Chl-a concentration decreased from 0.07–4.7 (on average 1.41) to 0.01–1.18 (on average0.26) (Fig. 3.1.15; Zhang and Wang, 2000a).
In the southern Yellow Sea, ciliates exhibited a seasonal cycle, with two peaks in abundance (Mayand August) but only one biomass peak (May). Larger ciliates were more prevalent in spring, whilesmaller ones dominated in the summer. Abundance and biomass of total ciliates were associatedwith different water masses, which then influenced the entire seasonal pattern in the basin. TheYSCWM had an important influence on the distribution of ciliates. High ciliate abundance mainly
occurred around a thermal front from December to April. In August through October, low ciliate
abundance was observed inside the YSCWM (Yu et al., 2014).
In spring, ciliate distribution pattern along the coast of the Shandong Peninsula was coincident withthat of the anchovy larva (Zhang et al., 2002). Ding and Xu (2012) attributed the low ciliate
abundance in some transects in summer to the possible grazing effects by jellyfish, which has been
an emerging ecological nuisance recently. During the spring phytoplankton bloom in the central
Yellow Sea, high ciliate abundance occurred in the bloom area. In October, there was very low
abundance in the central deep area (Zhang et al., 2017).
The mixotrophic ciliate Laboea strobila also occurred in the Yellow Sea (Zhang et al., 2002; Zhang
et al., 2017). With large size (body length 80–145 µm) and unique spiral body, this species was
easily identified. Possibly due to the need of light to photosynthesize, it mainly occurred in theupper 20 m. Abundance as high as 10,000 ind./L was found in the Yellow Sea in spring. Itsabundance dropped in autumn (Zhang et al., 2017).
In the East China Sea, planktonic ciliate populations had a distinct seasonal cycle. The abundance ofciliates increased by 3- to 5-fold in summer compared to the other seasons. The high abundance area
was in the Changjiang River Plume with salinity <31. The Three Gorges Dam appears to have an
important influence on ciliates in the river plume. Before the completion of the Three Gorges Dam,heterotrophic ciliates were more abundant (1000–2000 ind./L) in the river plume (salinity <32) thanin the waters outside of the plume (salinity >32) in Junes of 1998 and 2003. After the completion of
the Three Gorges Dam, the abundance of ciliates in the river plume became comparable with thatoutside of the plume in summer (2003–2007) (Tsai et al., 2011a). However, in 2011 the Changjiang

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River plume had higher ciliate abundance than in waters outside of the plume (Yu et al., 2016). In
cruises during 2006–2009, higher ciliate biomass was found in areas affected by the ChangjiangRiver discharge (Choi et al., 2012).

Fig. 3.1.15 Surface biomass (mg C/m3) and column biomass (mg C/m2) of planktonic ciliates in the
Bohai Sea during two grid investigations. From Zhang and Wang (2000a). Reproduced with permission ofInter-Research Science.

Mixotrophic ciliate abundance using the epifluorescence microscope was also investigated. In
summer, abundance (>1200 ind./L) was higher in the Changjiang plume than in waters outside of
the plume. Correspondingly, mixotrophic ciliate abundance occupied >50% of total ciliate
abundance in the plume compared with <30% outside of the plume (Chiang et al., 2003). The sizeof the ciliates had a regional trend: ciliates in the coastal shallow waters were larger than those in
the deeper waters. This trend in summer was not as obvious as in other seasons (Chiang et al., 2003).
Ciliate abundance in the shelf region of the East China Sea was high in the upper 50 m watercolumn. In waters deeper than 50 m, ciliate abundance decreased sharply. There usually was asubsurface abundance peak around 25 m depth (Ota and Taniguchi, 2003; Tsai et al., 2011a) but
ciliate abundance showed surface peaks in some stations (Zhang et al., 2001; Ota and Taniguchi,
2003). The vertical peak of ciliate abundance was not coincident with the Chl-a concentration peakin most of the stations. Although both ciliate biomass and pico-/nano-plankton biomass decreased
with depth, ciliate biomass decreased more rapidly. As a result, the ratio between ciliate biomass andpico-/nano-plankton biomass decreased with depth (Suzuki and Miyabe, 2007).

3.1.7 Tintinnids
In spring, Codonellopsis mobilis (Fig. 3.1.16) was the dominant tintinnid species in the Bohai Sea.
In a cruise in 1–15 June 1997, tintinnid abundance was investigated at five stations in the Bohai Sea.
C. mobilis was the dominant tintinnid species with 981, 200 and 30 ind./L in surface waters of Stn. 1,

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Stn. 5 and Stn. 3, respectively. Tintinnid abundance was lower in deep waters than in surface waters(Zhang and Wang, 2000b). From 28 April to 1 May, 1999, tintinnids were sampled using a verticalnet (mesh size 76 µm) towed from bottom to surface in 30 stations in Bohai Sea. Almost all thetintinnids were C. mobilis. Other species were rarely found. C. mobilis occurred in 27 out of the 30
occupied stations with maximum abundance of 10,731 ind./m3 (Zhang et al., 2004).
C. mobilis was also dominant in Laizhou Bay. Large-sized tintinnids were collected by verticaltowing a shallow sea type III plankton net (open area 0.1 m2, mesh size 76 μm) during 9 cruises
between May and November of 2011 and from March to April 2012. The abundance of C. mobilis
was more than 50 ind./L. C. mobilis was a dominant species during all time periods andsignificantly influenced the pattern of total ciliate abundance. The highest abundance of C. mobilis(489 ind./L) occurred in May (Chen et al., 2014).

Fig. 3.1.16 (Left) Tintinnid species Codonellopsis mobilis. Photo from Wuchang Zhang and (right)
stations in the Bohai Sea. From Zhang and Wang (2000b).

Tintinnids in the Yellow Sea were composed of boreal genera Parafavella spp. (Fig. 3.1.17a) and
Ptychocyli obtusa (Fig. 3.1.17b). Parafavella elongata and C. mobilis were reported only in the
Bohai Sea and Yellow Sea (Zhang et al., 2008; Yu et al., 2013). Parafavella spp. occurred in waters
north of 36°N in June (Zhang et al., 2009) and in waters north of 34.5°N in November and January(Zhang et al., 2008; Fig. 3.1.18). Ptychocyli obtusa occurred in deep waters (>50 m) north of 35°N
in high abundance (503 ± 874 ind./L) in April (Zhang et al., 2018).

Fig. 3.1.17
Zhang.

Tintinnid species Parafavella elongata (a) and Ptychocyli obtusa (b). Photos from Wuchang

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Fig. 3.1.18 Distributions of Parafavella spp. (ind./L in the upper panel and ind./m3 in the bottom panels)in the Yellow Sea. Solid circles indicate the positions of sampling stations. From Zhang et al. (2008, 2009).
Reproduced with permission of Springer Nature.

Tintinnids and aloricate ciliates had different vertical distributions in the East China Sea. Tintinnids
were distributed mainly near the bottom waters, while a higher abundance of aloricate ciliates wasfound in waters 30–70 m above the bottom. The contribution of tintinnids to the total ciliate
abundance decreased with increasing distance from the bottom. In waters with distance from thebottom >80 m, tintinnids accounted for <10% of the total ciliate abundance. The Changjiang River
discharge in summer enhanced aloricate ciliate abundance compared with spring and autumn,
although tintinnid abundance did not change significantly. Tintinnidium primitivum (Fig. 3.1.19a)
was the dominant tintinnid near the bottom waters. Aloricate ciliates and tintinnids had different
vertical distributions and responses to freshwater discharge in the East China Sea (Yu et al., 2016).
A total of 94 species in 36 genera of tintinnids were found in the East China Sea in three cruises inMay, August and October, respectively (Li et al., 2016). The tintinnid assemblage was the result of
the interaction between neritic and warm water tintinnids. Factors influencing neritic tintinnid
species richness and abundance were water depth while salinity influenced species richness andabundance of warm water tintinnids. Most neritic genera are agglutinated species with particles on
the loricae, which add additional weight to the cell. These genera settled down faster than hyaline
genera. Therefore, the neritic genera had lower mobility compared with warm water genera. Thus,
neritic genera were adapted to a turbulent environment in order to stay in the water column. Neritic
genera stayed in shallow waters which had a higher turbulence due to tidal mixing. According to thedifference in their expansion ability, neritic and warm water genera could be divided into core
genera (with smallest expansion range from its distribution center) and pioneer genera (with larger
expansion range than that of the core genera). In the case of neritic genera, core and pioneer neritic
genera stayed together in May and August while pioneer neritic genera expanded into oceanic waterin October. In the case of warm water genera, the pioneer genera always expanded toward the coastin all three cruises. In May and August, the pioneers of neritic and warm water genera did not meet.
Thus, there was a low species richness area in the middle of the East China Sea. However, in
October, the cores of neritic and warm water genera collided. The Changjiang River freshwater214

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plume is not suitable for both neritic and warm-water genera tintinnids. Warm-water genera cannot
live in diluted water because of its low salinity (<32), but neritic genera lived well in the diluted
waters quite near river mouth because of its high turbulence. However, at some distance from theriver mouth, where water became stratified because of the low salinity of the diluted water, neritic
genera could not thrive due to low turbulence (Zhang et al., 2015; Li et al., 2016).
However, there was neritic genera tintinnid species adapted to live in the estuarine low salinity
waters. A new tintinnid ciliate, Tintinnopsis estuariensis (Fig. 3.1.19b) was found recently in theestuary of the Changjiang River. It was most abundant in September. Its arenaceous lorica is
cylindro-conical in shape, (120–180) μm × (50–75) μm in size, agglutinated with mineral particles,
and open at both ends. Two characters distinguish it from others: the unique lateral flattened
posterior portion with a vertical aboral opening and wider oral diameter. It occurred in water withsurface temperature of 14–27°C and surface salinity of 3.9–29.8. This species was also found in
Yellow River Estuarine (Zhang Wuchang, unpublished data).

Fig. 3.1.19 Tintinnid species Tintinnidium primitivum (a) and Tintinnopsis estuariensis (b). Photos from
Wuchang Zhang.

3.1.8 Trophodynamics
In addition to the distribution patterns of the component organisms in the microbial food webs,
some authors have made a probe into the trophodynamics of the microbial food web. This probefocused on two aspects: one is the numerical relationships between (among) abundances (orbiomass) of the organisms; another one is the grazing impact of the grazers on their prey.
Huang et al. (2003) reported the abundance ratio of heterotrophic bacteria : Synechococcus :flagellates : dinoflagellates : ciliates = 105:103:102:10:1 in the southern Yellow Sea (Fig. 3.1.20).
Therefore, the numerical relationship among these organisms was comparatively stable. However,there was weak coupling between heterotrophic nanoflagellates and bacteria in the YSCWM area
(Lin et al., 2014).
Suzuki and Miyabe (2007) provided a different view. In the deep eastern part of the East China Sea,
the ratio of ciliates:prey candidates tend to increase with the increase of prey candidates. That means
when prey candidates decrease, ciliates decrease more rapidly. As a result, the ratio of ciliates : preycandidates decreased with depth to 400 m (Fig. 3.1.21; Suzuki and Miyabe, 2007).

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Fig. 3.1.20 Relationship of marine flagellate abundance with that of heterotrophic bacteria,
cyanobacteria, dinoflagellates and ciliates in the southern Yellow Sea. From Huang et al. (2003).

Fig. 3.1.21 Biovolume ratio (left) of planktonic ciliates to prey candidates (picoplankton +nanoplankton) on vertical proﬁle at Stns. 1–5 in the East China Sea (right) in May 1999. From Suzuki and
Miyabe (2007). Reproduced with permission of Springer Nature.

A number of dilution incubation experiments were carried out in the Yellow Sea (Zhang et al.,2002c; Yu et al., 2010; Sun et al., 2013) and East China Sea (Zhang et al., 2001; Zhang et al., 2006;
Sun and Song, 2009) to study microzooplankton grazing on phytoplankton. These studies showedthat microzooplankton was the major grazer of phytoplankton and had a higher grazing pressure on
phytoplankton than mesozooplankton did. However, mesozooplankton might enhance
phytoplankton growth by removing microzooplankton (Zhang et al., 2006; Yu et al., 2010).
Dilution incubations also were used to estimate microzooplankton grazing rates (0.02–0.19 h–1) on
bacteria and Synechococcus (0.01–0.13 h–1) in the East China Sea. Nanoflagellates largely dependon heterotrophic bacteria as an important energy source contributing 76% and 59% of carbon
consumed by nanoflagellates within the Changjiang River Plume (salinity <31) and outside of theplume (salinity >31), respectively (Tsai et al., 2012).

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Paralleled dilution incubations (dilution with 0.2-µm filtered seawater and 30-dDa filtered seawater,
respectively) estimated the contribution of microzooplankton and viruses to bacteria mortality. In
summer, flagellate grazing was responsible for most of the bacterial mortality, accounting for 59%
and 66% of total mortality within the plume (salinity <31) and outside of the plume (salinity >31),
respectively (Tsai et al., 2013a,c).
In another study, fluorescently labeled bacteria was used to determine the grazing rates ofheterotrophic flagellates on bacteria (Lin et al., 2014). In the Yellow Sea, the grazing rate fell to arange of 3.92–15.57 bacteria HNF–1 h–1. The grazing rate was high in the surface waters anddecreased with depth. Heterotrophic flagellates grazed 9.27–33.80% of bacteria standing stock and8.12–16.09% of bacteria production per day in autumn. Therefore, heterotrophic flagellate grazing
might not have a major impact on planktonic bacteria (Lin et al., 2014).
In the East China Sea from May to September, PNF had a larger grazing rate (0.9–15.5 bacteria
PNF–1 h–1) than that of HNF (0.3–5.8 bacteria HNF–1 h–1). The 3–6 µm PNF, 2–3 µm HNF, and 3–
6 µm HNF were major consumers in the nanoflagellate community and were responsible for an
average of 52, 28 and 16% of the total consumption of bacteria, respectively (Tsai et al., 2011b).

3.1.9 Planktonic community respiration
Planktonic community respiration (CR) rates were measured using the oxygen methods in autumn
1998 (Chen et al., 2003), June and August 2003 (Chen et al., 2006, 2009), June and July 2004, June
2005 (Chen et al., 2009) in the euphotic zone of East China Sea. Seawater samples were incubatedin the dark for 12 h or 24 h. CR rate was 127.6–4728.6 mg C m–2 d–1 in autumn (Chen et al., 2003).
CR was higher in June (∼114 mg C m–3 d–1) than in August (∼40 mg C m–3 d–1) (Chen et al., 2006).
CR in surface waters was higher in June (128.4 in 2003, 91.4 in 2004, 103.8 in 2005) than in July
(89.5 in 2004) and August (52.2 in 2003) (Chen et al., 2009). Spatially, CR was higher in the innershelf and Changjiang Diluted Water than in the middle and outer shelf area. Changjiang River
discharge governed the CR pattern. Phytoplankton and bacteria were the main contributors, which
accounted for >96% of CR. CR occupied about 35–42% of primary production. Therefore, CR was
supported more by riverine organic carbon input than in situ autotrophic production. ChangjiangRiver discharge may also stimulate high primary autotrophic production by providing nutrients,
which in turn, enhance the CR.

3.1.10 Synthesis and perspectives
The microbial food web and its function in Yellow Sea and East China Sea have been studied forabout 40 years. However, this information is fragmented and there are a lot of unknowns about the
microbial food web in this area. It is very difficult to make a synthesis in order to compare thedifferences among the Bohai Sea, Yellow Sea and East China Sea. There are no taxonomic data formost of the organisms. Their abundance, biomass and production has the largest dataset. However,
we could not compare their abundance because (1) they are not from the same season, (2) we do not
yet know the annual evolution of these organisms, and (3) viruses, bacteria, flagellates and ciliates
occurred everywhere (whenever you sampled, you get a value larger than 0). The only group we
could compare is the present/absent of Prochlorococcus and some tintinnids. Some topics wererarely studied, and we could not find corresponding data in other study areas. As a result, we could
not make a synthesis about these topics such as biovolume ratio of planktonic ciliates to preycandidates (Fig. 3.1.21).

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In response to the above-mentioned knowledge gap, some topics are proposed as future research
priorities. The taxonomic compositions of these groups need more study, perhaps with the help ofmolecular methods. The coupling between microbial organisms and water mass and currents is still
obscure. The seasonal variation of the microbial food web in different parts of the research area is
not clear. Different groups of the organisms in the microbial food web were studied separately inmost of the reports, And end-to-end food web research was absent. The influence of dust storms on
microbial organisms was not yet studied in these marginal seas right after facing the famous East
Asian Dust Storm. The energy transfer between different compartments still need to be clarified.
Finally, determining how the microbial food web will change facing global warming and otherforcings (acidification, pollution) is the most significant challenge.

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3.2 Phytoplankton
Joji Ishizaka
Institute for Space-Earth Environmental Research, Nagoya University, Japan

3.2.1 Introduction
Recently, as many red tide events are happening in the East China Sea and Yellow Sea (Fig. 3.2.1),
more attention is being paid to the ecosystem and phytoplankton in this area and their relationship tohuman activities and climate change. This area is a region of complex current systems, and it is
known that discharge from the Changjiang River and Kuroshio Intermediate Water are major
sources of nutrients (see Section 2.2). More recently, atmospheric input is also recognized as animportant contributor (also see Section 2.6). Ning et al. (1988) described the chlorophyll-a andprimary production in the Changjiang Estuary extending to offshore of the East China Sea inJanuary and July 1986. They found smaller phytoplankton (<10 µm) dominated in the estuary andnearshore in winter whereas diatoms dominated at 100 km offshore where chlorophyll-a andprimary production was maximum and salinity was 25–30. They concluded that turbidity limits thelight for primary producers in the coastal area, and nutrients are poor in the offshore area. A large
amount of data was accumulated after this study describing more complex temporal and spatial
variability of phytoplankton, including the influence of strong anthropogenic and climatic forcing inthis area. In this section, our present understanding of phytoplankton abundance, primary
production, and phytoplankton community in the East China Sea and Yellow Sea are summarized,
as well as the influence of the Three Gorge Dam and typhoons as sources of interannual variation.

Fig. 3.2.1 Map of the East China Sea, Yellow
Sea, and Bohai Sea, with names of location andcurrents used in the text.

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3.2.2 Chlorophyll-a
Phytoplankton abundance is often expressed by the photosynthetic pigment, chlorophyll-a (Chl-a).
Because of the complex and large variability of Chl-a in the East China Sea and Yellow Sea,
recently, satellite ocean color data is often used to understand the over-all structures. Ning et al.
(1998) and Tang et al. (1998) are the pioneers to describe satellite-derived phytoplankton abundancein this area in combination with satellite-derived sea surface temperature (SST). Using Coastal
Zone Color Scanner (CZCS) phytoplankton pigments (Chl-a and pheopigments) data from 1978 to
1986, they clearly showed various water mass structures and associated phytoplankton distributions,especially the extension of high phytoplankton-rich water from the Changjiang Estuary to theoffshore. However, ocean color remote sensing data overestimates Chl-a near the coastal area
because of the high amount of suspended matter near the coast (Kiyomoto et al., 2001) and highamount of colored dissolved organic matter from river discharge (Gong, 2004), as well as the
problem of atmospheric correction (Shi and Wang, 2009). Many attempts have been made to
reduce the error of satellite-derived Chl-a in this area. For example, Siswanto et al. (2011)
developed local algorithms under the Yellow Sea Large Marine Ecosystem Project (YOC algorithm),
specifically for high turbidity areas identified with bright (high remote sensing reflectance) water, toreduce the influence of high suspended matter.
Knowing the presence of errors in ocean color data, Yamaguchi et al. (2012, 2013) describedseasonal variation of NASA ocean color sensor SeaWiFS Chl-a in the East China Sea and Yellow
Sea from January 1997 to December 2006 with standard product and locally tuned product by YOC
algorithm, respectively. (Figs. 3.2.2 and 3.2.3). They recognized the overestimation of the satellite

Fig. 3.2.2 Distribution of 10-year (1997– 2006) average of monthly chlorophyll-a (Chl-a, mg m−3) from(a–d) standard MODIS algorithm and from (e–h) YOC algorithm, (i–l) the difference between the two a(Chl-a, mg m−3), and (m–p) total suspended matter (TSM, g m−3) with bathymetry. (a, e, i, m) January,(b, f, j, n) April, (c, g, k, o) July, and (d, h, l, p) October. Redrawn from Yamaguchi et al. (2013).
Reproduced with permission of Elsevier.

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Fig. 3.2.3 Seasonal variation in 10-year (1997–2006) averaged monthly chlorophyll-a (Chl-a, mg m−3)
and total suspended matter (TSM, g m−3) along (A) 124.5°E and (B) 31.75°N lines. Gray color in bottom
plots indicates no data. From Yamaguchi et al. (2013). Reproduced with permission of Elsevier.

Chl-a data caused by suspended matter near coastal areas as well as on the Changiiang Bank during
winter. They reported a spring bloom in large parts of this areas; however, the summer maximum of
Chl-a was also seen from the Changjiang Estuary to near the east of Jeju Island from July to
September. The summer maximum shifts eastward, coinciding with the movement of the
Changjiang Diluted Water (CDW), taking approximately 2 months to move from the ChangjiangRiver mouth to around Jeju Island. Yamaguchi et al. (2012) also reported that the summer Chl-a waspositively correlated with Changjiang River Discharge with a time lag of 0 to 2 months between
1998 and 2006, and they showed that Changjiang River Discharge is one of the most important226

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factors for the interannual variation of Chl-a in the middle East China Sea. Yamaguchi et al. (2013)
found relatively low Chl-a with high suspended matter near the coast and Changjiang Bank in
winter (Fig. 3.2.4). The high suspended matter decreases in spring to summer in the offshore area,and phytoplankton increase with the decrease; a spring bloom can be seen covering a large area,
indicating suspended matter is important for phytoplankton dynamics in the shallow area.

Fig. 3.2.4 Seasonal variation in 10-year (1998–2007) averaged monthly satellite chlorophyll (Chl-a, mgm−3) and total suspended matter (TSM, g m−3) from January to December in the middle of the Yellow Sea(A), south of the Changjiang Bank (B), offshore of the Changjiang River mouth (C), around theChangjiang Bank (D), and east of the Changjiang Bank (E). Black and white symbols indicate Chl-a andTSM, respectively. From Yamaguchi et al. (2013). Reproduced with permission of Elsevier.

