Marine Pollution Bulletin 130 (2018) 40–54

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Marine Pollution Bulletinjournal homepage: www.elsevier.com/locate/marpolbul

Residual eﬀects of treated eﬄuent diversion on a seaweed farm in a tidal
strait using a multi-nested high-resolution 3-D circulation-dispersal modelT⁎

Yusuke Uchiyamaa,b, , Xu Zhanga, Yota Suzuea,1, Taichi Kosakob, Yasumasa Miyazawac,
Akihiko NakayamadaDepartment of Civil Engineering, Kobe University, Kobe, Japan
Coastal and Estuarine Environmental Department, Port and Airport Research Institute, Yokosuka, Japanc
Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japand
Department of Environmental Engineering, Universiti Tunku Abdul Rahman, Kampar, Perak, Malaysiab

A R T I C LE I N FO

A B S T R A C T

Keywords:
Treated sewage eﬄuent
Coastal dispersal
Diversion
Tidal strait
Estuary
ROMS

A high-resolution 3-D model was developed to assess the impact of a diversion outfall at the Tarumi Sewage
Treatment Plant (TSTP) on an adjacent seaweed farm in Osaka Bay, Japan. The model was extensively validated
to ensure a reasonable agreement with in situ observations. The western part of the farm is largely inﬂuenced bytidal currents, whereas the eastern area is mainly aﬀected by subtidal residual currents that are primarily due tosurface wind stress. The released eﬄuent is transported by counterclockwise residual circulation formed oﬀ theTSTP. The model reveals that the diversion adequately suppresses the inﬂuence on the farm. While the instantaneous eﬄuent concentration is diminished by about 50%, the eﬄuent accumulated on the farm decreased
from 2.83 × 104 m3 to 2.01 × 104 m3 due to the diversion, demonstrating an approximately 28% reduction of
the eﬄuent from the TSTP by the diversion outfall.

1. Introduction
The Seto Inland Sea (SIS) is the largest semi-enclosed estuary inJapan, encompassing a total water area of 23,203 km2 (Fig. 1a). It has
unique geographical characteristics and consists of seven main subbasins interconnected by narrow straits and > 3000 islands, which
form a complex “basin–strait” system. The SIS is a partially stratiﬁed
tidal estuary that is concurrently aﬀected by: 1) energetic tidal currents
up to 3 ms−1 near straits (Fujiwara et al., 1994) under dominant
semidiurnal, mesotidal conditions with a maximum tidal range of ±1.2 m; 2) seasonally varying freshwater discharge from 28 major rivers;and 3) quasi-persistent eastward residual circulation (the SIS throughﬂow) primarily driven by the eastward drifting Kuroshio oﬀshore of thetwo openings that connect the SIS with the Paciﬁc (e.g., Kosako et al.,2016). The Kuroshio is known to have a considerable impact on the SIS
circulation due to sporadic intrusions (Takeoka et al., 1993; Tada et al.,2017) and meandering of the Kuroshio path (e.g., Kawabe, 1987,
1995). In such a strait–basin system, the water is well mixed near straits
due to strong tidal currents, while it is rather stratiﬁed in basins undermore quiescent conditions (e.g., Guo et al., 2004; Chang et al., 2009).
Approximately 24% of Japan's population resides within its⁎

1watershed basins (Tsuge and Washida, 2003), which leads to heavy
nutrient loading of the SIS (Miller et al., 2010) and thus an increased
occurrence of harmful algal bloom (HABs; e.g., Imai et al., 2006).
Historically, the SIS has been extensively used by ﬁsheries and inaquaculture, although it is viewed as a general dumpsite for wastewaterfrom sewage treatment plants. This has caused the critical deteriorationof the water quality and coastal marine ecosystems for many years(Tsujimoto et al., 2006). The number of “red tide” events due to HAB,
an indicator of eutrophication, has raised remarkably with the increased nutrient loading (Takeoka, 2002; Imai et al., 2006). Red tides
have occurred much more frequently and in a larger area in the SIS
since the 1960s due to Japan's economic development and the associated pollutant emission, with the maximum of 299 incidents in 1976
(Imai et al., 2006). The aquaculture and marine ecosystem have been
seriously aﬀected by deterioration. Terawaki et al. (2003) reported that
~6400 ha of seaweed and seagrass beds along the Japanese coast havebeen destroyed from 1978 to 1991. A series of environmental preservation policies that regulate the total amount of nitrogen andphosphorus in eﬄuent has been enacted, which had adequately improved the water quality in recent years (Tomita et al., 2016). However,deterioration is not entirely avoidable; ~100 red tides have occurred

Corresponding author at: Department of Civil Engineering, Kobe University, Kobe, Japan.
E-mail address: uchiyama@harbor.kobe-u.ac.jp (Y. Uchiyama).
Present aﬃliation: CTI Engineering Co., Ltd., Nagoya, Japan.

https://doi.org/10.1016/j.marpolbul.2018.03.007
Received 28 December 2017; Received in revised form 3 March 2018; Accepted 5 March 20180025-326X/ © 2018 Elsevier Ltd. All rights reserved.

Marine Pollution Bulletin 130 (2018) 40–54

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Fig. 1. Quadruple-nested ROMS model domains and bathymetry (color: m). (a) The ROMS-L1, L2, L3, and L4 domains (black boxes) embedded in the JCOPE2 domain (outside of theperimeter of the L1). The gray contours are the isobaths at intervals of 1000 m, while the red contours indicate the approximate Kuroshio region with surface velocity magnitudes ≥ 0.5 m/s at intervals of 0.25 m/s. (b) The enlarged area around Osaka Bay encompassing the L3 and L4 domains. The red dot and cross indicate the locations of the Kobe tidal
gauge and shallowest part of the “Okinose” shoal at a depth of 23 m. (c) The entire L4 domain with isobaths at intervals of 20 m. The blue box is the approximate extent of the seaweedfarm area; TFP is the Tarumi Fishery Port area; the red star shows the location of the Tarumi Sewage Treatment Plant (TSTP); the blue dots are the two outfalls (D: diversion, N: normal);the red dots (Stations A-C) are used for the time series plots in Figs. 5, 6, 7 and 11; and the red lines are two transects used for the cross-sectional plots of Figs. 9 and 10. (For interpretationof the references to color in this ﬁgure legend, the reader is referred to the web version of this article.)

