Ocean Dynamics
https://doi.org/10.1007/s10236-023-01551-1

July 2020 heavy rainfall in Japan: effect of real‑time river discharge
on ocean circulation based on a coupled river‑ocean model
Yu‑Lin K. Chang1 · Sergey M. Varlamov1 · Xinyu Guo1,2 · Toru Miyama1 · Yasumasa Miyazawa1
Received: 1 November 2022 / Accepted: 4 April 2023
© The Author(s) 2023

Abstract
In July 2020, a stationary atmospheric front over Japan caused persistent, nearly continuous rain for most of the month that
resulted in new historical highest rainfall records in several areas and caused serious river floods, landslides, and debrisflow events. An existing hindcast and forecast ocean circulation model that includes climatological discharge informationof major rivers failed to represent the extreme river discharge under heavy rainfall. New experiments were conducted using
real-time river discharge information based on Today's Earth CaMa-Flood simulation that includes 368 rivers in Japan. The
inclusion of real-time river discharge improved the salinity bias in the near-surface waters. The differences were significant
compared to the observations in the heavy rain region (i.e., Ariake Bay and Tosa Bay), and insignificant at offshore stations.
The ensemble experiments of real-time river discharge cases suggested the difference between the climatological and thereal-time river discharge experiments was not random, but was robust. The freshening water changed the shelf circulation,and its far-reaching effect appeared hundreds of kilometers away from the shore. Passive particle tracking was conducted
for examining the cross-shelf exchange. More particles released from Bungo Channel went offshore in near-surface water
when the real-time river discharge was used compared to using the climatology discharge. Particles released in Tosa Bay,
Seto Inland Sea, and Kii Channel showed the opposite tendency. The real-time river discharge not only changed the modeled
coastal salinity distribution, but also the coastal and offshore currents. The role of the real-time river discharge on modelingnormal flow periods or drought events, and its influence on a longer time scale model simulation remain to be explored.
Keywords River discharge · Extreme rainfall · Ocean modeling

1 Introduction
In July 2020, a stationary atmospheric front formed in East
Asia, extending from China to the east of Japan (Zhao et al.
2021). The front stayed above Japan for nearly a month,which brought warm and humid air towards Japan, resulting in persistent heavy rainfall. The front first hit southern and western Japan, starting on 3 July. The continuous
rain resulted in the historical maximum rainfall record in
Kyushu of 496 mm in 24 h. After that, with a minor shifting
Responsible Editor: Fanghua Xu* Yu‑Lin K. Chang
ychang@jamstec.go.jp
1

Application Laboratory, Japan Agency for Marine-Earth
Science and Technology, Yokohama 236‑0001, Japan

2

Center for Marine Environmental Studies, Ehime University,
Matsuyama 790‑8577, Japanin position, it stayed above Japan until 28 July. The 1-monthof rain led to hazards, such as floods, landslides, and mud
flows that resulted in a loss of 84 lives, and thousands ofhouses were destroyed (https://​www.​data.​jma.​go.​jp/​obd/​
stats/d​ ata/b​ osai/r​ eport/2​ 020/2​ 02008​ 11/2​ 02008​ 11.h​ tml). The
heavy rainfall also induced 1–2 cm crustal subsidence in theflooded region, which was recovered after the water drained
into the ocean (Heki and Arief 2022).
The heavy rain not only influenced the terrestrial areas ofJapan, but it also affected the ocean. Freshwater discharges,
however, are often neglected in global climate models,
because the scale of the atmosphere and ocean are muchgreater than the rivers (Miller et al. 1994). Global rivers
carry more than one-third of land-based precipitation to theocean (Trenberth et al. 2007). The buoyant freshwater affects
the coastal dynamics as it merges with deeper and saltier
ocean water, and the materials carried by the rivers also
influence coastal ecosystems (Horner-Devine et al. 2015).
The river plumes are mostly directed offshore and then turn

