TYPE Original Research
PUBLISHED 09 February 2024
DOI 10.3389/fmars.2024.1338835

OPEN ACCESSEDITED BY

Ricardo Torres,
Plymouth Marine Laboratory, United Kingdom
REVIEWED BY

Jianzhong Ge,
East China Normal University, China
Jenny Jardine,
University of Southampton, United Kingdom*CORRESPONDENCE

Liang Zhao
zhaoliang@tust.edu.cn
Yucheng Wang
ycwang@qnlm.ac
RECEIVED 15 November 2023
ACCEPTED 25 January 2024
PUBLISHED 09 February 2024
CITATION

Zhang J, Zhu L, Guo X, Wang Y, Feng J andZhao L (2024) Export production in acontinental shelf with multisource
nutrient supply.
Front. Mar. Sci. 11:1338835.
doi: 10.3389/fmars.2024.1338835
COPYRIGHT© 2024 Zhang, Zhu, Guo, Wang, Feng andZhao. This is an open-access article distributed
under the terms of the Creative Commons
Attribution License (CC BY). The use,
distribution or reproduction in other forums
is permitted, provided the original author(s)and the copyright owner(s) are credited andthat the original publication in this journal iscited, in accordance with accepted academic
practice. No use, distribution or reproduction
is permitted which does not comply withthese terms.

Export production in acontinental shelf withmultisource nutrient supply
Jing Zhang 1,2, Lei Zhu 2, Xinyu Guo 3, Yucheng Wang 4*,
Jianlong Feng 1,2 and Liang Zhao 1,2*
1
Key Laboratory of Marine Resource Chemistry and Food Technology (TUST), Ministry of Education,
Tianjin, China, 2 College of Marine and Environmental Science, Tianjin University of Science andTechnology, Tianjin, China, 3 Center for Marine Environmental Studies, Ehime University,
Matsuyama, Japan, 4 Laoshan Laboratory, Qingdao, China

Export production, which is deﬁned as the export of organic matter ﬁxed byphotosynthesis, is crucial for sustaining oceanic carbon uptake. The export route in
the open ocean is the sinking of biogenic particles through the bottom of theeuphotic layer. In contrast, the export routes in the shelf seas are the sinking of
biogenic particles to the sediment and the horizontal transport of biogenic
particles across the boundary of the shelf seas to the open ocean. The biogenic
particles in the shelf seas are supported by multisource nutrients including riverine
and oceanic ones. Their exports depend on the hydrodynamic conditions andbiogeochemical processes responsible for different sources of nutrients. Here, aunique physical-biological coupled model with a tracking approach is applied toevaluate the export production supported by multisource dissolved inorganic
nitrogen (DIN) over the East China Sea. The total export production is 6.83 kmol
N s-1 (=17.16 Tg C yr-1), which is slightly lower than the reported atmospheric CO2
absorption. Approximately 80% of particulate organic nitrogen (PON) is exported
via off-shelf transport, and the remaining 20% is buried in the sediment. The PON
supported by DIN from rivers accounts for 8% of export production, with an e-ratio(export production/primary production) of 0.09. In comparison, that from theKuroshio accounts for 64%, with an e-ratio of 0.22. This suggests that offshore
areas here are more efﬁcient in exporting local production than nearshore ones,
largely supported by oceanic nutrients.

KEYWORDS

export production, continental shelf pump, biological pump, the Kuroshio, the East
China Sea

1 Introduction
Continental shelf seas are responsible for 40% of the carbon sequestration in the ocean(carbon sinking below the thermocline) and play an important role in the global carbon
budget and climate change (Muller-Karger et al., 2005; Laruelle et al., 2018). The key to thecontinuous absorption of atmospheric CO2 by shelf seas is off-site carbon export, which is

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The East China Sea (ECS) is one of the principal carbon sinks
among the shelf seas in the northwestern Paciﬁc Ocean (Guo et al.,2015). Through the Taiwan Strait and the Tsushima Strait, it isconnected to the South China Sea and the Japan Sea, respectively.
At the edge of the ECS shelf, the vigorous western boundary
current, Kuroshio, continually exchanges water with the shelf(Zhang et al., 2017). Uncertainty exists in the assessment ofexport production in this region due to the complexhydrodynamic environment and multisource input of dissolved
inorganic nitrogen (DIN). The DIN concentrations are potentiallyaffected by anthropogenic activities and climate change and affectthe primary production over the ECS (Zhang et al., 2019). It is
unknown how much of the primary production supported bydifferent DINs can be exported. When, where, and in which form
the primary production is exported is unclear. Which export route—the open ocean or the seaﬂoor—is of greater importance for the
ECS? Which nutrient source has the highest capacity for export
production or export efﬁciency?
In this study, we evaluated export production over the ECS
using a physical-biological coupled model with a tracking
technique. We ﬁrst provided the distributions of particulateorganic nitrogen (PON) and the seasonal variations in thenitrogen budget. Then, the export production over the ECS andthe contributions of different DIN sources were evaluated.