Yamaguchi et al. (2013) further suggested that the increase of summer Chl-a in the Yellow Sea overthe last 10 years is possibly caused by eutrophication. In addition, Xu et al. (2013) analyzed
bloom conditions in this area with a consistent 13-year (1998 to 2010) time series from SeaWiFS to
MODIS to understand the relation of phytoplankton blooms to the giant jellyfish blooms, and foundthat the phytoplankton bloom magnitude increased in the Yellow Sea and Bohai Sea. The Northwest
Pacific Action Plan (NOWPAP) of the United Nations Environment Programme (UNEP) has usedchanges of Chl-a concentrations over time to monitor eutrophication of this area. They found anincreasing trend of Chl-a during 1998 to 2016 in the Yellow Sea and Bohai Sea (Fig. 3.2.5; Terauchi
et al., 2018). However, an increasing trend in the Chinese coastal area was not persistent, maybe
because of high turbidity.

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Fig. 3.2.5 Results of a preliminary assessment of eutrophication
status conducted by NOWPAP for the Bohai, Yellow and East Chinaseas, by ocean color data (Terauchi et al., 2018). H (high) and L (low)indicate that average chlorophyll-a during 2014–2016 was more andless than 5 mg m–3, respectively. D, N, and I indicate decreasing, no,and increasing trend during 1998–2016.

3.2.3 Primary production
Primary production in the Yellow Sea and East China Sea has been measured since the 1970s withthe 14C method (Guo et al., 1991), and later with the 13C method (Hama et al., 1997). Ning et al.
(1988) described very low primary production of 5 to 40 mgC m–2 d–1 on the shelf and higher
offshore (mid-shelf) during winter. On the other hand, it was higher (50–2000 mg C m–2 d–1) during
summer, with the low and high production observed in the offshore and nearshore, respectively.
Hama et al. (1997) reported 220 to 350 mgC m–2 d–1 at the Kuroshio, with little seasonal variability
whereas high primary productivity (above 570 mgC m–2 d–1) at the center of the continental shelfand nearest to the Changjiang Estuary exhibited a distinctive seasonal change from 68 to 1,500 mgC
m–2 d–1. Depth-integrated primary productivity was 2.7 times higher in the shelf area than the rates
at the Kuroshio, and high Chl-a specific productivity (mgC mgChl-a–1 d–1) throughout the euphotic
zone was found mainly in the shelf area. Gong et al. (1997) reported high primary production of1540 and 1900 mgC m–2 d–1 in the upwelling region north of Taiwan and in a dinoflagellate bloomin the coastal waters off China, respectively, whereas the Kuroshio water had a low value of 420
mgC m–2 d–1.
Because of the importance of annual euphotic zone averaged primary production, Gong et al. (1999,
2000, 2003) conducted extensive estimations of seasonal primary production with photosynthesis–
irradiance relationships. Gong et al. (1999) first estimated 104 gC m–2 y–1 in Kuroshio water near
Taiwan with little seasonal variation. Next, Gong et al. (2000) observed the west–east section
north of Taiwan and found the annual average in the shelf waters to be quite uniform 549 ± 84 mgC
m–2 d–1 (200 gC m–2 y–1). The offshore Kuroshio water showed a weak seasonal variation of 292 ±15 mgC m–2 d–1 (107 gC m–2 y–1). Then, Gong et al. (2003) covered the large area of thesouthwestern East China Sea from Taiwan to the mouth of the Changjiang River. They foundseasonal variation with high primary production with annual primary production of 155 gC m–2 y–1
in the northwestern area (Changjiang Estuary), where nutrients are high year-round. In thesoutheastern area, the seasonal variation in production was small and the annual value was 144 gC
m–2 y–1, although nutrients varied seasonally. They estimated 145 gC m–2 y–1 for annual primary
production of the entire area. They also reported the controlling factors of the primary production
were temperature, phosphate, and turbidity in the inner shelf.

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Although extensive measurements have been made, it is difficult to cover the large variation ofprimary production in space and time. Thus, it is natural to use satellite Chl-a data to estimate
euphotic zone integrated primary production. Gong and Liu (2003) first suggested a simple
empirical algorithm for satellite data with specific parameters on the continental shelf of the East
China Sea, taking into account the high turbidity in the Changjiang Estuary. Son et al. (2005) usedsatellite Chl-a data derived by a local algorithm with an approach using local
photosynthesis-irradiance (P-E) parameters and vertical profile models to estimate the primary
production in May and September. They pointed out that the primary production is lower in the
coastal area than the middle of the Yellow Sea where the turbidity was low and Chl-a was higher.
They also estimated the daily total primary production for the entire Yellow Sea: 19.7 × 104 tC d–1 in
May and 15.8 × 104 tC d–1 in September.
Siswanto et al. (2005) used a vertically resolved primary production model and found thesubsurface Chl-a maximum contribution to be 20% and 30% for the Kuroshio and Kuroshio frontal
area, respectively, although the difference was not easily distinguished. Then, Siswanto et al.
(2006) tested several different satellite primary production algorithms, including that of Gong and
Liu (2003), the vertically resolving model of Siswanto et al. (2005) and the Vertically Generalized
Production Model (VGPM) of Behrenfeld and Falkowski (1997) with the data of southeastern East
China Sea. They concluded the VGPM is the most effective after local tuning of one parameter, andthey used it to study the influence of typhoons, as described in subsection 3.2.7. Around the
Korean Peninsula, including the Yellow Sea, Yoon et al. (2012) tested several primary production
models with in situ Chl-a and euphotic zone depth and found the VGPM modified by Kameda andIshizaka (2005) works better than with the parameter modified by Siswanto et al. (2006) for net
primary production. The Kameda and Ishizaka (2005) model was also used by Tan et al. (2011)
and Tan and Shi (2012) for their study of influence of atmospheric input described later in the
middle of the Yellow Sea and in the entire Bohai Sea, Yellow Sea and East China Sea, respectively.
Even though Tan et al. (2011) and Tan and Shi (2012) verified the satellite Chl-a and primary
production data with few in situ data, as described in the previous section, it is known that there arelarge errors for the Chl-a and euphotic zone depth from the standard algorithm in coastal turbid
water, and the results need to be further verified.
There are still few observations of primary production of different phytoplankton groups. Chen,
Y.L.L. (2000) measure size fractionated primary production in the Kuroshio and upwelling off
Taiwan and shelf area. They found picoplankton (<3 µm) dominated primary productivity (54–83%) as well as Chl-a concentration (67–89%) in the Kuroshio water. They also reported that
although the picoplankton contribution was less in the Kuroshio, they made important contributionsin shelf and upwelling areas, accounting for 29–86% of the Chl-a contribution and 19–72% of the
primary production. Microplankton was the dominant contributor in the upwelling.
Recently, the use of fluorescence to understand the photophysiology of phytoplankton is becoming
popular. Wang, Z. et al. (2014) used a phyto-PAM (Pulse Amplitude Modulation) fluorometer toobserve the change of maximum photochemical efficiency of photosystem II (Fv/Fm) during spring
blooms of Skeletomema spp. and Prorocentrum donghaiense in the Changjiang Estuary. They found
Fv/Fm varied with temperature, turbidity and nutrients with a strong diel cycle, and reported it
increased to more than 0.6 before the Chl-a increase and start of the bloom, and declined after the
bloom. Li et al. (2016) also used the phyto-PAM fluorometer to derive various photophysiologicalparameters of the summer phytoplankton population in the Yellow Sea and East China Sea. They
found the Fv/Fm was generally less than 0.5 and photosynthesis activity was relatively low.
Recently, Zhu et al. (2017) used the Fast Repetitive Rate Fluorometer (FRRF) to derive the ElectronTransfer Rate (ETR) of phytoplankton in the East China Sea to compare with carbon uptake, andthey found that the electron requirement for carbon uptake is linearly correlated to the light intensity,

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and that the slope was different with phytoplankton size structure. They also suggested using the
FRRF method to estimate carbon uptake.

3.2.4 New production and nitrogen fixation
Nutrient sources to support primary production are important to understand the ecosystem. The
amount of the production supported by external nutrients, such as upwelling, mixing, and riverdischarge from outside of the euphotic zone is called new production, and the ratio to the total
production is the f-ratio. Chen et al. (1999, 2001, 2004), Chen and Chen (2003) and Kanda et al.
(2003) intensively studied this subject in the south of the East China Sea by measuring nitrate
uptake rate with addition of 15NO3. They found that the f-ratio, compared to the total primary
production measured by carbon isotope incubations after converting with the Redfield ratio, wasabout 0.3 in a large part of the mid-shelf area. This indicates that two thirds of the production was
supported by nitrogen other than nitrate, and regenerated production was dominant. Exceptions
were upwelling areas of north of Taiwan and at the mouth of the Changjiang River in winter andsummer, respectively, where the f-ratio can be higher, and production supported by nitrate can be
dominant. Depending on the season and region, there are several different causes for the low
f-ratio. In the mouth of the Changjiang, phosphate limitation is probably the reason for the low
f-ratio during summer because the production is supported mostly by the regeneration of both
nitrogen and phosphate even when the nitrate iss high. Phosphate supply by wind-induced
upwelling produces a higher f-ratio with use of abundant nitrate. Light limitation caused by turbidity
is also an important factor. Low nitrate is the major factor for the low f-ratio in the area not
influenced by high nitrate Changjiang discharge, high nitrate and phosphate upwelling, andlow-light turbid water.
Shiozaki et al. (2011) compared nitrate assimilation with diapycnal nitrate flux in the eastern East
China Sea, and found a large discrepancy. The nitrate assimilation was more than 10 times higher
than the diapycnal nitrate flux. They also detected a significant amount of archaeal amoA gene at
10 to 1% light depth, and concluded that the nitrification in the euphotic zone may be an important
process to keep high nitrate assimilation in this area.
Nitrogen (N2) fixation is another important biological activity to introduce new nitrogen into theecosystem. Marumo and Asaoka (1974) reported the importance of the Trichodesmium spp. in the
East China Sea, which was later reported as a major N2 fixer in this area by Minagawa and Wada
(1986) using nitrogen isotope ratio, in the Kuroshio area of the East China Sea. Shiozaki et al.
(2010) recently confirmed N2 fixation in the Kuroshio and eastern East China Sea was 10 times
higher than the western and central oligotrophic North Pacific, and mainly associated with
Trichodesimum spp. Zhang et al. (2012a) also measured N2 fixation in the Yellow Sea andnorthwest of the East China Sea. They found the maximum N2 fixation rate in the stratified
Kuroshio in the northeastern East China Sea, and very little N2 fixation in the low salinity water on
the shelf. The rate covers 4.6% for Kuroshio and 0.01 to 0.78% of the nitrogen demand for primary
production, and the nitrogen flux in this area in summer is equivalent to the atmospheric input andmuch less than Kuroshio upwelling and Changjiang River input.
Liu et al. (2013) compared upward turbulent nitrate flux across the pycnocline, nitrate assimilation,
N2 fixation and primary production to compare the nitrogen sources for new production in summer
at the shelf edge of the East China Sea across the Kuroshio. They found high turbulent nitrate flux
at the coastal side of the Kuroshio jet which supports high nitrate assimilation and primary
production. Although N2 fixation was low near the jet, it is more important off the shelf andaround islands where there is less nitrate flux. They concluded that the source for new production inthis area was highly variable.

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3.2.5 Community structure
Phytoplankton community structure has been studied by microscope for a long time (cf. Ning et al.,1988). More recently, epifluorescence microscopy and flow cytometry have been used todiscriminate non-photosynthetic microorganisms with photosynthetic organisms, and to determine
small phytoplankton efficiently, respectively (see Section 3.1). However, phytoplankton containvarious taxonomic groups and determining the whole community structure has not been easy. It is
now possible to determine the concentrations of phytoplankton pigments with High Performance
Liquid Chlromatography (HPLC), and thus phytoplankton pigments can be used an indicator ofeach phytoplankton group. CHEMTAX software (Mackey et al., 1996) can statistically estimate
the percentage of each phytoplankton group of total Chl-a concentration. Some studies have been
conducted in this area (Table 3.2.1).
Furuya et al. (2003) first reported the phytoplankton community measured by HPLC and analyzed
by CHEMTAX in an early spring bloom and in well-stratified summer water along the PN line (theChangjiang mouth to the northeast of Okinawa Island, Fig. 3.2.1). During spring, they found adiatom spring bloom on the shelf, and in the contrast, abundant prochlorophytes, chrysophytes,
prymnesiophytes and chlorophytes and only patchy diatoms in off-shelf Kuroshio water. During
summer, they found a two-layer distribution with cyanobacteria and prochlorophytes in the surface
and prochlorophytes, chrysophytes, prymnesiophytes in the subsurface chlorophyll maximum bothoff and on the shelf.
Lee et al. (2009) reported the difference in phytoplankton communities in the northern and southerncoastal waters of Jeju Island in May 2005: a predominance (>50%) of diatoms followed byprasinophytes (~14%) and fewer diatoms (<30%) with cryptophytes, prymnesiophytes andcyanobacteria (10–15%) in the higher Chl-a northern and lower Chl-a southern waters, respectively.
They concluded that the discrepancy was caused by the slight difference in source waters. Liu etal. (2015a) studied the influence of the Yellow Sea Warm Water from the southeast to thephytoplankton community of the western Yellow Sea in early spring. They found a highconcentration of diatoms and dinoflagellates in the Yellow Sea Cold Water to the west and moreprasinophytes and cyanobacteria in the Yellow Sea Warm Current to the east. They also foundhigh nutrient content in the Yellow Sea Warm Current compared to the Yellow Sea Cold Water, andremarked about the complexity of the coastal water.
Zhu et al. (2009) studied surface phytoplankton pigments of the Changjiang Estuary and adjacentarea in spring and summer. They reported that diatoms (34%) and chlorophytes (33%) accounted forequal amounts followed by cryptophytes, prasinophytes, dinoflagellates, chrysophytes andcyanobacteria in spring, and offshore in summer the community was mainly composed by diatoms(74.6%) and dinoflagellates (11.3%) with minor amount of prasinophytes, cryptophytes,
cyanobacteria, prochlorophytes and chlorophytes.
Recently, Liu et al. (2015b, 2016) extensively studied the seasonal change of phytoplankton
pigments in the Yellow Sea and East China Sea. In the southwestern Yellow Sea, Liu et al.
(2015b) found generally high abundance of diatoms with similar variation with Chl-a. In April, aspring bloom was observed with a maximum and average of 98% and 76% of diatom dominances,respectively, with Thalassiosira pacific, specifically in the central part of the Yellow Sea influenced
by the Yellow Sea Warm Current from the southwest. In August, when stratification was strong
and the Yellow Sea Cold Water Mass prevailed, Chl-a and diatoms (30%) decreased andcyanobacteria (36%) increased. In October, with the decaying of the Yellow Sea Cold Water Mass,chrysophytes (38%) and pracinophyte (18%) increased. In March, pracinophytes (29%) increased
along with diatoms (50%), such as Paralia sulcata and Coscinodiscus spp. Therefore, the strong
seasonality of phytoplankton variability is clear in the central part of the Yellow Sea.

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Liu et al., 2015bYSCWM

South of Jeju

North of Jeju

Liu et al., 2015a

YSWC

Lee et al., 2009

Liu et al., 2015b

Liu et al., 2015a

Frontal area of
YCC and YSWC

Spring bloom
Liu et al., 2015bYSCWM, YSWC

Liu et al., 2015a

Reference

YSCC

Water mass

Yellow Sea

Diatom (60%)
Cyano (40%)
Chloro (12%)
Diatom (35%)
Cyano (35%)
Chloro (30%)
Diatom (50%)
Pracino (30%)

Diatom (68%)

March

Diatom (80%)

April

SpringDiatom (45%)
Prasino (20%)
Prasino (30%)
Diatom (20%)
Crypto (15%)
Pelago (15%)
Cyano (15%)

May

Table 3.2.1 Dominant phytoplankton groups in the Yellow Sea and East China Sea.

May–
early
Jun.

late
Jun.–
Aug. Jul.

Diatom (30%)
Cyano (20%)

Jul.–Aug.

SummerAug.

Chryso (40%)
Diatom (30%)
Prasino (20%)

Oct.

Fall

late Nov.– late Dec.–
early Dec. early Jan.

WinterPhytoplankton
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Xu et al.,2018

SW 2009

SW 2013

SW 2011

Liu et al.,2016
Furuya etal., 2003

Furuya etal., 2003
Xu et al.,2018

Zhu et al.,2009

Mid-shelfwater

CDW 2011

CDW 2010

CDW 2013

CDW 2009

Changjiang
Estuarine

Water mass Reference March

East China Sea

Table 3.2.1 Continued.

Diatom (50%)
Chloro (15%)

Diatom (34%)
Chloro (33%)
Crypto (12%)
Diatom (50%)
Chloro (25%)

April

May

Springlate Jun.–Aug

Diatom (40%) Diatom (60%)
Dino (40%)

May–early
Jun.

Datom (54%)
Dino (14%)
Cyano (32%)
Prymnesio (29%)
Prymnesio (43%)

Diatom (44%)
Dino (18%)
Diatom (50%)
Dino (14%)
Prymnesio (26%)
Cyano (47%)
Chloro (16%)
Cyano (21%)
Chloro (17%)

Jul.

Jul.–Aug.

Diatom (15%)
Cyano (25%)
Prochloro (25%)

Diatom (90%)

SummerDiatom (75%)
Dino (11%)

Aug.

Oct.

Fall

late Dec.–
early Jan.

Diatom (60%) Diatom (60%)
Crypto (15%) Crypto (15%)

late Nov.–
early Dec.

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Chryso (15%)
Prymnesio (15%)

Chloro (20%)
Prochloro (15%)
Cyano (51%)
Prochloro (28%)

Cyano (20%)
Prochloro (30%)

Chryso (15%) Chryso (15%)
Chrypto (12%) Chrypto (12%)

Prochloro (10%)

Jul.–Aug.

Winterlate Dec.–late Nov.–
Aug. Oct.
early Dec.
early Jan.
Diatom (40%) Diatom (42%)

Fall

Prasino (18%) Prasinos (20%)

Jul.

SummerSynecho (30%)

May–earlyMay
Jun.
late Jun.–Aug
Diatom (55%) Diatom (35%)

Chryso (25%)
Prymnesio (20%)

April

SpringPercentages are approximated from the description in the references.
YSCC: Yellow Sea Cold Current, YSCWM: Yellow Sea Cold Water Mass, YSWC: Yellow Sea Warm Current, CDW: Changjiang Diluted Water, SW: Shelf Water. Diatom(orange), Dino (Dinoflagellate, red), Crypoto (Crypotophyte, yellow), Chloro (Chlorophyte, green), Pracino (Pracinophyte, green), Cyano (Cyanobacteria, blue), Synecho(Synechococcus, blue), Prochloro (Prochlorophyte, light green), Chryso (Chrysophyte), Prymnesio (Prymnesiophyte), Pelago (Pelagophyte), Chryso (Chrysophyte),
Cyanobacteria may include Synechococcus but not Prochlorococcus.

Xu et al.,2018

Furuya etal., 2003

Water mass Reference March
Kuroshio
Liu et al.,
Water
2016

East China Sea

Table 3.2.1 Continued.

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Liu et al. (2016) studied the seasonal and spatial patterns of phytoplankton variability in the East
China Sea. They found three patterns of seasonal variability: (i) blooms in the Changjiang River
plume with high biomass of dinoflagellates and diatoms in spring; (ii) significant spatial differences,including high biomass of diatoms in the river plume and low biomass and picophytoplankton, suchas Prochlorococcus dominance in the Kuroshio water during summer and early autumn; and (iii)
relatively low biomass and uniform distributions but higher contributions of cryptophytes,
prasinophytes, and chrysophytes during late autumn and winter.
With the large dataset, Liu et al. (2016) used the Generalized Additive Model (GAM) to elucidate
the specific environmental factors related to the phytoplankton community structure. Diatoms
dominated, especially during the summer months, influenced by the Changjiang River plume.
Dinoflagellates, a important group during spring blooms, showed a clear salinity optimum of around
31, and were also affected by temperature. Prochlorococcus and Synechococcus abundance
increased strongly with higher temperature; while Prochlorococcus was abundant in the Kuroshio,
Synechococcus was abundant in the Changjiang River plume during summer, with sensitivity tonutrient variability and with broader tolerance to low-salinity conditions. Chrysophytes (~17%)and prasinophytes (~15%) were abundant in the Kuroshio during autumn and winter, whereas
cryptophytes (~20%) were higher in winter in the plume and at mid-shelf.
More recently, Xu et al. (2018) reported the interannual variation of the summer phytoplankton
community south of Jeju Island where Changjiang Diluted Water can be seen. In general, thespatial distribution was similar to the previous reports, with more diatoms in the west andProchlorococcus and other cyanobacteria abundant in Kuroshio-originated water in the east.
However, there was significant interannaual differences, where diatoms were more abundant in2009 and 2013 while a mixed population of cyanobacteria, chlorophytes and other groups wereobserved in 2010 and 2011. They also found a significant difference in inorganic phosphateamounts between those different years: high inorganic phosphate in diatom-dominated years andlow inorganic phosphate in other phytoplankton-dominated years. They further discussed thepossibility that mixing of high N:P ratio Changjiang River waters with low N:P ratio coastal
upwelling water along the Chinese coast may influence the N:P ratio and consequently thevariability of the phytoplankton community in the south of Jeju Island.
Using HPLC phytoplankton pigment information, Wang, S.Q. et al. (2014) calculated thephytoplankton community by size, separating micro- (diatoms and dinoflagellates), nano(cryptophytes, chromophytes, green flagellates), and pico- (cyanobacteria including
Prochlorococcus) sized phytoplankton. They compared the relation between size fraction to Chl-a
concentration between waters from the south of Jeju to Tsushima Strait. It is known that in the
global ocean large phytoplankton dominate when the Chl-a concentration is increased. However,they found the relationship was consistent in the water in Tsushima Strait, but not in the East China
Sea. This finding agrees with the report of Xu et al. (2018) who found diatoms were less abundantin phosphate-limited Changjiang Diluted Water. Wang, S.Q. et al. (2014) further found that thedifference in the size structure was also noticeable from the optical properties. Wang, S.Q. et al.
(2015) and Wang, S. (2016) and Zhang et al. (2018) further indicated that the phytoplankton size
structure can be observable by multi-spectral fluorescence as well as from ocean color satellite data.
In the near future, it is expected that those methods will be used to understand the variability of thephytoplankton community structure.