widely cultivated Porphyra yezoensis and Porphyra tenera (Niwa andAguga, 2006). Local ﬁshermen keep complaining about the possible
impact from sewage eﬄuent on seaweed farming to the east of the TSTP
(blue box in Fig. 1c). In seaweed farming, spores are rather vulnerable
to the surrounding environment, in particular to nutrients and salinity.
They grow rapidly from December to March and are harvested in April.
Therefore, to avoid overdose that stunts seaweeds, a reduction of nutrients in the eﬄuent is inevitable in fall. The City of Kobe, the local
government, has constructed a new western diversion outfall (“D” in
Fig. 1c) 500 m away from the central normal outfall (“N” in Fig. 1c) in
the TSTP to reduce the possible impact of the eﬄuent on farming by
seasonal diversion from October to December. However, the eﬀects of
the western diversion have not been fully investigated, particularly inper year in the SIS since the 2000s. Osaka Bay, located in the easternpart (Fig. 1b) with the most densely populated hinterland around the
SIS, still suﬀers from severe water pollution.
The Tarumi Sewage Treatment Plant (TSTP), one of the largest
wastewater treatment plants in Osaka Bay, is situated in the 3.6 km
narrow Akashi Strait with a maximum depth of 140 m (Fig. 1c) andprevailing energetic and complex tidal ﬂow. A sandbank known as the“Okinose” shoal (Fig. 1b) with the shallowest depth of 23 m is formed
southeast of the strait, inﬂuenced by intense tidal currents and residual
clockwise circulation (Fujiwara and Nakata, 1991; Fujiwara et al.,1994). The area around the Akashi Strait is famous for seaweed farmingin the nation. Edible seaweed (“nori” in Japanese) is very common in
Asia, which is made from a type of red algae, Porphyra, including
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2. The nested modelcomparison to the case with eﬄuent from the normal outfall.
Bricker et al. (2006) described the general behavior of wastewater
released from the western outfall of the TSTP based on conductivity,
temperature, and depth (CTD) ﬁeld measurements for one M2 tidal
cycle under mixed inﬂuences from riverine water from the adjacentsmall stream (Fukuda River). Bricker and Nakayama (2007; hereinafterBN07) further diagnosed the sewage eﬄuent transport and diﬀusionfrom dilution quantiﬁed based on the ﬁltered temperature and salinity
ﬁelds that enable the separation of wastewater from ambient seawaterinﬂuenced by the stream. Although both studies were conducted over ashort duration under well-mixed winter conditions without any consideration of the normal outfall, the far-ﬁeld freshwater plume behaves
like those in a canonical open channel ﬂow with respect to the verticalmixing, while horizontal mixing occurs with a diﬀusivity two (one)orders of magnitude larger than that of meandering (straight) channel
ﬂows. They anticipated that the estimated large lateral diﬀusivity may
be due to topographically generated eddies, transient tidal ﬂow, andbaroclinicity in the buoyant plume. In contrast, multi-nested high-resolution numerical ocean modeling is useful to examine the far-ﬁeld
dispersal of eﬄuent under such complex dynamical conditions.
Uchiyama et al. (2014) comprehensively assessed the eﬀects of diversion and dispersal of eﬄuent with a quadruple-nested ocean circulation
model coupled with a buoyant plume and passive Eulerian tracer in an
open ocean conﬁguration for Southern California bays. They found thatlateral mixing and the associated eﬄuent dispersal predominantly
occur with submesoscale turbulence rather than tidal mixing.
In the present study, we conducted multi-nested, high-resolution,
synoptic ocean circulation modeling to assess the dilution and far-ﬁeld
dispersal of the bottom-released buoyant eﬄuent discharged from thecentral and western outfalls corresponding to normal and diversion
discharges at the TSTP. The extrinsic eﬀects of large-scale oceanic information, including the transient Kuroshio that determines the residual, eastward SIS throughﬂow, and the intrinsic variability, such as
the ﬂuctuating tidal ﬂow and associated 3-D mixing, were thoroughly
considered. We primarily aimed to examine possible driving mechanisms of the ﬂow ﬁeld near the TSTP and to quantify the impact of thediversion of sewage eﬄuent on the seaweed farm. The remainder of thepresent paper is organized as follows. The conﬁguration of the numerical model is given in Section 2. Section 3 illustrates and validates
the model results followed by a discussion of possible driving mechanisms of the near-surface and 3-D ﬂow ﬁelds around the TSTP in
Section 4. A quantitative assessment of inﬂuences of the TSTP on theseaweed farm for cases with normal and diversion outfalls is conductedwith a ﬂux budget analysis in Section 5. The conclusions are provided in
Section 6.

2.1. The ocean circulation model
We developed a quadruple-nested downscaling ocean model(hereinafter L1–L4 models; Table 1 and Fig. 1) based on the Regional
Oceanic Modeling System (ROMS; Shchepetkin and McWilliams, 2005,2008) coupled with an Eulerian passive tracer model (Section 2.2)
proposed in Uchiyama et al. (2014), which enables us to keep track of
the eﬄuent conservatively. The outermost boundary conditions for the
ROMS models were imposed from the assimilative Japan Coastal Ocean
Predictability Experiments (JCOPE2) oceanic reanalysis (Miyazawa
et al., 2009) at the perimeter of the L1 model, as described in Uchiyama
et al. (2017a, 2017b). Subsequently, the one-way oﬄine nesting technique was used to successively convey large-scale information into theembedded child models with gradual grid resolution reﬁnement (e.g.,
Mason et al., 2010; Buijsman et al., 2012; Romero et al., 2013;
Kamidaira et al., 2017). The lateral grid spacing was thus reduced from
1/12° (~10 km, JCOPE2) to 2 km (L1), 600 m (L2), 100 m (L3), and20 m (L4). The surface- and bottom-following, vertically stretched scoordinate (Shchepetkin and McWilliams, 2005) with 32 s-layers wasused for all four ROMS models with grid height reﬁnement near thesurface and bottom to resolve the boundary layers adequately. The
bathymetry for the L1 and L2 models was provided by the
SRTM30_PLUS product (Rodriguez et al., 2005; Rodriguez et al., 2006;
Becker et al., 2009), which covers the global ocean at 30 geographic arc
seconds (~1 km), supplemented by the J-EGG500 (http://www.jodc.
go.jp/data_set/jodc/jegg_intro.html) at 500 m resolution for the coastal
region. The bathymetry for the L3 and L4 models was obtained from thedataset compiled by the Central Disaster Prevention Council, Japan,
with a horizontal resolution of 50 m.
The surface momentum ﬂux (viz., wind stress) was taken from thehourly operational atmospheric product of the Grid-Point
Values–MesoScale Model (GPV–MSM; Isoguchi et al., 2010) of theJapan Meteorological Agency (JMA). The surface heat and freshwater
ﬂuxes for L1 and L2 were obtained using the bulk formula that has been
implemented in the Coupled Ocean/Atmosphere Mesoscale Prediction
System (COAMPS; Hodur, 1997), while the surface ﬂuxes for L3 and L4
were taken from the monthly climatology of the Comprehensive OceanAtmosphere DataSet (COADS; Woodruﬀ et al., 1987) with the ﬂux
correction described in Uchiyama et al. (2017a). The principal ten tidal
constituents from the TPXO7.0 global reanalysis (Egbert et al., 1994;
Egbert and Erofeeva, 2002) were imposed as additional surface elevations along the perimeter of the L2 model. The freshwater discharge
from all major rivers in each model domain is given as monthly climatological freshwater mass point source inputs based on the Annual
Record of Rainfall and Discharge Database issued by the Japan River
Association (http://www.japanriver.or.jp/publish/book/nenpyou_dvd.
htm). Wave-driven currents (e.g., Uchiyama et al., 2010, 2017c) wereomitted because of limited fetch eﬀects that signiﬁcantly diminish theTable 1Model conﬁgurations.
Models

L1

L2

L3

L4

Computational period
Grid cells
Horizontal grid resolution
Baroclinic time step

3/1/2006–11/31/2015
320 × 320 (×32 layers)
2 km
120 s

9/1/2006–11/9/2015
800 × 480 (×32 layers)
600 m
30 s

9/1/2015–11/9/2015
400 × 400 (×32 layers)
100 m
8.0 s

Surface wind stress
Surface ﬂux
Boundary/initial condition
T-S nudging
Topography

JMA-MSM (hourly)
COAMPS bulk formula
JCOPE2 (daily)
JCOPE2 (10-day averaged)
SRTM30 + JEGG500

JMA-MSM (hourly)
COAMPS bulk formula
ROMS-L1 (daily)
None
SRTM30 + JEGG500

JMA-MSM (hourly)
COADS (monthly climatology)
ROMS-L2 (hourly)
None
CDPC

9/21/2015–11/9/2015
696 × 400 (×32 layers)
20 m
1.0 s (diversion);
1.5 s (normal)
JMA-MSM (hourly)
COADS (monthly climatology)
ROMS-L3 (hourly)
None
CDPC

CDPC: Central Disaster Prevention Council, Japan (50 m resolution).