referred to as export production. Evaluating carbon export over thecontinental shelf is essential for understanding carbon cycling andbiological pump efﬁciency.
Export production was originally deﬁned as the net verticaltransport of particulate organic carbon produced by phytoplankton
exported out of the euphotic zone (Eppley and Peterson, 1979). Thisdeﬁnition can be easily applied to the open ocean but not to shelfseas. Unlike the open ocean, the exported carbon below theeuphotic layer over the continental shelf can return to theeuphotic layer and may be utilized by phytoplankton again(Chen, 2003). Additionally, the continental shelf pump tends totransport carbon-rich shelf water horizontally into the open ocean(Tsunogai et al., 1999; Fennel, 2010). Thus, carbon export to thesediment and open ocean instead of to below the euphotic layer
effectively contributes to long-term carbon sequestration over the
shelf sea (Simpson and Sharples, 2012; Stukel et al., 2015). The
cross-shelf output to the open ocean seems to play a more
important role than the sediment in export production. Honget al. (2021) demonstrated that cross-shelf particulate organiccarbon transport in the South China Sea was responsible forcarbon storage on seasonal to longer time scales. Legge et al.
(2020) estimated that 60-100% of pelagic carbon ﬂows were as
off-shore transport over the northwest European shelf, and theremaining 0 to 40% was buried in the sediment.
The export production is part of primary production.
Multisource nutrients over the continental shelf contribute toprimary production and export production. The shelf seas work
as a large processing workshop. The inorganic nutrients fromexternal sources are processed into organic forms throughphotosynthesis and then exported with varying export efﬁciency.
Particulate organic matter generated from different origins usually
has different spatial-temporal variations. If multisource particulateorganic matter is not distinguished in calculating the export
production, these different variations will be obscured. There isno information on whether their export routes differ. Also, it isunclear which nutrient source has the greatest impact on export
production and where the hotspot for carbon sequestration is.
Resolving the relative importance of different nutrient sources toexport production is essential for understanding the export
mechanisms and their future inﬂuences.
The estimation of export production in shelf seas fromobservations has a large degree of uncertainty. The exchanges
between the shelf and the open ocean are complex due to thestrong spatial-temporal variability of the hydrodynamics over the
shelf slope (Doney, 2010). In shallow areas of the continental shelf,vertical mixing can affect the whole water column and induce anintense resuspension of sediment that makes the calculation ofparticulate ﬂux across the water-sediment interface challenging.
Many studies have used numerical models to calculate the export
production of shelf seas, as the export production can be thoroughly
examined in the model, and investigations of the relevantmechanisms are easy to undertake (e.g., Liu and Chai, 2009; Kelly
et al., 2018).

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2 Methods
2.1 The physical-biological module
The model domain covers the area of the Bohai Sea, the Yellow
Sea, and the ECS, ranging from 24 to 41° N and 117.5 to 131.5° Ewith a horizontal resolution of 1/18° (Figure 1). The model usedhere is the same as the one used by Zhang et al. (2019) and Zhang
et al. (2021). It consists of two parts. The ﬁrst part is a physicalbiological coupled module. The physical module is based on the
Princeton Ocean Model (Guo et al., 2003; Guo et al., 2006). The
biological module is based on NORWECOM (Skogen et al., 1995;
Skogen and Søiland, 1998). The model is driven by monthly
climatological forcing involving wind, freshwater ﬂux, heat ﬂux,
nutrient deposition at the sea surface, ocean currents,
temperature, salinity, and nutrient concentrations along lateral
open boundaries. The main state variables in the module include
DIN, dissolved inorganic phosphorus, two kinds of phytoplankton(diatoms, DIA and ﬂagellates, FLA) that are collectively referred toas chlorophyll (CHL), and detritus (DET). The zooplankton is notincluded in the model because of the bottom-up control in the
low-trophic ecosystem of the ECS (Noman et al., 2019). In
comparison to zooplankton fecal pellets and other particles,
phytoplankton contributes substantially more to the particulateorganic carbon (POC) pool in the ECS (Qiu et al., 2018).
Biogeochemical processes in the water column involve
photosynthesis, respiration, mortality of phytoplankton, and02

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B

A

FIGURE 1(A) Model domain and bathymetric map of the ECS. (B) Schematic illustration of the biological module. The black contour lines in (A) represent theisobaths. The black dot on the coast denotes the location of the inﬂow from the Changjiang River (CJ), which is the main source of the riverine DIN.
The thin black lines show the positions of the Taiwan Strait (TAS), Tsushima Strait (TUS), 34.7° N section (34N), PN section, and designated 200-m
isobath section. Black arrows denote the Kuroshio and its branches (dashed lines), the Taiwan Warm Current (TWC), and the Yellow Sea Coastal
Current (YSCC). Blue and red arrows denote the summer and winter directions of the Min-Zhe Coastal Current (MZCC) and the Changjiang Diluted
Water (CDW), respectively.

∂FLAi
DINi
=dif f (FLAi )−adv(FLAi )+pp(FLA)
∂t
DIN

remineralization of detritus, while those in the benthic layer
include remineralization and denitriﬁcation.

−resp(FLA)

2.2 The tracking module

∂DINi
FLAi
=dif f (DINi )−adv(DINi )+resp(FLA)
∂t
FLA+resp(DIA)

−resp(DIA)

Frontiers in Marine Science(4)+mort(DIAi )−remi(DETi )where the subscript i represents the DIN from the ith source.
The adv and diff represent the physical terms advection anddiffusion. The resp, pp, remi, and mort indicate the biologicalterms respiration, primary production, remineralization, andmortality. The terms for biological processes are separated
following the ratio of each source of DIN concentration to thetotal DIN concentration.
Four external DIN sources in the ECS are considered here,
namely, the Kuroshio (K), the Taiwan Strait (T), atmosphericdeposition (A), and rivers (R). The nitrogen cycle of each source
is processed independently by independent equations. For example,
the DINK denotes DIN from the Kuroshio. The phytoplankton
assimilates DINK and produces CHLK. Then CHLK becomes DETK
after mortality. DETK is further decomposed and regenerates DINK.
The DIN concentration, phytoplankton, and detritus of the four
external sources are set to zero at the beginning of the model run.
The input ﬂuxes of DIN from ten main rivers were from publisheddata (Zhang, 1996; Liu et al., 2009). The total riverine DIN ﬂux into
the ECS is 1.24 kmol s-1. The DIN ﬂux from the Changjiang River
into the ECS is 0.89 kmol s-1, which accounts for approximately
80% of the total and plays a dominant role among all the rivers. The
climatology data of atmospheric wet and dry depositions was fromobservations (Zhang et al., 2011). The nutrient concentrations
needed for the lateral boundary conditions are climatological