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3.2.6 Influence of the Three Gorge Dam
As we described earlier, Changjiang River Discharge is known as one of the most important factors
for phytoplankton variability in the East China Sea. Much attention has been paid to the impact
the construction of the Three Gorges Dam has had on the East China Sea ecosystem because it has
drastically changed the nutrient supply, including upwelling, and sediment input as well as
freshwater discharge (Chen, C.T.A. 2000). Gong et al. (2006) suggested a reduction of the Si:N
ratio from 1.5 to 0.4 with sediment loading of 55% at the Datong station from 1998 to 2004,
specifically after the first filling phase of June 2003. Comparing results of six cruises during June
1998 and July 2003 in the traditionally high nutrient Changjiang Estuary, they also showed an 86%
reduction in primary production as well as a reduction of nutrients and diatoms associated with theincrease of salinity. Jiao et al. (2007) suggested significant differences in ecological parameters
between September 2003, two months after the first filling of the dam in June 2003, and September
2002, before the filling. They found higher salinity, expected from low freshwater discharge andpossible expansion of the Taiwan Warm Current in 2003, and higher Chl-a, particulate organiccarbon and transparency estimated by satellite. They also found high Chl-a with larger size
fraction (>20 µm), including Chaetoceros spp. and Pseudo-nitzschia spp. in the estuary, whereas
smaller size Synechococcus was abundant offshore, and this result seems to contradict the earlier
observation of Gong et al. (2006). They also reported more Prochlorococcus on the shelf, moreheterotrophic bacteria both in the estuary and on the shelf in 2003, and lower diversity of microbialcommunity by the 16s rDNA analysis in 2003.
On the other hand, Yuan et al. (2007) argued that the change in June 2003 was within the large
variation shown by satellite Chl-a from 1997 September to June 2006, although Gong et al. (2007)
denied the objection because of the inaccurate satellite data with a non-focused studied area.
Yamaguchi et al. (2012) carefully analyzed the satellite Chl-a data, specifically focusing on arelatively accurate mid-shelf region and showed that Chl-a in 2003 was within the 10-year variationand that Chl-a was lower in 2006, when the second filling phase of the dam took place. However,they concluded that even the change in 2006 was mostly not caused by the filling because Dai et al.
(2008) indicated that the reduction of Changjiang freshwater discharge from 2005 to 2006 was
mostly caused by a severe drought in 2006 and that the influence of the filling of the dam was only
1/5 that of the drought.
More recently, Chen et al. (2017) analyzed the satellite Chl-a of the Changjiang Estuary and where
hypoxia frequently occurs, and they concluded that the reduction of suspended matter caused by theconstruction of Three Gorges Dam was what caused the shift of high Chl-a and hypoxia area to amore shoreward direction. It is necessary to do a long-term study on the influence of theconstruction of the Three Gorge Dam not only by the change in freshwater discharge, but alsochanges in the amount of sediment and nutrients, as well as the composition of nutrients.

3.2.7 Influence of typhoons
It has been suggested that global warming may enhance the intensity of tropical cyclones, such as
typhoons in the western Pacific. It is also suggested that the strong disturbance of the tropical
cyclone may influence the marine ecosystem, specifically by increasing primary production becauseof strong mixing and/or upwelling caused by the strong wind. The East China Sea is one of thelocations where several typhoons often pass and turn in a year, and the influence of the typhoon is
expected to be large. Studying the influence of typhoons on the marine ecosystem is notparticularly easy because of its unpredictability and destructive capabilities. It is becoming
popular to study the influence of typhoons on primary production by ocean color satellite; however,

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scientists from Chinese Taipei are fairly active in investigating typhoons, even by ship, in the East
China Sea.
Chang et al. (1996) is a pioneer in studying the influence of typhoons in the region northeast of
Taiwan. They conducted four cruises before and after the typhoon in 1994 and reported the initial
decrease in phytoplankton and increase in nutrients following the increase of larger (>5 µm)
phytoplankton. Shiah et al. (2000) also noted an increase in Chl-a, nitrate, particulate organiccarbon, primary production, bacteria production and bacteria biomass by ship observation northwestof Taiwan before and after typhoons Gloria and Herb in 1996, and suggested wind mixing,
resuspension and terrestrial runoff are the important factors. Chenet al. (2003) suggested
upwelling enhanced by buoyancy effects as well as the coastward movement of the Kuroshio for thesame typhoons in 1996. The shelfward movement of the Kuroshio caused by the long-lasting
southerly wind by typhoon was confirmed by Morimoto et al. (2009) through high frequency (HF)
radar observation and a coupled atmospheric–ocean model, and Chang et al. (2008) confirmed thephytoplankton responses to the upwelling by satellite observations. Siswanto et al. (2009) usedsatellite observations of 16 typhoons from 2003 to 2007 in the upwelling region northeast of Taiwan
and estimated enhancement of the upwelling of nutrients promoted new productivity, which consists
0.6 to 11.8% of the East China Sea summer–fall new production.
Further observations were conducted in the southern East China Sea to verify the increase ofparticulate organic carbon flux by typhons in 2008 (Hung et al., 2010, 2013, 2014; Hung and Gong,
2011; Shih et al., 2013). Chung et al. (2012) and Jang et al. (2013) observed that upwelling andfloodwater enhanced by typhoon Morakot in 2009 produced a low N:P ratio and an increase inchain-forming centric diatoms (Chaetoceros spp.) instead of the cyanobacteria, Trichodesmium, anddinoflagellates, Gymnodinium spp. Furthermore, they suggested copepod grazing terminated thediatom bloom. He et al. (2014) also showed, by transparency images from ocean color satellite,
transport of terrestrial material to the northeast of Taiwan from east and west of Taiwan Island
coasts and Mainland China coast, and a bloom of phytoplankton by typhoon Morakot in 2009.
Zhao et al. (2015) observed the influence of typhoon Marat in 2005 and compared the offshore eastof Taiwan Island and nearshore of Mainland China and showed a decrease in satellite Chl-a in thenearshore area. Wang, T. et al. (2016) also observed the influence of typhoon Haikui in 2012 in
the Changjiang River Estuary and found vertical mixing was strong during the passing of thetyphoon, and that both terrestrial runoff and intrusion of offshore water were important for thenutrients and phytoplankton dynamics after the typhoon.
Although the ship observations were conducted in the coastal area to the north of Taiwan, satellite is
the still the only tool for most of the other areas, including outer shelf. In the outer shelf of theEast China Sea, Siswanto et al. (2008) conducted a detailed observation of upwelling/mixing causedby typhoon Meari in 2004. They were lucky to find Argo float data on time, as well as the satellite
microwave SST and ocean color Chl-a/primary production data. They estimated nitrateconcentration from the temperature change and 3.8% of annual carbon export caused by this onestrong typhoon. Siswanto et al. (2007) analyzed 13 typhoons from 1998 to 2004 using ocean color
Chl-a and primary production and information of the typhoon passing through the outer shelf ofEast China Sea. They formulated a simple equation of estimation of primary production
enhancement from maximum wind speed, typhoon transit speed, and bottom depth andreconstructed the enhancement from 1980 to 2004. They estimated the enhancement was 0.4 to39.7% of new production in the East China Sea. They found that the enhancement was muchhigher for El Niño years because of the occurrence of strong winds, and that the enhancement
increased during the 1990s because of more typhoons passing over the East China Sea.

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One interesting style of observations was conducted on the northwestern coast of Okinawa Island,
Japan, in which Grossmann et al. (2015) set a camera mounted on an underwater observatory
system and reported the change of 20-m near-bottom mesoplankton compositions after a series oftyphoons in 2013. They observed an increase in Trichodesmium, diatom, and radiolarianabundances after a series of September typhoons although Chl-a concentrations were not increased.

3.2.8 Future perspective
Development of ocean color satellite data is helping us to understand large-scale seasonal-todecadal variations and some event-scale variations in chlorophyll-a in the Yellow Sea and East
China Sea. Associated variations in primary production and phytoplankton size structure are also
promising to be understood. However, ocean color satellite information is only limited to thesurface. Subsurface chlorophyll-a maximum is often reported in this area and detailed behavior ofthis phenomena is still not well studied. HPLC pigment analysis gives a clearer view of thephytoplankton community structure, although the dynamics of this complicated physical andchemical regime is far from being understood. Recent improvement of sensors, to measure thephysiological parameters, and of molecular biological techniques are definitely opening newparadigms of phytoplankton studies. It is obvious that phytoplankton and primary production is
the key process onnecting physical oceanographic research on variabilities, including climatechange, and ecosystem dynamics, including carbon cycles. Those processes should be studied
simultaneously to understand the close coupling between physics, chemistry and biology.

3.2.9 References
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Chang, J., Chung, C.C. and Gong, G.C. 1996. Influences of cyclones on chlorophyll a concentration and
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Chen, C.-T.A. 2000. The Three Gorges Dam: Reducing the upwelling and thus productivity in the East
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Chen, Y.L.L., Chen, H.Y., Lee, W.H., Hung, C.C., Wong, G.T.F. and Kanda, J. 2001. New production in
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Chung, C.C., Gong, G.C. and Hung, C.-C. 2012. Effect of Typhoon Morakot on microphytoplankton
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3.3 Zooplankton
Guang-Tao Zhang
Institute of Oceaonology, Chinese Academy of Sciences, China

3.3.1 Introduction
Zooplankton refer to metazoa that swim weakly or drift passively in the water column. Owing totheir linking role in the marine food web, their diversity and biomass are commonly used in the
5-module (productivity, fish and fisheries, pollution and ecosystem health, socioeconomics andgovernance) strategy for measuring the changing states of the Large Marine Ecosystem, aiming at
taking remedial actions toward the recovery and sustainability of degraded goods and services(Sherman, 1994). As an ecological rather than strict taxonomic categorization, zooplankton cover alarge size range from single-cell heterotrophic flagellates to giant jellyfish, and a broad evolution
echelon from protozoan to chordate, with complex inter-specific interactions among those sharing
the same water column through competition and predation. Meanwhile, owing to lack of or weakswimming ability, their distribution pattern and life strategies are mostly determined by physicaloceanographic characteristics. These are the main obstacles preventing a full evaluation of thezooplankton contribution in marine ecological dynamics.
As two of the 64 Large Marine Ecosystems around the world coastal seas, zooplankton diversity islower in the Yellow Sea (YS) compared to that in the East China Sea (ECS) where it is significantly
influenced by the Kuroshio Current. Among 1140 metazoan species recorded in the YS,
approximately 300 species are planktonic (Huang, 1994). During investigations with limited spatial
and temporal coverage, species sampled in the YS by traditional conical net (mesh size 500 µm anddiameter 0.8 m) methods were approximately 100. Total zooplankton biomass varied between 5 and1000 mg m–3, commonly dominated by Calanus sinicus, Sagitta crassa, Euphausia pacifica andThemisto gracilipes (Tang, 2003). However, a total of 611 zooplankton species (excluding
meroplankton and protozoa) were recorded in the ECS during a two-year investigation, anddominant species also varied seasonally.
Therefore, we focus this section on distinguishing features of zooplankton communities in thecontext of physical–biological coupling rather than species composition. Impacts of climate change
and human activities are also discussed, based on existing reports, with anomalies at both species
and community levels.

3.3.2 Adaptation to distinct habitats
Yellow Sea Cold Water MassThe Yellow Sea Cold Water Mass (YSCWM) is formed in winter by vertical mixing under strong
winds. It remains in a bottom layer under the thermocline at 15–30 m depth from May to November
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Zooplankton(Lee and Beardsley, 1999). The low-temperature conditions of the YSCWM protect cold water
fauna, with C. sinicus as the most prominent representative (Fig. 3.3.1). When sea surface
temperature (SST) was over 20ºC in summer and autumn, E. pacifica could be found mainly in the
Yellow Sea Bottom Cold Water (YSBCW) area too.
As a common adaptation behavior in most zooplankton species, diel vertical migration patterns canbe influenced by strong stratification in YSCWM. According to the amplitude of diel verticalmigration (DVM) in relation to depth of the thermocline, four DVM patterns were defined (Zuo etal., 2004). DVM of Doliolum denticulatum, Penilia avirostris, Lucifer intermedius and Sagitta
enflata was limited to above the thermocline, with S. enflata reaching the deepest of 26 m depth.
The second group distributed inside the thermocline, including Sagitta crassa, Paracalanus parvus
and Oithona similis. Preference to cold bottom water was detected in Themisto gracilipes, C. sinicus
and E. pacifica, which were completely absent above the thermocline. Reversed DVM wasobserved only in the hydrozoan Muggiaea atlantica, which presented in the surface layer during
daytime and descended to the bottom layer at night.
Echo sounding also detected a different DVM pattern in the YSBCW area in spring and summer. In
the spring of 2010 and 2011, the sound-scattering layer (SSL) clearly showed vertical migration
throughout the entire water column, with zooplankton moving from the surface layer at night to near
the bottom during the day. However, in the summer of 2010, when SST increased to 28°C, the SSL
migrated to near the bottom during the day, but two SSLs occurred above and below thethermocline at a depth of 10–30 m at night (Lee et al., 2013).

Fig. 3.3.1 Diel vertical migration of copepodite stage 5 and females of Calanus sinicus inside (upper
panel) and outside (lower panel) of the Yellow Sea Cold Water Mass. Line at the bottom of each figure
indicates the scale, and shaded areas represent the thermocline. From Zhang et al. (2007). Reproduced withpermission of Oxford University Press.

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Physiological adaptations were also detected in association with the YSCWM, but evidence exists
overwhelmingly in C. sinicus. Over-summering in the YSCWM is actually a tradeoff between
thermal fitness and food limitation. Along with a retreat from coastal waters and a concentration in
the YSCWM, the dominance of copepodite stage 5 suggests a diapause state for theover-summering population (Zhang et al., 2007). Meanwhile, unfavorable food conditions wereconfirmed by decreased egg production and feeding rate (Zhang et al., 2005). Over-summering of
C. sinicus is often compared with the seasonal descent to deep waters of C. finmarchicus in the
North Atlantic, but instead of real dormancy, quiescence that can be awoken rapidly byenvironmental amelioration was proposed for C. sinicus (Yin et al., 2013; Zhou et al., 2016).
Besides affording an over-summering site, the YSCWM can also influence the seasonal pattern of
zooplankton through cascading effects. As vertical mixing is prevented by strong thermal
stratification, nutrients are exhausted in the upper layer (0–30 m) and accumulate in the bottom
layer. With the disappearance of the YSCWM in late autumn, strengthened vertical mixing carries
the enriched bottom nutrients into the surface layer and further to coastal waters, intensifying thephytoplankton bloom in autumn and even next spring. This process was extremely evident in a sea
ranching area in the northern YS (Yin et al., 2013). Though the cascading effects to zooplankton
were not as significant as for phytoplankton, the egg production rate of the dominant C. sinicus
peaked in spring and autumn (Zhang et al., 2005).

Yellow Sea Warm Current
The Yellow Sea Warm Current (YSWC) is an asymmetric upwind flow that intrudes into the central
YS along the western side of the Yellow Sea Trough from winter to early spring (Lin et al., 2011).
The most attractive issue to address is the simultaneous invasion of organisms and nutrients.
Remotely sensed SST has revealed that the YSWC can reach the Bohai Sea in the surface in winter,but for warm water species, mostly pelagic tunicates, remote sensing indicated a northward
extension only to the northern YS (Franco et al., 2014; Yin et al., 2017). To date, however, little is
known concerning biogeochemical behavior of the YSWC (Liu et al., 2015). Chen (2009) revealed
that the YSWC originates from the Kuroshio and Taiwan Warm Current and thus it is relatively
warmer, saltier, and nutrient-poor. Recent studies have revealed that the nitrate concentrations in theYSWC can reach up to 8 μM (Fu et al., 2009; He et al., 2013; Jin et al., 2013). It has been
established that cyanobacteria and eukaryotic picoplankton, dominated mostly by prasinophytes, issignificantly more abundant in the YSWC (Lü et al., 2013). Meanwhile, low zooplankton biomass
was observed in the YSWC area, especially at stations along the axis of the warm saline water
tongue (Liu et al., 2015). However, the DVM weakened markedly while warm water prevailed at
the mooring sites. The DVM change was believed to result from the YSWC, rather than fromsunlight (Lü et al., 2013).

Changjiang River diluted water
Changjiang River diluted water (CDW) forms a typical estuarine zooplankton community in thisregion (Zhang et al., 2005). Freshwater species, represented by Brachionus falcatus, B. calyciflorus,
Sinocalanus dorrii, Mesocycolops leuckarti and Thermocyctops taihokuensis, occupy regions withsalinity lower than 2. Between 122–122.5ºE, where salinity ranges from 2 to 10, the zooplankton
community was dominated by Tortanus vermiculus, Schmackeria poplesis and Monoculodes
limnophilus. In the plume region with salinity of 10–25, common coastal species were dominant,including Labidocera euchaeta, Sagitta nagae, Pseudeuphausia sinica and Acanthomysis sinensis.
Oceanic species were comparatively scarce in the estuary area, and the occurrence of Sagitta enflata
and C. sincus was proposed, as a result of their exchange with open waters.

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As transportation of estuarine species into the oceanic environment is difficult to detect, theecological impacts of CDW on zooplankton has been recorded mainly as cascading effects throughincreased nutrient input and thereafter primary production. Another influence of CDW on theconfiguration of the zooplankton community in the Yellow Sea is that the northward invasion ofwarm water species is prevented on the coastal side.

Kuroshio Current
The Kuroshio Current (KC) is a warm current (24°C in annual average SST) carrying nutrient-richwaters. Confirmation of the Nearshore Kuroshio Branch Current has stimulated much researchinterest in China, as it can at least partially explain the observed phosphate-rich bottom water off thecoast of Zhejiang province (Yang et al., 2012). Phosphate limitation has been proposed as the main
reason for the massive dinoflagellate bloom in this region (Li et al., 2009).
However, KC-driven changes in zooplankton assemblage were detected mostly in the south of the
ECS. Both copepods and jellyfish are used as indicator species of the KC. Meanwhile, the KC has
been known to transport tropical or subtropical species to water bodies on both the eastern andwestern sides of Taiwan Island, as it can flow northward through the continental shelf breaknortheast of Taiwan and the Luzon Strait into the Taiwan Strait.
The intrusion of warm water into the central ECS and north of it has been shown by the interaction
between neritic and warm water tintinnids in the surface waters (Li et al., 2016). Warm water
genera can reach the coast of Zhejiang province. Consistent with the seasonal change in KC strength,
the largest coverage of warm water genera was observed in August and October, compared to May(Fig. 3.3.2). In May and August, neritic and warm-water genera were completely separated in
geographical distribution, whereas in October pioneer genera that overlapped geographical coverage
was also observed (Li et al., 2016).

Fig. 3.3.2 Intrusion of the Kuroshio Current into the
ECS indicated by the distribution of warm-water
tintinnid genera. Colored lines and arrows denoteboundary and direction of the warm water intrusion,respectively. Redrawn from Li et al. (2016).

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3.3.3 Responses to climate change
According to report of the Scientific Committee on Oceanic Research (SCOR) Working Group 125(Global Comparisons of Zooplankton Time Series, https://scor-int.org/group/125/), long-term
zooplankton time series in the YS and ECS exist only near the Korean coast, where sampling has
been conducted bimonthly by Korea’s National Institute of Fisheries Science (formerly known as
the National Fisheries Research and Development Institute) since 1965. Biomass of
mesozooplankton in the eastern YS and northern ECS began to increase after the early 1990s, withsharp increases after the late 1990s, indicating a regime shift triggered by the increased SST(Fig. 3.3.3). Unusually higher biomass was also occasionally observed in April, June or Octoberafter the late 1990s. Abundances of the four major zooplankton groups (copepods, amphipods,
chaetognaths and euphausiids) have generally increased since the late 1990s (Kang et al., 2012).

Fig. 3.3.3 Standardized (SCOR WG 125) time-series plots of monthly anomalies and annual anomalies
for zooplankton biomass from 1967 to 2006 in the eastern Yellow Sea (EYS) and the northern East China
Sea (NECS). From Kang et al. (2012). Reproduced with permission of Elsevier.

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On the Chinese side, detection of zooplankton responses to climate change has been impeded by alack of time series. However, the comprehensive marine investigation from 1958–1959 provides abaseline for zooplankton change, as this monthly sampling covered a large area from the Bohai Sea
to South China Sea. Through comparisons, a decline was observed in Euphausia pacifica andEuchaeta plana in the coastal waters of ECS (Xu et al., 2013, 2014).
The northward extension of warm-water species and changed seasonality were also reported in thisregion. A subtropical estuarine copepod, Temora turbinata, was found to be the most abundant
zooplankton species in this area for the first time in August 2004 (Zhang et al., 2010), the first year
after the Three Gorges Reservoir was impounded to a water level of 135 m. Temora turbinata’s
unexpected dominance suggests a northward extension of warm-water species resulting from global
warming, although it appeared immediately following water control at the Three Gorges Reservoir.
The changes in peaked abundance of C. sinicus in the Changjiang River (Yangtze River) Estuary
were compared between 1959 and 2002. In spring 2005, the time for occurrence and decrease of thepeak in C. sinicus abundance advanced about one month, accompanied by an increase in SST.
Abundance peaked in June and decreased in July in 1959; however, in 2005, it peaked in May andattenuated sharply in early June (Xu et al., 2011).