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wind waves and swells in the SIS. The innermost L4 model encompasses
the TSTP and whole seaweed farm area (Fig. 1c) at a 20 m horizontal
grid spacing, which suﬃciently represents the complex bathymetry andhorizontal topography including coastal structures. Treated freshwater
discharge from the TSTP is also considered based on the measured data
(City of Kobe, unpublished data).
Because seaweed spores are vulnerable to environmental stresses
when they are sown on the farm in fall, we focused on boreal fallconditions from September to November when diversion occurs. The L4model was run for ~50 days to include the whole month of October
2015 when the sewage eﬄuent is most inﬂuential to the seaweed farm.
Table 1 summarizes the numerical conﬁguration of the L1–L4 models.

H (z ) = exp ⎡−⎢⎣Sewage eﬄuent from diﬀusers at the sea bottom immediately rises
and advects as buoyant plume. While releasing from diﬀusers andreaching the sea surface, the eﬄuent also mixes with ambient seawater,which is called near-ﬁeld dilution. Subsequently, far-ﬁeld dilution takes
over due to advection and diﬀusion by background currents. Because
ROMS relies on a hydrostatic assumption and the L4 model still underresolves the detailed design of diﬀusers, it is inappropriate to explicitly model the near-ﬁeld dilution, which is accompanied by intensebuoyancy-driven vertical convective transport. Therefore, we made useof the near-ﬁeld dilution model proposed by Uchiyama et al. (2014)
embedded in the 3-D transport equation of the buoyant eﬄuent, whichis explicitly applicable to far-ﬁeld behavior. A submodel for buoyant
sewage eﬄuent was implemented in the L4 model as an additional 3-D
advection–diﬀusion model for an arbitrary conservative Eulerian passive tracer, in which the near-ﬁeld dilution is modeled as an arbitrary
source term P :3. Model validation
3.1. Hydrodynamics
The newly developed L3 and L4 models were validated with observed data. The parent L1 and L2 models agree well with in situ
measurements of temperature, salinity, and tides for the 9-year period
from 2006 to 2015 in Kosako et al. (2016). Fig. 2 shows scatter plots ofthe modeled and measured sea surface temperature (SST) and salinity(SSS). The observed data are based on repeated CTD measurementsfrom vessels and are available as a part of “The Comprehensive Water
Quality Survey in the Seto Inland Sea” collected and maintained by the
Ministry of Land Infrastructure and Transport, Japan (http://www.pa.
cgr.mlit.go.jp/chiki/suishitu/). The areas outside of the L3 and L4 domains are supplemented by the L2 result. The model shows an outstanding SST reproducibility (Figs. 2a and 3), whereas it is moderate for
SSS with a correlation coeﬃcient of 0.379 (Fig. 2b). Such an overallreasonable reproducibility is inherited from their parent models(Kosako et al., 2016). The modeled SSS is overestimated because ourmodel merely accounts for the monthly climatology of the major rivers(Section 2.1) that results in the underestimation of freshwater input tothe SIS. Nevertheless, the estimated average density error of the surface
layer caused by the erroneous freshwater input is only 0.14%, leading
to subtle eﬀects on the near-ﬁeld submodel (Section 2.2) and the result
presented below. The inclusion of temporally varying synoptic freshwater discharge from the rivers, hundreds of unnamed small streams,
and groundwater seepage ubiquitous along the shoreline should improve the salinity reproducibility. Our implementation is apparently
suboptimal; however, detailed freshwater data are not publicly available for the SIS. Kurosawa et al. (2017) supported this speculation
based on the same L2 SIS model with data assimilation and indicatedthat heavy precipitation has a prominent inﬂuence on the river discharge in September and October, which appropriately corrects biases
in the simulated SSS. Our choice is considered as the least bad option,
which must be reconciled in future based on data availability.
We next veriﬁed the time-varying surface elevations mainly due totides at the Kobe tide gauge station (Fig. 1b) operated by the JMA andmaintained by the Japan Oceanographic Data Center (JODC; http://
jdoss1.jodc.go.jp/vpage/tide_j.html). Tidal harmonic analysis was
conducted with the modeled and measured hourly surface elevation, assummarized in Table 2, for four principal tidal constituents (M2, S2, K1,(1)

where c is the pollutant concentration normalized by Cp, u is the 3-D
velocity of the ambient ﬂow, D is the diﬀusion term, ∇ is the 3-D
gradient operator, and Cp [kg m−3] is the input pollutant concentration
at the outfall. The underresolved nondimensional tracer source function
P [s−1] may be represented as follows:

P (x , y, z; t ) = Ps (t ) A (x , y ) H (z ),(2)where Ps is the normalized tracer ﬂux from the diﬀusers and A and H
are the prescribed spatial shape functions of the plume above the outfall
diﬀusers. The values of A and H are nondimensional, that is, close to
one in the source region and zero outside of it. They were also utilized
to resolve the spatial distribution in the source region. The associatedspatial integral relations are:

∬ A dx dy = As , ∫ H dz = Hs,(4)

where zs is the plume center height and ds is the vertical scale of theplume. For our outfall design, zs was set to be 0 m for the surfaceconﬁned shape with ds of 7 m, the local mean depth. The sewage efﬂuent was applied at the locations of the two outfalls (normal and diversion) as bottom-released freshwater plume at a constant volume rateof 1.80 × 105 m3 d−1. The discharged nondimensional passive tracer
was always set to unity (one) with the temperature of the ambient
seawater (i.e., no net heat supply) and zero salinity. The diversion andnormal outfalls are represented by two and four grid points, respectively (viz., footprint of 20 m × 40 m for the diversion and 20 m × 80 m
for the normal outfall). Their centers are located 80 m and 40 m oﬀshore from the sea wall of the TSTP, which approximately corresponds
to the pipe lengths of the central (normal) and western (diversion)
outfalls, respectively.
It is worth mentioning that a more elaborate near-ﬁeld mixing
model could be alternatively used for the source term P in order toreﬂect realistic behavior of a transient buoyancy plume at the outfall
pipes. A possible direction is to employ a sophisticated 3-D near-ﬁeld
model such as the one based on a non-hydrostatic LES model(Nakayama and Nizamani, 2016). Such an implementation into thepresent system is viable, and should be conducted in future work.

2.2. Sewage eﬄuent model∂c+ ∇∙uc = D + P ,∂t(z − z s )2 ⎤
,
⎥
ds2
⎦

(3)

where As is the horizontal area, Hs is the vertical size, and Vs = As Hs isthe volume of the prescribed plume. Hence, Ps(t) = Qp(t)/Vs, where Qp(t) [m3 s−1] is the volume ﬂux. Because the variable c is a nondimensional concentration, one can retrieve any arbitrary conservative concentration, such as colored dissolved organic matter (CDOM), nitrate,
and ammonium, by multiplication with a factor Cp, which corresponds
to the mean concentration at the point source. Note that a similarsource term was added to the 3-D mass, momentum, salinity, andtemperature conservation equations to account for near-ﬁeld dilution
eﬀects.
The shape functions A and H were also described according to
Uchiyama et al. (2014). We assumed that the peak of the released
sewage ﬂux occurs at the sea surface with downward exponential decay
following a Gaussian distribution:
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Fig. 2. Scatter plots of (a) SST (temperature) and (b) SSS (salinity) at the surface from discrete ship observation versus the corresponding model results. In each panel, R indicates thecorrelation coeﬃcient between the model and observations.

and O1). The tidal amplitudes and phases were reasonably well reproduced by the model, with correlation coeﬃcients of 0.9785 and0.9996, respectively. Slightly less agreement was obtained for the two
semidiurnal constituents. These biases are already included in the
TPXO7.0 that provides lateral boundary conditions for barotropic tides
along the L2 perimeter (Section 2.1). The direct comparison of theelevations before applying the harmonic decomposition including
winds and other subtidal variability yields a correlation coeﬃcient of
0.887 (not shown). In general, the high-resolution L3 and L4 models
exhibit a reasonable reproducibility of the mean and transient hydrodynamics around Osaka Bay, although there certainly is room for improvement. We leave these issues for future work.

