Research article

Biogeosciences, 20, 4323–4338, 2023
https://doi.org/10.5194/bg-20-4323-2023© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Contribution of the open ocean to the nutrient and phytoplankton
inventory in a semi-enclosed coastal sea
Qian Leng1 , Xinyu Guo2 , Junying Zhu3 , and Akihiko Morimoto2
1 Graduate School of Science and Engineering, Ehime University, 2-5 Bunkyo-cho, Matsuyama, Ehime 790-8577, Japan
2 Center for Marine Environmental Studies, Ehime University, 2-5 Bunkyo-cho, Matsuyama, Ehime 790-8577, Japan
3 State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570228, China

Correspondence: Xinyu Guo (guo.xinyu.mz@ehime-u.ac.jp)
Received: 17 April 2023 – Discussion started: 2 May 2023
Revised: 6 August 2023 – Accepted: 5 September 2023 – Published: 24 October 2023

Abstract. The semi-enclosed coastal seas serve as a transition zone between land and the open ocean, and their environments are therefore affected by both. The influencesof land were noticed but that of the open ocean were usually neglected. The Seto Inland Sea (SIS), which is connected to the Pacific Ocean, is a typical representative ofsemi-enclosed seas. To quantitatively assess the inventory ofnutrients originating from land and the open ocean, as wellas their supported phytoplankton in the SIS, we developeda three-dimensional coupled hydrodynamic–biogeochemical
model and embedded a tracking technique in it. Model results
showed that the open ocean contributes 61 % and 46 % to theannual inventory of dissolved inorganic nitrogen (DIN) andphytoplankton in the SIS, respectively. This proportion has
apparent spatial variations, being highest near the boundarywith the open ocean, decreasing from there towards the interior area of the SIS, and being lowest in the nearshore areas.
The open ocean imports 799 mol s−1 of DIN to the SIS, 25 %of which is consumed by biogeochemical processes and 75 %of which is delivered again to the open ocean. Such a large
amount of oceanic nutrient input and its large contribution
to the inventory of DIN and phytoplankton suggest the necessity of considering the impact of open-ocean variabilities
in the management of the land loading of nutrients for thesemi-enclosed seas.

1

Introduction

The semi-enclosed coastal seas geographically refer to theseas surrounded by land and connected to the open ocean

by one or more narrow entrances (Healy and Harada, 1991;
Leppäkoski et al., 2009). These regions are highly productive due to their location at the transition zone between land
and the open ocean, which results in the receipt of nutrients from both terrestrial and oceanic sources (Bauer et al.,2013; Jickells, 1998). While it was previously believed thatthe narrow entrances limited the exchange between the semienclosed seas and the open ocean (Caddy, 2000; Statham,2012), recent studies have highlighted the significant contribution of oceanic nutrients to the nutrient inventory in thesemi-enclosed seas (Anderson et al., 2008; Mackas and Harrison, 1997; Townsend, 1998). Therefore, the role of oceanic
nutrients cannot be disregarded in semi-enclosed seas. In
fact, it has been known that the changes in terrestrial nutrients alone cannot fully explain ecological variations in theseregions (Nakai et al., 2018).
Unlike terrestrial nutrients, which are susceptible to anthropogenic activities, oceanic nutrients are typically influenced by climate change, which affects ocean currents andother environmental variables, including nutrient concentrations in the open ocean (Jickells, 1998; Vermaat et al.,2008). The transport process of oceanic nutrients into thecoastal seas depends on the water exchange between thesemi-enclosed seas and the open ocean, so their import has
a seasonality that is different from the terrestrial nutrients(Morimoto et al., 2022; Zhang et al., 2019). Because of its
complex nature, the transport of oceanic nutrients into thesemi-enclosed seas and their role in these seas are not easily
known. One example is the Seto Inland Sea (SIS), where thepresence of oceanic nutrients has been recognized for a longtime (Hayami et al., 2004; Yanagi and Ishii, 2004). However,

Published by Copernicus Publications on behalf of the European Geosciences Union.

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their transport flux into the SIS as well as their contribution
to the primary production there have not been documented.
The impacts of an external source of nutrients are usuallyassessed by the amount of input into the coastal seas (Aokiet al., 2022), which actually reflects only its potential impact,
not its real impact. One reason is that not all nutrients entering the coastal seas are available for phytoplankton growth(Zhang et al., 2019). Thus, the evaluation of the input amountof oceanic or terrestrial nutrients is only the first step in understanding their effects in semi-enclosed seas. Their real effects should be evaluated by their inventory of nutrients andbiological particles (phytoplankton, zooplankton, and detritus) in the semi-enclosed seas, which is associated with thematerial flows in biogeochemical cycling and the material
exchange at the boundary between semi-enclosed seas andthe open ocean.
In situ observations can often be insufficient to differentiate materials originating from different sources of nutrients.
To address this issue, researchers have proposed introducing the tracking modules into the coupled hydrodynamic–
biogeochemical models to trace the nutrients from different sources (Kawamiya, 2001; Ménesguen et al., 2006). Recently, this type of model has demonstrated their performance in quantifying the contribution of riverine and oceanic
nitrogen to hypoxia formation in the northern Gulf of Mexico, where the oceanic nitrogen supports 16±2 % of summer
sediment oxygen consumption (Große et al., 2019). Similar models have also been used to assess the contribution ofdifferent nutrient sources to primary production in the East
China Sea (ECS), where the nutrients from the Kuroshio
Current support up to 50 % of primary production (Zhang
et al., 2019).
This study focuses on the SIS, a semi-enclosed sea thatis connected to the Pacific Ocean. Heavy eutrophication occurred in the SIS during the rapid economic growth of the
1960s–1970s due to urbanization and concentrations of industry and population around it. After that, the Temporary
Law for Environment Conservation in the SIS and the Special Law for Environment Conservation in the SIS were madein 1973 and 1978, respectively. Based on these laws, total
amount controls for chemical oxygen demand (COD), nitrogen, and phosphorus have been conducted since then. As aresult of effort over a long time, the nutrient concentration
in some areas of the SIS has largely decreased, which raised
a social concern about the possibility of oligotrophication in
the SIS (Yamamoto, 2003).
To initiate our understanding of such a long-term change
in the nutrient concentrations in the SIS, we use a coupledhydrodynamic–biogeochemical model with a tracking technique to quantitatively evaluate the inventory of materials
originating from the open ocean, rivers, and sediment. In addition, we emphasize the material flows involved in biogeochemical cycling and material exchange through the boundary with the open ocean. Through these analyses, we want toBiogeosciences, 20, 4323–4338, 2023

Q. Leng et al.: A semi-enclosed coastal sea
present an example to demonstrate the role of oceanic nutrients in semi-enclosed seas.
2
2.1

Methods
Study area

The SIS is the largest semi-enclosed coastal sea in westernJapan. It has a surface area of approximately 23 203 km2 andan average depth of 38 m (Fig. 1a). The sea is surrounded bythree major islands, namely Honshu, Kyushu, and Shikoku,
and is connected to the Pacific Ocean through two straits,
the Bungo Channel and Kii Channel (Fig. 1b). During thesummer, the cold and nutrient-rich Kuroshio Current subsurface water from the Pacific Ocean intrudes into the SIS
through these two straits (Kobayashi and Fujiwara, 2009;
Takashi et al., 2006; Guo et al., 2004). The SIS is surrounded
by a highly industrialized area in Japan where more than 10
million people live. Consequently, the nutrients discharged
from land are high (Yamamoto, 2003). Furthermore, the organic material in the sediment also releases nutrients (Tadaet al., 2018; Yamamoto, 2003). Therefore, we consider threesources of nutrients for the SIS in this study.
2.2