3.3.4 Anthropogenic impacts
Commercial harvesting of zooplankton species is scarce. In the YS and ECS, fishing target species
include euphausiids, shrimps, and giant jellyfish. However, zooplankton can still be severely
impacted by human activities through decreased top-down control (overfishing) and increased
bottom-up control (eutrophication).
Considering decreased top-down control (overfishing) and increased bottom-up control(eutrophication), an increase in zooplankton abundance is expected. However, despite thewell-documented overfishing and eutrophication studies, the increase in zooplankton biomass has
not observed. It may be attributed to the change in community composition, notably giant jellyfish
blooms.
Among the four giant jellyfish species, Nemopilema nomurai, Cyanea nozakii, Aurelia aurita andRhopilema esculentum, commonly observed in the YS and ECS, the edible R. esculentum isoverfished, and the fishing population is supported mostly by artificial release. Blooms of
N. nomurai, C. nozakii and A. aurita, reported frequently since this century, have had negative
effects on fishing, tourism and coastal industries. However, polyps, the benthic larvae of jellyfish,
are found in the natural environment only for A. aurita. It is quite certain that artificial constructions
in coastal regions have contributed significantly to its bloom.
The damming of major rivers can have a broad impact on ecological processes in adjacent marine
ecosystems. Evidence shows that the recent impounding of water by the Three Gorges Dam on the
Changjiang River can have a dramatic impact on the mesozooplankton community. The operation of
the reservoir has significantly altered the fluctuation of freshwater discharge. Reduction of riverine
nutrients, especially dissolved silicate loads into the Changjiang River Estuary could contribute to
the decline in Bacillariophyta species. Consequently, decreased zooplankton and increased benthos
biomass has been observed (Fig. 3.3.4; Chen et al., 2015).

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Fig. 3.3.4 Changes in (a) number of phytoplankton species and (b) their abundance, (c) number ofzooplankton species and (d) their abundance, and (e) number of benthos species and (f) their biomass in the
Changjiang (Yangtze) estuarine ecosystem. From Chen et al. (2015). Reproduced with permission of
Elsevier.

In another study in the northern YS, the reduction in freshwater discharge and subsequent increases
in phytoplankton biomass have resulted in a substantial increase in total mesozooplankton wet
weight but sharp declines in the abundance of all of the crustacean components (Hwang et al., 2013).
The increase in mesozooplankton biomass is probably associated with an increase in small,gelatinous zooplankton. Together with an increase in SST in the region, the reduced freshwater andsediment discharge might have led to the proliferation of gelatinous mesozooplankton in thishistorically overfished region, generating significantly increased grazing pressure on planktonic
crustaceans (Hwang et al., 2013).
Human activities are also responsible for introducing persistent organic pollutants into the ocean.
For instance, total concentrations of polycyclic aromatic hydrocarbons (PAHs) in zooplankton fromsurface waters of ECS were measured (Fig. 3.3.5; Hung et al., 2014). Values of PAHs ranged from
2 to 3500 ng m–3 in the ECS, with highest PAH levels located at the salinity front between the CDW
and mid-shelf waters. In contrast, concentrations of zooplankton PAHs in the mid-shelf andouter-shelf waters were significantly lower (2–23 ng m–3) than those in the CDW. These results
demonstrate that PAHs are conspicuously accumulated in zooplankton at the salinity front between
the CDW and the mid-shelf waters. As both large and tiny non-zooplankton particles were removedby visual check and high-speed centrifugation, there is convincing evidence that these higher levelsof PAHs in zooplankton at the salinity front may be further biomagnified in marine organisms ofhigher trophic levels through their feeding activities (Hung et al., 2014).

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Water depth (m)Salinity

Water PAHs (mg m-3)

Zooplankton PAHs
(ng g-1)

Fig. 3.3.5 Water depth, salinity (S) and total polycyclicaromatic hydrocarbons (PAHs) in surface water andzooplankton along a transect in the ECS, with the inlet
map showing the location of sampling stations. CDW =Changjiang Diluted Water, YSW = Yellow Sea Water,
TCWW = Taiwan Current Warm Water, KW = Kuroshio
Water. Redrawn from Hung et al. (2014).

3.3.5 Attractive issues and future perspective
The most attractive issue in zooplankton study is blooms of unwanted species, such as jellyfish.
Another jelly-like species, Salpa fusiformis, is a pelagic tunicate. A massive aggregation of thistunicate was recorded in the YS in 1965 and 2007 (Chen, 1978; Liu et al., 2012). In coastal Koreanwaters, a dense swarm was observed in 2003, clogging the screen system of a nuclear power plant(Lee et al., 2005). The negative impacts are obvious, but the reasons for jellyfish blooms are poorly
understood.
The function of zooplankton in the marine ecosystem is well understood, but it is still hard to
integrate them into a numerical ecological model. The obstacles lie mostly in quantification ofcritical parameters, such as feeding pressure on phytoplankton and mortality caused by predationand natural death.
Potential responses to climate change and anthropogenic stresses depend critically on time series. To
establish long-term observation plans and free access to the existing plans are important steps
towards resolution of such scientific questions.

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Introduction of new technology and methods for measuring zooplankton production is also
important. The Continuous Plankton Recorder (CPR), equipped on commercial ships, has been usedin the North Atlantic for many years, and has been introduced in North Pacific in the recent decades.
According to the existing literature, most of the long-term zooplankton time series was obtained by
CPR, showing very attractive results on climate change impacts. The application of molecularmethods in zooplankton research can also help in many aspects other than biodiversity.

3.3.6 References
Chen, D., Dai, Z., Xu, R., Li, D. and Mei, X. 2015. Impacts of anthropogenic activities on the Changjiang(Yangtze) estuarine ecosystem (1998–2012). Acta Oceanol. Sin. 34: 86–93,
doi:10.1007/s13131-015-0679-7.
Chen C.T.A. 2009. Chemical and physical fronts in the Bohai, Yellow and East China seas. J. Mar. Syst.
78: 394–410, doi:10.1016/j.jmarsys.2008.11.016.
Chen, J.K. 1978. Two salps observed in the northern Yellow Sea. Chin. J. Zool. 2: 13–16 (in Chinese).
Franco, P., Chen, H.J. and Liu, G,X. 2014. Distribution and abundance of pelagic tunicates in the North
Yellow Sea. J. Ocean Univ. China 13: 782–790.
Fu, M., Wang, Z., Li., Sun, P., Li, Y. and Li, R. 2009. Spatial distribution characteristics and theenvironmental regulation mechanisms of phytoplankton chlorophyll a in Southern Yellow Sea during
summer 2006. Acta Ecol. Sin. 29: 5366–5375 (in Chinese with English abstract).
He, X., Bai, Y. and Gong, F. 2013. Satellite views of the seasonal and interannual variability ofphytoplankton blooms in the eastern China seas over the past 14 yr (1998–2011). Biogeosciences 10:
4721-4739, doi:10.5194/bg-10-4721-2013.
Huang, Z.G. 1994. General characteristics of the species in China seas. Chinese Biodiv. 2: 63–67 (inChinese).
Hung, C.C., Ko, F.C., Gong, G.C., Chen, K.S., Wu, J.M., Chiang, H.L., Peng, S.C. and Santschi, P.H.
2014. Increased zooplankton PAH concentrations across hydrographic fronts in the East China Sea.
Mar. Pollut. Bull. 83: 248–257, doi:10.1016/j.marpolbul.2014.03.045.
Hwang, S.-W., Kang, H.-K., Son, Y.-B., Jang, M.-C. and Choi, K.-H. 2013. Collapse of the crustaceanmesozooplankton in the northern East China Sea: effects of Three Gorges Dam? J. Coast. Res. 29:
1464–1469, doi:10.2112/JCOASTRES-D-13-00011.1.
Jin, J., Liu, S.M., Ren, J.L., Liu, C.G., Zhang, J., Zhang, G.L. and Huang, D.J. 2013. Nutrient dynamics
and coupling with phytoplankton species composition during the spring blooms in the Yellow Sea.
Deep Sea Res. II 97: 16–32, doi:10.1016/j.dsr2.2013.05.002.
Kang, Y.S., Jung, S., Zuenko, Y., Choi, I. and Dolganov, N. 2012. Regional differences in the response of
mesozooplankton to oceanographic regime shifts in the northeast Asian marginal seas. Prog.
Oceanogr. 97–100: 120–134, doi:10.1016/j.pocean.2011.11.012.
Lee, S.H. and Beardsley R.C., 1999. Influence of stratification on residual tidal currents in the Yellow
Sea. J. Geophys. Res. Oceans 104: 679–701, doi:10.1029/1999JC900108.
Lee, J.H., Kim, D., Shin, K.S., Choi H.W. and Chae, J. 2005. Prevention of bio-impingement against
power plant intake. KORDI Report BSPN52700-1716-3.
Lee, H., Cho, S., Kim, W. and Kang, D. 2013. The diel vertical migration of the sound-scattering layer in
the Yellow Sea Bottom Cold Water of the southeastern Yellow sea: focus on its relationship with atemperature structure. Acta Oceanol. Sin. 32: 44–49, doi:10.1007/s13131-013-0351-z.

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Li, H., Zhao, Y., Chen, X., Zhang, W., Xua, J., Li, J. and Xiao, T. 2016. Interaction between neritic andwarm water tintinnids in surface waters of East China Sea. Deep Sea Res. II 124: 84–92, doi:
10.1016/j.dsr2.2015.06.008.
Li, J., Glibert, P.M., Zhou, M., Lu, S. and Lu, D. 2009. Relationships between nitrogen and phosphorus
forms and ratios and the development of dinoflagellate blooms in the East China Sea. Mar. Ecol. Prog.
Ser. 383: 11–26, doi:10.3354/meps07975.
Lin, X., Yang, J., Guo, J., Zhang, Z., Yin, Y. Song, X. and Zhang, X. 2011. An asymmetric upwind flow,
Yellow Sea Warm Current: 1. New observations in the western Yellow Sea. J. Geophys. Res. Oceans
116: doi:10.1029/2010JC006513.
Liu, Y., Sun, S. and Zhang, G. 2012. Seasonal variation in abundance, diel vertical migration and bodysize of pelagic tunicate Salpa fusiformis in the Southern Yellow Sea. Chin. J. Ocean. Limnol. 30: 92–
104, doi:10.1007/s00343-012-1048-4.
Liu, X, Chiang, K.-P., Liu, S.-M., Wei, H., Zhao, Y. and Huang, B.-Q. 2015. Influence of the Yellow Sea
Warm Current on phytoplankton community in the central Yellow Sea. Deep Sea Res. 106: 17–29,
doi:10.1016/j.dsr.2015.09.008.
Lü, L.-G., Wang, X., Wang, H., Li, L. and Yang, G. 2013. The variations of zooplankton biomass andtheir migration associated with the Yellow Sea Warm Current. Cont. Shelf Res. 64: 10–19, doi:
10.1016/j.csr.2013.05.007.
Sherman, K. 1994. Sustainability, biomass yields, and health of coastal ecosystems: an ecologicalperspective. Mar. Ecol. Prog. Ser. 112: 277–301.
Tang, Q. 2003. The Yellow Sea LME and mitigation action, pp. 121–144 in: Large Marine Ecosystems of
the World – Trends in Exploitation, Protection and Research, edited by G. Hempel and K. Sherman,
Elsevier, Amsterdam, the Netherlands.
Wang, B. 2000. Characteristics of variations and interrelations of biogenic elements in the Huanghai Sea
Cold Water Mass. Acta Oceanol. Sin. 22: 47–54.
Xu, Z.-L. and Zhang, D. 2014. Dramatic declines in Euphausia pacifica abundance in the East China Sea:
Response to recent regional climate change. Zool. Sci. 31: 135–142, doi:10.2108/zsj.31.135.
Xu, Z.L., Ma Z.L. and Wu Y.M. 2011. Peaked abundance of Calanus sinicus earlier shifted in the
Changjiang River (Yangtze River) Estuary: a comparable study between 1959, 2002 and 2005. Acta
Oceanol. Sin. 30: 84–91.
Xu, Z.-L., Gao, Q., Kang, W. and Zhou, J. 2013. Regional warming and decline in abundance of
Euchaeta plana (Copepoda, Calanoida) in the nearshore waters of the East China Sea. J. Crust. Biol.
33: 323–331, doi:10.1163/1937240X-00002140.
Yang, D., Yin, B., Liu, Z., Bai, T., Qi, J. and Chen, H. 2012. Numerical study on the pattern and origins
of Kuroshio branches in the bottom water of southern East China Sea in summer. J. Geophys. Res.
Oceans 117: C02014, doi:10.1029/2011JC007528.
Yin, J., Zhang, G., Zhao, Z., Wang, S. and Wan, A. 2013. Annual variation in Calanus sinicus abundance
and population structure in the northern boundary area of the Yellow Sea Cold Water Mass. Chin. J.
Oceanol. Limnol. 31: 1284–1294, doi:10.1007/s00343-013-3004-3.
Yin, J., Zhang, G., Li, C., Wang, S., Zhao, Z. and Wan, A. 2017. Community composition, abundanceand biomass of zooplankton in Zhangzi Island waters, Northern Yellow Sea. Chin. J. Oceanol. Limnol.
35: 1144–1151, doi:10.1007/s00343-017-6135-0.
Zhang, G.T., Sun, S. and Zhang, F. 2005. Seasonal variation of reproduction rates and body size of
Calanus sinicus in the Southern Yellow Sea, China. J. Plankt. Res. 27: 135–143,
doi:10.1093/plankt/fbh164.

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Zhang, G.T., Sun, S. and Yang, B. 2007. Summer reproduction of the planktonic copepod Calanus
sinicus in the Yellow Sea: influences of high surface temperature and cold bottom water. J. Plankt.
Res. 29: 179–186, doi:10.1093/plankt/fbm005.
Zhang, G.T., Sun, S., Xu, Z.L. and Zhang Q.L. 2010. Unexpected dominance of the subtropical copepod
Temora turbinata in the temperate Changjiang River Estuary and its possible causes. Zool. Stud. 49:
492–503.
Zhou, K.L., Sun, S., Wang, M.X., Wang, S.W. and Li, C.L. 2016. Differences in the physiologicalprocesses of Calanus sinicus inside and outside the Yellow Sea Cold Water Mass. J. Plankt. Res. 38:
551–563, doi:10.1093/plankt/fbw011.
Zuo, T., Wang R., Wang, K. and Gao, S.-W. 2004. Vertical distribution and migration of zooplankton in
the southern Yellow Sea in summer. Acta Ecol. Sin. 24: 524-530 (in Chinese).

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Macrobenthos

3.4 Macrobenthos
Xinzheng Li and Yong Xu
Institute of Oceanology, Chinese Academy of Sciences, China

3.4.1 Introduction
Marine benthos are those organisms living on, in, or near the seabed. They are widely distributed
from the intertidal zone to the abyss and compose the most abundant group of marine organisms.
Marine benthos play important roles in energy flow and material circulation in marine ecosystems.
They make full use of the organic detritus settled on the seafloor from the water column andfacilitate the decomposition of nutrients.
According to body size, benthos is classified into macrobenthos, meiobenthos and microbenthos.
Macrobenthos are defined as benthos which cannot pass through 0.5 mm mesh sieve (Shen et al.,2010). Other literature defines the macrobenthos with body size not passing through a 1 mm meshsize (Lalli and Parsons, 2000). The main taxonomic groups of macrobenthos are Polychaeta,
Mollusca, Crustacea and Echinodermata. Other taxonomic groups such as Porifera, Cnidaria,
Sipuncula, Echiura, Nemertea and Pisces are also common in the benthic habitat. Activities ofmacrobenthic fauna, such as burrowing, crawling, feeding and avoiding enemies can change thesediment structure and result in the deformation or fracture of the seabed. The mobility of mostmacrobenthos is limited and some of them are sessile, the lifestyle of which is significantly different
from plankton and nekton. Because of limited mobility, macrobenthos cannot escape fromenvironmental stresses immediately and are impacted particularly profoundly when the environment
deteriorates. So, the long-term variation of species composition, community structure and secondary
productivity of macrobenthos can reflect the long-term variation of the environment.
The study of the macrobenthos in the Yellow Sea and East China Sea dates back to the 1930s and40s. Researchers investigated the macrobenthos in the adjacent waters of Shandong Province(Tchang, 1935; Tchang and Ma, 1936, 1949). However, large spatial-scale investigations ofmacrobenthos started in the late 1950s. These large spatial-scale investigations carried out in the
Yellow Sea and East China Sea are the Marine Comprehensive Scientific Survey from Coast of
China (National Ocean Census) (1958–1959), the Continental Shelf Marine Comprehensive Survey
from the East China Sea (1975–1976), the National Survey of Coastal Resource (1980–1985), theNational Insular Comprehensive Survey (1989–1993), the National Basic Research Program ofChina–The Ecosystem Dynamics and Sustainable Use of Marine Biological Resources from theEast China Sea and the Yellow Sea (1999–2004), the Marine Comprehensive Scientific Survey andAssessment from Coast of China (also called “the second National Ocean Census”) (2004–2009),
the National Basic Research Program of China–The Key Process, Mechanism and Effect of
Ecological Environment of the Jellyfish Bloom from the Coast of China (2011–2015), and theongoing “Strategic Priority Research Program” of the Chinese Academy of Sciences–Western
Pacific Ocean System: Structure, Dynamics and Consequences (2013–2018). Large amounts ofmacrobenthic samples were collected in these investigations and data have been accumulated fromPICES Scientific Report No. 62255

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the Yellow Sea and East China Sea. According to these samples and data, many researchers havereported the phased investigation results of macrobenthos, including macrobenthic community
structure (Gurjanova and Wu, 1963; Liu et al., 1986; Zhang et al., 1990; Sui et al., 2010), species
composition and distribution pattern (Liu et al., 1986; Qi et al., 1989; Liu and Li, 2002, 2003; Li etal., 2007; Liu et al., 2008; Gao et al., 2009), species diversity (Li et al., 2006; Liu et al., 2009),
secondary productivity (Li et al., 2005,b,c,d), biomass and stock (Liu, 1992; Han et al., 2001; Hanet al., 2004), the relationship between macrobenthos and environmental factors such as sediment,
temperature, salinity, heavy metal, current, and marine disasters (Li, B.Q. et al., 2005; Wang et al.,2006; Li et al., 2010; Wang, et al., 2011), thus achieving much knowledge.
However, there are also some issues that need to be addressed urgently in the study of macrobenthic
ecology and diversity in the Yellow Sea and East China Sea. The first problem is the systematicalanalysis and arrangement of the historical materials, that is, exploring the dynamic regularity of themacrobenthic community structure, diversity and secondary productivity using the data obtainedsince the 1950s, predicting the direction of changes in the future, and assessing the influence ofclimate change and human activities. The second problem is the coupling between macrobenthos
and plankton, meiobenthos, hydrologic and sedimentary characteristics, especially the analysis of
the effects of physical and chemical variables on the ecology of macrobenthos. The objective of thissection is to show the status of the macrobenthic community in different decades. The area surveyed
in the Yellow Sea and East China Sea covered most of the sub-tidal area. The gear used to sample
macrobenthos was grab or box corer unless specifically stated otherwise.

3.4.2 Yellow Sea
Taxonomic groups
From October 1997 to December 2000, macrobenthos in the Yellow Sea were investigated in a large
spatial scale. A total of 414 species were collected. Polychaeta, Crustacea and Mollusca were thepredominant groups and accounted for 89.37% of all species, with 194 species of Polychaeta, 90
Crustacea, 86 Mollusca, 21 Echinodermata and 23 species in other groups (Li, 2003). Species
numbers showed a decreasing trend from spring to winter, with 247 species in spring, 206 insummer, 181 in autumn, and 178 in winter (Table 3.4.1).

Table 3.4.1 Seasonal variation in species numbers for marcrobenthos in the Yellow Sea from 1998 to
2000. From Li (2003).
SeasonPolychaeta

Mollusca

Crustacea

Echinodermata

OthersTotal

SpringSummerAutumnWinterFull year

125
103
88
92
194

47
46
19
30
86

52
41
54
34
90

13
9
9
13
21

10
7
11
9
23

247
206
181
178
414

During 2006 and 2007 in the project “the second National Ocean Census”, 853 macrobenthic
species were recorded in the Yellow Sea. Seasonal variation in species numbers is shown in
Table 3.4.2, with higher values in autumn and winter and lower values in spring and summer (Sun,2012).

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Table 3.4.2 Seasonal variation in species numbers for macrobenthos in the Yellow Sea during 2006 and2007. From Sun (2012).
SeasonPolychaeta

Mollusca

Crustacea

Echinodermata

OthersTotal

SpringSummerAutumnWinterFull year

184
201
218
207
313

104
97
121
102
206

114
101
137
104
198

27
23
24
30
39

44
54
61
48
97

473
476
561
491
853

Table 3.4.3 Number and percentage (%) of species in the northern and southern Yellow Sea during
2006 and 2007. From Sun (2012).
Area

Northern Yellow Sea
Southern Yellow Sea

Polychaeta

Mollusca

Crustacea

Echinodermata

Others

S

P

S

P

S

P

S

P

S

P

261
199

39.67
47.84

124
81

18.84
19.47

178
78

27.05
18.75

33
25

5.02
6.01

62
33

9.42
7.93

Total

658
416

S: Number of species; P: Percentage of species

In the northern Yellow Sea, 658 species were collected. Polychaeta (261 species) was the dominantgroup, followed by Crustacea (178), Mollusca (124), Echinodermata (33) and other groups (62)
(Table 3.4.3). In the southern Yellow Sea, 416 species were collected. Polychaeta (199 species) was
the dominant group, followed by Mollusca (81), Crustacea (78), Echinodermata (25) and othergroups (33) (Table 3.4.3).
In spring, a higher number of species were located in the southern area and northern area of the
Yellow Sea, while a lower number were found in the middle area (Li, 2003, Fig. 3.4.1). In summer,
the species number was higher in southwestern and northern area and lower in middle area. In
autumn, a higher species number occurred in the southwestern and northeastern area, and a lower
number occurred in the middle and eastern area. In winter, the number was higher in the southernarea of the Yellow Sea.
It may be difficult to understand the seasonal variation in species number for macrobenthos,
especially for those non-migrating polychaetes. It is true that some species are non-migrating
species, but there are some polychaetes that could still migrate within a limited spatial area. Because
the sampling area for each station was only 0.2 m2 for the quantitative sampling methods (grab orbox corer, 0.1 m2 for each sample and two samples collected for each station), the limited samplingarea might also contribute to the seasonal variation if there was spatial heterogeneity in a limited
area.

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Fig. 3.4.1 The distribution of macrobenthic species numbers in the Yellow Sea in each season from 1998
to 2000. From Li (2003).