Table 2
Result of the tidal harmonic analysis of the modeled (ROMS-L3) and observed (JMA)hourly surface elevation at the Kobe tidal gauge.
Amplitude (m)

M2
S2
K1
O1

Phase (deg.)

OBS

MDL

DIF

OBS

MDL

DIF

0.31120.18460.19150.14050.4216
0.26470.23450.2116−0.1104−0.0801−0.0430−0.0711307.8853
310.6101
53.915447.0540

300.0558
304.0042
61.842545.04137.82956.6059−7.9271
2.0127

OBS: JMA observation data; MDL: ROMS-L3 model data;
DIF: Diﬀerence (observation - model).

3.2. Sewage eﬄuent dispersal

freshwater eﬄuent represented by the ﬁltered salinity fraction weredetected mostly in this 1 km × 1 km box and barely extended further inthe oﬀshore and alongshore directions. The most striking ﬁnding ofBN07 (see their Fig. 4) is the asymmetric, alongshore-conﬁned eﬄuent
transport due to prevailing alongshore currents constrained by the topography. During phases with slack to ﬂood tides, westward alongshore
currents developed inducing westward eﬄuent transport toward the
Akashi Strait, while the breakwater of the adjacent ﬁshing port locatedin the west of the TSTP notably aﬀected both of them to regulate
southward oﬀshore current and transport. As a result, the eﬄuent
mostly stayed within ~400 m from the outfall. Subsequently, an ebbing

The bulk behavior of the modeled sewage eﬄuent was comparedwith the measured freshwater plume from the western diversion outfallof the TSTP (Fig. 1c). We chose to qualitatively compare it with the insitu measurement described in BN07, where an empirical, ad hoc ﬁlter
was applied to the T–S diagram to separate the sewage eﬄuent fromambient seawater and riverine freshwater. Vessel-based acoustic Doppler current proﬁler (ADCP) and tow-body CTD measurements wererepeatedly conducted on December 1, 2005, ~3 days before the springtide, for almost one M2 tidal cycle (~10 h) in a narrow 1 km × 1 km
region oﬀ the TSTP diversion outfall. Nevertheless, the signals of theFig. 3. Spatial maps of the (a) observed SST shown in Fig. 2 and (b) corresponding computed SST using the ROMS-L4. The black boxes are the L3 and L4 domains. Note that theobservations were conducted by vessels for seven days in October 2015.

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Fig. 4. (a) Time series plot of the sea surface elevation 1 km oﬀshore of the TSTP starting at 1:00 UTC on October 1, 2015. The ﬁve
red vertical lines indicate the selected surveys at 9:00, 12:00, 15:00,
19:00, and 22:00 UTC on October 1, 2015, as shown in the panels(b)–(k). Snapshots (hourly-averaged data) of the vertical component of relative vorticity (s−1) at the surface normalized by the
local Coriolis parameter (color) on the left during each survey at theindicated time. The right panels show the corresponding normalized passive tracer concentrations, c (color), in the enlarged area
near the TSTP (white boxes in the left panels). The red dots represent the location of the western diversion outfall. The vectors aresuperposed to show the subsampled instantaneous surface velocity.
The arrows in the top-left corner of each plot indicate the velocity
scale of 0.5 m/s. (For interpretation of the references to color in thisﬁgure legend, the reader is referred to the web version of this article.)45

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0.6 m/ s and 0.2 m/ s, respectively. Such a velocity variation is consistent with the ADCP measurement in the SIS (e.g., Kobayashi et al.,2009).
The residual currents have a complex distribution (Fig. 5a), primarily consisting of the 1) northwestward coastal current along thenortheastern shore of Awaji Island; 2) clockwise circulation oﬀ Awaji
Island and the TSTP, which is often referred to as the “Okinose Circulation” (e.g., Nakatsuji et al., 1994; Mishima et al., 1999), while only
the northern part is depicted here; and 3) counterclockwise circulationoﬀ the TSTP, consistent with the model result in Imasato (1983). The
velocity magnitudes of the three predominant currents are the largest.
The coastal current along Awaji Island veers clockwise and points
northeastward at y ≈ 5 km. It then further turns clockwise to form thesoutheastward current that detaches from Honshu near the bend of thecoastline at x ≈ 3 km next to the TSTP and eventually merges with the
Okinose Circulation. The counterclockwise circulation near the TSTP isaccompanied by the westward nearshore current, which veers left to theoﬀshore and then coalesces with the Okinose Circulation oﬀ the TSTP.
Therefore, the surface currents mostly converge oﬀ the coastline bend
near the TSTP. Fig. 4 shows that the eﬄuent from the TSTP drifts
westward near the shore after the release and turns oﬀshore. Subsequently, it is entrained in the Okinose Circulation and transported
southeastward toward Osaka Bay. Such a transport pattern is anticipated to reduce the direct impact of the eﬄuent on the seaweed farm
located in the east of the TSTP.

phase manifested to promote shore-conﬁned eastward currents andassociated shore-trapped eastward eﬄuent transport, followed by another ﬂow reversal with a more prominent ﬂood tide than the previous
ﬂooding. Interestingly, the shore-trapped eﬄuent in the east of theoutfall remained there for several hours, even at reverted westward
oﬀshore currents of the ﬂooding tide. BN07 speculated that nearshore
clockwise recirculation with the cross-shore extent of ~200 m formed
to entrain the eﬄuent within the overall westward current. Honda et al.
(2007) extended the Eulerian analysis of BN07 by using Lagrangiansurface drifters equipped with conductivity–temperature sensors, reassuring the alongshore asymmetric eﬄuent dilution, where relatively
stagnated, less diﬀusive eastward transport took place during an ebb,
while more diﬀusive westward transport was provoked in conjunction
with remote inﬂuence of intense oﬀshore tidal currents generated in the
Akashi Strait. Such a small-scale, coastally trapped dispersal of thebuoyant eﬄuent is solid evidence for the fact that we need high-resolution modeling with larger-scale ambient currents.
Fig. 4 shows the sea surface elevation in the top panel and sequential ﬁve pairs of snapshots of the relative vorticity (left panels) andcorresponding normalized passive tracer concentration c in the enlarged area near the TSTP (right panels) at the surface with subsampled
surface velocity vectors for the western diversion case on October 1,
2015. Because the prevailing westward currents formed during ﬂood
tide (Fig. 4b and c) and reach a maximum at the highest slack tide
(Fig. 4d and e), the surface sewage released from bottom-mounted
diﬀusers is also transported westward at the surface and conﬁned near
the shore (Fig. 4c and e). Subsequently, the currents weaken andchange direction during ebb, resulting in eastward sewage transport(Fig. 4f and g). The wastewater distribution is largely aﬀected by theﬁshing port just west of the TSTP during this period, in particular by its
detached breakwater located ~150 m oﬀshore to notably entrain a
tracer in the port (Fig. 4g). Then, a ﬂow reversal occurs at the lowest
slack tide (Fig. 4h and i) retaining the eastward currents formed aroundthe oﬀshore deep channel of the strait, while westward ﬂow is predominant in the nearshore shallow area. Such a cross-shore reversal of
the alongshore ﬂow direction results in the promotion of rather strong
vorticity in the channel and near the coastline bend next to the TSTP
(Fig. 4h). Counterclockwise circulation also forms around the eastern
tip of the breakwater transporting the eﬄuent oﬀshore (Fig. 4i).
Therefore, the tracer is widely distributed in the cross-shore direction at
low tide. The sewage is transported westward during the ﬂood due tothe counterclockwise lateral circulation formed oﬀ the TSTP that promotes nearshore westward ﬂow (Fig. 4j and k). In summary, the model
successfully reproduces tidally-driven, asymmetric, alongshore-dominated, shore-trapped eﬄuent dispersal patterns consistent with thoseobserved in BN07; however, there are several minor discrepancies
mainly due to diﬀerent periods.