13

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Ocean Dynamics

to form shore-parallel coastal currents that are driven by
the Earth’s rotation and centrifugal acceleration (Garvine
1987(. In estuaries, dense shelf water flows into the estuary below the outflowing fresher surface layer. This bidirectional flow is the estuarine circulation, sometimes called
the exchange flow (Geyer and MacCready 2014). Althoughriver discharge occurs locally, it could affect coastal circulation by changing offshore salinity and therefore density (Daiet al. 2009). The near-surface currents could be modified byriver-induced momentum flux and turbulence (Carniel et al.
2009). High-volume river discharge from large rivers or due
to extreme rainfall can not only affect the coastal region, butalso has a nonnegligible far-reaching effects (Urakawa et al.
2015). A recent study investigated the 2018 July heavy rain
event in the Seto Inland Sea and found that the 4-day rainfall
caused a long-lasting change in temperature and salinity in
Bungo Channel and therefore modified the circulation andenhanced the cross-shelf exchange between the Seto Inland
Sea and the Pacific Ocean for about 2 months (Morimoto
et al. 2022). In July 2020, there was more precipitation and it
lasted longer than during the 2018 event in the same region,
so the cross-shelf exchange between the Seto Inland Sea andPacific Ocean via the Bungo Channel and Kii Channel mightalso be modified.
A high-resolution ocean hindcast and forecast system
has been used to monitor the seas around Japan (JCOPE-T
DA, (Miyazawa et al. 2021)) since it came online in 2019(https://​www.​eorc.​jaxa.​jp/​ptree/​ocean_​model/​index.​html).
JCOPE-T DA has the most updated satellite information
assimilated and is expected to provide coastal prediction
information during heavy rain periods. Although JCOPE-T
DA did include the freshwater discharge, its river discharge
input was based on monthly climatology, which was likely
unable to reproduce the actual coastal circulation condition
due to the overestimation and/or underestimation of shortterm river discharge. Similar to JCOPE-T DA, monthly climatological river runoff was also used in other major ocean
models, such as the Ocean General Circulation Model for the
Earth Simulator OFES (Sasaki et al. 2020) and Global Reanalysis Hybrid Coordinate Ocean Model HYCOM (Chassignet et al. 2009), due to the limited access to real-timeriver information.
Underestimates of river discharge often occurred during the typhoon season. A previous study investigatedthe coastal salinity variation from two selected typhoons(Troselj et al. 2017). They ran an offline river runoff model
based on the observed rainfall data and made an optimized
parameterization for deriving the best-fit river discharge.
The modeled river discharge was then used in the ocean
model, which changed the near-coast salinity distributions
compared to when the climatological river discharge wasused. However, they did not describe details of the oceanic
conditions affected by the extreme river discharge together

13with quantitative validation using in-situ salinity data. A
similar method that introduced the river discharge data fromthe river runoff model to the ocean model was also applied
by Urakawa et al. (2015). Instead of focusing on an extreme
weather condition, they presented the long-term mean condition. They suggested that the case with river discharge
helped to reduce the coastal salinity error compared to thecase without river input. Extreme precipitation events haveoccurred more frequently in Japan in recent years becauseof the warming of air and ocean temperatures, as well as
atmospheric moisture stability near Japan (Kawase et al.
2020). Therefore, a more accurate river discharge dataset isneeded to replace the long-used monthly climatology riverdischarge data.
The objective of this study was to improve the ability toaccurately simulate the coastal salinity and currents during heavy rain periods. We introduced real-time hourly
river discharge from a river routing model into JCOPE-T
DA for simulating the July 2020 heavy rain event in thesouthwestern Japan region. How the coastal and offshore
salinity changed with a shift from monthly climatology riverdischarge to using the real-time hourly river discharge wasinvestigated. Changes of salinity and coastal currents, aswell as their far-reaching effects are presented. Changes incross-shelf exchange in heavy rain regions are shown basedon the particle tracking method.

2 Data and methods
2.1 Observation data
Salinity data collected from various sources was used
for comparing to the model performance under different
sources of river discharge input. Salinity was measured
by CTD casts from ships or fixed recording locations at a
total of 36 stations during the target period of July 2020.
Salinity data at the mouth of Ise Bay (Fig. 1, red dot) was
obtained from the Ise Bay environmental database (http://​www.​isewan-​db.​go.​jp/), and it was recorded by a bottommounted tower that provided near surface (~ 3 m), andsubsurface (around 15 m and 25 m depth) information.
Data at Kojima and Nagahama in Ariake Bay in Kyushu(Fig. 1 magenta dots) were recorded from buoys at the sea
surface, and was provided by the Fisheries Research Centerof Kumamoto Prefecture. Ship observations were available
in Tosa Bay (Fig. 1, blue dots) and offshore Japan (Fig. 1,
green dots). Salinity in Tosa Bay was observed by the ship
Tosa-Kaiyo Maru, and the data was provided by the Fisheries Center of Kochi Prefecture. Data for offshore Japan
was provided by the Japan Coast Guard. Since the fresh andlow-density river discharge was mainly distributed in theOcean Dynamics