The species number for taxonomic groups of macrobenthos from eight cruises (March 2001 (Li etal., 2014), June–August 2006 (Xu et al., 2009), September–October 2008 (Jia et al., 2010), April
and August 2011 (Li et al., 2014), June, August and October 2012 (Xu et al., 2016)) in the southern
Yellow Sea are shown in Figure 3.4.2. Polychaeta showed the highest proportion of species number
in each cruise, while Echinodermata showed the lowest proportion. There were no obvious changes
in species numbers for Mollusca among cruises compared to other taxonomic groups.

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OthersEchinodermata
Mollusca
Crustacea
Polychaeta

200
180

Number of species

160
140
120
100
80
60
40
20

ly
-A

Sp

Ju

M

ar
ch

20
01
et
ug
em
us
be
t2
r-O
00
6
ct
ob
er
20
08
A
pr
il
20
11
A
ug
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t2
01
1
Ju
ne
20
12
A
ug
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t2
01
O
2
ct
ob
er
20
12

0

Fig. 3.4.2 Number of species of the main taxonomic groups in the southern Yellow Sea collected during
eight cruises.

Dominant species
The dominant species in the Yellow Sea were stenohaline northern temperate species, including
Goniada maculata, Aricidea fragilis, Ninoe palmata, Terebellides stroemii, Thyasira tokunagaii,
Eudorella pacifica, Callianassa japonica, Ophiura sarsii vadicola, etc. (Table 3.4.4, Sun, 2012).
In the survey for macrobenthos carried out during 1997–1998 in the coastal area of the Yellow Sea,
the species compositions and distribution patterns in the southern and northern areas showed
obvious differences (Hu et al., 2000). In the coastal area of the northern Yellow Sea, because of theinfluence of the Yellow Sea Cold Water Mass (YSCWM), the bottom temperature was low and thecold water species such as Crangon affinis, Heptacarpus geniculatus, Palaemon ortmanni, Ophiura
sarsii vadicola, Amphipholis kochii, Thyasira sp., Volutharpa ampullaceal perryi, Yoldia notabilis,
etc. increased in abundance. The temperate species of the North Pacific Ocean, such as Ophiopholis
mirabilis, Acila mirabilis, etc. dominated in number and abundance. In the coastal area of thesouthern Yellow Sea, the marine environment was characterized by complex composition of
sediment and low salinity, so the macrobenthos in this area were dominated by eurythermal and low
saline species. Because of the influence of warm currents in summer, some species in the East China
Sea or South China Sea, such as Nephtys ciliata (Polychaeta) and Temnopleurus hardwickii(Echinodermata) appeared in this area.
During 2006 and 2007, in the project “the second National Ocean Census”, the dominant
macrobenthos in the northern Yellow Sea included Sternaspis scutata, Melinna cristata, Thyasira
tokunagai, Laternula anatina, Pagurus ochotensis, Alpheus japonicus, Echinocardium cordatum,
Ophiura sarsii vadicola, Actiniaria, Urechis unicinctus, etc. (Table 3.4.5, Sun, 2012). The dominant
species in the southern Yellow Sea were Notomastus latericeus, Lumbrineris latreilli, Cadellanarutoensis, Theora fragilis, Alpheus japonicus, Callianassa harmandi, Stegophiura sladeni,
Ophiura sarsii vadicola, Antillesoma antillarum, etc. (Sun, 2012).

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Table 3.4.4 Dominant species in the Yellow Sea. From Sun (2012).
Taxonomic group

Dominant species

Taxonomic group

Dominant species

Polychaeta

Sigambra hanaokai
Nereis longior
Goniada maculata
Aricidea fragilis
Ophelia acuminata
Ninoe palmata
Terebellides stroemii
Lumbrineris sp.
Thyasira tokunagaii
Raetellops pulchella
Calliodentalium crocinum
Episiphon kiaochowwanensis
Cryptonatica andoi
Natica janthostomoides
Philine orientalis
Philine japonica

Crustacea

Eudorella pacifica
Harpinopsis sp.
Ampelisca brevicornis
Ampelisca miharaensis
Byblis japonicus
Eriopisella sechellensis
Photis hawaiensis
Caprella equilibra
Callianassa japonica
Ophiura sarsii vadicola

Mollusca

Echinodermata

Table 3.4.5 Dominant species in the Yellow Sea during 2006 and 2007. From Sun (2012).
Taxonomic group

Northern Yellow Sea

Southern Yellow Sea

Polychaeta

Sternaspis scutata
Melinna cristata
Laonice cirrata
Glycera chirori
Aphrodita australis

Notomastus latericeus
Lumbrineris latreilli
Ophelina acuminata
Euclymene lombricoides
Terebellides stroemii
Ninoe palmata

Mollusca

Thyasira tokunagai
Laternula anatina
Raetellops pulchella

Cadella narutoensis
Theora fragilis
Nucula nipponica

Crustacea

Pagurus ochotensis
Alpheus japonicus
Eudorella pacifica
Ampelisca brevicornis
Crangon affinis

Alpheus japonicus
Callianassa harmandi
Ampelisca bocki

Echinodermata

Echinocardium cordatum
Ophiura sarsii vadicola
Ophiopholis mirabilis

Stegophiura sladeni
Ophiura sarsii vadicola
Ophiopholis mirabilis

OthersActiniaria
Urechis unicinctus
Nemertinea

Antillesoma antillarum

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The long-term variations of dominant species in the southern Yellow Sea were described by Xu et al.
(2016). The dominant species in the western region (WR, depth < 40 m, Figure 3.4.3) changed from
Scapharca and Nassarius (Mollusca) and sea urchin (Echinodermata) in the 1950s (Liu et al., 1986),
and Praxillella praetermissa, Sternaspis scutata, Praxillella pacifica, and Glycera chirori(Polychaeta with big body size) in 1992 (Zhang et al., 2012) to Ninoe palmata and Notomastus
latericeus (Polychaeta with small body size) in 2012. Small species with short life cycles (small
Polychaeta) had replaced big species with long life cycles (Mollusca, Echinodermata and large
Polychaeta) in the WR from the 1950s to 2012. The inshore WR area of the southern Yellow Sea isalso experiencing environmental disturbances, and short-life species with high competitiveness andadaptability can adapt to the increasingly unstable environment. Thus, we predict that macrobenthos
living in the WR of the southern Yellow Sea will become smaller and have short life cycles. In the
middle region (MR, 40 ≤ depth < 70 m, Figure 3.4.3), dominant species in 2012 (e.g., Ophiura
sarsii vadicola, O. acuminate and Ninoe palmata) were also different from those in 1950s (e.g.,
Ditrupa arientina, Travisia pupa and Eunephythya sp.) (Liu et al., 1986) and those in 1992 (e.g.,
Terebellides stroemii and Thyasira tokunagai) (Zhang et al., 2012). However, they were notsignificantly different in body size from the 1950s to 2012. The species dominated in eastern region(ER, depth > 70 m, Figure 3.4.3) (e.g., Ophiura sarsii vadicola and Thyasira tokunagai) remained
unchanged from the 1950s to 2012 (Liu et al., 1986; Zhang et al., 2012). The YSCWM in the ER
remained stable all the year round and had a low bottom temperature in areas deeper than 40–50 m(Liu and Hsu, 1963; Weng et al., 1988). Cold water species like Ophiura sarsii vadicola andThyasira tokunagai were typical species of YSCWM in the southern Yellow Sea. Furthermore, theER is the farthest offshore area of the southern Yellow Sea, and had the least human influence. As aresult, the environment of the ER remains stable for a typical macrobenthic community to inhabit.
Thus, we predict that the stability of the ER environment will continue and cold water species like
Ophiura sarsii vadicola and Thyasira tokunagai will dominate the macrobenthic community in thenext 50 years, unless heavy pollution or other devastating events occur at the bottom of the southern
Yellow Sea.

Fig. 3.4.3

Long-term variations of dominant species in the three regions in the southern Yellow Sea.

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Community structure
The macrobenthic species in the coastal area of the Yellow Sea were classified into 4 communities
according to the species collected by box corer and Agassiz trawl (Fig. 3.4.4, Hu et al., 2000).
1) Crangon affinis-Ophiuroidea community
This community was located in the northern area of the northern Yellow Sea and was mainlycomposed of cold water species. The representative species included Crangon affinis, Amphioplus
japonicus, Stegophiura sladeni, Ophiopholis mirabilis, Ophiura sarsii vadicola, etc.
2) Echinocardium cordatum-Ophiuroidea community
This community was situated in the southern area of the northern Yellow Sea, north of Shandong
Province and the representative species were Echinocardium cordatum, Temnopleurus hardwickii,
Distolasterias nipon, Bullacta exarata, Stegophiura sladeni, etc.
3) Alpheus japonicas-Nassarius spp. community
This community was located in the northern area of the southern Yellow Sea and from Weihai (onthe northern coast of Shandong peninsula) south to Lianyungang. The representative species werecomposed of Alpheus japonicus, Odontamblyopus rubicundus, Nassarius succinctus, Nassarius
variciferus, etc.
4) Palaemon gravieri-Collichthys spp. community
This community was situated along the coast in the southern area of the southern Yellow Sea from
Sheyang to Shanghai. The representative species were Collichthys lucidus, Palaemon gravieri,
Sternaspis scutata, Metapenaeopsis dalei, etc., with the obvious component of subtropical flora.

Fig. 3.4.4 The distribution of macrobenthic communities in the coastal area of the Yellow Sea, as
sampled by box corer and Agassiz trawl net from 1997 to 1998. From Hu et al. (2000).

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Li (2003) classified the macrobenthos into 7 communities according to the species collected by box
corer, and these communities distributed in the northern, middle, and southern areas in the Yellow
Sea (Fig. 3.4.5; Li, 2003).
There were two communities in the northern area of the Yellow Sea, named as Spiophanes bombyx–Mactra chinensis–Ampelisca brevicornis–Echinocardium cordatum community (Community I)
and Goniada maculata–Thyasira tokunagai–Ophiura sarsii vadicola community (Community II).
Four communities were located in the middle area of the Yellow Sea, including Mediomastus
californiensis–Thyasira tokunagai–Metridium sp. community (Community I), Praxillella
praetermissa–Thyasira tokunagai–Leptochela aculeocaudata community (Community II), Onuphis
geophiliformis–Raeta pulchella–Cerianthus filiformis community (Community III), andLumbrineris japonica–Thyasira tokunagai–Molgula manhattensis community (Community IV).
In the southern area of the Yellow Sea, the macrobenthic community was Terebellides stroemii–
Raeta pulchella–Eudorella pacifica–Amphioplus depressus–Listriolobus brevirostris community.

Fig. 3.4.5 The distribution pattern of macrobenthic communities in the Yellow Sea, as sampled by box
corer from 1998 to 2000. From Li (2003).

The long-term variations in the macrofaunal community structure at the species, genus and familylevels in three regions (WR, MR, ER; Fig. 3.4.3) in the southern Yellow Sea were described by Xu
et al. (2017, Fig. 2.4.6). Two groups were at the species level (Fig. 3.4.6a). One group wascomposed of the samples collected during 1958 and 1959, and the other consisted of the samples
collected from 2000 to 2014. A regional difference in the community structure was observed in two
groups (1958–1959 and 2000–2014), while a seasonal difference was not obvious.

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At the genus level, two groups (1958–1959 and 2000–2014) were also identified and regional
differences were also detected in each group, while seasonal variations were not visually
identifiable (Fig. 3.4.6b).
The community structure at the family level was slightly different from that at the species and genus
levels, for which regional differences were the most obvious (Fig. 3.4.6c). Two groups wereidentified at the family level. One group was composed of the samples collected in the WR, and theother contained the samples collected in the MR and ER. Two subgroups (1958–1959 and 2000–
2014) showing temporal differences in each group were observed.

0a Species level

Transform: Square root
Resemblance: S17 Bray Curtis similarity

Species level 2D Stress: 0.13

14AE

20

14SE

58AE

01SE

Similarity

11SE

59SE

00AM

40

00AE 12AE
12AM
11SM

60

01SM

59SM 58AM

14SM
14AM

59SW
12AW

80

58AW

11SW
14SW
14AW

Region

01SW

59SW
58AW
58AM
59SM
59SE
58AE
14SW
14AW
11SW
12AW
01SW
00AW
01SM
14AM
14SM
11SM
12AM
00AM
01SE
00AE
14AE
12AE
11SE
14SE

100

Western Region
Middle Region
Eastern Region

00AW

Samples0b Genus level

Transform: Square root
Resemblance: S17 Bray Curtis similarity

20

12AM 00AM

14AW

11SM
14SM

12AW

Similarity

Genus level 2D Stress: 0.12

01SM

14SW

01SW

40

14AM
00AW

01SE
11SE

12AE

11SW

14SE

00AE

60

14AE

59SE
58AE

80

59SW
59SM

Region58AW

100

Western Region
Middle Region
Eastern Region

59SW
58AW
58AM
59SM
59SE
58AE
14SW
14AW
11SW
12AW
01SW
00AW
01SM
14SM
12AM
11SM
14AM
00AM
01SE
00AE
14AE
11SE
12AE
14SE

58AM

Samples0c Family level

Transform: Square root
Resemblance: S17 Bray Curtis similarity

58AM

Family level 2D Stress: 0.12

20
58AW

Similarity

58AE

40

59SW

59SM
59SE
14SE

60
14AE

01SE

11SE

80

11SW

12AE
12AM

00AE

14AM

14SM

59SW
58AW
01SW
00AW
11SW
14SW
12AW
14AW
58AM
59SM
59SE
58AE
00AM
01SM
14SM
12AM
11SM
14AM
01SE
00AE
14AE
11SE
12AE
14SE

01SM
00AM

12AW
14SW

11SM

100

00AW
01SW

14AW

RegionWestern Region
Middle Region
Eastern Region

SamplesFig. 3.4.6 Cluster analysis (left) and non-metric multidimensional scaling (nMDS) ordination (right) of
macrofaunal community structure based on pooled abundance data for each region in the southern
Yellow Sea at the (a) species level, (b) genus level and (c) family level. From Xu et al. (2017). 58: 1958;
59: 1959; 00: 2000; 01: 2001; 11: 2011; 12: 2012; 14: 2014; S: spring; A: autumn; W: western region; M:
middle region; E: eastern region. Reproduced with permission of Elsevier.

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Abundance, biomass and diversity
Abundance
The mean abundance of macrobenthos in the Yellow Sea was 250 ind./m2. Polychaeta was the mostabundant taxonomic group, followed by Crustacea and Mollusca. The seasonal variations ofmacrobenthic abundance was spring (358 ind./m2) > winter (289 ind./m2) > summer (187 ind./m2) >autumn (164 ind./m2) (Table 3.4.6, Li, 2003).
Macrobenthic abundance showed an obvious spatial variation in the coastal area of the Yellow Sea(Fig. 3.4.7, Hu et al., 2000), with a decreasing trend from the northern area to the southern area,
which was in accordance with the spatial distribution of organic matter in the sediment. The meanabundance in the northern Yellow Sea was 511.0 ind./m2, and in the southern Yellow Sea was 129.4
ind./m2.

Table 3.4.6 Seasonal variation of macrobenthic abundance (ind./m2) in the Yellow Sea from 1998 to
2000. From Li (2003).
SeasonPolychaeta

Mollusca

Crustacea

Echinodermata

OthersTotal

SpringSummerAutumnWinterAverage

202
109
130
131
143

57
15
7
72
38

70
55
13
37
44

24
5
11
11
13

5
2
3
38
12

358
187
164
289
249

Fig. 3.4.7 The distribution of macrobenthic abundance in the coastal area of the Yellow Sea from June
1997 to July 1998. From Hu et al. (2000).

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During 2006 and 2007 in the project “the second National Ocean Census”, the mean abundance was2017.41 ind./m2 in the northern Yellow Sea. Polychaeta was the most abundant taxonomic group,
followed by Crustacea and Mollusca. Echinodermata and others groups showed low abundance(Table 3.4.8, Sun, 2012). Compared with the northern Yellow Sea, the abundance in the southern
Yellow Sea was lower, with a mean abundance of 88.67 ind./m2. Polychaeta accounted for morethan half of the total abundance and Mollusca, Crustacea, Echinodermata and other groups showed
low abundance. The seasonal variation of mean abundance is shown in Table 3.4.9.
The abundance of macrobenthos taxonomic groups from eight cruises (March 2001 (Li et al., 2014),
June–August 2006 (Xu et al., 2009), September–October 2008 (Jia et al., 2010), April and August
2011 (Li et al., 2014), June, August and October 2012 (Xu et al., 2016)) in the southern Yellow Sea
is shown in Figure 3.4.8. Polychaeta had the highest abundance in each cruise, while Echinodermata
had the lowest proportion, except during August 2012. There were no obvious changes inabundance of Mollusca among cruises compared to other taxonomic groups.

Table 3.4.8 Mean abundance (ind./m2) and its percentage (%) in the northern and southern Yellow Sea
during 2006 and 2007. From Sun (2012).
Polychaeta

Mollusca

Crustacea

Echinodermata

Others

Ab

P

Ab

P

Ab

P

Ab

P

Ab

P

865.9242.92

415.04

20.57

568.61

28.19

112.30

5.57

55.54

2.75

2017.40

51.79

58.41

13.95

15.73

10.89

12.28

6.64

7.49

5.40

6.09

88.67

Area
Northern
Yellow Sea
Southern
Yellow Sea

Total

Ab: Abundance; P: Percentage of abundance
Table 3.4.9 Seasonal variation of macrobenthic abundance (ind./m2) in the Yellow Sea during 2006 and2007. From Sun (2012).
Polychaeta

Mollusca

Crustacea

Echinodermata

OthersTotal

SpringSummerAutumnWinter

218
250
217
468

80
173
88
164

145
178
123
283

28
44
29
39

4
11
18
26

476
655
476
979

OthersEchinodermata
Mollusca
Crustacea
Polychaeta

20
01
et
ug
em
us
be
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6
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20
08
A
pr
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20
11
A
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t2
01
1
Ju
ne
20
12
A
ug
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t2
01
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ob
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20
12

280
260
240
220
200
180
160
140
120
100
80
60
40
20
0

Sp

M

266

Ju

Fig. 3.4.8 Abundance of the main taxonomic groups
in the southern Yellow Sea.

ly
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ar
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Abundance (ind./m2)

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Biomass
The mean biomass of macrobenthos in the Yellow Sea was 37.09 g/m2. Echinodermata showed thehighest biomass, followed by Polychaeta. The seasonal variations of macrobenthic biomass was
spring (51.01 g/m2) > autumn (35.34 g/m2) > summer (32.06 g/m2) > winter (29.94 g/m2) (Table
3.4.10; Li, 2003).
The macrobenthic biomass also showed an obvious spatial variation in the coastal area of the
Yellow Sea (Fig. 3.4.9) (Hu et al., 2000). A decreasing trend from the northern area to the southernarea was detected, in accordance with the spatial distribution of organic matter in the sediment andabundance. The mean biomass in the northern Yellow Sea was 106.1 g/m2, dominated by
Echinodermata (55.0% of total biomass), and in the southern Yellow Sea was 13.36 g/m2,
dominated by Polychaeta (44.3% of total biomass).

Table 3.4.10 Seasonal variation of macrobenthic biomass (g/m2) in the Yellow Sea from 1998 to 2000.
From Li (2003).
SeasonPolychaeta

Mollusca

Crustacea

Echinodermata

OthersTotal

SpringSummerAutumnWinterAverage

11.07
10.39
10.75
7.89
10.03

8.54
3.11
1.78
3.69
4.28

3.81
6.36
2.56
1.56
3.57

18.99
6.25
11.56
9.48
11.57

8.60
5.94
8.69
7.33
7.64

51.01
32.06
35.34
29.94
37.09

Fig. 3.4.9 The distribution of macrobenthic biomass in the coastal area of the Yellow Sea from June
1997 to July 1998. From Hu et al. (2000).

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During 2006 and 2007 in the project “the second National Ocean Census”, the mean biomass was
99.66 g/m2 in the northern Yellow Sea (Sun, 2012). Echinodermata was the taxonomic group
showing highest biomass, followed by Polychaeta and other groups. Crustacea and Mollusca
showed low biomass (Table 3.4.11). The mean biomass in the southern Yellow Sea was 27.67 g/m2
(Sun, 2012). The rank of the biomass for each taxonomic group was Polychaeta > Echinodermata >Mollusca > other groups > Crustacea. The seasonal variation of mean biomass is shown in Table 3.4.12.
Table 3.4.11 Mean biomass (g/m2) and its percentage (%) in the northern and southern Yellow Sea
during 2006 and 2007. From Sun (2012).
Area

Northern Yellow Sea
Southern Yellow Sea

Polychaeta

Mollusca

Crustacea

Echinodermata

Others

Total

Bio

P

Bio

P

Bio

P

Bio

P

Bio

P

20.41
9.25

20.48
33.40

12.99
5.64

13.03
20.38

7.70
2.18

7.73
7.86

36.42
7.64

36.55
27.61

22.14
2.98

22.21
10.76

99.66
27.69

Bio: biomass; P: Percentage of biomass

Table 3.4.12 Seasonal variation of macrobenthic biomass (g/m2) in the Yellow Sea during 2006 and2007. From Sun (2012).
SeasonPolychaeta

Mollusca

Crustacea

Echinodermata

OthersTotal

SpringSummerAutumnWinter

7.50
11.37
7.55
8.94

4.44
5.85
7.02
8.95

3.45
2.26
2.90
2.34

11.88
10.09
12.63
11.01

5.36
5.67
8.46
8.07

32.63
35.23
38.55
39.32

The biomass of macrobenthos taxonomic groups from eight cruises (March 2001 (Li et al., 2014),
June–August 2006 (Xu et al., 2009), September–October 2008 (Jia et al., 2010), April and August
2011 (Li et al., 2014), June, August and October 2012 (Xu et al., 2012)) in the southern Yellow Sea
is shown in Fig. 3.4.10. Echinodermata and Mollusca had the highest abundance in most cruises.
During August 2012, the biomass of Echinodermata showed an overwhelming advantage in biomass
compared to other taxonomic groups.