4.2. Underlying mechanisms
We carried out a spectral analysis to examine possible driving mechanisms of the ﬂow ﬁeld around the seaweed farm. Fig. 6 comparesthe frequency spectra of the surface currents us = (us, vs), wind stress at
10 m above the sea surface τs = (τu, τv), and surface elevation ζ at
Station A (Fig. 1c) for October 2015. The variables us and τu represent
the zonal (eastward) component, while vs and τv reﬂect the meridional(northward) component. Two distinctive peaks commonly appear inboth components at diurnal and semidiurnal frequencies of the surface
velocity and ζ with moderate semidiurnal predominance. The windstress spectra are “red” with low-frequency prevalence and no notablepeaks, whereas the diurnal variability manifests modestly, presumably
reﬂecting land–sea breeze cycles.
We further conducted a spectral coherence analysis among thevariables (Fig. 7) at the three selected locations on the seaweed farm,i.e., Stations A–C, (Fig. 1c). The coherences of the alongshore velocity us
against τu and ζ (left column) are distributed similarly at all three stations. The coherences are commonly high at diurnal and semidiurnal
frequencies. However, the coherences between us and ζ are muchhigher (> 0.9) than those between us and τu (< 0.5), demonstratingthat tidal currents dominate wind stresses and generate alongshore
surface currents on the farm and thus the resultant eﬄuent dispersal. In
the higher subtidal frequency band at ~0.2–0.5 cpd, us is inﬂuenced byboth tidal current and wind stress at Station A (Fig. 7a). In contrast, thecoherence versus τu decreases gradually at Stations B and C whenmoving away from the TSTP (Fig. 7c and e), indicating that wind becomes less inﬂuential. In the frequency band much lower than 0.2 cpd,
the lower subtidal ζ and low-frequency wind jointly aﬀect the surface
current at Station C with coherence by ~0.4, which in turn decreases at
Stations A and B. Nevertheless, the coherence of the cross-shore velocity vs (right column) substantially diﬀers from that of us. Although thecoherence between vs and ζ is high at diurnal and semidiurnal frequencies near the TSTP (viz., Station A), it rapidly decreases whenleaving the TSTP. The semidiurnal and diurnal coherence against windstress is always low. In contrast, the subtidal coherence against ζ and τv
for the band below 0.5 cpd, particularly 0.2–0.5 cpd, is much larger
than that for us.
These results clearly indicate that tidal currents prevail near the
TSTP, primarily provoking alongshore currents that are rectiﬁed by the

4. Dynamics
4.1. Surface currents
Spatiotemporally varying currents mainly driven by tides near
narrow straits have been of great interest (e.g., Imasato, 1983; Imasato
et al., 1994; Li et al., 2015). Our study area is in such a setup, where thenarrow Akashi Strait repeatedly generates quite intense tidally driven
currents that aﬀect both the transient and residual dynamics and theresultant eﬄuent dispersal near the TSTP (Section 3). Fig. 5 shows thesurface residual velocity vectors as monthly averaged velocity withcolors illustrating their magnitude and a time series plot of hourly
surface velocity at Station A (Fig. 1c) for the diversion case. Strong tidal
currents are repeatedly formed and intrude southeastward (northwestward) into Osaka Bay (Harima Sea) through the narrow Akashi
Strait ﬂanked by the Honshu and Awaji islands (Fig. 1b). Both the zonal(alongshore) and meridional (cross-shore) surface velocities considerably ﬂuctuate at the semidiurnal period with a peak speed of46

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Y. Uchiyama et al.

Fig. 5. (a) Monthly mean surface velocity vectors (white arrows) on their own magnitude (color). (b) Temporal variability of the eastward (us, black) and northward (vs, red) surface
currents at Station A (Fig. 1c) for the diversion case. (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.)while the cross-shore currents are inﬂuenced by low-frequency wind,
not by tides. In the subtidal frequency band lower than 0.2 cpd, thesubtidal ζ solely contributes to cross-shore subtidal currents, reﬂecting
much larger-scale ﬂows including the quasi-persistent eastward SIS
throughﬂow maintained by the transient Kuroshio (e.g., Kosako et al.,2016).

coastline and topography, whereas such topographic constraint is relaxed at Station A, where the tidal ellipse is more circular (not shown).
At locations distant from the Akashi Strait on the shallow shelf (Stations
B and C), tidal currents are intensely constrained by topography and thecross-shore currents vary mostly depending on low-frequency wind andζ. The low-frequency ζ variability is due to subinertial motions forcedby the low-frequency wind variability. Eddies may play another important role. The seaweed farm experiences unique hydrodynamic
conditions on a shallow shelf in the north of the deep channel, where
the Okinose Circulation develops (Fig. 5a). The western part of the farm
(viz., Station A) is controlled by tides and subtidal cross-shore currents.
The central (Station B) and eastern (Station C) parts of the farm are inmixed conﬁguration, where the alongshore currents are tidally driven,(a) zonal

PSD

10
10
10
10

5. Impact of diversion
5.1. Time-averaged horizontal structures at the surface
Because seaweed growth relies on adequate light driving photosynthesis, the near-surface euphotic layer is most inhabitable forFig. 6. Power spectral densities (PSDs) of the (a) eastward
surface velocity, us, eastward wind stress, τu, and sea surface
elevation, ζ, and the (b) northward surface velocity, vs,northward wind stress, τv, and ζ at Station A (Fig. 1c). The
units of the PSDs for velocity, wind stress, and elevation are(m2/s2)/cpd, (N2/m4)/cpd, and (m2)/cpd, respectively,
where cpd stands for cycles per day.

(b) meridional

-2

-4

-6

-8

u

v

τ (× 10)uτ (× 10)ζ (× 0.1)

ζ (× 0.1)s

0.1 0.2 0.5 12
frequency (cpd)

s

v

4

8 12

0.1 0.2
0.5 1
2
frequency (cpd)

47

4

8 12

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Y. Uchiyama et al.