Coupled hydrodynamic–biogeochemical model

A hydrodynamic model based on the Princeton Ocean Model(Blumberg and Mellor, 1987) has been developed to simulate the climatological seasonal variation of water circulation in the SIS (Chang et al., 2009; Zhu et al., 2019).
The model covered the entire SIS at 32.8–34.8◦ N, 130.98–
135.5◦ E (Fig. 1), with two open boundaries south of the
Bungo Channel and Kii Channel connecting to the Pacific
Ocean. The Kanmon Strait, which connects to the Sea ofJapan, was closed due to the poor water exchange there(Takeoka, 1984); 21 first-class rivers monitored by the Ministry of Land, Infrastructure, Transport and Tourism (MLIT)
and 45 second-class rivers monitored by the local government were specified along the coastline (Fig. 1b). The
fluxes across the air–sea interface, including the momentum
flux, mass flux, and heat flux, were from a daily dataset
named the Grid Point Value of Meso-Scale Model (GPVMSM) provided by the Japan Meteorological Agency (http://www.jmbsc.or.jp/jp/online/file/f-online10200.html, last access: 13 October 2023) (Zhu et al., 2019). Four tidal constituents, M2 , S2 , O1 , and K1 , were specified at the open
boundary of this model (Chang et al., 2009). In addition to
the tidal components, the open-boundary conditions also include the subtidal current velocity, temperature, and salinityfrom a diagnostic model (Guo et al., 2004). The hydrodynamic model provided the current field, temperature field,
and diffusion coefficients for the biogeochemical model. For
further information on the hydrodynamic model, please refer
to Chang et al. (2009) and Zhu et al. (2019).

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Figure 1. (a) Location of the Seto Inland Sea. (b) Bathymetry of the Seto Inland Sea. The solid orange circles with lowercase letters represent
the locations of first-class rivers, and the solid purple circles represent the locations of second-class rivers. The names of 21 first-class rivers
are shown in Table S2. The dashed red line in the west represents the open boundary between the Bungo Channel and the open ocean, andthe red dashed line in the east represents the open boundary between the Kii Channel and the open ocean. The solid black line represents thelocation of the Kanmon Strait. (c) Division of subregions and locations of in situ observation stations. Solid red lines represent dividing linesof different subregions. Blue crosses represent in situ observation stations with a depth of less than 50 m, and orange crosses represent in situ
observation stations with a depth greater than 50 m.

The biogeochemical model used in this study is a lowertrophic level model, which was employed to depict the annual nutrient and phytoplankton cycle in the coastal seas. It isa nitrogen-based model, and its structure is similar to the one
proposed by Fasham et al. (1990). The model comprises fivestate variables, namely dissolved inorganic nitrogen (DIN),
dissolved inorganic phosphate (DIP), phytoplankton (PHY),
zooplankton (ZOO), and particulate organic matter (PON)(Fig. S1 in the Supplement). The equations and parametric formulas governing the model are given in the Supplement. The parameter values were obtained from other marine
ecosystem models (Fennel et al., 2006; Kishi et al., 2007; Xiu
and Chai, 2014), and adjustments were slightly made for the
SIS (Table S1).
The initial fields of DIN, DIP, and PHY were obtained
from the Broad Comprehensive Water Quality Survey
conducted by the Ministry of the Environment, Japan(https://water-pub.env.go.jp/water-pub/mizu-site/mizu/
kouiki/dataMap.asp, last access: 13 October 2023). Specifically, DIN, DIP, and PHY concentrations in January wereaveraged from 1981 to 2018 and used as the initial fields.
As data for PON and ZOO were not available, the initial
fields for these state variables were set to 0. This approach
was considered reasonable as these variables are known toincrease rapidly during the spin-up time (Fennel et al., 2006).
In this study, we examined three primary nutrient sourcesin the SIS: land, open ocean, and sediment. We disregarded
the DIN loads from the atmospheric deposition in the model.

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This is because only limited studies have been conducted toobserve the atmospheric nitrogen deposition flux throughout
the SIS, and the reported data covered only a small area andone season (Nakamura et al., 2020). Furthermore, an estimation of the nitrogen budget in the SIS reported that the nitrogen loads from atmospheric deposition were significantly
smaller than that from the land through rivers (Yanagi, 1997).
As we present later, because of its small contribution of thenutrient loads into the SIS, the model without atmosphericnitrogen deposition flux can give reasonable results for the
distribution of nutrients and phytoplankton as compared tothe observations.
Furthermore, we also excluded nitrogen fixation as a nutrient source for the SIS. Hashimoto et al. (2016) reported thatthe nitrogen load from nitrogen fixation was much lower thannitrogen input from rivers in Osaka Bay, and Lee et al. (1996)
indicated that there was no nitrogen fixation in Hiroshima
Bay. In a previous study estimating the nitrogen budget for
the SIS, nitrogen fixation was assumed to be 0 (Yamamoto etal., 2008).
Regarding the air–sea interface, we used the thirdgeneration of a daily dataset named the Japanese Ocean Flux
Data Sets with the Use of Remote-Sensing Observations (JOFURO3) to provide the shortwave radiation flux that is usedin the photosynthesis (Tomita et al., 2019). The daily dataset
was averaged from 2002 to 2013 to obtain a daily climatological value.