OthersEchinodermata
Mollusca
Crustacea
Polychaeta

60
55
50
45

Biomass (g/m2)

40
35
30
25
20
15
10
5

Sp

M

268

Ju

Fig. 3.4.10 Biomass of the main taxonomic
groups in the southern Yellow Sea.

ly
-A

ar
ch

20
01
et
ug
em
us
be
t2
r-O
00
6
ct
ob
er
20
08
A
pr
il
20
11
A
ug
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t2
01
1
Ju
ne
20
12
A
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t2
01
O
2
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20
12

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Diversity
According to the Shannon-Wiener diversity index (H’), Margalef richness index (d), and Pielou
evenness index (J’), the offshore area of Dalian–Dandong and the coastal area of Qiangdao–Rushan,
the Qidong–Yangtze River estuary showed high macrobenthic diversity and richness (H’ >3; d:
2.07–4.36), with a relatively homogeneous (J’: 0.71–0.97) distribution of species (Fig. 3.4.11) (Huet al., 2000). This phenomenon was related to the richness in types of species, high organic matter
content and less pollution in the sediment (Hu et al., 2000). The coastal area in Dandong, Yantai–
Weihai, Rizhao–Sheyang showed the lowest species diversity and richness (H’: 0.48–1.87; d: 0.37–1.75), with a heterogeneous (J’: 0.12–0.57) distribution of species. This phenomenon was related toheavy pollution and instability of the sediment (Hu et al., 2000).

42°N
41°N
40°N
39°N
38°N
37°N
36°N
35°N
34°N
33°N
32°N
H' ≥ 331°N

2 ≤ H' < 3

H' < 230°N
118°E 119°E 120°E 121°E 122°E 123°E 124°E 125°E 126°E

Fig. 3.4.11 Distribution of the Shannon-Wiener diversity index (H’) in the coastal area of the Yellow
Sea during June 1997 and July 1998. From Hu et al. (2000).

The diversity indexes (Margalef richness index, Shannon-Wiener index and Pielou’s evenness index)
from six cruises (March 2001, April and August 2011, June, August and October 2012 (Xu et al.,2016)) in the southern Yellow Sea are shown in Figure 3.4.12.
The mean value of Margalef richness index (d) was highest (3.72) in October 2012 and lowest (3.12)
in June 2012, with values fluctuating from 2001 to 2012. The mean value of Shannon-Wiener index(H’) showed the same trend with d, having the highest value in October 2012 (3.23) and the lowest
value (2.85) in June 2012. The mean value of Pielou’s evenness index (J’) was relative stable during
the six cruises, with the highest mean value (0.86) occurring in October 2012 and the lowest meanvalue (0.83) in August 2011.

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J'

H'

d
d

H'
J'
3.5 0.9

4.0
3.8

3.4

3.6

3.3

3.4

3.2

3.2

3.1

3.0

3.0

2.8

2.9

2.6

2.8

2.4

2.7

2.2

2.6
2.5

2.0

M

h2
arc

001
A

20
pril

11
Au

t 20
gus

11
J

2
une

012
Au

t 20
gus

12

r 20
obe
Oct

12

0.8

0.7

0.6

0.5

0.4

Fig. 3.4.12 Diversity indexes (Margalef richness index (d), Shannon-Wiener index (H’) and Pielou’s
evenness index (J’)) in the southern Yellow Sea.

Secondary productivity
Secondary production is the conversion of energy from primary producers by consumers to produce
new biomass (Blomberg and Montagna, 2014). In this section, all productivity occurring higher thanat the primary level is referred to as secondary productivity of macrobenthos. Secondary
productivity (g(AFDW)/(m2·a)) is calculated using the formula (Li et al., 2005d): Secondary
productivity = Abundance0.27 × Biomass0.737/100.4, which was transformed from Brey’s empiricalformula (Brey, 1990): log(Secondary productivity) = –0.4 + 1.007log(Biomass) – 0.27log(Biomass/
Abundance).
As shown in Fig. 3.4.13, the mean secondary productivity in the southern Yellow Sea was 3.00
g(AFDW)/(m2·a) in April 2011. In August 2011, the mean value increased to 4.77 g(AFDW)/(m2·a).
The mean of the secondary productivity in June, August and October, 2012 was 2.53, 5.74 and 4.12g(AFDW)/(m2·a), respectively. It can be concluded that in summer (August), the macrobenthos inthe southern Yellow Sea had the highest secondary productivity.

Secondary productivityg(AFDW)/(m2·a)

7
6
5
4
3
2
1
0
April 2011 August
2011

June 2012

August2012

October2012

Fig. 3.4.13 Secondary productivity (g(AFDW)/(m2·a)) of macrobenthos during five cruises in thesouthern Yellow Sea.

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3.4.3 East China Sea
Taxonomic groups
From October 1997 to March 2000, a total of 855 macrobenthic species were collected in the East
China Sea. Polychaeta, Mollusca and Crustacea were the predominant groups which accounted for
85.94% of all species, with 268 species of Polychaeta, 283 Mollusca, 171 Crustacea, 68
Echinodermata and 65 species in other groups (Li, 2003).
Compared to the species composition in the Bohai Sea and the Yellow Sea, the species compositionin the East China Sea was more diverse. The East China Sea shared 209 species with the Yellow Sea,
among which were 139 species of Polychaeta, 31 Mollusca, 18 Crustacea, 16 Echinodermata and 5
species in other groups (Li, 2003).
The species composition of macrobenthos in the East China Sea had obvious seasonal variations,with the rank of species number being spring (452 species) > autumn (435) > summer (380) >winter (288) (Table 3.4.13, Li, 2003).

Table 3.4.13 Seasonal variation in species numbers for macrobenthos in the East China Sea from 1997
to 2000. From Li (2003).
SeasonPolychaeta

Mollusca

Crustacea

Echinodermata

OthersTotal

SpringSummerAutumnWinterFull year

155
139
153
129
268

147
92
126
66
283

92
86
100
48
171

37
36
25
24
68

21
27
31
21
65

452
380
435
288
855

Figure 3.4.13 shows the seasonal distribution pattern of macrobenthic species numbers in the East
China Sea. In spring, a high number of species was found in the southern area and the northern areaof the East China Sea and the northern area of the Taiwan Strait, but in the middle area of the East
China Sea, the species number was low (Li, 2003). In summer, a high number of species was foundin the middle and northern area of the East China Sea and the northern area of the Taiwan Strait. In
autumn, the middle and northeastern area showed high species number, but the number in thesouthern area was low. In winter, the distribution of species numbers was relatively homogenous.

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Fig. 3.4.13(2003).

Biological Oceanography

The distribution pattern of macrobenthic species numbers in the East China Sea. From Li

During 2006 and 2007 in the project “the second National Ocean Census”, 1300 macrobenthic
species were recorded in the East China Sea, including 428 species of Polychaeta, 291 of Mollusca,
283 of Crustacea, 80 of Echinodermata, and 218 species in other groups (Sun, 2012). Seasonalvariation in species numbers is shown in Table 3.4.14, with higher values in summer and winter andlower values in spring and autumn.
In the sea area adjacent to the Yangtze River estuary, the area off the Zhejiang coast and the Taiwan
Strait, the number of species was 418, 327 and 492, respectively, with Polychaeta being thedominant taxonomic group (Table 3.4.15).

Table 3.4.14 Seasonal variation of the species number of macrobenthos in the East China Sea during
2006 and 2007 (Sun, 2012).
SeasonPolychaeta

Mollusca

Crustacea

Echinodermata

OthersTotal

SpringSummerAutumnWinterFull year

239
274
256
260
428

108
169
102
121
291

157
170
133
156
283

41
43
46
44
80

86
117
82
76
218

631
773
619
657
1300

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Table 3.4.15 Number and percentage (%) of species in the sea adjacent to the Yangtze River estuary,
the area off the Zhejiang coast, and the Taiwan Strait during 2006 and 2007. From Sun (2012).
Polychaeta

Mollusca

Crustacea

Echinodermata

Others

S

P

S

P

S

P

S

P

S

P

148
187
249

35.41
57.19
50.61

135
46
50

32.30
14.07
10.16

69
49
135

16.51
14.98
27.44

27
29
31

6.46
8.87
6.30

39
16
27

9.33
4.89
5.49

Area

Total

Sea adjacent to Yangtze
River estuary
Area off Zhejiang coast
Taiwan Strait

418
327
492

S: Number of species; P: Percentage of species

220
200
180

OthersEchinodermata
Mollusca
Crustacea
Polychaeta

Number of species

160
140
120
100
80
60
40
20
0
March 2001

Fig. 3.4.14

April 2011

August 2011

Number of species of the main taxonomic groups in the East China Sea.

The main taxonomic groups of macrobenthos from three cruises (March 2001, April and August2011) in the East China Sea are shown in Figure 3.4.14. The proportion of species number of eachtaxonomic group was relative stable among cruises.

Dominant species
The dominant, and most common, macrobenthic species are listed in Table 3.4.16. The spatial
distributions of different dominant species were also different.
During 2006 and 2007, in the project “the second National Ocean Census”, researchers found thatsome dominant species in the sea adjacent to the Yangtze River estuary, the area off the Zhejiang
coast and the Taiwan Strait had similar distributions, while species in the others group had different
distributions. Sternaspis scutata and Nemertinea had high dominance in all three areas (Table 3.4.17,
Sun, 2012).

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Table 3.4.16 Dominant species in the East China Sea from 1997 to 2000 (Li, 2003).
Taxonomic group Dominant species

Taxonomic group

Dominant species

Polychaeta

Paralacydonia paradoxa
Sigambra hanaokai
Goniada maculata
Glycera sp.
Glycera chirori
Prionospio malmgreni
Prionospio pygmaea
Paraprionospio pinnata
Paraprionospio sp.
Lumbrineris latreilli
Magelona sp.
Notomastus aberans
Onuphis eremita
Aglaophamus dibranchis
Aricidea fragilis
Angulus lanceolatus

Mollusca

Dentalium octangulatum
Bursa rana
Mitrella burchardi
Nassarius siquijorensis
Zeuxis sp.
Inquisitor flavidula
Lophiotoma leucotropis
Ampelisca cyclops
Ampelisca miharaensis
Eriopisella sechellensis
Leptochela acculeocaudata
Alpheus distinguendus
Callianassa japonica
Jassa falcata
Byblis japonicus
Grandidierella japonica

Spiniplicatula muricata

Echinodermata

Mollusca

Crustacea

Amphioplus laevis

Episiphon kiaochowwanensis

Amphioplus depressus

Graptacme buccinulum

Amphioplus ancistrotus

Calliodentalium crocinum

Amphiura vadicola

Solen canaliculatus

Amphiura digitula

Olivella sp.

Ophiura kinbergi

Table 3.4.17 Dominant species in the sea adjacent to the Yangtze River estuary, the area off theZhejiang coast and the Taiwan Strait during 2006 and 2007. From Sun (2012).
Sea adjacent to Yangtze Riverestuary

Area off the Zhejiang coast

Paraprionospio pinnata
Cossurella dimorpha
Capitella capitata
Sternaspis scutata
Laonice cirrata

Polychaeta
Sternaspis scutata
Notomastus latericeus
Laonice cirrata
Cossurella dimorpha
Magelona cincta

Nassarius variciferus
Nassarius succinctus
Moerella jedoensis

Hemigrapsus peniciillatus
Alpheus distinguendus
Ampelisca cyclops
Leptochela gracilis

274

Mollusca
Eocylichna braunsi
Nassarius siquijorensis

Crustacea
Gammaridea
Xenophthalmus pinnotheroides
Diogenidae

Taiwan Strait

Sternaspis scutata
Paralacydonia paradoxa
Paralacydonia ehlersi
Mediomastus californiensis
Notomastus aberans

Modiolus comptus
Moerella culter
Angulus vestalis

Eriopisella sechellensis
Urothoe grimaldii
Harpiniopsis sp.
Byblis japonicus

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Table 3.4.17 Contined.
Sea adjacent to Yangtze Riverestuary

Area off the Zhejiang coast

Amphiura vadicola
Amphioplus depressus

Echinodermata
Protankyra bidentata
Amphiura vadicola

Nemertinea
Odontamblyopus rubicundus
Actiniaria

OthersNemertinea
Actiniaria

Taiwan Strait

Amphiura sp.
Ophiactis affinis
Amphioplus depressus

Apionsoma trichocephala
Nemertinea
Branchiostoma belcheri

Community structure
Li (2003) classified the macrobenthos in the East China Sea into 5 communities named as northinshore community, north offshore community, south inshore community, south offshore community
and Taiwan Strait community (Fig. 3.4.15).

Fig. 3.4.15 The distribution pattern of macrobenthic communities in the East China Sea from 1997 to
2000. From Li (2003).

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1) North inshore community, including 2 sub-communities
Sub-community I, Aricidea fragilis–Calliodentalium crocinum–Raetellops pulchella–Callianassa
japonica –Amphioplus depressus community.
Sub-community II, Micronephtys sphaerocirrata–Eulima bifascialis–Scapharca inaequivalvis–
Ogyrides orientalis community.
2) South inshore community, including 2 sub-communities
Sub-community I, Onuphis eremita–Calliodentalium crocinum–Callianassa japonica–Amphioplus
ancistrotus community.
Sub-community II, Aricidea fragilis–Calliodentalium
Amphioplus ancistrotus community.

crocinum–Grandidierellajaponica–

3) North offshore community, including 4 sub-communities
Sub-community I, Mediomastus californiensis–Byblis japonicus–Golfingia sp. community.
Sub-community II, Amphictene sp.–Calliodentalium crocinum–Leptochelia aculeocauclata–
Placophiothrix striolata community.
Sub-community III, Sigambra hanaokai–Calliodentalium crocinum–Callianassa japonica–
Amphiura digitula community.
Sub-community IV, Aricidea fragilis–Calliodentalium crocinum–Callianassa japonica–Amphioplus
sp. community.
4) South offshore community, including 2 sub-communities
Sub-community I, Aglaophamus dicirris–Turritella fascialis–Callianassa japonica–Placophiothrix
striolata community.
Sub-community II, Paraprionospio pinnata–Spiniplicatula muricata–Solenocera pectinulata–
Ophionephthys difficilis community.
5) Taiwan Strait community, including 2 sub-communities
Sub-community I, Tharyx acutus–Calliodentalium crocinum–Ampelisca cyclops–Amphioplus
depressus community.
Sub-community II, Haploscoloplos elongatus–Nucula convexa–Ampelisca brevicornis community.

Abundance, biomass and diversity
Abundance
The mean abundance of macrobenthos in the East China Sea was 280 ind./m2, among which
Polychaeta showed the highest abundance, followed by Crustacea and Mollusca. The seasonalvariation of macrobenthic abundance was autumn (461 ind./m2) > spring (336 ind./m2) > summer(178 ind./m2) > winter (146 ind./m2). The seasonal variations of the abundance of five taxonomic
groups are shown in Table 3.4.18 (Li, 2003).

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Table 3.4.18 Seasonal variations of the macrobenthic abundance (ind./m2) in the East China Sea from1997 to 2000. From Li (2003).
SeasonPolychaeta

Mollusca

Crustacea

Echinodermata

OthersTotal

SpringSummerAutumnWinterAverage

151
99
134
81
116

69
6
108
3
47

69
50
203
47
92

13
12
6
5
9

34
11
10
10
16

336
178
461
146
280

The spatial distribution pattern of macrobenthic abundance showed that high abundance existed in
the inshore area and low abundance in the offshore area. There were also obvious seasonal andlatitudinal patterns for abundance. In spring, the abundance was high in the middle area of the East
China Sea, the inshore area of the Yangtze River estuary, and the north of the Taiwan Strait, with thehighest value of 1000 ind./m2, showing a decreasing trend from inshore to offshore. In summer,
there were two high abundance areas. One was located in the inshore area of the Yangtze Riverestuary and the other was situated from the Yangtze River estuary to the north of the Taiwan Strait,
both of which had the highest value of 250 ind./m2. In autumn, the abundance in the inshore area
(highest abundance was up to 1500 ind./m2) was higher than that in the offshore area. In winter,high abundance occurred in the coastal area of Zhejiang province and in the offshore area ofHangzhou Bay, with the high abundance of 250 ind./m2 (Li, 2003).
During 2006 and 2007 in the project “the second National Ocean Census”, the mean abundance was
164 ind./m2 in the East China Sea (Sun, 2012). The seasonal variation of mean abundance is shownin Table 3.4.19.
The macrobenthic abundance in the sea adjacent to the Yangtze River estuary, the area off theZhejiang coast and the Taiwan Strait had its own characteristics, with the total abundance showing
an increasing trend from the north to the south. The mean abundance was 92.06 ind./m2 in the sea
adjacent to the Yangtze River estuary, 140.87 ind./m2 in the area off the Zhejiang coast, and 330.52
ind./m2 in the Taiwan Strait (Sun, 2012). The main contributor for abundance in the three areas was
Polychaeta, corresponding to its small size and centralized distribution. The reason for the highabundance in the Taiwan Strait was the existence of large amounts of small Crustacea, such as
Gammaridea, Cumacea, Limnoria, etc. In winter, these small Crustacea accounted for more thanhalf of the total abundance (Table 3.4.20).

Table 3.4.19 Seasonal variation of macrobenthic abundance (ind./m2) in the East China Sea during 2006
and 2007. From Sun (2012).
SeasonPolychaeta

Mollusca

Crustacea

Echinodermata

OthersTotal

SpringSummerAutumnWinter

61
123
65
122

8
11
8
8

29
47
16
65

12
10
9
14

10
9
10
15

120
200
108
224

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Table 3.4.20 Mean abundance (ind./m2) and its percentage (%) in the adjacent sea area of the Yangtze
River estuary, the area off the Zhejiang coast and the Taiwan Strait during 2006 and 2007.
Polychaeta

Mollusca

Crustacea

Ab

Ab

Ab

Echinodermata

Others

Area

Total

Sea adjacemt toYangtze Riverestuary
Area off
Zhejiang coast
Taiwan Strait

P

P

Ab

P

Ab

19.48 21.16

4.32

4.69

2.88 3.13

92.06

4.67 20.50 14.55
0.87 128.21 38.79

9.88
26.70

7.01
8.08

11.10 7.88
24.61 7.44

140.87330.5153.60 58.23

11.78 12.79

92.81 65.88
148.13 44.82

6.58
2.88

P

P

Ab: Abundance; P: Percentage of abundance

The abundance of each taxonomic group from three cruises (March 2001, April and August 2011) inthe East China Sea is shown in Figure 3.4.16. All taxonomic groups had obviously higher
abundance values in summer than in spring, while almost no long-term variation of abundance inspring was detected.

240
220
200

OthersEchinodermata
Mollusca
Crustacea
Polychaeta

Abundance (ind./m2)

180
160
140
120
100
80
60
40
20
0
March 2001

Fig. 3.4.16

April 2011

August 2011

Abundance of the main taxonomic groups in the East China Sea.

Biomass
The mean biomass of macrobenthos in the East China Sea was 20.84 g/m2, among which Mollusca
showed the highest biomass, followed by Echinodermata and species in other groups (Li, 2003).
The seasonal variation of macrobenthic biomass was spring (41.24 g/m2) > autumn (19.10 g/m2) >summer (12.45 g/m2) > winter (10.58 g/m2). The seasonal variations of the biomass for different
taxonomic groups were shown in Table 3.4.21.

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Table 3.4.21 Seasonal variation of macrobenthic biomass (g/m2) in the East China Sea from 1997 to
2000. From Li (2003).
SeasonPolychaeta

Mollusca

Crustacea

Echinodermata

OthersTotal

SpringSummerAutumnWinterAverage

3.27
3.49
3.32
2.42
3.13

29.3
1.29
5.74
1.69
9.51

2.14
1.53
1.55
1.17
1.60

2.91
3.65
3.51
3.75
3.46

3.62
2.48
4.98
1.55
3.16

41.24
12.45
19.10
10.58
20.84

The spatial distribution of macrobenthic biomass also showed obvious seasonal and latitudinalpatterns. In spring, the biomass was high in the inshore area of the East China Sea, showing adecreasing trend from inshore to offshore; the biomass had highest values (250 g/m2) in the coastal
area of Zhejiang province and formed high value areas in the north and middle of the Taiwan Strait.
In summer, the biomass was lower than that in spring; high biomass areas were located in the
middle area of the Yangtze River estuary and the offshore area of Hangzhou Bay, and low biomass
area in the north of the Taiwan Strait. In autumn, high biomass areas were formed in the coastal
areas of the Yangtze River estuary and Hangzhou Bay with the highest value being 150 g/m2, andlow biomass area in the north of the Taiwan Strait. In winter, high biomass areas were the middle
area of the Yangtze River estuary and the inshore area of the Hangzhou Bay, with the highest value
being 90 g/m2 (Li, 2003).
During 2006 and 2007 in the project “the second National Ocean Census”, detailed investigations ofmacrobenthos were made in the East China Sea. The seasonal variation of mean biomass is shownin Table 3.4.22 (Sun, 2012).
The macrobenthic biomass in the sea adjacent to the Yangtze River estuary, the area off the Zhejiang
coast and the Taiwan Strait were different (Table 3.4.23). The mean biomass was highest in the area
off the Zhejiang coast, and lowest in the Taiwan Strait (Sun, 2012). The biomass of each taxonomic
group in the three areas was also different. Mollusca and Echinodermata accounted for the highest
proportion of biomass for its comparatively large size. Polychaeta only accounted for a small
proportion of biomass for its small size though its abundance was high.

Table 3.4.22 Seasonal variation of macrobenthic biomass (g/m2) in the East China Sea during 2006 and2007. From Sun (2012).
SeasonPolychaeta

Mollusca

Crustacea

Echinodermata

OthersTotal

SpringSummerAutumnWinter

2.06
2.84
1.81
2.69

3.31
3.20
3.08
2.74

1.17
2.06
1.96
1.20

11.26
3.92
3.53
6.43

1.59
2.05
1.32
2.00

19.39
14.07
11.71
15.06

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Table 3.4.23 Mean biomass (g/m2) and its percentage (%) in the sea adjacent to the Yangtze Riverestuary, the area off the Zhejiang coast and the Taiwan Strait during 2006 and 2007. From Sun (2012).
Polychaeta

Mollusca

Crustacea

Bio

P

Bio

P

Bio

Sea adjacent toYangtze River estuary

2.85

18.33

4.78

30.74

Area off Zhejiang coast

1.49

5.28

2.77

Taiwan Strait

1.46

16.26

2.01

Echinodermata

Others

Area

Total
P

Bio

P

Bio

P

2.75 17.72

3.53

22.71

1.63

10.50 15.55

9.81

1.36

4.82

19.73

69.92

2.87

10.16

28.22

22.42

1.01 11.20

3.27

36.46

1.23

13.66

8.98

Bio: biomass; P: Percentage of biomass

The biomass of each taxonomic group from three cruises (March 2001, April and August 2011) inthe East China Sea are shown in Figure 3.4.17. The biomass showed high difference among cruises
for each taxonomic group, especially for Mollusca, Echinodermata, and others (species notbelonging to Polychaeta, Crustacea, Mollusca and Echinodermata).