(1)

DIAi
DINi
−pp(FLA+DIA)
+remi(DETi )
DIA
DIN

∂DIAi
DINi
=dif f (DIAi )−adv(DIAi )+pp(DIA)
∂t
DIN

FLAi
−mort(FLAi )
FLA

∂DETi
=dif f (DETi )−adv(DETi )+mort(FLAi )∂t

The second part is a tracking module following the methods ofMé nesguen et al. (2006). Biological state variables from different
external sources are separately calculated in this module rather thanas a whole. Only DIN-related variables are tracked here. Because, incontrast to nearshore ECS, offshore ECS is the key region for export
production and DIN is a limiting nutrient there. In addition, theinput ﬂuxes of the DIN from various sources in the ECS arecomparable. Since the Kuroshio contributes a signiﬁcantly greater
amount to DIP than rivers and the atmosphere, DIP-related
tracking may result in an expected Kuroshio-dominated result.
The DIN from any external sources has the same physical andbiogeochemical processes as the total DIN. The governing
equations in the tracking module are the same as those in theﬁrst module. A complete subset of equations (Equations 1-4) is
added to address the DIN-related tracking variables DIN, DIA,
FLA, and DET for each source:

(3)

(2)

DIAi
−mort(DIAi )DIA

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et al., 2019). We also made comparison between the modeled andobserved DIN and CHL concentration distributions at PN line andnorthern ECS (Wang et al., 2019; Zhang et al., 2019). Similar features
were identiﬁed for DIN and CHL in both our model results andobservations. The tracking module has been applied to evaluate theroles of multisource nutrients in primary production (Zhang et al.,2019) and the riverine nitrogen budgets in the ECS (Zhang et al., 2021).
These factors are critical for the reliability of the tracking module.
Here, we compare the annual-mean density, velocity normal to
the section, DIN and DIP concentrations, and DIN and DIP ﬂuxes
at the PN section in Figure 2 with the measurements described in(Guo et al., 2012, Figure 3) to validate the model’s capability. The
location of the PN section is shown in Figure 1. The potential
density increases from around 23 sq at the surface layer to over 27
sq at the bottom (Figure 2A). Both the range and isopycnal
patterns match the measurement. The maximum velocity over
0.8 m s-1 is located at the surface layer of the shelf break (Figure 2B)mean of observation data for the Taiwan Strait (Huang et al., 2019)and from the Japan Meteorological Agency for the Kuroshio east of
the Taiwan Island.

2.3 Model validation
The tracking module is run for over 5 years until it reaches
equilibrium, and the outcomes in the ﬁnal year are analyzed here.
The physical-biological coupled model has been fully veriﬁed that it
can reproduce the climatology features of physical and biologicalvariables in the ECS (Zhao and Guo, 2011; Wang et al., 2019; Zhang
et al., 2019; Shen et al., 2021).
We compared the modelled surface climatology temperature andsalinity with Chen (2009). The model results are similar with those inChen (2009). The circulation of the ECS from the model results candemonstrate the most well-known features in current ﬁeld (Zhang

FIGURE 2

Annual-mean (A) potential density, (B) velocity normal to the section (m s-1), (C) DIN and (D) DIP concentrations (mmol m-3), (E) DIN and (F) DIP
ﬂuxes normal to the section (mmol m-2s-1) at the PN section from the model results.

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A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

FIGURE 3

Seasonal distributions of depth-integrated PON concentrations (unit: mmol N m-2) from different sources: (A–D) rivers (PONR), (E–H) atmosphericdeposition (PONA), (I–L) the Kuroshio (PONK), and (M–P) the Taiwan Strait (PONT). The black contours denote the 50-m, 100-m, and 200-m
isobaths. The concentration outside the shelf area is not shown. Cross hatching denotes that the PON concentration from a speciﬁc sourcecomprises more than 50% of the total PON.

which is the same as the observation. The velocity coredemonstrates the main axis of the Kuroshio, and the velocity
decreases from the core to its ﬂanks and depth. The DIN and DIP
concentrations generally rise with depth (Figures 2C, D). While theconcentrations in the upper 200 m are not as low as those from themeasurements, the ranges of DIN (5~35 mmol m-3) and DIP(0.5~2.5 mmol m -3 ) concentrations are comparable to theobservations. The product of the DIN or DIP concentration andthe velocity normal to the section is used to determine the DIN and
DIP ﬂuxes (Figures 2E, F). In both model and observation results,
the largest DIN and DIP ﬂuxes are seen at a depth of around 400 m.
The maximum DIN and DIP ﬂuxes (>8.0 and >0.6 mmol m-2s-1)
have magnitudes that are in agreement with the measurements(Figure 3 in Guo et al., 2012).