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The Ministry of the Environment, Japan, estimated total
nitrogen (TN) loads with an average value of 471 mol s−1
from land to the SIS from 1979 to 2014 based on the unit
method (Abo and Yamamoto, 2019; Tomita et al., 2016; Yamamoto, 2003). Yanagi and Ishii (2004) indicated that the
TN load estimated by this unit load method did not represent the input of TN into the coastal sea since some partsof the TN load remained on land. Yamamoto et al. (1996)
recommended the use of the river flow rate to calculate theactual inflow TN load into the SIS and reported that the actual TN load into the SIS calculated using this method wasabout 48 % of that given by the unit load method. Based on
this proportion, the average input of the TN load to the SIS
was 471 × 48 % = 226 mol s−1 .
The proportion of DIN concentration in the TN concentration at the first-order rivers of the SIS is about 77 %,which was estimated by the nutrient data from MLIT. Applying this proportion to the TN load from land, the DIN
load from land is 226×77 % = 174 mol s−1 . In our study, weused the daily river discharge and monthly nutrient concentration from MLIT averaged over the period from the 1990s
to the 2010s to estimate the DIN load from rivers into the SIS.
The annual mean of DIN loads from rivers is 63.85 mol s−1 ,which was 37 % of the expected DIN load from land into the
SIS. To consider the DIN load from land as realistically as
possible, the DIN load from rivers was increased to 3 timesof the original value to represent the DIN load from land.
Consequently, the annual mean of DIN loads from land becomes about 192 mol s−1 . There is a clear spatial variationin DIN loads from land, which shows high DIN loads in theeastern part such as Osaka Bay and Harima Nada (Fig. S2).
The seasonal variation in DIN loads, showing high values in
July and September and a low value in January, is primarily
controlled by the seasonal variation in river discharge. We
did not include particle nitrogen input from land.
At the interface between the SIS and the open ocean, it isnecessary to specify the nutrient concentrations at two open
boundaries. Previous studies have shown that there is a good
relationship between nutrient concentration and water temperature in the subsurface water of the Kuroshio Current(Morimoto et al., 2022; Takashi et al., 2006). In this study,
we followed this idea and used the water temperature fromthe hydrodynamic model as a substitute for the observed water temperature to obtain nutrient concentrations at the open
boundaries. To establish the relationship between water temperature and nutrient concentrations, we used the observed
monthly water temperature and nutrient concentrations in the
Bungo Channel from 1991 to 2005, provided by the Fisheries Research Center, Ehime Prefectural Institute of Agriculture, Forestry and Fisheries, Japan (Fig. S3). Their strong
correlation at the range of low temperatures reflects the inherent nature of the water temperature and DIN concentration of the Kuroshio Current subsurface water. At the hightemperature range, the DIN concentration is low, which reflects the nutrient-poor Kuroshio Current surface water. As
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Q. Leng et al.: A semi-enclosed coastal sea
we lacked observed nutrient concentration data in the Kii
Channel, we applied the Bungo Channel relationship to theKii Channel, given that both channels are influenced by thesubsurface water of the Kuroshio Current (Takashi et al.,2006). At the open boundaries in the Bungo Channel and Kii
Channel, DIN concentration was high at the bottom throughout the year, with much higher concentrations in summer(Fig. S4). On the other hand, we lacked data for the concentrations of PHY, PON, and ZOO at the open boundaries, so
we set their concentrations to 0. While we did not include theinput of organic matter from the open ocean into the SIS, weallowed for the export of organic matter from the SIS by using the upwind scheme for the tracer at the open boundaries.
At the water–sediment interface, the fluxes of PON(mmol m−2 d−1 ) and DIN (mg m−2 d−1 ) were specified. To
determine the downward PON flux to the sediment surface, we followed the method proposed by Ariathurai andKrone (1977) and used Eq. (1):(
Cb ws |ττbc | − 1 , |τb | < τc
, (1)sediment PON flux =|τb | ≥ τc0,where τc is the critical bottom stress. If the magnitude of bottom stress (τb ) is larger than it, we assume no deposition occurs. We assigned τc a value of 0.14 N m−2 , by considering
the sediment distribution in the SIS. τb (N m−2 ) is calculated
from the hydrodynamic model. Cb is the PON concentration(mol m−3 ) in the bottom water. ws is the sinking velocity ofPON, which was set at 1 m d−1 .
In addition to the downward PON flux, we also specify
an upward DIN flux from the sediment using an empiricalformula based on observation data (Tada et al., 2018). For
the upward DIN flux from the sediment, we used Eq. (2):sediment DIN flux
0.1277T · (TN − 1.301) ,= 1.802 · exp
0,

TN > 1.301 ,TN ≤ 1.301(2)

where T is the water temperature (◦ ) of the bottom water and TN is the total nitrogen concentration (mg g−1 )at the sediment surface. T was calculated by the hydrodynamic model and applied to the biogeochemical modelat each time step. The TN concentration at the sediment surface was the average value from the 1980s to
2010s obtained from the Seto Inland Sea Environmental Information Basic Survey (Ministry of the Environment, Japan; https://www.env.go.jp/water/heisa/heisa_net/
setouchiNet/seto/g2/g2cat01/teishitsuodaku/index.html, lastaccess: 13 October 2023). The annual mean TN concentration was high at Suo Nada, Hiroshima Bay, Harima Nada,
and Osaka Bay, but it was below the value of 1.301 mg g−1 inthe Bungo Channel, Kii Channel, and Bisan Strait, where no
DIN was released from the sediment (Fig. S5). It should be
noted that the calculation of DIN flux released from the sediment is somewhat simple in our model. Based on an empirical function obtained from experiments (Tada et al., 2018),
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Q. Leng et al.: A semi-enclosed coastal sea
we used the annual mean TN concentration and bottom temperature to calculate the sediment DIN flux. This means that
we did not consider the instant effect of PON settled to thesurface sediment, which can increase the reactive TN concentration in the surface sediment and subsequently raise
DIN flux released from the sediment. In fact, it is difficult
to treat such short-term responses of benthic DIN flux to thesettled PON (Soetaert et al., 2000) as a source of nutrients
because they can be a part of the nutrient cycle within thewater column.
The hydrodynamic–biogeochemical model was initiatedon the first day of January and stabilized from the thirdyear onwards. Therefore, the simulation results of the thirdyear were used to analyze the seasonal variations of DIN
and PHY.

2.3

Tracking technique

In our study, we utilized the tracking technique proposedby Kawamiya (2001) and Ménesguen et al. (2006) in conjunction with the hydrodynamic–biogeochemical model. The
nutrients in the SIS were considered to have three sources:
oceanic, riverine, and benthic nutrients. It needs to be notedthat the nutrients in the sediment are initially from either landor the open ocean, and the sediment acts a temporary storage
or a permanent sink of these nutrients. In this study, however, we treat the sediment as the third source to track. Thisis because the sediment-released nutrients gain more attention and are particularly important in shallow waters (Radtke
et al., 2019). To represent these sources, we introduced three
additional sets of variables, namely DINocean , PHYocean ,PONocean , and ZOOocean ; DINriver , PHYriver , PONriver , andZOOriver ; and DINsediment , PHYsediment , PONsediment , andZOOsediment , where the subscripts indicate the source. Eachset of variables has its independent equations that are similar
to the original equations for the sum of three sources of nutrients, i.e., DIN, PHY, PON, ZOO. The complete governing
equations for the tracked variables are provided in the Supplement. Regarding the hydrodynamic processes, all tracked
variables have the same advection and diffusion terms. However, for the biogeochemical processes, the tracked variables
have a different form. Here, we describe how the biogeochemical terms of tracked variables were treated in our study.
Regarding the biogeochemical processes in the model, we
calculated the flux that converts one variable to another one(Fig. S1), denoted as FluxA→B , where A was the variablebeing converted and B was the target variable. The contribution of each nutrient source to the biogeochemical process depends on its proportion in the total concentration of
the variable being converted, as described by Eqs. (3), (4),https://doi.org/10.5194/bg-20-4323-2023

4327
and (5):[Aocean ],[A][Ariver ]Fluxriver,
A→B = FluxA→B[A][Asediment ],Fluxsediment
A→B = FluxA→B[A]
Fluxocean
A→B = FluxA→B

(3)
(4)
(5)

where the square bracket denotes the concentration and[A] = [Aocean ] + [Ariver ] + [Asediment ]. [A] can be any of four
variables (DIN, PHY, PON, ZOO) used in the model.
We initiated the tracking technique from the first day of
the fourth year of the hydrodynamic–biogeochemical model,
with a value of 0 for the initial fields of all the variables
originating from the three sources of nutrients. For the openboundary conditions, during the time of inflow, the DINocean
flux had the same values as those used at the open boundaries of the hydrodynamic–biogeochemical model; during
the time of outflow, the DINocean flux was given as the product of DINocean and the outflow velocity. The DINocean flux
was specified as 0 at the land–sea interface and the water–
sediment interface. DINriver at the land–sea interface was
identical to that used in the hydrodynamic–biogeochemical
model, but it was set to 0 at the water–sediment interface. In the open boundaries, during the time of inflow, theDINriver flux was set to 0, and during the time of outflow,
the DINriver flux was given as the product of DINriver and theoutflow velocity. DINsediment was set to have the same flux
at the sediment–water interface as that in the hydrodynamic–
biogeochemical model, but it was set to have zero flux at theland–sea interface. At the open boundaries, during the timeof inflow, the DINsediment flux was set to 0, and during thetime of outflow, the DINsediment flux was given as the productof DINsediment and the outflow velocity. The other model configurations of the tracking technique were the same as thoseof the hydrodynamic–biogeochemical model. After a spin-upof 3 years, the annual cycle of each source of nutrients andrelated particles became stationary.
2.4