horizontal resolution. The domain covered East Asia from
123°E to 148°E, and from 24 to 46°N, which included Japan,
Korea, and part of China. The hourly data was available
from September 2018 to the present. CaMa-Flood model
received input from the weather reanalysis of the Japan
Meteorological Agency and satellite land information. Basedon the calculated runoff amounts, CaMa-Flood solved the
local inertial equation and enabled hydrodynamic simulation within the river and over the floodplain. The CaMaFlood river discharge output was validated against observations, which had a reasonable correlation coefficient of 0.55
among all Japanese rivers for the period between 2010 and2015 (Yoshimura et al., 2008; Ma et al., 2021). The freshwater discharge at the river mouth used in the ocean model
included both the river discharge and floodplain flow. The
floodplain flow was generally smaller than river discharge.
It accounted for less than 5% of total discharge without rainfall, but could increase to 25–35% of total discharge during heavy rain periods. 368 rivers in Japan were included
from the CaMa Flood model. Two of the largest rivers, theShimanto River in Shikoku and the Kuma River in Kyushu
were selected for showing the discharge difference between
monthly climatology and real-time discharge.
Fig. 1  a Ocean bathymetry in the western North Pacific Ocean forthe model domain. The region in the black dotted box was enlargedin (b). b The climatology salinity in July and the locations for salinity observations: bottom-mounted tower at Ise bay (red dot), buoys in
the Kojima and Nagahama in Ariake Sea in Kyushu (magenta dots),ship observations in Tosa Bay (blue dots) and off southern Japan(green dots), selected rivers: Shimanto River (red star) and Kuma
River (green star) used in Fig. 2, and the sections used for computing
transport in Fig. 9. c Particle tracking release map. Color represents
different release regions: Bungo Channel (zone 1, green), Tosa Bay(zone 2, blue), Kii Channel (zone 3, red), and Seto Inland Sea (zone4, magenta)

shallow water, the near-surface (depth ≤ 10 m) observed
salinity data were used.
Satellite-observed gridded Multi-Mission Optically Interpolated Sea Surface Salinity (OISSS) from NASA was used
to compare the general performance of the model simulation. OISSS has a 0.25-degree resolution, and the timeinterval is 4 days. Note that the salinity data used were notassimilated into the hindcast runs, and they are thus considered independent from the simulation results.

3 Real‑time river discharge
The river discharge output from the river routing model(CaMa-Flood; (Yamazaki et al., 2011)) operated by Japan
Aerospace Exploration Agency was used in the present
study. CaMa-Flood was a gridded model with 1/60° (~ 2 km)

4 The general ocean circulation model
The ocean circulation model used for this study was basedon the tide-resolving data-assimilative nowcast and forecast
system (JCOPE-T DA; (Miyazawa et al. 2021)). JCOPET DA was constructed from the Princeton Ocean Model(Blumberg and Mellor 1987) with a generalized coordinate
system (Miyazawa et al. 2009). The model domain encompassed East Asia (17–50°N and 117–150°E), with a horizontal resolution of 1/36° (~ 3 km) and 46 vertical layers. The
external forcing to drive the model included wind stresses
and net heat/freshwater fluxes at the sea surface converted
from the hourly atmospheric reanalysis and forecast produced by the Global Forecast System (GFS) of the National
Centers for Environmental Prediction (NCEP). Satellite andin situ temperature and salinity data were assimilated into
the model based on a multi-scale three-dimension variational
method (Miyazawa et al. 2017). The tidal components were
provided by the 1/12-degree Oregon State University Tidal
Inversion Software OTIS, (Egbert and Erofeeva 2002). The
freshwater discharge from the land was represented as water
volume fluxes at river mouth grids with monthly mean climatological discharge volumes (Varlamov et al. 2015). The
monthly climatology included 108 rivers in Japan and 43
rivers in China, Korea, Taiwan, and Russia. The operational
JCOPE-T DA had an initial run in June 2018. It allowed
daily and hourly output and could cover a 3-weeks maximum
forecast. The restart files were kept every 7 days based on