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2.4 The calculation of DIN, CHL and
DET inventories
The inventory is deﬁned as the volume-integrated values for avariable concentration (Equation 5). Here we deﬁne the area shown in
Figure 3 as our target region and calculate the monthly inventories of
DIN, CHL, and DET, whose temporal variations are affected bybiological and physical processes (Equation 6). Biological processes(denoted as Bio) are responsible for the transformation of DIN, CHL,
and DET. The Bio term of DIN includes the remineralization of
detritus, phytoplankton photosynthesis, and respiration (Equation 7).
The Bio term for CHL involves respiration, mortality, andphotosynthesis (Equation 8). The Bio term for DET includes its
remineralization and phytoplankton mortality (Equation 9). The

05

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10.3389/fmars.2024.1338835

physical ﬂuxes (denoted as Phy) include river input or atmosphericdeposition (only for DIN), lateral transport across boundaries, andwater–sediment ﬂux at the sea bottom. A positive (negative) value of
ﬂux represents an increase (reduction) in the variable inventory. The
time variation term (denoted by Tendency) refers to the sum of thephysical and biological ﬂuxes, which corresponds to the temporalchange in inventory. The export production is based on the output ﬂux
of PON, which is referred to here as the sum of CHL and DET.
inventoryi =∫∫∫ concentrationi ·dxdydz

Tendency =∂inventoryi=Phyi +Bioi∂twhere the upwelling DINK can be converted to CHLK and the DETK
effectively (Figure 3K and Supplementary Figures 2, 3). The obvious
difference between the distributions of PONK in winter and summer
largely corresponds to seasonal variations in the Kuroshio
intrusion, which ﬂows onshore from the surface in winter andfrom the bottom in summer (Yang et al., 2012; Zhang et al., 2017).
The pattern of PONK in autumn is similar to that in summer butwith a lower concentration (Figure 3L).
The concentration of PONT is lowest among the four kinds of
PON except in summer when the CHLT and DETT are largely
produced (Supplementary Figures 2, 3) and has a maximum valueon the southern middle shelf (Figure 3O). The high-concentration
areas of PONT mainly follow the Taiwan Warm Current and its
branches (Liu et al., 2021).

(5)

(6)

=input−output+biological source=sink
Bio(DINi )=∫∫∫ (resp(FLA)


FLAi+resp(DIA)
FLA

3.2 Seasonal variations of inventories of
DIN, CHL, and DET from each source

(7)

DIAi
DINi
−pp(FLA+DIA)
+remi(DETi ))·dxdydz
DIA
DIN
Bio(CHLi )=∫∫∫ (pp(DIA)
−mort(DIAi )+pp(FLA)

The seasonal variations of inventories of DIN, CHL, and DET
are quite different between different sources (Figure 4). They areaffected by biological and physical processes (bars in Figure 4,
Equation 5). The inventory of DINR shows a peak value in Marchand a valley value in July (Figure 4A). The Bio term controls thevariation of DINR and makes the inventory decrease in spring andsummer. The enhancement of DINR from July to the next March is
attributed to the reduced consumption by biological processes andgain from the river input in summer. The inventory of DINA shares
similar seasonal patterns with DINR (Figure 4D). The correlation
coefﬁcient between DINA and DINR inventories is 0.97, whichindicates a signiﬁcant relationship. There was no signiﬁcantcorrelation between the other DIN inventories.
The inventory of DINK plays a leading role in the quantity andseasonal amplitude (Figure 4B). It is highest in winter and lowest in
April-May. The Bio term of DINK shows a negative value all year
round, suggesting that the total biological processes spend the
DINK. The seasonal variations of DINK follow its Bio term. The
DINK inventory decreases from January to April due to the Bioterm, then it increases sharply after May resulting from thephysical ﬂux.
The inventory of DINT is relatively low and changes weakly(Figure 4C). The DINT inventory reaches its maximum in October
and its minimum in May-June. Unlike the Bio terms of the othersources with only one peak in spring, the Bio term of DINT has two
peaks in April and July while the latter one is larger. The primary
production supported by DINT mainly happens in the middle shelfresulting from the large volume transport and corresponding DINT
input from the Taiwan Strait in summer (Zhang et al., 2019). The
large DINT input in July makes its tendency term positive andincreases the DINT inventory, even though the DINT consumption
by biological processes also peaks.
The inventories of CHLR and CHLA change in phase withrespect to DINA and DINR. Both of them peak in May and dissipate
away in winter with a correlation coefﬁcient of 0.95 (Figures 4E, H).
The strong Bio terms of CHLR and CHLA dominate the time
variations of their inventories. The CHLK is still the largest

DINi
DIAi
−resp(DIA)
(8)
DIN
DIA

DINi
FLAi
−resp(FLA)
DIN
FLA−mort(FLAi ))·dxdydz

Bio(DETi )=∫∫∫ (mort(FLAi )+mort(DIAi )(9)−remi(DETi ))·dxdydz

3 Results
3.1 Horizontal distributions of depthintegrated PON concentration
The detailed distributions of the depth-integrated PON
concentrations from different sources are shown in Figure 3. The
horizontal distributions of the depth-integrated DIN, CHL, andDET are shown in Supplementary Figures 1–3.
The PONR is concentrated along the inner shelf (0~50 m) andplays a dominant role. The higher concentration of PONR appears in
the coastal areas north of the Changjiang Estuary in summer andautumn as the Changjiang Diluted Water (Figures 3C, D). The seasonalpatterns of PONR mainly follow those of DETR (Supplementary
Figure 3). The concentration of PONA is higher on the northern
middle shelf (50~100 m) and then decreases southward. It expands
further in spring and summer (Figures 3F, G). Both PONA and PONR
show the lowest concentrations in winter (Figures 3A, E).
The highest concentration of PONK appears on the outer shelf(100~200 m) in winter (Figure 3I). In spring, the highconcentration area of PONK narrows down to the southwest of
Kyushu (Figure 3J). Unlike in winter and spring, the highconcentration area in summer appears northeast of Taiwan Island