Validation data

Chang et al. (2009) and Zhu et al. (2019) have conducted
a comprehensive comparison between the simulated residual current pattern, monthly water temperature, and monthly
salinity and the corresponding observations. We provided aconcise summary of their main results in Sect. 3.1. Additionally, to check whether the mixing ratio between the riverine
and oceanic water masses was realistically captured by themodel, we compared the simulated sea surface salinity (SSS)with the observations. The observed sea surface salinity wasprovided by the Fisheries Research Center, Ehime Prefectural Institute of Agriculture, Forestry and Fisheries, Japan,
and these data were averaged from 1980 to 2001 to derive theclimatological distribution.
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Q. Leng et al.: A semi-enclosed coastal sea

Figure 2. Monthly mean (a–d) observed and (e–h) simulated sea
surface salinity.

We obtained the observation data to validate the seasonal and spatial distribution of DIN and PHY from theBroad Comprehensive Water Quality Survey, Ministry of theEnvironment, Japan (https://water-pub.env.go.jp/water-pub/mizu-site/mizu/kouiki/dataMap.asp, last access: 13 October 2023). The original data were collected at 121 stations
distributed throughout the SIS during January (winter), May(spring), July (summer), and October (autumn) (Fig. 1c). At
each station, the data were sampled from two layers, the upper layer, located 1 m below the sea surface, and the lower
layer, positioned 1 m above the seafloor for stations shallower than 50 m. For stations deeper than 50 m, the data forthe lower layer were obtained at a fixed depth of 50 m (https://www.env.go.jp/content/900530598.pdf, last access: 13 October 2023). We used the averaged field from 1981 to 2021,
i.e., a climatological field, to validate our model results.
2.5

Sensitivity experiments

Understanding the response of the SIS to the changes and uncertainty in nutrient inputs from different nutrient sources is
crucial for effective nutrient management. To investigate this,we conducted sensitivity experiments by varying the input
amount of each nutrient source individually. Nutrient inputs
were altered by adding or subtracting the standard deviationof the long-term variation of nutrient input based on the climatological input amount. This allowed us to simulate theresponses of the SIS to the larger and smaller inputs that may
occur.
For the DIN load from rivers, we added twice the standard
deviation to the climatological value for the upper limit but
removed only 1 standard deviation from the climatological
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Figure 3. Monthly mean DIN concentration (mmol m−3 ) at the (a–d) upper and the (e–h) lower layers. Solid circles represent the observed values, and the background color represents the simulation
values. (i) Comparison between observed DIN concentrations andsimulated DIN concentrations.

value for the lower limit to avoid negative values. The variation of DIN loads from rivers was specified in the model bychanging the nutrient concentration in river water to avoid
changes in the hydrodynamic fields due to the change in riverdischarge. For the DIN load from the open ocean, the nutrient concentration at the open boundaries was added or subtracted by 1 standard deviation to obtain the upper limit orlower limit, and the open-boundary conditions for the hydrodynamic model were not changed. For the DIN load from thesediment, the TN concentration at the sediment surface in the
1980s was selected as the upper limit and TN concentrationin the 2010s was selected as the lower limit because the TNdata in the 1980s, 1990s, 2000s, and 2010s show a reduction
trend throughout these years. Table 1 summarizes the input
amount of DIN from the open ocean, rivers, and sediment ineach sensitivity experiment.

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Q. Leng et al.: A semi-enclosed coastal sea
3
3.1

Results
Seasonal and spatial variations of sea surface
salinity

Chang et al. (2009) demonstrated that the summer and wintercirculation patterns were reproduced by this hydrodynamic
model. Significant cyclonic and anticyclonic eddies were developed near the entrance and inner part of Suo Nada, respectively. Moreover, the model also captured the southward current flowing into the western Bungo Channel and the southwestward current in the northern Iyo Nada. Zhu et al. (2019)
reported that the warm and saline waters flowed into the SIS
through the Bungo Channel and Kii Channel in winter. For
the vertical distributions in winter, both the simulated and observed results showed that the water column was well mixed
throughout the whole SIS. In summer, both temperature andsalinity exhibited a well-mixed pattern around the straits anda well-stratified pattern in the broad basins.
We conducted a comparison between the simulated SSS
and the observed one in January (winter), May (spring), July(summer), and October (autumn) (Fig. 2). Throughout theyear, the water with a salinity of 33–34 covered the surface
layer of the Bungo Channel and Kii Channel, with the SSS
being higher in the Bungo Channel than in the Kii Channel. In January, the SSS was consistently around 33–34 inmost areas of the SIS. In May, the SSS decreased in the inshore areas, particularly in the eastern part. In July, the SSS
in most inshore areas dropped below 32. In October, the water of lower salinity was refined to the nearshore areas. The
hydrodynamic model of this study effectively reproduced theobserved SSS distribution in the SIS, indicating a realistic
capture of the mixing ratio between riverine and oceanic water masses.
3.2

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Figure 4. Monthly mean PHY concentration (mg Chl a m−3 ) at the(a–d) upper and the (e–h) lower layers. Solid circles represent theobserved values, and the background color represents the simulation
values. (i) Comparison between observed PHY concentrations andsimulated PHY concentrations.

Seasonal and spatial variations of DIN and PHY

In the upper layer, the observed DIN concentration was
low in May (Fig. 3b) and July (Fig. 3c), with a value ofaround 1.0 mmol m−3 , as most of DIN was consumed by
PHY for its growth. In the lower layer, the observed DIN
concentration remained relatively stable throughout the year,with a value of over 2 mmol m−3 in most areas, except for
May, when it appeared slightly lower at a value of around
1.5 mmol m−3 (Fig. 3f). The observed DIN concentration
was well mixed vertically in January (Fig. 3a, e) because thestratification disappeared from late autumn to winter (Changet al., 2009; Takeoka, 1985). In July, the high DIN concentration in the bottom layers of the Bungo Channel and Kii
Channel (Fig. 3g) was due to the intrusion of nutrient-richopen-ocean water. Additionally, the observed DIN concentration in July was higher in the eastern part of the SIS, suchas Osaka Bay, than in other regions because the rivers withhigh DIN loads in the eastern area discharge more DIN into
the SIS in summer. The observed DIN concentration was also
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high in the bottom layer of the central part of Harima Nada
in May and July, due to the presence of bottom cold water
there. To visualize seasonal patterns, we calculated the meanvalues of observed and simulated DIN and PHY for the upper and lower layers of each subregion. These results are illustrated in Fig. S6. In general, our model reproduced themain seasonal variation and spatial distribution of the DIN
concentration in the SIS, except for the Bisan Strait and Osaka Bay, where the simulation results were lower than theobservations. This was probably because the DIN loads fromsewage sources were not considered in the model. The comparison of simulated DIN concentration versus observations
suggests that the model works well in the areas with low
concentrations (< 10 mmol m−3 ) but underestimates the highconcentrations in some nearshore areas. Their linear correlation coefficient was 0.82 (Fig. 3i).
In the upper layer, the observed PHY concentration washigh in May and October, with values over 1 mg Chl a m−3
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Table 1. Annual mean input amount of DIN (mol s−1 ) from the open ocean, rivers, and sediment in sensitivity experiments. “L” means thelower-limit case; “U” means the upper-limit case. The percentages mean the relative change from the value in the control case.
Name of cases