(a) zonal: stn. A
1coherence

0.8

u vs. τsu0.8

us vs. ζ

0.6

0.6

0.4

0.4

0.2

0.2

0

coherence

0.8

u vs. τsu0.8

us vs. ζ

0.6

0.4

0.4

0.2

0.2

0

coherencev

v vs. τsv

vs vs. ζ0(e) zonal: stn. C
0.8s

vs vs. ζ(d) meridional: stn. B
1

0.6

1

v vs. τ0(c) zonal: stn. B
1

Fig. 7. Left: spectral coherences of us versus τu (black) and ζ(red); right: spectral coherences of vs versus τv (black) and ζ(red) at Station A (panels a and b), Station B (c and d), andStation C (e and f). The locations of the three stations areshown in Fig. 1c. (For interpretation of the references tocolor in this ﬁgure legend, the reader is referred to the web
version of this article.)(b) meridional: stn. A
1(f) meridional: stn. C1

u vs. τsuv vs. τs

0.8

us vs. ζ

0.6

0.6

0.4

0.4

0.2

0.2

0

v

vs vs. ζ0
0.1 0.2
0.5 1
2
frequency (cpd)

4

8 12

0.1 0.2
0.5 1
2
frequency (cpd)

4

8 12

TSTP (warm color) in an oﬀshore extent of ~3 km. Such spatial differences in the eﬄuent concentration and salinity are considered to be
due to counterclockwise circulation formed oﬀ the TSTP as part of the
Okinose Circulation, where westward currents that develop near theeastern shore transport the released tracers westward and then seaward
oﬀ the TSTP and entrain in the circulation to promote eastward
transport in the oﬀshore area.

seaweeds (Abowei and Ezekiel, 2013). Therefore, surface eﬄuent
transport and its accumulation are the most essential factors to be assessed. Fig. 8 shows the monthly averaged SSS (contours) and passive
tracer concentration c normalized by the source concentration Cp(Section 2.2) at the surface, representing eﬄuent far-ﬁeld dilution(color) averaged for the month of October 2015. The result for the casewith diversion discharge from the western outfall is depicted in the top,while the diﬀerences (diversion case – normal case) are shown in thebottom. A weak lateral salinity gradient is formed with lower SSS in thenortheast (Fig. 8a), primarily due to riverine freshwater discharge frommajor rivers in the inner Osaka Bay further east and inﬂuences from thebuoyant plume of the freshwater eﬄuent. The SSS diﬀerence between
the two cases is nontrivial, with a factor of several percent relative tothe spatial SSS diﬀerence (Fig. 8b). The western diversion results in an
overall salinity increase near the coast including the eastern shore close
to the seaweed farm, except for the area just oﬀ the TSTP, where
freshening occurs approximately 3 km from the shore. A similar patternis observed in the temperature ﬁeld, although it is subtle with SST
diﬀerences of no more than 0.01 °C (not shown). Hence, the eﬀect of thelocations of the outfall on buoyancy-driven dynamics is not signiﬁcant.
The surface eﬄuent dilution c is distributed similarly to SSS in thediversion case and is conﬁned mostly near the shore on both zonal sides
relative to the TSTP, with a pronounced eastward bias, in particulararound the TSTP (Fig. 8a). In the eastern shallow area, c is widely extended oﬀshore, with notable cross-shore dilution at depths below~40 m (see Fig. 1c). This pattern is consistent with that of surface residual currents (Section 4), where strong currents are mostly formed in
the deeper channel constrained by topography. The diversion signiﬁcantly reduces the nearshore c (cold color), particularly near the
shore including the majority of the seaweed farm area (white box in
Fig. 8b). Similarly to SSS, the diversion increases c just oﬀshore the5.2. Time-averaged vertical structures
While our primary interest is the near-surface processes, verticalstructures of the time-averaged c and salinity (S) are brieﬂy discussed.
We introduce two nearshore transects normal to the shore approximately deﬁned at the western and eastern boundaries of the seaweedfarm area, indicated by the red lines in Fig. 1c. Fig. 9 shows the crosssectional plots of the monthly averaged c (color) and S (contours) along
the western transect for October 2015 for the diversion case (Fig. 9a),diﬀerence between two cases (diversion – normal; Fig. 9b; denoted by
ΔS and Δc), and corresponding enlarged plots for the nearshore area
(Fig. 9c and d). The red contours in Fig. 9b and d represent the isohalines for ΔS = 0 psu, while the solid and dotted white contours are
the positive and negative values (intervals: 0.005 psu). The spatial
distributions of c and S are largely consistent with each other (Fig. 9a),exhibiting that the buoyant eﬄuent plume from the outfall transports
increased c and decreased S simultaneously. The eﬄuent plume is mostconsiderable in the nearshore area, where S is lowered near the surface
to form buoyantly stable stratiﬁcation. Therefore, c is consistently distributed, mostly in the area in which freshening takes place. The diversion alters c and S only in a limited area (~1 km from the shore),with somewhat diﬀerent spatial patterns (Fig. 9b and d). With the diversion, S increases near the surface due to the reduced freshwater48

Marine Pollution Bulletin 130 (2018) 40–54

Y. Uchiyama et al.

Fig. 8. (a) Monthly mean passive tracer concentration, c (color, nondimensional), at the surface for the diversion case. The black contours and labels denote the sea surface salinity (SSS;intervals: 0.02 psu). (b) The diﬀerence of c (color, nondimensional) and SSS (contours) between the two cases (diversion - normal). The red and blue contours are the positive andnegative values (intervals: 0.001 psu), respectively. (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.)c due to the diversion are more conﬁned to the shore (~1 km to thesea), whereas the former is negligibly small. The surface-trapped diversion eﬀects in Fig. 10b are not as evident as that observed along thewestern transect (Fig. 9b), supporting that the TSTP-originated buoyant
plume is much less inﬂuential near the surface along the easterntransect. These results suggest that buoyancy eﬀects due to freshwatereﬄuent moderately alter the nearshore 3-D ﬂow and associated c distribution, particularly near the TSTP.

eﬀects from the TSTP (solid white contours), while it decreases in thelower layer near the shore (dotted white contours). Likewise, the diversion generally decreases (increases) c when S increases (decreased).
In particular, c is always diminished in the near-surface layer shallower
than ~3 m depth with the diversion outfall, which reduces the inﬂuenceof the eﬄuent from the TSTP on the seaweed farm. However, the diversion lowers c in the shallow area within ~200 m from the shore,
where S is enhanced (Fig. 9d). This minor diﬀerence in the cross-sectional spatial patterns of the deviations in c and S due to the diversion ismost likely attributed to the river-originated fresher S source mainly
from the east, which promotes more intense recurring westward efﬂuent transport in front of the TSTP extending to the northwestern
corner of the farm compared with that of c (Fig. 8b). On the other hand,the diversion results in an increase of the near-surface S to promote a
vertical overturning circulation cell that entrains c in the middle of thecell and forms the positive subsurface Δc maximum.
Fig. 10 shows that the cross-sectional structures of c and S along theeastern transect (Fig. 1c) and their diﬀerences associated with the diversion are much simpler at smaller values than those along the westerntransect. In the diversion case (Fig. 10a), freshening due to the riverinewater from the east is notable within ~2 km from the shore, as shownby the SSS in Fig. 8a. The oﬀshore extent of the eﬄuent c is approximately in the same range as that of S. The increase of S and decrease of

5.3. Spatiotemporal variability
The eﬀects of the sewage eﬄuent from the two outfalls on theseaweed farm were examined using time series plots of the surface c(Fig. 11) at the three selected farm locations deﬁned in Fig. 1c. The
eﬄuent concentration c gradually decreases from Stations A to C withincreasing distance from the TSTP. Because the diﬀerences of the surface c at the three stations depicted by the green curves have apparent
negative biases, the diversion outfall adequately reduces c in the farm.
The temporal ﬂuctuations of c for both the normal and diversion cases
diﬀer at the three stations. Although high-frequency tidal variability isobserved at the westernmost Station A located ~1 km from the TSTP,
much lower-frequency subtidal ﬂuctuations are superposed on the tidal
signals at the central Station B. Furthermore, at the easternmost Station
49

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Y. Uchiyama et al.