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A

E

I

B

F

J

C

G

K

D

H

L

FIGURE 4

Monthly inventories (107 kmol) of (A–D) DIN, (E–H) CHL, and (I–L) DET from sources of the rivers (R), the Kuroshio (K), the Taiwan Strait (T), and theatmospheric deposition (A) which are denoted by red lines. The blue, cyan, and yellow bars represent the time variation of inventory (Tendency),
biological ﬂux (Bio), and physical ﬂux (Phy) with a unit of kmol s-1. The biological and physical ﬂuxes add up to the time variation of inventory. The
positive (negative) value of ﬂux induces an increase (decrease) in inventory.

of DET inventories are not much different. The superiority for
Kuroshio source in DINK and CHLK no longer exists in DETK
(Figure 4J). In March, the inventory of DET K reaches its
maximum, while the other three inventories of DET are thelowest. In the following June, July, and August, the maximum of
DETA, DETR, and DETT inventories appear one after another. The
DETR inventory is signiﬁcantly large in summer and autumn due
to the Bio term and then drops down with gradually increasing
Phy term (Figure 4I). The Bio term of DETR has the largest value
in May, which is one month after the peak of CHLR. In wintertime,
the Bio term of DET R is negative, indicating a stronger
remineralization process than mortality. The Bio term of DETT
is largest in July when the CHLT are produced in large numbers
and a high temperature causes more mortality of phytoplankton(Figure 4K). The tendency term is therefore signiﬁcantly above
zero in spring and summer, which causes the DETT inventory toincrease and then drops to below zero in autumn. Comparing ofthese four kinds of DET, it is found that DETR, DETA and DETT

among all the CHL at any time with a maximum in March and aminimum in autumn (Figure 4F). However, unlike CHLR and
CHLA, the seasonal variations of the CHLK inventory do not fully
follow the Bio term, which is positive the whole year, even in winter.
The strong Bio term of CHLK gives a positive tendency term fromJanuary to March. But after April, the combined effects of enhanced
Phy term and weakened Bio term make the tendency term of CHLK
below zero. The CHLT inventory also shows low value in winter,and peaks in August (Figure 4G). It is signiﬁcantly correlated to
CHLR and CHLA with correlation coefﬁcients of 0.91 and 0.77. The
Bio term of CHLT dominates the seasonal variations of tendency
term, unlike the DINT situation.
The Bio term for DET comprises the mortality ofphytoplankton and remineralization to DIN (Equation 9). The
DET Bio term is positive most of the time, its peak time agrees
with that of mortality of CHL (ﬁgure is not shown). The DET
inventory from a speciﬁc source is markedly larger than the CHL
inventory from the same source. The amplitudes of the four kinds

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lateral transports across the Taiwan Strait (TAS), the 200-m isobath(200m), the Tsushima Strait (TUS) and the 34.7° N section (34N).
The return of DIN from the sediment to the water column denotes
the Bot ﬂux of DIN, while that for CHL and DET is the net ﬂux ofresuspension and sinking.
The main physical ﬂux of DINR is river input (Figure 5A). It is
highest in summer like the river discharge. While beneﬁting from thelarge input from the river, the total inventory of DINR increases
slowly after spring (Figure 4A). Meanwhile the high export of DINR
to Tsushima Strait begins. The output ﬂux of DINR through the
Tsushima Strait is larger than other ﬂuxes in the summer half year.
The patterns of DINA ﬂuxes are similar to those of DINR (Figure 5D)
but it has a larger ﬂux across 200-m isobath. The variation ranges of
DINK ﬂuxes are the largest among all the DIN sources (Figure 5B).
The ﬂuxes through the 200-m isobath and Tsushima Strait represent
the main pathway of DINK, which intrudes across the 200-m isobathand exits via the Tsushima Strait. Therefore, their seasonal variations
are almost in phase. Both reach a maximum in autumn when the
Kuroshio onshore intrusion is highest (Guo et al., 2006; Zhao andGuo, 2011). The most prominent ﬂuxes of DINT are input from the
Taiwan Strait (Figure 5C).

are correlated two by two, while DETK is not correlated with anyof the remaining three DET.
The annual means of each ﬂux and inventory are shown in
Supplementary Table 1. In the climatological model result, theinventories of the variables change little and the physical andbiological ﬂuxes are equivalent on a yearly basis. Over such atime scale, physical ﬂuxes raise DIN inventories while biologicalactivities reduce them. The CHL and DET, on the other hand, areproduced by biological activities and exported by physical ﬂuxes.
Returning to seasonal variations, the peak times of Phy terms of
CHL and DET lag those of Bio terms by one or a few months. Thatis because the outputs of the CHL and DET occur after a certain
amount of accumulation of their inventory.

3.3 Seasonal variations of physical ﬂuxes of
DIN, CHL, and DET
The seasonal variations of each physical ﬂux are shown in
Figure 5. The physical ﬂuxes include the DIN from rivers (Riv) andatmospheric deposition (Atm), the benthic ﬂuxes (Bot), and the

A

E

I

B

F

J

C

G

K

D

H

L

FIGURE 5

Monthly variations of physical ﬂuxes of (A–D) DIN, (E–H) CHL, and (I–L) DET over the ECS shelf. The Riv and Atm terms refer to DIN input ﬂuxes
from rivers and the atmosphere. The ﬂuxes across lateral sections are indicated by 200 m, TUS, TAS, and 34N (see Figure 1 for the position of eachsection). The ﬂux across the water-sediment interface is denoted by Bot. Export production-related ﬂuxes (200 m, TUS, and Bot) are shown by lines
with markers. The positive (negative) value of ﬂux induces an increase (decrease) in inventory.