From rivers

From the open ocean

From sediment

Total input

Control
L open ocean
U open ocean
L rivers
U rivers
L sediment
U sediment

192
192
192
130 (−33 %)
314 (+64 %)
192
192

799
527 (−34 %)
1073 (+34 %)
799
799
799
799

86
86
86
86
86
60 (+30 %)
136 (+58 %)

1077
805 (−25 %)
1351 (+25 %)1015 (−5.8 %)1199 (+11 %)1051 (−2.4 %)1127 (+4.6 %)(Fig. 4b, d). In the lower layer, the observed PHY concentration remained at a low value below 0.5 mg Chl a m−3 allyear round in most areas (Fig. 4e–h). However, it was high inthe nearshore areas, particularly around the estuaries. In January, the observed PHY concentration was similar between
the upper and lower layers (Fig. 4a, e). The difference in
PHY concentration between the upper and lower layers waslarger in the simulation than in the observation. The simulated PHY concentration was lower than the observationsin the nearshore areas. The mean values of observed andsimulated DIN and PHY for the upper and lower years ofeach subregion are illustrated in Fig. S7. The comparison ofsimulated PHY concentration versus observations suggeststhat the model works well in areas with low concentrations(< 5 mg Chl a m−3 ) but underestimates the high concentrations in the nearshore areas. Their linear correlation coefficient was 0.63 (Fig. 4i).
It should be noted that the underestimations for DIN and
PHY occur only in the nearshore areas (surface salinity of< 30.6 in July refers to the Ministry of the Environment,
Japan), whose volume shares 5 % of the whole SIS. In otherwords, the model can reproduce the seasonal variations andthe spatial distribution of DIN and PHY in the other 95 % of
the volume of the SIS, which is sufficient for our purpose of
examining the impacts of oceanic nutrients on the inventories
of DIN and PHY in the SIS.
3.3

Figure 5. The water-column-averaged DIN concentration(mmol m−3 ) from the open ocean, rivers, and sediment in thefirst year of the tracking simulation.

Distribution of oceanic, riverine, and benthic DIN
in the first year of simulations

The materials related to the DIN originating from the open
ocean, rivers, and sediment were all set to 0 at the initial stageof the simulation. After DIN loads were transported into the
SIS, DIN concentration from each nutrient source gradually
increased in the SIS until reaching the steady state of the annual cycle. The water-column-averaged DIN concentrationin the first year of tracking simulation was used to exhibit
the pathway of DIN from each nutrient source into the SIS(Fig. 5).
The oceanic DIN intruded into the SIS from the Bungo
Channel and Kii Channel and then entered Iyo Nada and Osaka Bay. Gradually, it arrived at Suo Nada, Aki Nada, andBiogeosciences, 20, 4323–4338, 2023

Harima Nada. In the western part of the SIS, the oceanic
DIN continued to intrude into Hiroshima Bay and Hiuchi
Nada. On the other hand, in the eastern part of the SIS,
the oceanic DIN was mainly concentrated in the Kii Channel, at Osaka Bay, and at Harima Nada, but its concentration increased (Fig. 5a–f). The oceanic DIN concentrationin the Bisan Strait started to increase at the end of the firstyear. On the other hand, the riverine DIN intruded into the
SIS from the estuaries, especially in Osaka Bay and Harima
Nada (Fig. 5g–l). The benthic DIN concentration increased
from the nearshore areas of Suo Nada, Hiroshima Bay, Hiuchi Nada, Harima Nada, and Osaka Bay (Fig. 5m–p).
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After 1 year of simulation, DIN concentrations from theopen ocean, rivers, and sediment already occupied most areasof the SIS (Fig. S8). We used the results in the third year of
the tracking simulation, which had reached the steady stateof the annual cycle, for the following sections.
3.4

Contribution of different nutrient sources to DIN
and PHY

The oceanic DIN concentration in the entire SIS showeda significant seasonal variation, with a decrease from
2.7 mmol m−3 in January to 1.7 mmol m−3 in June, followedby an increase until December, similar to the total DIN concentration (Fig. 6h). The proportion of oceanic DIN to thetotal DIN did not show an obvious seasonal variation, ranging between 57 % and 66 %, with an annual mean of 61 %(Fig. 6i). The riverine and benthic DIN concentrations weremuch smaller than the oceanic DIN concentration and remained around 0.89 and 0.52 mmol m−3 , respectively. The
annual average contribution of riverine and benthic sources
to DIN concentration was 25 % and 14 %, respectively.
Regarding the annual mean spatial variation, the oceanic
DIN concentration averaged over the entire depth was higher
than 2.5 mmol m−3 in the Bungo Channel and Kii Channel,
while it was lower than 0.5 mmol m−3 in the Bisan Strait andat Bingo Nada (Fig. 6b). The oceanic contribution to DIN
concentration decreased from the southern boundary areas tothe inner part of the SIS and exhibited lower values in thenearshore areas. For different subregions, the proportion ofoceanic nutrients was high in the Bungo Channel (83 %), in
the Kii Channel (68 %), and at Iyo Nada (65 %) and low in
the Bisan Strait (17 %) and at Bingo Nada (22 %) (Fig. 8a).
The riverine concentration was highest in Harima Nada
and Osaka Bay, due to the large terrestrial input, and the corresponding contribution was also highest in these two subregions with a value of about 50 %. The benthic concentration was higher in Bingo Nada, Harima Nada, and Hiroshima
Bay, while its contribution was higher in Bingo Nada, with avalue of about 46 %.
The volume-averaged PHY concentration supported byoceanic DIN also exhibited a significant seasonal variation,with an obvious spring bloom in March (0.61 mg Chl a m−3 )(Fig. 7h). However, the seasonal variation in the oceanic contribution to the total PHY concentration remained close to
the annual mean value of 46 % (Fig. 7i). The PHY concentrations supported by riverine DIN and benthic DIN alsochange a lot with the season, with an annual average of0.38 mg Chl a m−3 and 0.20 mg Chl a m−3 , respectively. The
annual average contribution of riverine and benthic sources
to PHY concentration was 35 % and 19 %, respectively.
For the spatial variation of the annual mean, the oceanic
PHY concentration averaged over the entire depth remained
at 0.5 mg Chl a m−3 in most areas of the SIS (Fig. 7b). However, the oceanic contribution to PHY concentration exhibited an obvious spatial pattern, decreasing from the southern
https://doi.org/10.5194/bg-20-4323-2023

Figure 6. (a–d) The annual mean of the water-column-averaged
DIN concentration (mmol m−3 ) originating in the open ocean,rivers, and sediment. (e–g) Contributions from the open ocean,rivers, and sediment to the total DIN concentration. (h) The
monthly average oceanic, riverine, and benthic DIN concentration (mmol m−3 ) in the whole SIS. (i) The monthly variations ofoceanic, riverine, and benthic contributions to the total DIN concentration in the whole SIS.

boundary areas into the inner part of the SIS and showing
lower values in the nearshore areas, similar to DIN. For different subregions, the oceanic contribution was much higher
in the Bungo Channel (78 %), in the Kii Channel (46 %), andat Iyo Nada (59 %) and lower in the Bisan Strait (15 %) and at
Bingo Nada (21 %) (Fig. 8b). The riverine contribution was
higher at Harima Nada (56 %) and Osaka Bay (62 %), while
the benthic contribution was higher at Bingo Nada (42 %)and in the Bisan Strait (32 %).