13

Ocean Dynamics

storage-volume constraints. Comparison between observational data and JCOPET DA was performed in a previous
study (Miyazawa et al. 2021), which showed satisfactory
performance in simulating the three-dimensional circulation
over the western North Pacific Ocean.
The hindcast result of JCOPE-T DA served as the initialcondition for the river discharge experiments in the present study. The initial date for the experiments was set on
27 June 2020 when the restart file became available. Twoexperiments were conducted: (1) the river discharge used thedefault monthly climatology (Exp. Clim), (2) the river discharge used the hourly real-time CaMa Flood (Exp. CamaF)
data. The two experiments restarted from the same initialcondition, then ran freely until 31 July 2020 when the heavy
rain ended. Apart from the river discharge, all other parameters were identical among the experiments. Note that the
CaMa Flood model had a smaller domain than the model, sothat only the river discharges in Japan were used, and the climatology discharge remained in use outside of Japan. Therefore, this study focused on the variation near Japan where
input sources of rivers were different, and especially on theheavy rain region near Shikoku and Kyushu. Hourly data
was used for the analyses shown below. Daily data was used
for sensitivity experiments that are shown in the discussion.

5 Ensemble simulation experiments
Ensemble experiments were conducted to examine the reliability of Exp. CamaF. Eight ensemble experiments were
set up by perturbing their initial conditions in Exp. CamaF.
The perturbed initial conditions were simply extracted fromdaily snapshot model conditions of 7, 12, 17, 22 June, and2, 7, 12, 17 July 2020, which were close to the starting timeof 27 June 2020. Apart from the initial conditions, all othersettings in the ensemble experiments remained identical toExp. CamaF.

locations, but at depth of 50 m. The particles were releasedin hourly intervals from 00:00 to 23:00 on 1 July 2020 toaccount for different tidal phases, and were tracked at the
fixed depths until 31 July 2020 after the heavy rain ended.
Particles were released in a 2-km horizontal interval andabout 88,000 particles were released in each experiment.
Note that if particles encountered local water depths shallower than the assigned water depth, in order to avoid hitting the sea floor, particles would then be tracked at 90% ofwater depth.
For sensitivity experiments, particle tracking based ondaily-mean data was conducted. Particles were released
at the same locations and depths as described above andalso tracked for the same period. The tracking results basedon daily-mean data were presented in the discussion. The
significance of the difference between the experiments wasassessed using the chi-square test.

7 Results
The largest river discharge occurred in June, July, or August
depending on the region based on the monthly climatology.
The overall July average discharge in the Shimanto River is
327 ­m3/s, but in 2020, the real-time discharge went beyond
the long-term mean from 3 July when heavy rain started
and even exceeded 2000 ­m3/s on 4, 6, and 10 July (Fig. 2,blue). A relatively dry period occurred between 15 and 23
July before the next rain arrived. Similarly, the heavy rainalso resulted in extraordinary maximum discharge of over
5000 ­m3/s in the Kuma River in Kyushu, whereas its climatological July average was 355 m­ 3/s. The monthly averaged
real-time discharge in July 2020 was 690 ­m3/s and 765 ­m3/s
for Shimanto and Kuma rivers, respectively. During July
2020, the real-time discharge had an excessive total volumeof 1.23 × ­106 ­m3 from all targeted rivers in Japan that was

6 Particle tracking scheme
The passive particle-tracking method was used to investigate the cross-shelf exchange in the heavy rain region near
Shikoku. The two model experiments described above provided the background hourly ocean current. The tracking
scheme was based on the fourth-order Runge–Kutta method(Press et al. 1992) with a tracking time step of 1 h. The
vertical motion was not included in this study, and particles were tracked horizontally. Two experiments were conducted that both released particles in 4 zones surrounding
Shikoku (Fig. 1c). The first experiment released particles
at depth of 5 m in the heavy rain region near Shikoku. The
second experiment released particles at the same horizontal

13

Fig. 2  River discharge (­m3/s) in July 2020 for the Shimanto River insouthwestern Shikoku (blue) and the Kuma River that flows into the
Ariake Sea in western Kyushu (red). Dotted lines showed the climatological average river discharges in July (not daily data). Note that thevalues in 18–23 July were small but non-zeroOcean Dynamics