26
24
22
20

OthersEchinodermata
Mollusca
Crustacea
Polychaeta

Biomass (g/m2)

18
16
14
12
10
8
6
4
2
0
March 2001

Fig. 3.4.17

April 2011

August 2011

Biomass of the main taxonomic groups in the East China Sea.

Diversity
The diversity indexes (Margalef richness index (d), Shannon-Wiener index (H’) and Pielou’s
evenness index (J’)) from three cruises (March 2001, April and August 2011) in the East China Sea
are shown in Figure 3.4.18.
The mean value of Margalef richness index (d) was highest (3.81) in March 2001 and lowest (3.05)
in April 2011 (Li et al., 2014). The mean value of the Shannon-Wiener index (H’) also had thehighest value (3.06) in March 2001, but the lowest value (2.36) in August 2011. The mean value of
Pielou’s evenness index (J’) was highest (0.88) in March 2001 and lowest (0.65) in August 2011.

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d
4.0

H'

J'

d

H'
J'
3.4 0.9

3.8

3.2

0.8

3.0

0.7

2.8

0.6

2.6

0.5

2.4

0.4

2.2

0.3

2.0

0.2

3.6
3.4
3.2
3.0
2.8
2.6
2.4
2.2
2.0
March 2001

April 2011

August 2011

Fig. 3.4.18 Diversity indexes (Margalef richness index (d), Shannon-Wiener index (H’) and Pielou’s
evenness index (J’)) in the East China Sea.

Secondary productivity
The mean secondary productivity in the East China Sea was 1.37 g(AFDW)/(m2·a) in April 2011
(Li et al., 2014). In August 2011, the mean value of secondary productivity increased to
4.11 g(AFDW)/(m2·a).

3.4.4 Summary and prospects
In this section, we discussed the number of species, dominant species, community structure,abundance, biomass, diversity and secondary productivity of macrobenthos in the Yellow Sea andEast China Sea. The macrobenthic community changed through time and areas, together with thesecommunity parameters. However, there were still some things that did not change. For example, inboth the southern Yellow Sea and the East China Sea, the macrobenthos had the highest secondary
productivity in summer.
Up to now, we still lack the long-term, large spatial scale investigations of macrobenthos in the
Yellow Sea and East China Sea. The forming mechanism and the variation regularity of themacrobenthic community are still short of macroscopic analysis. However, many studies have been
conducted on macrobenthic ecology and biodiversity in the Yellow Sea and East China Sea. A
geographic information system (GIS) database has also been established. We think that in the future
it is essential to conduct research in the following directions (Li, 2011).
1) First, data from macrobenthos investigations collected since 1956 should be collected andintegrated, and a new ecological theory and new data analyzing method should be applied toanalyze the temporal and spatial variations of macrobenthic community structure of more than halfa century, combined with environmental variables such as primary productivity, hydrology, andsediment characters. The relationship between macrobenthic variation and global climate variation
also requires on-going research. Second, the bio-indicating function of the macrobenthic community
to marine environmental pollution needs to be studied; the indicator species, dominant species andtheir variations over time also need to be clarified. Third, ecological models should be developed to
predict the variation trends of the macrobenthic community; the role and status of the macrobenthos
in nutrient flow and material flow in the ecosystem and their relationship with plankton and nekton
as well as hydrology also requires explanation.

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Specific researches for future investigation are as follows:
1) Drawing the diagrams to depict the distribution pattern of macrobenthic abundance, biomass,
diversity indices and other ecological indices in the Yellow Sea and East China Sea and illustrate
their temporal and spatial variations. The main indices include species composition, the abundanceand distribution of dominant species, biodiversity indices, secondary productivity, the contributionof the four main taxonomic groups of macrobenthos to community structure and quantity
distribution. As we know, the species composition of macrobenthos has changed a lot since the1950s.
2) Data standardization. The mesh size used in most macrobenthic investigations was 1.0 mm
before 1995 and 0.5 mm after 1995. The difference in mesh size may cause variations in species
number, abundance, biomass and other indices. So the data should be standardized when using thehistorical data to analyze the temporal variation trend. Li et al. (2005) compared the macrobenthic
investigation results with the 1.0 mm mesh size and the 0.5 mm mesh size in Jiaozhou Bay, which
could be used as a reference (Li et al., 2005a).
3) Collecting the historical data on: water masses, currents, sediment characteristics, temperature,salinity, planktons, meiobenthos and other environmental and biological variables; analyzing therelationship between these variables and macrobenthos; coupling the regularities of environmental
variation and the macrobenthic community variation; exploring the relationship between theseregularities, and establishing a GIS database.
4) Conducting macrobenthic investigations, collecting samples, and clarifying the current status ofmacrobenthos in the Yellow Sea and East China Sea. The sampling methods should focus on
quantitative grabbing, quantitative and qualitative trawling, and diving in special marine habitats.
The status of macrobenthic community structure, biodiversity and secondary productivity and theirrelationship with environmental variables need to be studied.
Through research of the above, we hope to clarify the regularity of macrobenthic ecologicalvariations in the Yellow Sea and East China Sea, confirm the relationship between macrobenthic
community variations and the environmental variables, establish a GIS database of macrobenthos in
the Yellow Sea and East China Sea, explore the mechanism of variation of the macrobenthic
community and predict variation trends, and study the ecological services of the macrobenthos andput forward advice for natural resource protection to management and policy makers.

3.4.5 References
Blomberg, B.N. and Montagna, P.A. 2014. Meta-analysis of Ecopath models reveals secondary
productivity patterns across the Gulf of Mexico. Ocean Coast. Mgmt. 100: 32–40,
doi:10.1016/j.ocecoaman.2014.07.014.
Brey, T. 1990. Estimating productivity of macrobenthos invertebrates from biomass and mean individual
weight. Meeresforschung 32: 329–343.
Gao, A.G., Yang, J.Y., Zeng, J.N., Shou, L., Liao, Y.B., Xu, X.Q., Chen, Q.Z. and Hu, X.G. 2009.
Distribution of the intertidal macrobenthos in the Haizhouwan Bay. J. Mar. Sci. 27: 22–29.
Gurjanova, P.V. and Wu, B.L. 1963. Benthic community from Jiaozhou Bay. Studia Mar. Sin. 3: 51–57.
Han, J., Zhang, Z.N. and Yu, Z.S. 2001. Study on the macrobenthic abundance and biomass in Bohai Sea.
J. Ocean Univ. Qingdao 31: 889–896.
Han, Q.X., Gao, W.F., Li, B.Q. and Li, X.Z. 2004. Evaluation on the biomass and resource of Ruditapes
philippinarum from Jiaozhou Bay. Chin. J. Zool. 39: 60–62 (in Chinese).

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Hu, H.Y., Huang, B., Tang, J.L., Ren, S.J. and Shao, X.W. 2000. Studies on benthic ecology in coastal
waters of Bohai and Yellow Sea. Donghai Mar. Sci. 18: 39–46.
Jia, H.B., Hu, H.Y., Tang, J.L., Huang, B., Mao, H.Y., Wang, J.Y. and Wei, N. 2010. The macrobenthos
ecology of the southern Yellow Sea. Oceanol. Limnol. Sin. 41: 842–849.
Lalli, C.M. and Parsons, T.R. 2000. Biological Oceanography: An Introduction. Publishing House of
Qingdao Ocean University, Qingdao.
Li, B.Q., Li, X.Z., Yu, H.Y., Wang, H.F., Wang, J.B., Xu, F.S. and Zhang, B.L. 2005. Macrobenthic
mollusca fauna and its relations to environmental factors in Jiaozhou Bay. Oceanol. Limnol. Sin. 36:
193–198 (in Chinese with English abstract).
Li, B.Q., Li, X.Z., Wang, H.F. and Zhang, B.L. 2006. Species diversity of macrobenthic mollusk fauna in
Jiaozhou Bay, Shandong. Biodiv. Sci. 14: 136–144, doi:10.1360/biodiv.050100.
Li, B.Q., Li, X.Z., Wang, H.F. and Jiang, Y.X. 2010. Situation and assessment on heavy metal contents infive important marine animal species in sea area near Rushan, Shandong Peninsula. Mar. Environ. Sci.
29: 392–395.
Li, R.G. 2003. Macrobenthos on the Continental Shelves and Adjacent Waters, China Sea. Ocean Press,
Beijing.
Li X.Z. 2011. An overview of studies on marine macrobenthic biodiversity from Chinese waters:
principally from the Yellow Sea. Biodiv. Sci. 19: 676–684, doi:10.3724/SP.J.1003.2011.09126.
Li, X.Z., Wang, H.F., Wang, J.B., Zhang, B.L. and Li, B.Q. 2005a. Comparison of sampling results ofmacrobenthos using different apertures of sampling sieve in Jiaozhou Bay. Mar. Sci. Beijing 29: 68–74.
Li, X.Z., Wang, H.F. and Zhang, B.L. 2005b. The secondary production of macrobenthos in Jiaozhou Bay,
Shandong. Oceanol. Limnol. Sin. 36: 527–533.
Li, X.Z., Wang, J.B., Wang, H.F. and Zhang, B.L. 2005c. Secondary production of macrobenthos fromthe East China Sea. Chin. J. Appl. Environ. Biol. 11: 459–462.
Li, X.Z., Yu, Z.S., Wang, J.B. and Wang, H.F. 2005d. Secondary production of macrobenthos in southern
Yellow Sea. Chin. J. Appl. Environ. Biol. 11: 702–705.
Li, X.Z., Zhang, B.L. and Wang, H.F. 2005e. Secondary production of macrobenthos from the anchovy
spawning ground in the southern Yellow Sea. Chin. J. Appl. Environ. Biol. 11: 324–327.
Li, X.Z., Zhang, B.L., Li, B.Q., Li, S.L., Wang, H.F. and Wang, J.B. 2007. Changes of body length andweight of Branchiostoma belcheri tsingtauense outside of Jiaozhou Bay and the environmental
factors causing the changes. Mar. Sci. 31: 55–59.
Li, X.Z., Wang, H.F., Wang, J.B., Dong, D., Ma, L., Kou, Q., Sui, J.X., Gan, Z.B. and Zhang, B.L. 2014.
Biodiversity variability of macrobenthic in the Yellow Sea and East China Sea between 2001 and2011. Zool. Systemat. 39: 459–484.
Liu, J.Y. and Hsu, F.S. 1963. Preliminary studies on the benthic fauna of the Yellow Sea and the East
China Sea. Oceanol. Limnol. Sin. 5: 306–321.
Liu, L.S. and Li, X.Z. 2002. Distribution of macrobenthos in spring and autumn in the East China Sea.
Biodiv. Sci. 10: 351–358, doi:10.17520/biods.2002049.
Liu, L.S. and Li, X.Z. 2003. Distribution of macrobenthos in spring and autumn in the southern Yellow
Sea. Oceanol. Limnol. Sin. 34: 26–32.
Liu, L.S., Meng, W., Tian, Z.Q. and Cai, Y.L. 2008. Distribution and variation of macrobenthos from the
Changjiang Estuary and its adjacent waters. Acta Ecol. Sin. 28: 3027–3034.
Liu, L.S., Meng, W., Li, X.Z., Li, Z.C., Zheng, B.H., Lei, K. and Li, Z.Y. 2009. Studies on macrobenthos
in the northern waters of Liaodong Bay. II. Biodiversity and community structure. Res. Environ. Sci.
22: 155–161 (in Chinese).

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Biological Oceanography

Liu, R.Y. 1992. Ecology and Living Resources of Jiaozhou Bay. Science Press, Beijing.
Liu, R.Y., Cui, Y.H., Xu, F.S. and Tang, Z.C. 1986. Ecological characteristics of macrobenthos of the
Yellow Sea and the East China Sea. Studia Mar. Sin. 27: 153–173.
Margalef, R. 1968. Perspective in Ecological Theory. University of Chicago Press.
Pielou, E.C. 1975. Ecological Diversity. Wiley, New York.
Qi, Z.Y., Ma, X.T., Wang, Z.R., Lin, G.Y., Xu, F.S., Dong, Z.Z., Li, F.L. and Lv, D.H. 1989. Mollusca of
Huanghai and Bohai. Science Press, Beijing.
Shannon, C.E. and Weaver W. 1949. The Mathematical Theory of Communication. University of Illinois
Press, Urbanna.
Shen, G.Y., Shi, B.Z., Guo, F. and Shi, B.Z. 2010. Marine Ecology. Science Press, Beijing.
Sui, J.X., Yu, Z.S., Qu, F.Y. and Liu, W.X. 2010. Preliminary ecological study of the macrobenthos in the
middle part of the Jiaozhou Bay. Mar. Sci. 34: 1–6.
Sun S. 2012. Regional Oceanography of China Seas - Biological Oceanography. Ocean Press, Beijing.
Tchang, S. 1935. Report on the first collection in the Kiaochow Bay and its vicinity. Bull. Inst. Zool. Nat.
Acad. Peiping 11: 195–262.
Tchang, S. and Ma, S.T. 1936. Report on the second and third collections in the Kiaochow Bay and its
vicinity. Bull. Inst. Zool. Nat. Acad. Peiping 17: 1176–1210.
Tchang, S. and Ma, S.T. 1949. Report on the fourth collection in the Kiaochow Bay and its vicinity. Bull.
Inst. Zool. Nat. Acad. Peiping 23: 1113–1180.
Wang, H.F., Li, X.Z., Wang, J.B. and Li, B.Q. 2011. The ecological research of the macrobenthic
community from sea areas around Qingdao during the upsurge of green seaweed Enteromorphaprolifera in summer of 2008. Mar. Sci. 35: 10–18.
Wang, J.B., Li, X.Z. and Wang, H.F. 2006. Ecological characteristics of dominant polychaete species
from the Jiaozhou Bay. Acta Zool. Sin. 52: 63–69.
Weng, X.C., Zhang, Y.K., Wang, C.M. and Zhang, Q.L. 1988. The variational characteristics of the
Huanghai (Yellow Sea) cold water mass. Oceanol. Limnol. Sin. 19: 368–379.
Xu, Q.Z., Li, R.X., Wang, Z.L., Fan, S.L. and Wang, Z.X. 2009. Macrobenthos distribution of the South
Yellow Sea in summer. Adv. Mar. Sci. 27: 393–399.
Xu, Y., Li, X.Z., Wang, H.F. and Zhang, B.L. 2016. Seasonal and spatial variations of macrobenthic
community structure and diversity in the South Yellow Sea. Aquat. Ecosyst. Health Mgmt. 19: 92–100,
doi:10.1080/14634988.2016.1139429.
Xu, Y., Sui, J., Yang, M., Sun, Y., Li, X., Wang, H. and Zhang, B. 2017. Variation in the macrofaunal
community over large temporal and spatial scales in the southern Yellow Sea. J. Mar. Syst. 173: 9–
220, doi:10.1016/j.jmarsys.2016.11.006.
Zhang, J.L., Xu, F.S. and Liu, R.Y. 2012. Community structure changes of macrobenthos in the South
Yellow Sea. Chin. J. Oceanol. Limnol. 30: 248–255, doi:10.1007/s00343-012-1076-0.
Zhang, Z.N., Tu, L.H. and Yu, Z.S. 1990. Preliminary study on the macrofauna in the Huanghe River
Estuary and its adjacent waters (I) The Biomass. J. Ocean Univ. Qingdao 20: 37–45.

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3.5 Fish and Invertebrate Fisheries
Weiwei Xian and Cui Liang
Institute of Oceanology, Chinese Academy of Sciences, China

3.5.1 Introduction
A large amount of freshwater and terrigenous materials are injected into the Yangtze River estuary
annually, which sustain the productive fishery grounds, Zhoushan and Lüsi fisheries, in the adjacentseas (Zhuang, 2006), and provide a changeable environmental condition for estuarine organisms. In
the offshore area of Yangtze River estuary, 16 stations were set to examine the fish and invertebrate
community composition and variation from 1985 to 2004 (Fig. 3.5.1). This section provides specificinformation of seasonal variation of fish and invertebrate communities in the offshore area of the
Yangtze River estuary based on data obtained in 2004. In addition, information and interannual
variation of the key species in this area were also described based on information from FishBase
and the co-author’s unpublished data.

Fig. 3.5.1
circles).

Map of the Changjiang River basin and estuary, with the sampling stations (solid black

3.5.2 Methods
From 1985 to 2004 (autumn in 1985; spring in 1986; autumn in 1998; spring in 1999; autumn in
2000; spring in 2001; spring, summer, autumn and winter in 2004), 16 stations in the offshore areaof Yangtze River estuary were sampled by trawl survey. At each station, a trawl survey was
conducted by two vessels with the vessel speed of approximately 3 knots (5.5 kph) for 1 hour. The
fish and invertebrate samples were weighed, counted and sorted to the species level according totheir morphological features.

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The ecological density of marine fishery species can be measured by two indexes: number
ecological density (NED, ×103 ind./km2) and biomass ecological density (BED, kg/km2) as follows:
NED = YN/[(A)(q)],BED = YW/[(A)(q)]where A is the swept area per hour, YN is the mean trawl fish capture number per hour, YW is themean trawl fish capture weight per hour, and q is the catchability coefficient, assumed to be 0.6.

3.5.3 Nekton assemblages in the Yangtze River estuary
Fish community
The particular geographical position generates a unique environment in the offshore area of the
Yangtze River estuary, and also results in distinguishing fish and invertebrate communities in thisarea. The fish community in the offshore area of the Yangtze River estuary includes bothwarm-water pelagic and cold-water demersal species, and includes both coastal species and offshore
migratory species. According to the fishery investigation in 2004, the fish community in theoffshore area of Yangtze River estuary included 47 species, representing 42 genera and 31 families,
among which Perciformes occupied the most, including 23 species, 21 genera and 15 families(Fig. 3.5.2). Fish community composition showed seasonal variation in the estuary, with thecommon feature that the community was comprised mainly by Perciformes (Fig. 3.5.3). All fish
species and their occurrence in different months are listed in Table 3.5.1.

Species
Genus
FamilyLophiiformes
Tetraodontiformes
Scorpaeniformes
Perciformes
Gasterosteiformes
Mugiliformes
Anguilliformes
Myctophiformes
Clupeiformes
0

5

10

15

20

25

NumberFig. 3.5.2

286

Fish catches composition in the offshore area of the Yangtze River estuary in 2004.

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Species
Genus
FamilyLophiiformes
Tetraodontiformes

Species
Genus
FamilyLophiiformes
Tetraodontiformes
Scorpaeniformes

Scorpaeniformes
Perciformes

Perciformes

Gasterosteiformes

Gasterosteiformes

Mugiliformes

Mugiliformes

Anguilliformes

Anguilliformes
Myctophiformes

Myctophiformes

A
04
Number2

6

B

Clupeiformes

Clupeiformes

0

8

2

4

6

8

10

NumberSpecies
Genus
FamilyLophiiformes
Tetraodontiformes

Species
Genus
FamilyLophiiformes
Tetraodontiformes

Scorpaeniformes

Scorpaeniformes

Perciformes

Perciformes

Gasterosteiformes

Gasterosteiformes

Mugiliformes

Mugiliformes

Anguilliformes

Anguilliformes

Myctophiformes

MyctophiformesC

Clupeiformes
0

3

6

9
Number12

15

D

Clupeiformes
18

0

3

6

9
Number12

15

18

Fig. 3.5.3 Fish community composition and monthly variation in the offshore area of the Yangtze Riverestuary in 2004 (A: spring, B: summer, C: autumn, D: winter).

Table 3.5.1 Fish species in the offshore area of Yangtze River estuary in 2004.
Scientific nameFebruary

May

AugustClupeiformes
Clupeidae
Ilisha elongata
Engraulidae
Engraulis japonicus
Thrissa kammalensis
Coilia ectenes
Coilia mystus
Setipinna taty
Anchoviella commersonii
Myctophiformes
Synpdidae
Saurida elongata
Harpadon nehereus
Myctophidae
Benthosema pterotum
Anguilliformes
Muraenesocidae
Muraenesox cinereus

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November

＋
＋
＋
＋
＋
＋

＋
＋

＋

＋

＋
＋
＋
＋
＋
＋

＋
＋

＋
＋

＋

＋

＋
＋
＋

＋
＋

＋

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Table 3.5.1 Continued.
Scientific nameFebruary

Mugiliformes
Sphyraenidae
Sphyraena pinguis
Polynemidae
Polynemus sextarius
Gasterosteiformes
Syngnathidae
Syngnathus acus

May

＋

＋

＋

＋

Perciformes
Pomadasyidae
Hapalogenys mucronatus
Priacanthidae
Priacanthus macracanthus
Acropomatidae
Acropoma japonicum
Sillaginidae
Sillago japonica

288

November＋

Kurtiformes
Apogonidae
Apogonichthys lineatus

Carangiformes
Carangidae
Selar crumenophthalmus
Decapterus maruadsi
Trachurus japonicus
Sciaenidae
Argyrosomus argentatus (Nibea albiflora
Collichthys lucidus
Johnius belengeri
Pseudosciaena polyactis
Trichiuridae
Trichiurus japonicus
Scombridae
Pneumatophorus japonicus
Scomberomorus niphonius
Theraponidae
Therapon jarbua
Stromateidae
Pampus nozawae
Pampus argenteus
Centrolophidae
Psenopsis anomala
Gobiidae
Chaeturichthys stigmatias
Chaeturichthys hexanema
Odontamblyopus rubicundus

August

＋

＋
＋
＋
＋

＋
＋
＋

＋
＋
＋

＋

＋

＋

＋
＋
＋
＋
＋

＋

＋

＋

＋

＋

＋
＋

＋
＋

＋

＋

＋
＋

＋
＋

＋
＋

＋

＋

＋

＋
＋

＋
＋
＋

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Table 3.5.1 Continued.
Scientific nameFebruary

Scorpaeniformes
Triglidae
Lepidotrigla punctipectoralis
Aploactidae
Erisphex potti
Synanceiidae
Minous monodactylus
Vespicula sinensis
Platycephlidae
Platycephalus indicus
Liparidae
Liparis tanakae＋

May

AugustNovember

＋

＋

＋

＋
＋

＋

＋

＋

＋

＋

Tetraodontiformes
Aluteridae
Navodon septentrionalis
Tetraodontidae
Fugu obscurusFugu alboplumbeus＋

Lophiiformes
Lophiidae
Lophius litulon

＋

＋
＋

＋

＋

+ indicates occurrence.