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Figure 8. Contributions from the open ocean, rivers, and sediment
to the total (a) DIN and (b) PHY concentrations in subregions of
the SIS.

Figure 7. (a–d) The annual mean of the water-column-averaged
PHY concentration (mg Chl a m−3 ) originating in the open ocean,rivers, and sediment. (e–g) Contributions from the open ocean,rivers, and sediment to the total PHY concentration. (h) The
monthly average oceanic, riverine, and benthic PHY concentration(mg Chl a m−3 ) in the whole SIS. (i) The monthly variations ofoceanic, riverine, and benthic contributions to the total PHY concentration in the whole SIS.

8
3.5

DIN transport across the boundary between the
SIS and open ocean

The oceanic DIN flux was primarily transported from theopen ocean to the SIS in the lower layers, reflecting the intrusion of nutrient-rich Kuroshio Current subsurface waterfrom the bottom and out of the SIS in the upper layers andeastern part of the open boundaries (Fig. S9). In terms of
the seasonal variation of DIN transport in the Bungo Channel, the onshore transport of oceanic DIN was highest in
Biogeosciences, 20, 4323–4338, 2023

January and July, with a value of 707 and 664 mol s−1 , respectively, and lowest in April and May, with a value of 236
and 226 mol s−1 , respectively (Fig. 9a). There is also offshoretransport of oceanic DIN across the open boundary. Its value
was highest in January and February, with a value of −648
and −550 mol s−1 , respectively, and lowest in April and May,
with a value of −244 and −215 mol s−1 , respectively. Consequently, the net oceanic DIN transport was highest in Julyand August, with a value of 275 and 215 mol s−1 , respectively, and lowest in April and May, with a value of −8 and
10 mol s−1 , respectively (Fig. 9a).
The seasonal variation of oceanic DIN transport across theKii Channel was somewhat different from that of the Bungo
Channel. The onshore transport of oceanic DIN was highest in July and August, with a value of 407 and 483 mol s−1 ,respectively, and lowest in April and May, with a value of
199 and 215 mol s−1 , respectively. The offshore transport ofoceanic DIN was highest in July and August, with a value of−277 and −329 mol s−1 , respectively, and lowest in Marchand April, with a value of −123 and −112 mol s−1 , respectively. Consequently, the net oceanic DIN transport was highest in July and August, with a value of 130 and 154 mol s−1 ,https://doi.org/10.5194/bg-20-4323-2023

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4333
model, which was larger than their DIN concentration. Another possible reason is that the simulation of July andAugust 2018 by Morimoto et al. (2022) represents an extremely heavy rain case, which induced a stronger outflow
in the surface layer and a stronger inflow in the bottom layer
than in our simulation. Fujiwara et al. (1997) reported about
168 mol s−1 of DIN through the Kii Channel from the open
ocean in August 1985. Kasai et al. (2001) estimated the net
DIN transport through the Kii Channel into the SIS in August
1996 was 111 mol s−1 , which was similar to that of Fujiwara
et al. (1997). We estimated about 150 mol s−1 of DIN wasfrom the open ocean to the SIS through the Kii Channel inAugust.

4
4.1

Figure 9. (a) Monthly variation of onshore, offshore, and net transport of ocean DIN (mol s−1 ) across the Bungo Channel and Kii
Channel. (b) Monthly variation of DIN loads (mol s−1 ) from theopen ocean, rivers, and sediment.

respectively, and lowest in May and November, with a valueof 70 and 67 mol s−1 , respectively (Fig. 9b).
The onshore transport of oceanic DIN has an annual meanof 507 mol s−1 in the Bungo Channel and 292 mol s−1 in theKii Channel, with a sum of 799 mol s−1 . The offshore transport of oceanic DIN has an annual mean of −397 mol s−1 inthe Bungo Channel and of −199 mol s−1 in the Kii Channel, with a sum of −596 mol s−1 . The values produce an annual mean of net oceanic DIN transport of 110 mol s−1 in the
Bungo Channel and 93 mol s−1 in the Kii Channel, with asum of 203 mol s−1 .
In the south of the Bungo Channel, Morimoto et al. (2022)
reported that a net of 385 mol s−1 oceanic DIN was transported from the open ocean to the SIS in July and August
based on simulated water volume and DIN concentration
derived from water temperature. It was 245 mol s−1 in ourstudy. The reason that our estimate is less than theirs may becaused by the outward DIN transport. We used the DIN concentration calculated by the hydrodynamic–biogeochemical
https://doi.org/10.5194/bg-20-4323-2023

Discussion
Role of the open ocean in the DIN inventory ofdifferent shelf seas

Our simulation reveals that the annual mean inventory of
DIN in the whole SIS was 31.7 × 108 mol, among whichthat originating from the open ocean, rivers, and sedimentwas 19.2 × 108 , 7.89 × 108 , and 4.62 × 108 mol, respectively,
sharing a proportion of 61 %, 25 %, and 14 %, respectively(Fig. 10). The oceanic contribution to the DIN inventory in
our simulation (61%) was less than the 81 % proposed by
Yanagi and Ishii (2004), who did not consider biogeochemical processes and nutrient release from sediment.
In terms of PHY, the annual mean inventory supported
by DIN from the open ocean, rivers, and sediment was
2.75 × 108 , 2.10 × 108 , and 1.11 × 108 mol N, respectively,
implying a proportion of 46 %, 35 %, and 19 %, respectively,in the whole inventory (Fig. 10). Although smaller than thecontribution to DIN, the open ocean is still a dominant contributor to PHY in the SIS. If we consider the total nitrogen,which is defined as the sum of DIN, PHY, ZOO, and PON inthis study, the open ocean contributes 53 % to its inventory in
the SIS.
In the ECS, a marginal sea in the northwestern Pacific
Ocean with a broad continental shelf, the open ocean plays
a dominant role in areas shallower than 200 m, contributing 57 % of the DIN inventory (Zhang et al., 2019). Even
though the ECS is more open to the open ocean, our simulation showed that the open ocean contributed more to the DIN
inventory in the SIS compared to the ECS. In a shallow estuary located on the eastern coast of Jutland, Denmark, which
is well ventilated with the open ocean, the open ocean contributed less than 40 % to the DIN inventory in most areas ofthe estuary (Timmermann et al., 2010). In the northern Gulf
of Mexico, another marginal sea receiving large amounts ofnutrients from rivers, the open ocean contributed less than
15 % of the total nitrogen inventory in the shallow regions,where rivers contribute more than 85 % (Große et al., 2019).
Our study also found that the open ocean contributed less
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Net biogeochemical processes of DIN =− photosynthesis + respiration + mineralization+ excretion

Figure 10. The annual mean of material inventories and material
flows in the whole SIS. (a) Total inventories and material flows.
(b) Oceanic inventories and material flows. (c) Riverine inventories
and material flows. (d) Benthic inventories and material flows. The
values in the rectangles represent the inventory (×108 mol) of eachmaterial type in the SIS. The values next to the black solid lines withan arrow represent the flux of biogeochemical processes (mol s−1 ).
The values next to the horizontal solid orange lines with an arrow represent the horizontal flux from the SIS to the open ocean(mol s−1 ). The values next to the vertical solid orange lines withan arrow represent the vertical flux to the sediment (mol s−1 ). The
values next to the solid blue line with a vertical arrow represent theflux of benthic DIN into the SIS.