significantly larger than the total climatological dischargesof 0.42 × ­106 ­m3.
The salinity in the heavy rain regions from the two modelexperiments was compared against the observations. Bothmodel experiments captured the major sea surface salinity
pattern, showing higher salinities in the Japan Sea and North
Pacific and lower values north of Kyushu and east of Hokkaido(Fig. 3a–c). The salinity bias showed a lower salinity; hence,
there was a greater negative bias in Exp. CamaF in comparison to Exp. Clim (Fig. 3d, c). The satellite image, however,
did not cover the shallow water region; therefore, it was difficult to judge if real-time discharge improved or degraded theaccuracy of coastal salinity by comparing against satellite dataalone. To examine salinity change in the coastal region, thein-situ observations were examined next. The salinity around3 m depth in Ise Bay was about 30–31 before the heavy rainstarted on 3 July and then it gradually dropped with the cumulative rains and reached its minimum of around 21 on 13 Julybefore returning to 30–32 in a week (Fig. 4a). Both modeledsalinities showed a positive bias. Salinity in Exp. Clim showed
a weak variation, with salinity being changed by the freshwater
input by less than 3. Exp. CamaF improved from Exp. Climby showing a maximum salinity decrease of 6, although thedecrease in salinity was still smaller than the observations.
Exp. CamaF resulted in a smaller root-mean-squared error(RMSE) of 4.52 in comparison to the 5.58 value of Exp. Clim,
yet the difference between the two experiments was not significant at the Ise Bay station (p > 0.1).
Salinity in Ariake Bay was observed at the sea surface,
and the model simulation also showed a general positive bias

Fig. 4  Time series of salinity at a Ise Bay, b Kojima, and c Nagahama in
the Ariake Sea. Salinity measurment observations are shown in green,Exp. Clim in black, and Exp. CamaF in red. Numbers shown in the bottom-right are the root-mean-squared error compared to observation

Fig. 3  Sea surface salinity
on 17 July 2020 based on asatellite OISSS, b Exp. Climc Exp. CamaF, and d the bias
and standard deviation error
between satellite OISSS andExp. Clim (black) and Exp.
CamaF (red) during July 202013

Ocean Dynamics(Fig. 4b, c). A recent observation recorded in the eastern
Pacific during a rainy period showed a salinity drop of 2–3
near the sea surface at 0.02 and 1 m depths (Clayson et al.
2019), with a rate of ~ 20 mm per hour. In the July 2020
event, the precipitation was 10–60 mm per hour near the
Ariake Sea, and the salinity decreased by 2 in Exp. Clim and5 in Exp.CamaF, but was more than 15 in the observations.
The observed salinity at these two stations even dropped tonearly zero on 12 July, suggesting the buoys were directly
affected by the heavy rainfall that greatly reduced the salinity
values. The modeled salinity, even in the layer nearest to thesurface, was below the surface by some distance (mean andstandard deviation depth of 0.72 ± 0.14 m), and therefore
had a limitation in representing the real surface salinity. Exp.
CamaF captured the general salinity variation trend of theobservations, and showed a smaller RMSE than Exp. Clim.
The salinity improvement from Exp. Clim to Exp. CamaF
was significant at both stations in Ariake bay (p < 0.01).
Although the real-time river discharge used in Exp. CamaF
improved the salinity, it still had a RMSE of 4–10.
The comparison between ship observations and model
simulations is shown in Fig. 5. Exp. CamaF showed a significantly lower RMSE (p < 0.01) than Exp. Clim at thenearshore stations in Tosa Bay (Fig. 5a, b). Salinity in offshore Japan showed an insignificant difference between thetwo experiments (Fig. 5c, d, p > 0.1). A slight improvement
was found in Exp. CamaF compared to Exp. Clim.
Although the modeled salinity values did not matchexactly with the observations, the real-time river discharge
Fig. 5  Salinity at a, b Tosa bay
and c, d offshore to the south ofsouthwestern Japan. Salinities were measured at depths
of a 0 m, b 10 m, c 1 m, and d
3 m. Observations are shownwith solid green circles, Exp.
Clim with open black circles,
and Exp. CamaF with open red
circles. Numbers shown in thebottom-right are the root-meansquared error compared toobservations