Fig. 9. (a) Cross-sectional plot of the monthly mean tracer concentration, c (color, nondimensional), at the western transect (WT in Fig. 1c) for the diversion case. The black contours andlabels denote the salinity at intervals of 0.03 psu. (b) Cross-sectional plot of the diﬀerence of c (color, nondimensional) between the two cases (diversion - normal). The red contours
represent zero, while the solid and dotted white contours are the positive and negative values at intervals of 0.005 psu, respectively. (c) and (d) show the enlarged nearshore areas
depicted by the thin black boxes around the upper-left corners of (a) and (b), respectively. Note that diﬀerent color scales are used. (For interpretation of the references to color in thisﬁgure legend, the reader is referred to the web version of this article.)considered section. Because c is dimensionless with normalization by
the source concentration Cp, F has the unit m3 s−1. We further introduce
the cumulative tracer ﬂux Q (m3) by integrating F over a given elapsed
period of t from t = 0 as follows.

C, the subtidal components dominate the tidal components. Overall, thediﬀerences of c at the three stations ﬂuctuate similarly to c, with asigniﬁcant reduction due to the diversion. The reduction intermittently
occurs with tides at Station A, most signiﬁcantly during strong tidal
currents (~50% relative to the normal case; Fig. 11a). At Station B,~3.5 km from the TSTP, the level of c decreases to approximately aquarter of that at Station A; yet, the reduction by the diversion reaches~50% (Fig. 11b). In contrast, the reduction of c at Station C occurs
mostly at subtidal low frequencies. The subtidal variability dominates
the tidal variability; thus, the reduction due to the diversion lasts much
longer (Fig. 11c).

Q (t ) =To quantify the impact of the sewage eﬄuent on the seaweed farmfor the two cases, we evaluated the eﬄuent concentration ﬂux crossing
an arbitrary section with an area Ac:∫A cun dAc ,c(6)

We show the time series of the incoming cumulative ﬂuxes Q, denoted by Qfarm, across the perimeter of the seaweed farm surrounded byfour cross sections (Fig. 1c) starting at 0:00 UTC on September 21 forthe two cases and their diﬀerence, ΔQfarm, deﬁned as diversion – normal(Fig. 12). Because the ﬂux budget Qfarm for both cases is mostly positive,the eﬄuent released from the TSTP gradually accumulates on the farmwith time. The diversion yields a lower Qfarm than the normal case,leading to a net reduction of the eﬄuent, viz., ΔQfarm < 0 (Fig. 12b).
The time-averaged Qfarm during the plotted period is also lower in thediversion case with 2.01 × 104 m3 than in the normal case with
2.83 × 104 m3 (the time-averaged ΔQfarm is −0.82 × 104 m3), as summarized in Table 3. The temporal variability in Qfarm and ΔQfarm consistsof tidal and subtidal components, where the latter contributes morethan the former. An increase and subsequent decrease of Qfarm and5.4. Eﬄuent ﬂux budget analysis

F=t∫0 Fdt.

(5)where Ac is the cross-sectional area and un is the velocity normal to theFig. 10. Same as Fig. 9a and b but for the eastern transect (ET in Fig. 1c).

50Marine Pollution Bulletin 130 (2018) 40–54

Y. Uchiyama et al.

(a) stn. A

Table 3Cumulative nondimensional tracer ﬂuxes in the seaweed farm, Qfarm (m3).

0.01

C (n.d.)0.005
Diversion
NormalDiﬀerence0-0.005-0.01
x 10

normalC (n.d.)0

diversion-2
x 10

normaldifference(c) stn. C-3

C (n.d.)

0.5
0
-0.5
diversion

normaldifference-1
Fig. 11. Time series plots of the surface tracer concentration, c, at Stations A, B, and C(Fig. 1c). The black, red, and green lines indicate the diversion, normal, and diﬀerence(diversion - normal), respectively. (For interpretation of the references to color in thisﬁgure legend, the reader is referred to the web version of this article.)3

cumulative tracer (m )ΔQfarm occurs periodically at a period of ~14 days, consistent with aspring–neap tidal cycle, with maxima on October 3, 17, and 31. The
peak Qfarm values are 1.84 × 104 m3, 3.67 × 104 m3, and 5.49 × 104 m3
in the diversion case, while they are 3.51 × 104 m3, 5.08 × 104 m3, and6.53 × 104 m3 in the normal case, respectively (Fig. 12a). These results
indicate that the diversion adequately reduces the sewage inﬂuence (by~28% on average) on the seaweed farm. It is worth addressing that theoutgoing Q at the perimeter of the L4 domain, QL4, is dominated by thatfrom the western open boundary toward the Harima Sea due to strong
tidal currents formed near the Akashi Strait (Fig. 7a), accounting
for > 75% of the total outgoing QL4 (not shown). The diﬀerence in QL4
between the two cases is subtle because of the short distance between
the normal and diversion outfalls (500 m) relative to the size of the L4
domain.
Doblin and Clayton (1995) reported that a high concentration of
sewage eﬄuent has a serious impact on the seaweed growth during its
early life circle (viz., in fall). Maruyama et al. (1985) showed that thegrowth rate of seaweed, Porphyra yezoensis, is exponentially retarded
due to the increased addition of sewage water under low sewage

8

Average

1.84 × 104
3.51 × 104−1.67 × 104

3.67 × 104
5.08 × 104−1.41 × 104

5.49 × 104
6.53 × 104−1.04 × 104

2.01 × 104
2.83 × 104−0.82 × 104

concentration conditions (> 5%). This is the case for the present conditions, where the surface concentration in the farm is only
0.1%–0.01% relative to that at the source (Fig. 8a). As stated in Section
2.2, the computed far-ﬁeld relative concentration of O (10−3) anddiﬀerences between the two cases of O (10−4) are translated into thecorresponding actual values by multiplying the source concentration
Cp. Typical Cp may be represented by the tolerable limits of biochemical
oxygen demand (BOD), nitrogen, and phosphorus contents are 160 mg/
L (or 160 ppm with the daily mean of 120 mg/L), 120 mg/L (60 mg/L),
and 16 mg/L (8 mg/L), respectively, as set in the wastewater standards(https://www.env.go.jp/en/focus/docs/ﬁles/20120801-51.pdf). This
leads to a rough estimate of far-ﬁeld concentration of 0.01–0.1 ppm,
which is generally one order of magnitude higher than the detection
limits (World Health Organization, 2011). The eﬀect of the diversion,i.e., the averaged reduction of ~28%, is anticipated to be marginallyfavorable to the growth of the seaweed, because local seaweed farmers
are generally satisﬁed with the fall diversion. Although the farmers
claim that the TSTP overdoses the seaweed culture, they occasionallycomplained once in several years that the diversion would not haveworked adequately. Apparently the seaweed cultures are inﬂuenced bymany factors, not only by the nutrient load from the adjacent treatment
plant, but also by light, turbidity, temperature, nutrients from the rivers
and the oﬀshore waters, etc. A farther detailed assessment is necessary
for more comprehensive protection of the seaweed culture in the future.
Fig. 13 shows the time series of instantaneous F and cumulative Q at
the four cross sections surrounding the seaweed farm. The subscripts
represent the western, northern, eastern, and southern cross sections,
respectively. For example, Fw denotes F at the western cross section. The
sum of the four cumulative ﬂuxes is identical to Qfarm. Tidal and subtidal ﬂuctuations are manifested in all four F values; yet, their contribution diﬀers from section to section. Fw and Fs ﬂuctuate primarily at
diurnal or semidiurnal tidal frequencies, while the lower-frequency
subtidal contribution is more prominent for Fn and Fe. Nevertheless,high-frequency tidal variability of the instantaneous ﬂuxes has a minor
or even negligible contribution to the net impact of the eﬄuent. For
instance, at the western section closest to the outfall, even if a negative
Fw prevails, the cumulative Qw is positive and has an apparent increasing trend because the positive Fw lasts longer than the negative Fw(Fig. 13a and b). The cumulative Q shows an increase, except for Qs,
where the largest amount of c discharges out of the farm, which iscompensated for by the incoming c at the three other sections. Therefore, the direct impact from the TSTP manifests at the western andnorthern sections showing the highest transport oﬀshore (southward)
and eastward, yielding the overall cross- and alongshore gradients ofthe surface c, as shown in Fig. 8a.