than 30 % to the nearshore areas with shallow water depth
and large riverine nutrient inputs (Figs. 6e, 7e). Apparently,
the oceanic contribution to the nutrient inventory in these waterbodies varies largely and may strongly depend on its cycling rate in the low-trophic system of the shelf sea.
4.2

To understand the different proportions of oceanic, riverine,and benthic nutrients in the inventory of DIN in the SIS, we
present its related material flows here (Fig. 10).
The annual mean budget flows of DIN in the SIS can be
described using Eq. (6), where the amount of DIN delivered
to the SIS from external sources is equal to the sum of theamount consumed by biogeochemical processes that transfer
DIN to the biological particles and the amount exported tothe open ocean through open boundaries in the form of DIN.
Input transport = output amount + net biogeochemical

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Our study found that the input amount of DIN from the open
ocean, rivers, and sediment was 799, 192, and 86 mol s−1 ,respectively, with a ratio of 74 %, 18 %, and 8 %. The output amount of oceanic, riverine, and benthic DIN to the open
ocean was −596, −42, and −17 mol s−1 , respectively, with aratio of 91 %, 6 %, and 3 %. The net biogeochemical processof DIN from the open ocean, rivers, and sediment was −202,−150, and −68 mol s−1 , respectively, with a ratio of 48 %,36 %, and 16 %. These results indicate that only 25 % of
the input oceanic DIN was involved in biogeochemical processes, and 75 % was returned to the open ocean (Fig. 10b).
Therefore, the role of oceanic nutrients may be overestimatedin terms of the input amount. In contrast, riverine and benthic DIN input transport was smaller than oceanic DIN, but
78 % and 79 % of their inputs were involved in biogeochemical processes, indicating more bioactive effects (Fig. 10c, d).
In the North Sea, which is a semi-enclosed sea connected
to North Atlantic Ocean through its northern boundary, theopen ocean contributed 75 % of the total nitrogen budget in
the northern open area (Vermaat et al., 2008). Similarly, in
the ECS, the open ocean contributed 72 % of the DIN input
amount in areas shallower than 200 m (Zhang et al., 2019).
As shown by Zhang et al. (2019), the contribution of oceanic
DIN to primary production in the ECS is lower than the proportions of DIN input (72 %). Such a difference suggests a
different efficiency of DIN with different origins.
In our simulation, we did not allow for external sources tobring PHY into the SIS through open boundaries. Therefore,
the PHY generated through biogeochemical processes inside
the SIS was transported to the open ocean through the open
boundaries. The equation governing the budget of PHY isrepresented by Eq. (8). The net biogeochemical processes of
PHY are defined in Eq. (9).
0 = output transport + net
biogeochemical processes of PHY

DIN and PHY material flows

processes of DIN

(7)

(6)

(8)

Net biogeochemical processes of PHY= photosynthesis − respiration − grazing− mortality(9)

The oceanic proportion of photosynthesis was 41 %, which islower than the proportion of oceanic DIN in the total inventory. This may be due to the fact that the oceanic DIN was
primarily distributed in the lower layer of areas with deep
depth in the SIS (Fig. 6b). As a result, some of the oceanic
DIN in the deep layer may not be available for photosynthesis due to light limitation, leading to a lower proportion
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Table 2. Annual mean oceanic, riverine, and benthic DIN concentration (mmol m−3 ) and PHY concentration (mg Chl a m−3 ) and their
proportion in the total DIN and PHY concentration in the entire SIS in the sensitivity experiments. The percentage in column 6 is the relativechange to the values in the case of “Control”.
Name

Oceanic

Riverine

Benthic

Total

Control

DIN
PHY

2.19 (61 %)0.49 (46 %)0.89 (25 %)0.38 (35 %)0.52 (14 %)0.20 (19 %)

3.61
1.07

L open ocean

DIN
PHY

1.44 (51 %)0.35 (37 %)0.88 (31 %)0.39 (41 %)0.52 (18 %)0.21 (22 %)2.85 (−21 %)0.96 (−10 %)U open ocean

DIN
PHY

2.95 (67 %)0.61 (52 %)0.91 (21 %)0.36 (31 %)0.53 (12 %)0.19 (16 %)4.40 (+22 %)1.17 (+9.4 %)L rivers

DIN
PHY

2.16 (65 %)0.50 (51 %)0.62 (19 %)0.28 (28 %)0.52 (16 %)0.13 (21 %)3.30 (−8.6 %)0.99 (−7.5 %)U rivers

DIN
PHY

2.22 (52 %)0.47 (39 %)1.53 (36 %)0.54 (45 %)0.54 (12 %)0.19 (16 %)4.30 (+19 %)1.20 (+12 %)L sediment

DIN
PHY

2.18 (62 %)0.50 (48 %)0.89 (26 %)0.38 (37 %)0.41 (12 %)0.16 (15 %)3.49 (−3.3 %)1.04 (−2.8 %)U sediment

DIN
PHY

2.19 (58 %)0.48(44 %)0.90 (24 %)0.37 (34 %)0.67 (18 %)0.25 (22 %)3.76 (+4.2 %)1.11 (+3.7 %)of oceanic photosynthesis compared to oceanic DIN. In contrast, riverine and benthic DIN were mainly distributed in theareas with shallow depths where the light condition is good(Fig. 6c, d).
The net biogeochemical process of PHY defined by Eq. (9)was 64, 20, and 6 mol s−1 for the oceanic, riverine, and benthic DIN, respectively, which are exactly the same as the output amount of PHY from the open boundaries. Their ratios of
71 %, 22 %, and 7 % suggest that the oceanic PHY was more
likely to leave the SIS.
The inventory of ZOO and PON depends on that of PHY,
and consequently the nutrients from the open ocean support more than half of ZOO and PON in the SIS. The export of biological particles from the SIS is important fora material balance in the sea. In the SIS, the horizontal export flux of biological particles (PHY + ZOO + PON)
to the open ocean is 229 mol s−1 (Fig. 10a), and the vertical export flux of biological particles to the sedimentis 190 mol s−1 , whose ratio is 229 mol s−1 / 190 mol s−1 ≈1.2 : 1. If we examine them for the different origins of nutrients, this ratio changes. For oceanic nutrients (Fig. 10b),
the horizontal export of biological particles has a fluxof 140 mol s−1 , while the vertical export has a value of
61 mol s−1 , whose ratio is 140 mol s−1 / 61 mol s−1 ≈ 2.3 : 1.
For the riverine nutrients (Fig. 10c), the horizontal export has
a flux of 68 mol s−1 , while the vertical export has a value of
82 mol s−1 , whose ratio is 68 mol s−1 / 82 mol s−1 ≈ 0.83 : 1.
For the benthic nutrients (Fig. 10d), the horizontal export has
a flux of 21 mol s−1 , while the vertical export has a value of
47 mol s−1 , whose ratio is 21 mol s−1 / 47 mol s−1 ≈ 0.45 :1. Considering the spatial distribution of these different ori-