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From June to December, the CHLR ﬂux through the Tsushima
Strait is distinct with a maximum in autumn (Figure 5E). The Bot
ﬂux of CHLR is largest in summer with weak seasonal variation. The
ﬂuxes of CHLK across the 200-m isobaths and the Tsushima Strait
reach a maximum in April after the peak of the CHLK inventory inMarch (Figures 4F, 5F). The ﬂux through the Tsushima Strait is alittle larger than that across 200-m isobaths throughout the year.
The input of CHLT from the Taiwan Strait is the dominant ﬂux likethe DINT situation (Figures 5C, G). It has two peaks in May andAugust. The maximum physical ﬂux of CHLA in summer resultsfrom the export through Tsushima Strait and 200-m
isobath (Figure 5H).
The Bot terms show their important roles in all DET ﬂuxes. The
striking ﬂux of DETR is in the Bot terms (Figure 5I). The Bot term of
the DETR increases from April when the DETR greatly increases.
The largest two ﬂuxes of DETA are the Bot term and export to the
Tsushima Strait (Figure 5L). Both the Bot term and export to the
Tsushima Strait reach the maximum in June. The peak months ofBot ﬂuxes of DETR and DETA are the same as those of theirinventories. The Bot term of DETT also plays a dominant role and it
reaches the maximum one month after DETT inventory peaks(Figure 5K). The seasonal variations in the DETK ﬂuxes are
similar to those in the CHLK ﬂuxes except for a larger Bot ﬂux(Figure 5J). The export ﬂuxes of DETK and CHLK through threedifferent exits (Bot, TUS, and 200m) show a comparable peak timein March or April.

1.95 kmol N s-1 (=4.89 Tg C yr-1) is between the reported POC
output ﬂux of 0.25~1.7 Tg C yr-1 (Deng et al., 2006; Jiao et al., 2018;
Yuan et al., 2018) and 12.61 Tg C yr-1 (Chen and Wang, 1999). The
net-Bot ﬂux and the 200 m ﬂux explain 20% and 29% of the totalexport production, respectively, while the TUS ﬂux accounts for
51%. Consequently, approximately 80% of the total export
production leaves by lateral transport. This value is consistent
with the percentages of off-shelf transport and sediment burial of
80% and 20%, respectively, in eastern North America (Najjar et al.,2018), 70% and 30%, respectively, on the whole North American
shelf (Fennel et al., 2019), and 60~100% and 40~0%, respectively, innorthwest European continental shelf seas (Legge et al., 2020).
However, this ratio varies among the PONs supported by four
sources of nutrients, which is covered in the following subsection.
The e-ratio is deﬁned as the ratio of export production toprimary production, which indicates the export efﬁciency of organic
matter output from the generated carbon. The total primary
production and export production are 39.9 and 6.83 kmol N s-1,
respectively, forming an e-ratio of 0.17. Our value is comparable to
the result from the carbon budget based on a box model used byChen and Wang (1999). They predicted an f-ratio of 0.15, which
was supposed to balance the e-ratio in a steady state. Zuo et al.
(2016) found that approximately 12% of the generated particulate
nitrogen was exported between May and October in the ECS, whichis less than our annual-averaged e-ratio due to the relatively hightemperature and remineralization during their observation period.
Based on corrected trap-collected POC ﬂuxes, Hung et al. (2016)
estimated an e-ratio of 0.59 nearshore and 0.16 offshore. The high eratio in the nearshore area demonstrated a signiﬁcant vertical ﬂux
across the euphotic layer depth rather than efﬁcient carbon export.
The export production values of PON from various sources
have a wide range of features. The export production of PONK is
4.40 kmol N s-1, accounting for almost 64% of the total export
production. The export production of PONA is 1.10 kmol N s-1, yet
it accounts for 16%. The export production of PONT accounts for
12%, while that of PONR makes up only 8%. The PONK net-Bot ﬂux(0.51 kmol N s-1) is the largest, accounting for nearly 36% of theoverall net-Bot ﬂux. The net-Bot ﬂuxes of PONR and PONA are
equivalent and account for approximately 23% of the total ﬂux. The
burial ﬂux is larger in the coastal and shelf-break areas (Deng et al.,2006). Thus, the PONT, which is mainly concentrated on the middle
shelf, has a slightly lower net-Bot ﬂux. The TUS ﬂux of the PONK
has a fraction of 64%, whereas that of the PONR has a fraction of
5%. The PONK ﬂux across the 200-m isobath accounts for 85% of
the total, and the PONR ﬂux accounts for only 0.1%.
The primary export pathway for the PONR is the sediment. The
net-Bot ﬂux of PONR accounts for over 60% of the total export
production of rivers. This ratio is disproportionate larger than the
20% for the total PON. The PONR ﬂux across the 200-m isobath isnegligible. On a yearly basis, the impact of DINR and PONR on the
Kuroshio region is insigniﬁcant. The excess DINR and a small
amount of PONR may be exported through the Tsushima Strait(Isobe and Matsuno, 2008; Zhang et al., 2021). The PONK ﬂux is thelargest through the Tsushima Strait, which beneﬁts from theKuroshio Branch Current west of Kyushu (Hsueh et al., 1996; Lieet al., 1998; Isobe, 2000) and the outﬂow through the Tsushima