Invertebrate community
In 2004, 27 species were sampled in the offshore area of the Yangtze River estuary, representing 19
Crustacea, 6 Mollusca and 2 Coelenterata (Fig. 3.5.4). Crustacea was the predominant category in
all four months, and no Coelenterates were observed during the survey conducted in February. All
invertebrate species and their occurrence in different months are listed in Table 3.5.2.

Crustacea

Nov.

Mollusca
Coelenterata

Month

Aug.

May

Feb.

Overall
0

5

10

15

20

NumberFig. 3.5.4 Invertebrate catches composition and monthly variation in the offshore area of the Yangtze
River estuary in 2004 (Xian, unpub.).

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Table 3.5.2 Invertebrate species in the offshore area of Yangtze River estuary in 2004.
Scientific nameFebruary

May

AugustNovemberOratosquilla oratoria

＋

＋

＋

＋

Charybdis bimaculata

＋

＋

＋

Charybdis japonica

＋

＋

＋

Portunus sanguinolentus

＋

＋

＋

Portunus trituberculatus

＋

＋

＋

Crustacea＋

Matuta planipes＋

Neodorippe japonicum＋＋

Calappidae＋

Eucrate crenata
Crangon affinis＋

Palaemon gravieri＋

Exopalaemon carinicauda＋

Alpheus distinguendus＋

Acetes chinensis

＋

＋

＋

＋

Penaeus spp.

＋

Penaeus orientalis＋

Metapenaeopsis dalei＋

Trachypenaeus curvirostris＋

Leptochela gracilis

＋

＋

＋

＋

＋

＋

＋

＋

＋

＋

＋

＋

Mollusca
Loligo japonica＋

Euprymna spp.

＋

Sepiola birostrata＋

Sepiola spp.

＋

Octopus variabilis＋

Hyriopsis cumingii＋

Coelenterata
Sanderia malayensis
Rhopilema esculenta＋＋＋+ indicates occurrence.

3.5.4 Key species
Sliver pomfret
Pampus argenteus
Sliver pomfret Pampus argenteus is a coastal species, inhabiting in areas with slow flow, and its
preferable water layer range is 30–70 m. In spring, sliver pomfret head north from the northern partof Taiwan waters to the north East China Sea, and move into the Yangtze River estuary to spawn inearly May. After spawning, they disperse for feeding and migrate to the south after autumn. Sliver
pomfret feed on small fishes and shrimps, as well as plankton, i.e., jellyfish, copepods and diatoms.

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In spring, the resource of sliver pomfret in the offshore area of the Yangtze River estuary, assessedboth by NED and BED, showed an increasing trend, and reached its peak value in 2001; while theresource decreased remarkably in 2004, and the NED and BED occupied only 0.01% and 0.69%,
respectively, of the corresponding values in 2001 (Fig. 3.5.5A).
Sliver pomfret resource in the offshore area of the Yangtze River estuary witnessed larger variation
in autumn from 1985 to 2004, with its peak value in 1998 and the lowest value in 2000. The BED in
2004 was only 4.28% of that in 2001, and NED was 1.00% (Fig 3.5.5B).

BED

200

A

NED

2500

B

BED

5000

160

2000
1500
1000

4000
120
3000
80
2000
40

500

1000

0

0
1986

1999

2001
Year

2004

6000

kg/km2

NED*103 ind./km2*103 ind./km2 or kg/km2

3000

0
1985

1998

2000

2004

Year

Fig. 3.5.5 Number ecological density (NED) and biomass ecological density (BED) variation of sliver
pomfret in the offshore area of the Yangtze River estuary in (A) spring and (B) autumn (Xian, unpub.).

Small yellow croaker
Larimichthys polyactis
Small yellow croaker Larimichthys polyactis was regarded as one of the four most importantcommercial fishery resources in China and was the main target fish of the bottom trawl fisherybefore the 1970s, while, with the repid increase of fishing intensity, their resources have greatly
declined in the last few decades (Cheng et al., 2003). Because of the significant role ofLarimichthys polyactis in Chinese fisheries, a number of studies have focused on their status andindicated a tendency of smaller size and faster growth (Liang and Pauly, 2017). Larimichthyspolyactis is always found in the sublittoral zone above 120 m sandy mud bottoms, and has verticalmovement habit. Every year in April or May, when the water temperature starts to rise, the small
yellow croaker community migrates north from the junction of the Yellow Sea and the East China
Sea to Jiangsu–Shandong coast for spawning, after which they disperse and prey near shore; when
the autumn comes, water temperature declines, and the community migrates back for overwintering.
Small yellow croaker feed mainly on shrimps and small fishes.
In spring, small yellow croaker BED in 1986 was quite low, only 134 kg/km2, then it increased
abruptly to 1993 kg/km2 in 1999. Afterwards, it decreased again in 2001, and further down in 2004;
the same variation trend was detected in NED in spring (Fig. 3.5.6A). In autumn, the resource
varied in the same way as spring (Fig. 3.5.6B).
The resource of small yellow croaker was low in the 1980s, which could be attributed to intensive
fishing activities, while, with the establishment of no-fishing zones, the resource started to rebuildin the late 1990s, although it declined again afterwards.

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A

BED

300

2500

80
*103 ind./km2

1000

kg/km2*103 ind./km2

60
40

B

BED

8000

250

2000
1500

NED

6000
200
4000

150

kg/km2

NED

Biological Oceanography

100

20

2000

500

0

50

0
1986

1999

2001

0

2004

0
1985

1998

2000

Year

2004

Year

Fig. 3.5.6 Number ecological density (NED) and biomass ecological density (BED) variation of small
yellow croaker in the offshore area of the Yangtze River estuary in (A) spring and (B) autumn (Xianunpub.).

Largehead hairtail
Trichiurus lepturus
Largehead hairtail Trichiurus lepturus is a migratory species, which is widespread in China’s seas,
especially in the East China Sea. Largehead hairtail has its main fishing grounds in the East China,
Yellow and Bohai seas, is the fish species with the highest national catch in China (Wu and Zhu
1979). In recent years, T. japonicus has produced high yields in Chinese waters, attributed to intense
fishing pressure, offshore expansion of the fisheries and high proportion of larvae (Zhang et al.
2006). In May–July, the largehead hairtail community spawns and feeds in the Yangtze Riverestuary, after which the community migrates northward to develop the summer fishing season. Then,in the late autumn and early winter, the community moves southward and forms the winter fishingseason in Zhoushan waters and its adjacent area. Largehead hairtail feed mainly on shrimps, squids
and other fishes, and is the most important commercial fish in China’s coastal area.
In spring, BED and NED of largehead hairtail were both stable at low levels, compared to autumn,in the offshore area of the Yangtze River estuary (Fig. 3.5.7A). In November, BED and NED had
the similar trends, with the highest value in 2004 and lowest in 1985 (Fig. 3.5.7B).

NED

BED

350

500

A

NED

5000

BED

B

300

4000

200
10

*103 ind./km2

300

kg/km2*103 ind./km2

400
20

250
200

3000

150

2000

kg/km2

30

100
100

0

0
1986

1999

2001
Year

2004

1000

50

0

0
1985

1998

2000

2004

Year

Fig. 3.5.7 Number ecological density (NED) and biomass ecological density (BED) variation oflargehead hairtail in the Yangtze River estuary in (A) spring and (B) autumn (Xian unpub.).

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Japanese grenadier anchovy
Coilia nasus
Japanese grenadier anchovy Coilia nasus is an estuarine migratory species, and spends its whole lifein the estuary area. In the early spring, the fish community gathers in the estuary and forms thespring fishing season in March–April. In the late April, Japanese grenadier anchovy migrate to the
freshwater area inside the estuary to spawn, with the main spawning season during early May toearly July. The main prey of Japanese grenadier anchovy are shrimps, copepods and juvenile fishes.
The maximum output of Japanese grenadier anchovy once reached 281.8 tonnes in 1973, but its
yield reduced in 1980s because of the intensive fishing pressure, and further sharply decreased in
1990s (Li and Huang, 2009). The output of Japanese grenadier anchovy was only 5 tonnes in 1994
and could barely form a fishing season, which suggests that it was in severe recession (Chen et al.,2002).
In May, the NED of Japanese grenadier anchovy had its highest value in 2001, while the peak valueof BED occurred in 1999, which indicates that its catches had larger individual size in 1999
compared to 2001; in 2004, both NED and BED experienced their lowest levels in spring
investigations (Fig. 3.5.8A).
In autumn, NED was highest in 1998 in the offshore area of Yangtze River estuary, while BED
decreased significantly from 1985 to 2004, which resulted in the conclusion that the catches in 1998
had smaller body size (Fig. 3.5.8B).

500

40

400

30

300

20

200

10

100

0

0
1986

1999

2001
Year

2004

400

NED

B

BED

2500
2000

300

1500
200

kg/km2A

BED*103 ind./km2

NED

kg/km2*103 ind./km2

50

1000
100

500

0

0
1985

1998

Year

2000

2004

Fig. 3.5.8 Number ecological density (NED) and biomass ecological density (BED) variation ofJapanese grenadier anchovy in the offshore area of the Yangtze River estuary in (A) spring and (B) autumn(Xian, unpub.).

Scaly hairfin anchovy
Setipinna taty
Scaly hairfin anchovy Setipinna taty is a warm water marine species which has a wide distribution;
it can be caught during the whole year in the Yangtze River estuary, with its fishing season
occurring in spring and autumn. Scaly hairfin anchovy feed mainly on plankton, and it can also be
the prey of carnivorous fishes in the estuary. With intense fishing activities, the spawning stock ofscaly hairfin anchovy has been experiencing an earlier sexual maturity and smaller individual size
(Xiong et al., 2009).

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In May, both NED and BED showed slight ascendant trends from 1986 to 2001, and had the lowest
values in 2004 while, in autumn, NED appeared a more fluctuate trend, with its highest and lowest
values occurred in 1998 and 2004, respectively (Fig. 3.5.9). BED shared the similar tendency with
NED, which implied that no significant change was observed in body size catches.

NED

A

BED

1200

100

1000

80

800

60

600

40

400

800

NED

B

BED

12000

kg/km2*103 ind./km2*103 ind./km2

600

9000
400
6000
200

20

3000

200

0

0
1986

1999

Year

2001

2004

15000

kg/km2

120

0

0
1985

1998

2000

2004

Year

Fig. 3.5.9 Number ecological density (NED) and biomass ecological density (BED) variation of scaly
hairfin anchovy in the offshore area of Yangtze River estuary in (A) spring and (B) autumn (Xian, unpub.).

Bombay-duckHarpadon nehereus
Bombay-duck Harpadon nehereu is a benthopelagic species living in warm oceans. It usually
inhabits deep water offshore on sandy mud bottom for most of the year, but also gathers in large
shoals in deltas of rivers to feed during monsoons. In China, Bombay-duck distributes in the South
China Sea, East China Sea and the southern Yellow Sea and their estuaries; in the investigation area,
it can be caught in both spring and autumn, with a large number occurring in autumn. It feeds
mainly on benthic shrimps and small fishes. As the catches of Bombay-duck has increased
significantly since the mid-1990s, its fishery resource value has been noticed (Li et al., 2004). The
catch of Bombay-duck has been stable, and capture size has not changed significantly, which meansthat there is a certain potential for its resource exploitation (Chen et al., 2012; Luo et al., 2012). On
the other hand, the proportion of the low-age groups of Bombay-duck has increased in the capture,
so attention should be paid to its resource management to guarantee its sustainable use (Chen et al.,2012).
The Bombay-duck resource in spring, measured by BED, remained stable at a low level; however,
the NED reached a high value in 2001, which indicated that the catch during this time wascharacterized by small body size (Fig. 3.5.10A).
Bombay-duck played a relatively important role in fishery resources in the offshore area of the
Yangtze River estuary in autumn. NED and BED shared similar trends, except in 1985, when BED
had lower proportion compared with NED, implying a smaller body size in catches in autumn(Fig. 3.5.10B).

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NED

A

BED

2500

1000

2000

1500

15000

1000

10000

200

500

5000

0

0

600
1000
400

0
1986

1999

2001

25000

B

BED

20000

1500

500

NED

2000

800

kg/km2*10 3 ind./km2

1200*103 ind./km2

2500

Fish and Invertebrates

2004

kg/km2Biological Oceanography

0
1985

1998

Year

Year

2000

2004

Fig. 3.5.10 Number ecological density (NED) and biomass ecological density (BED) variation of
bombay-duck in the offshore area of Yangtze River estuary in (A) spring and (B) autumn (Xian unpub.).

Horse crab
Portunus trituberculatus
Horse crab Portunus trituberculatus is distributed only incoastal areas of China, Japan and Democratic People’s
Republic of Korea, and inhabits sandy to sandy-muddy
substrates with depths of 7–100 m (Ng, 1998). Horse crab
has become one of the most important commercial
resources in the Yangtze River and its adjacent area sincethe 1970s, and can form two commercial aggregations
annually, with spawning stock in spring and feeding stockin autumn.
For both spring and autumn, the maximum resource of horse crab occurred in the 1980s. After that,the resource decreased abruptly and stayed stable at a low level compared to the 1980s. NED and
BED had the similar trend, indicating that no significant change in size (Fig. 3.5.11).

A

BED

120

30

3000

20

2000

10
0
1986

1999

Year

2001

2004

NED

B

BED

100

4000

*103 ind./km2*103 ind./km2

40

5000

10000
8000

80
6000
60

kg/km2NED

kg/km2

50

4000
40

1000

20

0

0

2000
0
1985

1998

Year

2000

2004

Fig. 3.5.11 Number ecological density (NED) and biomass ecological density (BED) variation of horsecrab in the offshore area of Yangtze River estuary in (A) spring and (B) autumn (Xian, unpub.).

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Japanese squillid mantis shrimp
Oratosquilla oratoria
Japanese squillid mantis shrimp Oratosquilla oratoriahas has become
an important invertebrate resource in the Yangtze River estuary since
the traditional commercial invertebrate species declined. It burrows innear-shore bottom substrates (Manning, 1998) in sheltered embayment
or estuaries (Ahyong, 2001), and occurs in muddy bottoms of bays andinlets (Hamano et al., 1987). Japanese squillid mantis shrimp feeds
mainly on crustaceans, mollusks, polychaetes, and fishes.
The stock of Japanese squillid mantis shrimp in the offshore area of the
Yangtze River estuary showed a stable increasing trend from 1998 to2001, except for mean individual biomass in autumn (Table 3.5.3).

Table 3.5.3 Resource estimation of Japanese squillid mantis shrimp in autumn and spring during 1998–
2001.
Parameter
NED
BED
Mean individual biomass
Resource (×104 kg)

Nov. 1998

Nov. 2000

May 1999

May 2001

5.28
44.08
9.52
62.6

11.64
66.05
9.50
93.8

0.17
3.05
13.74
4.33

1.34
22.94
19.23
32.6

NED: Number ecological density; BED: biomass ecological density

Chinese ditch prawn
Palaemon gravieri
Chinese ditch prawn Palaemon graviera is a pelagic
species, and is distributed in subtropical waters,
mainly in China and Korea (www.sealifebase.org).
The resource of Chinese ditch prawn fluctuatedduring 1998 to 2001. In autumn, NED, BED andresource increased from 1998 to 2000; however, theindividual size shrunk. In spring, NED, BED andresource decreased significantly from 1999 to 2001,
whereas the mean individual size increased (Table 3.5.4).
Table 3.5.4 Resource estimation of Chinese ditch prawn in autumn and spring during 1998–2001.
Parameter
NED
BED
Mean individual biomass
Resource (×104 kg)296

Nov. 1998

Nov. 2000

May 1999

May 2001

3.50
5.80
2.32
8.24

5.39
8.52
1.58
12.10

17.77
24.51
1.37
34.80

3.53
10.86
3.08
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Japanese squid
Loliolus japonica
Japanese squid Loliolus japonica is one of the most important marine
cephalopods in the Yangtze River estuary. Its male and female adults
usually die shortly after spawning and brooding, respectively. Embryos
hatch into planktonic stage and live for some time before they growlarger and take up a benthic existence as adults (Ruppert et al., 2004).
In autumn, NED, BED, mean individual size and resource of Japanesesquid in the offshore area of the Yangtze River estuary decreased in2000 compared to 1998, indicating declining stock in autumn. In spring,these parameters in 2001 were all higher than in 1999, except for mean
individual size, which was slightly lower than that in 1999 (Table
3.5.5).

Table 3.5.5 Resource estimation of Japanese squid in autumn and spring during 1998–2001.
Parameter
NED
BED
Mean individual biomass
Resource (×104 kg)

Nov. 1998

Nov. 2000

May 1999

May 2001

12.37
26.39
2.13
37.47

5.44
9.40
1.73
13.35

0.70
9.49
13.56
13.47

1.54
20.33
13.20
28.87

3.5.5 Summary and future perspective
The offshore area of the Yangtze River estuary, as the prime spawning and feeding ground for fish
and invertebrates, is one of the highest fishery production areas in China. On account of thecomplexity of terrain and water mass conditions, as well as fishing activities, fish and invertebrate
community structure have always been under a great variation.
Located in the junction of the Yellow Sea and East China Sea, the Yangtze River estuary provides
spawning ground, nursing ground, feeding ground and migratory passage for important commercial
species. Although studies focusing on fish and invertebrates in the Yangtze River estuary started in
the mid-1980s, there are still some unknowns need to be further explored. Multiple pressures, i.e.,
human activities, global warming and environmental pollution, are occurring in the Yangtze Riverestuary, but the adaptive mechanism of fish and invertebrates to them is still obscure. The number offish stocks is mainly driven by the amount of recruitment, and the recruitment mechanism of
population directly determines the sustainable yield of biological resources, while recruitment
process and mechanism of key resources still need to be clarified. Moreover, to explore thetheoretical basis of habitat restoration and resource conservation for inshore fishery stocks, resource
dynamic model analysis and comprehensive evaluation of resource effects are also necessary.

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3.5.6 References
Ahyong, S.T. 2001. Revision of the Australian stomatopod Crustacea. Rec. Austral. Museum 26(Suppl.):1–326, doi:10.3853/j.0812-7387.26.2001.1333.
Chen, D.Q., Duan, X.B. and Liu, S.P. 2002. On the dynamics of fishery resources of the Yangtze Riverand its management. Acta Hydrobiol. Sin. 26: 685–690 (in Chinese with English abstract).
Chen, L., Shui, B. and Dong, W. 2012. The growth characteristics and sustainable resource use of
Harpadon nehereus. Technol. Mgmt. 3: 68–70 (in Chinese).
Cheng, J., Lin, L., Ling, J., Li, J. and Ding, F. 2003. Effects of summer close season and rational
utilization on redlip croaker (Larimichthys polyactis Bleeker) resource in the East China Sea Region.
J. Fish. Sci. China 11: 554–560.
Hamano, T., Morrissy, N.M. and Matsuura, S. 1987. Ecological information on Oratosquilla oratoria(Stomatopoda, Crustacea) with an attempt to estimate the annual settlement date from growthparameters (Hakata bay, Japan). J. Shimonoseki Univ. Fish. 36: 9–27 (in Japanese with English
abstract).
Li, J., Li, S. and Cheng, J. 2004. Structure analysis on the utilization of trawl fishery resources on thefishing grounds of the Changjiang estuary. Mar. Fish. 26: 24–28 (in Chinese with English abstract).
Li, M. and Huang, S. 2009. Management of fishery resources in Yangtze River Estuary. Asian Agri. Res.
1: 31–35, 40.
Liang, C. and Pauly, D. 2017. Fisheries impacts on China’s coastal ecosystems: Unmasking a pervasive‘fishing down’ effect. PLOS One 12: e0173296, doi:10.1371/journal.pone.0173296.
Luo, H., Zhang, H., Li, P. and Zhou, Y. 2012. Analysis of the current situation of fishery biology ofHarpadon nehereus in the East China Sea. J. Zhejiang Ocean Univ. (Natural Science) 31: 202–205,
233 (in Chinese with English abstract).
Manning, R.B. 1998. Stromatopods, pp. 827–849 in: The Living Marine Resources of the Western
Central Pacific. Vol. 2. Cephalopods, Crustaceans, Holothurians and Sharks edited by K.E. Carpenter
and V.H. Niem, FAO Species Identification Guide for Fishery Purposes, FAO, Rome.
Ng, P.K.L. 1998. Crabs, pp. 1045–1155 in: The Living Marine Resources of the Western Central Pacific.
Vol. 2. Cephalopods, Crustaceans, Holothurians and Sharks edited by K.E. Carpenter and V.H. Niem,
FAO Species Identification Guide for Fishery Purposes, FAO, Rome.
Ruppert, E.E., Fox, R.S. and Barnes, R.D. 2004. Invertebrate Zoology. A Functional Evolutionary
Approach. 7th Ed. Brooks/Cole, Thomson Learning, Inc. 990 pp.
Wu, J.Z. and Zhu, D.L. 1979. Study on the change and rational utilization of fish resources in Zhejiang
offshore and adjacent sea areas. Mar. Fish. 3: 6–10 (in Chinese with English abstract).
Xiong, Y., Tang, J., Liu, P., Zhong, X., Yan, L., Gao, Y., Wu, L. and Mao, H. 2009. Resource estimate on
Setipinna taty in the Southern Yellow Sea. Oceanol. Limnol. Sin. 40: 500–505 (in Chinese withEnglish abstract).
Zhang, Q.H., Cheng, J.H., Xu, H.X., Shen, X.Q., Yu, G.P. and Zhen, Y.J. 2006. Fishery Resources andTheir Sustainable Utilization in the East China Sea. Fudan University Press.
Zhuang, P. 2006. Fishes of the Yangtze Estuary. Shanghai Scientific and Technical Publishers.

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