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gins of nutrients, the difference in ratios is likely a natural
result of water exchange between the SIS and the open ocean.
If the proportion of PON flux settled on the sediment surface produced by the oceanic and riverine nutrients was taken
as the proportion of DIN flux released from the sediment,
among the 86 mol s−1 of DIN from the sediment, 37 mol s−1
has an origin in the open ocean and 49 mol s−1 is from rivers.
There have been some studies that estimated the TN budget in the SIS based on certain assumptions. Yanagi (1997)
reported that 392 mol s−1 of TN was transported fromthe land and 31 mol s−1 was from atmospheric deposition.
Among these inputs, 358 mol s−1 of TN was transported tothe open ocean in the open boundaries, while 64 mol s−1 wasburied in the sediment. Our study revealed that 192 mol s−1of DIN was from rivers. After undergoing biogeochemical
processes, 110 mol s−1 was transported to the open ocean asa sum of DIN, PHY, PON, and ZOO, while 82 mol s−1 wasburied in the sediment in the form of PON. It is important toconsider a few factors when comparing our results to thosein Yanagi (1997). First, we did not introduce the other typesof nitrogen from the land. Additionally, the TN load used in
Yanagi (1994) was obtained in 1982, a period characterized
by severe terrestrial pollution. As a result, the nitrogen loadfrom land in our study was lower compared to that reported
by Yanagi (1997).
Fujiwara et al. (2006) also estimated the TN budget in the
SIS and clarified the origin as being either from land or the
open ocean, showing that 330 mol s−1 of TN was supplied
from the land to the SIS, of which 297 mol s−1 was transported to the open ocean and 33 mol s−1 was buried in thesediment. Fujiwara et al. (2006) also reported that the net in-

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put of TN from the open ocean was 50 mol s−1 , which wasburied in the sediment. In our study, the TN originating fromthe open ocean has a net input of 61 mol s−1 , all of which
was buried in the sediment.
4.3

The changes in riverine nutrients can cause a change of lessthan 20 % in the total DIN inventory and a change of about
10 % in the PHY inventory. However, the changes in benthic
nutrients can cause changes of only ∼ 4 % in the total DIN
inventory and PHY inventory.

Response of the SIS to changes in nutrient input

When the DIN load from the open ocean was reduced by
33 % (case of L open ocean in Table 1), there was a corresponding decrease in the concentration of oceanic DIN by
34 % ((1.44–2.19)/2.19), as well as a decrease in the concentration of oceanic PHY by 28 % ((0.35–0.49)/0.49) (Table 2).
As a result, the contribution of oceanic DIN and PHY to thetotal concentration decreased by 10 %, while that of riverine
and benthic DIN and PHY increased within a range of 3 %to 6 %. Conversely, when the DIN load from the open ocean
was increased by 34 % (case of U open ocean in Table 1),
the concentration of oceanic DIN increased by 35 % and thatof oceanic PHY increased by 24 % (Table 2). Then, the contribution of oceanic DIN and PHY to the total concentration
increased by 6 %, while that of riverine and benthic DIN and
PHY decreased by 2 % to 4 %.
Similarly, when the DIN load from rivers was reduced by
33 % (case of L rivers), the concentration of riverine DIN and
PHY decreased by 30 % ((0.62–0.89)/0.89) and 26 % ((0.28–
0.38)/0.38), respectively, and their contribution to the total
concentration decreased by 6 % and 7 %, respectively (Table 2). On the other hand, when the DIN load from rivers
was increased by 64 % (case of U rivers), the concentration ofriverine DIN and PHY increased by 72 % ((1.53–0.89)/0.89)
and 42 % ((0.54–0.38)/0.38), respectively, and their contribution to the total concentration increased by 11 % and 10 %,respectively. In these two cases, the contribution of oceanicand benthic DIN and PHY changed by less than 9 %.
When the DIN load from the sediment was reduced by
30 % (case of L sediment), the concentration of benthic DIN
and PHY decreased by about 20 % ((0.41–0.52)/0.52 and(0.16–0.20)/0.20), and their contribution to the total concentration decreased by about 4 %. When the DIN load from thesediment was increased by 58 % (case of U sediment), theconcentration of benthic DIN and PHY increased by 29 %((0.67–0.52)/0.52) and 25 % ((0.25–0.20)/0.20), respectively,
and their contribution to the total concentration increased byabout 4 %.
Overall, the change in the DIN load from a specific nutrient source led to the same order change in the corresponding
DIN and PHY concentrations but a small range of change inthe contribution of all nutrient sources. Specifically, despite
significant changes in DIN loads from rivers and sediment,
their contribution to the SIS was only minimally impacted
due to their low proportion. This can be understood by the
relative change in the inventory of total DIN and total PHY
in Table 2. In these experiments, the changes in oceanic nutrients can cause a change of more than a 20 % in the total DIN
inventory and a change of about 10 % in the PHY inventory.
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5

Conclusions

In this study, we investigate the behaviors of oceanic, riverine, and benthic nutrients regarding the inventory of nutrientsand phytoplankton in the SIS using a low-trophic model with
an embedded tracking module. Our study shows the largest
contribution of oceanic nutrients to the inventory of nutrientsand phytoplankton in the SIS and its lowest efficiency in the
primary production in the SIS. On the other hand, although
the riverine and benthic nutrients have a lower contribution to
the inventory of nutrients and phytoplankton in the SIS thanthe oceanic nutrients, they demonstrate a higher efficiency
in the primary production in the SIS. It must be noted that
their contributions have a strong spatial variation, in which
the oceanic nutrients range from > 90 % in the area close
to the open ocean to < 30 % in the nearshore areas, while
the riverine and benthic nutrients range from > 50 % in thenearshore areas to < 10 % in the areas far from the estuaries
and inner bays.
The above results give us several hints as to the management strategy for the SIS. Firstly, the oceanic nutrients provide a background of nutrients that supports the primary production in the SIS, and it can cause a temporal variation witha range of 20 % in the inventory of nutrients and phytoplankton. This nutrient part is mainly controlled by the variationsin the nutrient concentration in the open ocean and the waterexchange between the SIS and open ocean, and human-mademanagement of nutrient load from land cannot affect it. Secondly, the management should be easily applied to the riverine nutrients whose impact has a strong spatial distribution.
Therefore, the target of management should be carefully examined to confirm the local impacts of each river before thedetermination of the management plan. Thirdly, although it
is a little difficult, one needs to pay more attention to the sediments because their impacts are larger than those of riverine
nutrients in some places.

Code availability. The source code of the numerical model used inthis study is available on request. Please contact the corresponding
author.

Data availability. The data used in this study are available on request. Please contact the corresponding author.

Supplement. The supplement related to this article is available online at: https://doi.org/10.5194/bg-20-4323-2023-supplement.

https://doi.org/10.5194/bg-20-4323-2023

Q. Leng et al.: A semi-enclosed coastal sea
Author contributions. JZ developed the hydrodynamic model. AM
developed the low-trophic model. QL combined two models andintroduced the tracking module into them. QL and XG prepared themanuscript. All the authors reviewed the manuscript.

Competing interests. The contact author has declared that none of
the authors has any competing interests.

Disclaimer. Publisher’s note: Copernicus Publications remains
neutral with regard to jurisdictional claims in published maps andinstitutional affiliations.

Acknowledgements. We thank Yoshitsugu Koizumi, who worked in
the Fisheries Research Center, Ehime Prefectural Institute of Agriculture, Forestry and Fisheries, Japan, and provided us with the observed nutrients and water temperature data in the Bungo Channel.

Financial support. This research has been supported by the Environment Research and Technology Development Fund (grantnos. JPMEERF20205005 and JPMEERF20212001) of the Environmental Restoration and Conservation Agency provided by the Ministry of the Environment, Japan.

Review statement. This paper was edited by Emilio Marañón andreviewed by Hagen Radtke and one anonymous referee.

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