13

did indeed improve the modeled salinity near the coast. The
modeled salinity did not show the apparent difference at theoffshore observed stations (Fig. 5c, d). However, the difference did appear in other regions further offshore. Figure 6 shows the salinity and surface current speed differences between the two experiments. Before the heavy rainstarted, a small salinity difference was observed near thecoast (Fig. 6a). After a week of continuous rainfall, theheavy rain regions in the south and west of Japan showeda clear freshening. Eddies and filaments of fresher water
extended out from the Kii Channel (east of Shikoku) toabout 300 km offshore (Fig. 6b). The salinity difference further diffused offshore with time, eventually forming a large
circular oval patch of lower salinity surface water 400 km
offshore (Fig. 6c). A large region of complex eddy-like salinity differences appeared east of northern Japan where mesoscale eddies were often observed within or north of theKuroshio Extension (Chelton et al. 2011). Saltier water was
also observed in coastal Japan (Fig. 6c), because the riverdischarge in the monthly climatology could sometimes beoverestimated (i.e., 17–22 July, Fig. 2).
Comparing to the ocean currents in the southern region,
the fresher circular water area was located inside the Kuroshio large meander (Fig. 6d–f). The fresher water was
entrained into the Kuroshio and brought offshore withthe large meander and later returned to the nearshore by
the currents. Low salinity water had occasionally been
observed on the surface of the Kuroshio and was suggested
to originate from south and west of Japan (Imasato andOcean Dynamics
Fig. 6  Surface salinity difference a–c and current speed diffrence d–f between Exp. Climand Exp. CamaF on a, d 2 July,b, e 12 July, and c, f 22 July.
The superimposed trajectoriesin (d-f) are surface currents forExp. CamaF

Qiu 1987). The freshwater discharge not only changed
the salinity, but also influenced the currents (Fig. 6d–f).
The currents were modified from the nearshore region toa few hundred kilometers offshore. The surface speed difference from Exp. Clim could exceed 0.2 m/s. The currents in the center of the Kuroshio large meander were
weakened, and currents in the outer edge of the Kuroshio were strengthened (Fig. 6f). The absolute vorticity
changes from Exp.Clim to Exp. CamaF in the Kuroshio large meander region (135.5–139°E, 28.8–31.5°N)
were reduced by − 3.38 × ­1 0 −7 ­s −1. The trapping abilityof rotating fluid is determined by the rotation versus thetranslation speed (Flierl 1981), and the reduction of vorticity in Exp. CamaF suggested the amount of water parcels trapped in the Kuroshio large meander area may be
reduced.
The passive particle tracking experiments were conducted to examine the cross-shelf exchange in the heavy rainregion near Shikoku. The number of particles in each zone
depended on the size of each defined area. The percentageof particles of each defined area (Table 1) was calculatedby the number of particles in each domain divided by the

Table 1  Particle distribution (%) present in each zone after being
released and tracked at depths of 5 m and 50 m on the initial and finaldays, based on hourly and daily background currents. Zones 1 and4 were located on the shelf surrounding Shikoku Island, zones 5–7

were off the shelf in the south of Japan (Fig. 8). The percentages are
the number of particles in each zone divided by the total released particles