(b) stn. B

-3

-1

x 10

Until Oct. 31

difference

1

1

Until Oct. 17

Diﬀerence: diversion - normal.

diversion
2

Until Oct. 3rd(a)4diversion

normal1

x 10

Fig. 12. (a) Time series plot of temporally cumulative net
tracer ﬂuxes stored in the seaweed farm area, Qfarm (m3).
The black and red curves denote the diversion and normalcases. (b) The temporal changes of the diﬀerence of Qfarm(m3) for the two cases (diversion - normal). (For interpretation of the references to color in this ﬁgure legend, thereader is referred to the web version of this article.)

(b)

4

6
0

4
2

-1

0

-2
21/Sep1/Oct

11/Oct21/Oct-2
31/Oct 21/Sep

1/Oct

11/Oct51

21/Oct31/OctMarine Pollution Bulletin 130 (2018) 40–54

Y. Uchiyama et al.

(a) west
3

cumulative tracer (m )tracer flux (m3/s)

5
0-5
diversion-10

normal(c) north10diversion-1

normal30diversion-1

normal(g) south3

cumulative tracer (m )3tracer flux (m /s)

2
0
-2

-4
21/Sepdiversion
1/Oct

11/Octnormal21/Oct31/Oct20diversion-2
x 10

normal(d) north50diversion-5

cumulative tracer (m )3tracer flux (m /s)

1(b) west

55(e) east

2

x 10

4

10

cumulative tracer (m3)3

tracer flux (m /s)

2

6

3

x 10

normal(f) east

52
10
diversion
-1
5

x 10

normal(h) south

5

0
-5
-10
diversion-15
21/Sep

1/Oct

3 −1

normal11/Oct21/Oct31/OctFig. 13. Time series plot of the instantaneous tracer ﬂux F (m s , left panels) and corresponding cumulative tracer ﬂuxes Q (m , right panels) at the four boundaries surrounding theseaweed farm. From top to bottom, the ﬁgures show F and Q at the western, northern, eastern, and southern transects, respectively. The black and red curves denote the values for thediversion and normal cases. (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.)3

section (Fig. 10), with un being substantially unaltered (not shown).
These results indicate that the highly nonlinear, subtidal, and residual
variability in c and un and their phase relation included in the present
model are inevitable to accurately and quantitatively assess the ﬂux cun.

The eastern cumulative Qe has a remarkable time lag of ~14 days
behind Qw, Qn, and Qs; the eﬄuent appears immediately after the initialization. The farm is generally aﬀected by nearshore westward residual current (Fig. 7a) that initially carries no eﬄuent. Tidal currents
give rise to oscillatory eﬄuent transport back and forth in the alongshore direction within the farm. Hence, the farm is more inﬂuenced
near the outfall (Fig. 8a) due to the retarded initial arrival of the efﬂuent. However, the magnitude of Qe is smallest among the four sections. Because Qe increases with time, the incoming ﬂux dominates thedischarging ﬂux. This result implies that the primary source of Qe is therecurring eﬄuent discharged out of the farm, likely that passing
through the southern section.
Although the diversion leads to an overall reduction of the eﬄuentaccumulation in the farm relative to the case with the normal outfall(Fig. 12), it is not always the case in all four sections. The diversion
results in the increase of Q at the western and southern sections, while
the latter contributes less to the Q budget. Accordingly, the eﬄuent
from the diversion outfall induces more notable alongshore transport inand around the farm, whereas the cross-shore, seaward transport prevails for the normal outfall. The increased Qw of the diversion issomewhat counterintuitive to the overall reduced c in the western

6. Conclusions
We conducted a comprehensive, detailed assessment of the impactof the diversion at the TSTP on the adjacent seaweed farm in a narrowtidal channel connecting two subbasins in the SIS, Japan. We developed
a quadruple-nested, very high-resolution conﬁguration with a horizontal grid spacing down to 20 m based on a state-of-the-art 3-D ocean
model, ROMS (Shchepetkin and McWilliams, 2005, 2008). The Eulerian
near-ﬁeld eﬄuent dilution model of Uchiyama et al. (2014) was employed to reproduce 3-D advection–dispersion processes of the underresolved, bottom-released buoyant eﬄuent plume at the TSTP.
The reproducibility of the model was ﬁrst conﬁrmed with in situ
observations to ensure reasonable agreement. The time-averaged residual 3-D dynamics and their temporal variability were then extensively analyzed. The time-averaged surface currents are primarily
driven by the clockwise Okinose Circulation formed oﬀ the TSTP, which
52

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Y. Uchiyama et al.

substantially determines the large-scale, residual dynamics including
the nearshore counterclockwise recirculation manifested near the TSTP.
The currents ﬂuctuate prominently at semidiurnal and diurnal tidal
frequencies, with a notable inﬂuence of low-frequency subtidal variability. The TSTP and western portion of the seaweed farm located closer
to the tidal strait are largely aﬀected by tidal currents. In contrast,
subtidal residual currents induced by surface wind stresses and constrained by the Okinose Circulation have prevailing eﬀects in theeastern portion of the farm. The eﬄuent measured by non-dimensional
Eulerian passive tracer concentration generally follows these circulation patterns. The eﬄuent transport is mostly controlled by subtidal
residual currents, in particular by the oﬀshore-directed current of thenearshore counterclockwise recirculation that subsequently merges
with the Okinose Circulation to promote southeastward oﬀshore efﬂuent transport.
A twin numerical experiment for cases with normal and western
diversion outfalls enabled us to quantitatively evaluate the eﬀect of thediversion. However, the dynamical eﬀects due to the diversion were
found to be subtle for the relatively small freshwater discharge of
1.80×105 m3 d−1. An overall reduction of the eﬄuent aﬀecting thefarm was discovered, although the distance between the two outfalls is
only 500 m in the alongshore direction. The eﬄuent ﬂux budget analysis revealed that the cumulative nondimensional eﬄuent of the seaweed farm is reduced by ~0.81 × 104 m3 on average compared withthat of the normal outfall (~2.83 × 104 m3), exhibiting a ~28% reduction of the eﬄuent from the TSTP by the diversion, which could
lead to favorable inﬂuences on the growth of seaweeds. The farm located in the east of the TSTP encounters incoming eﬄuent ﬂuxes fromthe western, northern, and eastern cross sections, whereas outgoing ﬂux
is invoked at the southern section. The eastern positive ﬂux lags behind
the initialization, indicating the dominance of a recurring tracer thathas ﬂowed out of the farm or comes from the south.

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Acknowledgements
The present research was ﬁnancially supported by the City of Kobe
and Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for
Scientiﬁc Research 15KK0207 and 15H04049 at Kobe University. The
design and eﬄuent data for the Tarumi Sewage Treatment Plant were
provided by the City of Kobe.
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