3.4 Export production and e-ratio derived
from different DIN sources over the ECS
In terms of long-term export production, the biogenic particles
ﬂuxes that are removed through the sediment and lateralboundaries to the open ocean are characterized as export
production on the continental shelf. The main pathways of theexport production over the ECS shelf are via the sediment and theTsushima Strait to the Japan Sea and across the 200-m isobath to
the northwest Paciﬁc Ocean.
In the ECS, the particulate organic carbon to PON ratio
approaches the Redﬁeld ratio of 6.63, with a mean values ranging
from 4 to 8 (Hung et al., 2000; Zhu et al., 2006). The PON ﬂuxes
may be utilized to estimate the organic carbon ﬂux in this situation.
The overall export production over the ECS is 6.83 kmol N s-1(=17.16 Tg C yr-1) using a C/N ratio of 6.63, and ranging from10.35-20.71 Tg C yr-1 when considering a C/N ratio of 4~8. The
export production is composed of net-Bot, TUS, and 200 m ﬂuxes(Table 1). This value is slightly lower than the atmospheric CO2
absorption of 23.3 ± 13.50 Tg C yr-1 (Guo et al., 2015), suggestingthat export to the open ocean and seaﬂoor can reﬂect efﬁcientcarbon sequestration. The benthic ﬂux of 1.40 kmol N s-1(44.15×109 mol yr-1) is less than the burial ﬂux of organic
nitrogen at 75×109 mol N yr-1 (Chen and Wang, 1999) because
the benthic ﬂux here considers only ocean-generated nitrogen andexcludes the input of terrestrial organic nitrogen, because they only
comprise small portions of the total Changjiang River nitrogen
input ﬂux (Zhang et al., 2003; Kwon et al., 2018). The 200 m ﬂux of

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TABLE 1 Annual mean export production, primary production, and input ﬂuxes from the four sources and their sum over the ECS.

(kmol N s-1)

RiversKuroshio

Taiwan Strait

Atmosphere

Total

Bot of PON

0.66 (19%)

1.42 (41%)

0.67 (19%)

0.71 (21%)

3.46

Bot of DIN-0.33 (16%)-0.91 (44%)-0.43 (21%)-0.39 (19%)-2.06

net Bot

0.33 (24%)

0.51 (36%)

0.24 (17%)

0.32 (23%)

1.40

TUS

0.19 (5%)

2.22 (64%)

0.44 (13%)

0.63 (18%)

3.48

200m

0.003 (0.1%)

1.67 (85%)

0.13 (7%)

0.15 (8%)

1.95

export production

0.52 (8%)

4.40 (64%)

0.81 (12%)

1.10 (16%)

6.83

primary production

6.00 (15%)

20.10 (50%)

6.80 (17%)

7.00 (18%)

39.90

input ﬂux

1.24 (7%)

12.64 (71%)

1.75 (10%)

2.14 (12%)

17.77

e-ratio

0.09

0.22

0.12

0.16

0.17

EPQ

4.84

1.59

3.89

3.27

2.25

EEQ

0.42

0.35

0.46

0.51

0.38

The export production includes PON ﬂuxes across the 200-m isobath (200m), through the Tsushima Strait (TUS) and the net water-sediment interface ﬂux (net Bot), which is the difference
between the Bot ﬂux of PON and the Bot ﬂux of DIN. The export production-related ﬂuxes indicate the export production leaving the ECS, but the values are depicted as positive in the table forsimplicity. The Bot ﬂux of DIN is negative because it represents the return of PON after remineralization and the reduction of export production. The primary production is depth-integrated andsupported by each source of DIN. The units of ﬂuxes, export production, and primary production are kmol N s-1. The numbers in the parentheses represent the percentages of a particular source.
The EPQ is the ratio of primary production to input ﬂux. The EEQ is the ratio of export production to input ﬂux. The export production to primary production is known as the e-ratio.

Strait to the Japan Sea (Isobe et al., 2002; Teague et al., 2003). The
total lateral transport of PONK (TUS and 200m) accounts for nearly
90% of the export production. The Tsushima Strait is also theprincipal export pathway for PONT and PONA, and the sediment is
the second important export pathway. About 30% of the overall
export production of PONT and PONA are made up of their net Bot
ﬂuxes, respectively. PONT and PONA are mostly found on the
middle shelf (Figure 3), demonstrating a distinction between coastal
PONR and oceanic PONK.

of the total. The remaining half is shared evenly among DINA,
DINT, and DINR. The order of the primary production values
supported by various DIN sources corresponds to the order of theirexport production values. The e-ratio of DINK is greatest at 0.22,
followed by an e-ratio of DINA at 0.16. The DINT has a ratio of 0.12,
whereas the DINR has a ratio of 0.09.
The unique oceanic sources of DINK and coastal sources of
DINR provide the maximum and minimum e-ratios, respectively.
The high e-ratio of DINK is due to its proximity to the outlets of the
200-m isobath and the Tsushima Strait, where the horizontal
currents transport the PONK off the shelf. The EPQ and EEQ
values of DINK are both the lowest, most likely resulting from thenotably large DINK input. The highest EPQ of DINR (4.84) indicatesthat the input DINR is used by phytoplankton for nearly ﬁve times,
and the generated PONR is extensively recycled over the shelf andhas difﬁculty reaching the lateral export pathways. This suggeststhat offshore rather than nearshore locations are usually greatcarbon sequestration sites.
A schematic representation of the different patterns of thenitrogen budgets from the Kuroshio and the rivers is shown in
Figure 6. The cycle of DINK starts from the input from externalsources the Kuroshio with a value of 12.64 kmol s-1. A portion of
DINK (5.31 kmol s-1) is converted by biological processes into PON,
while the remaining is exported through the Tsushima Strait (8.17kmol s-1). A value of 0.91 kmol s-1 is provided to DINK by thebenthic ﬂux. The cycle of PONK begins with the biological ﬂux of
5.31 kmol s-1. This value is equivalent to the sum of the ﬂuxes
through the sediment (1.42 kmol s-1), the TUS (1.67 kmol s-1), and200m (2.22 kmol s-1) ﬂuxes. The ﬂuxes of DINK and PONK throughother boundaries are negligible. The input of DINR (1.24 kmol s-1) is
only one-tenth of that from the Kuroshio and most of them (0.98kmol s-1) turn into PONR. The primary exit of PONR is the

4 Discussion
We establish two other ratios to further compare the export
efﬁciency of different sources. The EPQ is deﬁned as the ratio ofprimary production to the nitrogen input ﬂux, and the EEQ is theratio of export production to the nitrogen input ﬂux. The two ratios
connect the input ﬂux to the primary production responses andthen to the ﬁnal export production. The input ﬂux of nitrogen ismuch smaller than that of primary production. The excess primary
production is supported by recycled DIN rather than external input.
Thus, the EPQ denotes how many times the loaded DIN is
assimilated for primary production (Takeoka, 1997). The annual
mean nitrogen inventory over the ECS is almost unchanged in a
climatological state, suggesting a balance between the input andoutput of nitrogen. Export production is an organic form of output.
Most of the remaining material leaves the shelf in inorganic form.
The EEQ represents the ratio of organic export to input and is the
product of the e-ratio and EPQ.
Although the export production and primary production ofdifferent sources are fairly different, the e-ratio difference is not big.
The primary production supported by DINK accounts for over half

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FIGURE 6

Annual-mean biological and export ﬂuxes of (A) DINK and PONK and (B) DINR and PONR (unit: kmol s-1). The yellow arrows represent the DIN input
from the (A) Kuroshio and (B) rivers. The ﬂuxes of DIN and PON through sediment, shelf break, and the Tsushima Strait are indicated by blue and redlines, respectively. The circles indicate the transformation from DIN to PON through biological activities.

The various roles of PON in export production and e-ratio areinﬂuenced by its distribution. The PON supported by the nutrientsfrom the Kuroshio concentrates on the outer shelf and contributes
to 64% of the overall export production, whereas the PON
supported by the nutrients from rivers concentrates in the coastal
area and accounts for only 8% of the overall export production,
which demonstrates that offshore areas are vital locations forcarbon sequestration.

sediment rather than lateral boundaries.
The magnitudes of the EPQ and e-ratio of DINA and DINT are
between those of DINR and DINK. However, the EEQ of DINA andDINT (0.51 and 0.46), which is the product of EPQ and the e-ratio, ishigher than that of DINR and DINK. These two DIN values havehigher primary production efﬁciency (Zhang et al., 2019) and an eratio that is not too low, thus approximately 50% of the input DINA
and DINT are exported in organic form. This suggests that the ECS
shelf efﬁciently processes them. The increasing inputs of DIN fromthe Taiwan Strait and atmosphere may lead to more export
production and carbon sequestration.

Data availability statement

5 Conclusions

The raw data supporting the conclusions of this article will bemade available by the authors, without undue reservation.

The export production values derived from various nitrogensources over the ECS are assessed using a three-dimensional
physical-biological coupled model and a tracking technique. We
begin with the distributions of PON supported by different sources.
PONK and PONR concentrate offshore and nearshore, respectively,
and PONA and PONT are found on the middle shelf. The variationsin DIN and PON inventories are the combined effects of physicaland biological processes. Physical ﬂuxes raise DIN inventories whilebiological activities reduce them. For PON, the situation is the exact
reverse. The export ﬂuxes of PON occur after a certain amount ofaccumulation of their inventory.
There are two ways in which the mechanism of export productionin the shelf seas differs from that in the ocean. Initially, organic matter inthe shelf seas should be exported through the sediment rather than
through the bottom of the euphotic layer. Second, the shelf seas pumporganic matter laterally into the ocean. We use the PON ﬂuxes to theseaﬂoor and open ocean (via the Tsushima Strait to the Japan Sea andthe shelf break to the Northwestern Paciﬁc Ocean) to calculate theexport production over the ECS, and the result is 6.83 kmol N s-1. Thisvalue is slightly lower than the atmospheric CO2 absorption. The
Tsushima Strait is the principal export route (51%) for PON, followedby the shelf break (29%) and sea bottom (20%), suggesting theimportant role of off-shelf transport.

Frontiers in Marine Science

Author contributions
JZ: Writing – original draft. LeZ: Visualization, Writing –review & editing. XG: Conceptualization, Writing – review &editing. YW: Methodology, Writing – review & editing. JF:
Writing – review & editing. LiZ: Conceptualization, Writing –review & editing.

Funding
The author(s) declare ﬁnancial support was received for the
research, authorship, and/or publication of this article. This word
was supported by the National Natural Science Foundation ofChina [grant numbers 42006018, 41876018, 42176198]; Grantsin-Aid for Scientiﬁc Research [MEXT KAKENHI, grant numbers
22H05206]; the Tianjin Municipal Education Commission
Scientiﬁc Research Project [grant number 2019KJ219]. JZ thanks
the support of the Ministry of Education, Culture, Sports, Scienceand Technology, Japan (MEXT) under a Joint Usage/ResearchCenter, Leading Academia in Marine and Environment Pollution
Research (LaMer) Project.

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organizations, or those of the publisher, the editors and thereviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.

Conﬂict of interest
The authors declare that the research was conducted in theabsence of any commercial or ﬁnancial relationships that could beconstrued as a potential conﬂict of interest.

Supplementary material
Publisher’s note
The Supplementary Material for this article can be found online
at: https://www.frontiersin.org/articles/10.3389/fmars.2024.1338835/
full#supplementary-material

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