Zone

InitialFinal: hourly data
Exp. Clim

1
2
3
4
5
6
7

19.20
17.61
14.41
48.77
–
–
–

Final: daily data
Exp. CamaF

Exp. Clim

Exp. CamaF

5m

50 m

5m

50 m

5m

50 m

5m

50 m

23.03
5.81
4.93
47.28
0.44
12.69
5.82

14.38
5.38
6.98
55.43
1.64
15.57
0.63

22.33
5.87
6.61
46.51
0.66
11.73
6.28

14.28
5.38
7.77
55.42
1.58
14.90
0.68

22.66
3.97
3.43
46.17
0.05
23.24
0.49

15.80
8.83
7.80
53.60
1.07
12.89
–

15.18
4.99
2.66
49.65
4.51
22.53
0.47

16.32
8.65
6.90
53.91
1.08
13.15
–

13

Ocean Dynamics

number of total particles released in all areas. Particles thatdeparted at a depth of 5 m mostly stayed on the shelf (> 70%,
Table 1). There were 18–19% of particles that went offshore,with part of them becoming trapped in the Kuroshio, others
were transported downstream into the Kuroshio Extension to
the east of Japan, and some of the particles were transported
southward to the east of Kyushu (Fig. 7). Particle distributions were different between the two experiments (Fig. 7).
Particles arrived east of Kyushu earlier and in larger numbers in Exp. CamaF than in Exp. Clim (Fig. 7, Table 1). Particles observed in the Kuroshio meander (zone 6, Table 1)
were reduced in Exp. CamaF (11.73%) compared to Exp.
Clim (12.69%). Fewer particles being trapped in the Kuroshio meander could be caused by the weakening of Kuroshio
vorticity. The Kuroshio extension region appeared to havemore particles in Exp. CamaF than in Exp. Clim (Fig. 7 andTable 1), especially for those that originated from Tosa Bay(zone 2). The particles redistribution on the shelf also varied
between experiments. At the end of the simulation, particles
in Bungo Channel (zone 1) showed a greater percentage inExp. Clim than in Exp. CamaF. Particles in Tosa Bay (zone2) and Kii Channel (zone 3) were reduced significantly fromthe initial date, with the reduction being less in Exp. CamaF
than in Exp. Clim. The amount of particles that remained in
the Seto Inland Sea was similar (> 45%) among the experiments, in which Exp. CamaF showed a slightly lower percentage of particles than Exp. Clim.
The connectivity matrix showed the detailed redistribution of particles between the different sub-domains(Table 2). In Exp. Clim, particles released in Bungo Channel(zone 1) mostly stayed in that zone (84.01%), a small percent of particles were transported eastward and northward to
Tosa Bay (zone 2) and Seto Inland Sea (zone 4), and some
went off-shelf or entered Kuroshio (zones 5, 6, 7). Zone 1
released particles in Exp. CamaF had slightly less retention (83.19%) in the same zone. Instead, more particles weretransported off-shelf to the east of Kyushu (zone 5) and into
the Kuroshio meander region (zone 6), which could also beseen in Fig. 7 (green dots).

Fig. 7  Passive particles releasedin the heavy rain region surrounding Shikoku at a depth of
5 m and their distributions ona, d 11 July, b, e 21 July, andc, f the final date on 31 Julyfor a–c Exp. Clim, and d–fExp. CamaF. Color represents
different release regions: Bungo
Channel (zone 1, green), Tosa
Bay (zone 2, blue), Kii Channel(zone 3, red), and Seto Inland
Sea (zone 4, magenta) as shownin Fig. 1cFig. 8. Percentages are the number of particles in each destination
zone divided by the number of particles released in each departurezone. The sum of each row is about 100 (%)

Table 2  Connectivity matrix showing the percentages of particles
that moved from one zone to another after being released and tracked
at a depth of 5 m. The zone definitions are the same as Table 1 andExp. Clim

Exp. CamaF

13

start
end

1
2
3
4
1
2
3
4

1

2

3

4

5

6

7

84.01
0.07
–
13.33
83.19
0.18
–
12.62

4.30
30.98
–
0.05
2.03
33.28
0.01
0.06

–
0.32
35.49
0.11
0.16
0.22
46.76
0.23

4.79
0.05
19.53
86.03
5.23
–
12.75
86.95

1.95
0.07
–
0.03
2.89
0.03
–
0.04

4.72
47.52
27.67
0.46
6.45
42.40
23.84
0.09

0.24
21.00
17.32
–
0.05
23.88
16.64
–

Ocean Dynamics

Particles released in Tosa Bay (zone 2) had a small
percentage (< 35%) remaining in the same area, and morethan 60% of those particles were taken offshore into the
Kuroshio (Table 2). Exp. Clim had less retention (30.98%)
in the released area than Exp. CamaF (33.28%), yet moreparticles were observed in the Kuroshio extension region(zone 7) in Exp. CamaF (23.88%) than Exp. Clim (21%)(Fig. 7 and Table 2). Less than half of the particles fromKii Channel (zone 3) remained there, others released
there became the major group that arrived in the Kuroshio (zone 6) and in the Kuroshio Extension (zone 7), andsome reached the Seto Inland Sea (zone 4). Exp. Clim had
less local retention for the particles that departed fromKii Channel, and more particles moved to the Seto Inland
Sea and the Kuroshio compared to Exp. CamaF (Table 2
and Fig. 7). Greater than 85% of the particles released in
the Seto Inland Sea (zone 4) remained in the same region.
Only about 13% of particles were transported southwest
to zone 1, less than 1% arrived at other zones, and noneof the particles reached the Kuroshio Extension in zone
7. Exp. CamaF had greater retention in zone 4 and lessparticles went offshore to zones 5–7 in comparison to Exp.
Clim (Table 2 and Fig. 7).
Particles were also released and tracked in sub-surface
water (50 m) at the same horizontal locations in the seasaround Shikoku Island to examine the subsurface current
variation from Exp. Clim to Exp. CamaF. Although the
freshwater discharge was mainly distributed near the surface, the particle distribution map also showed some different particle distributions in the subsurface water between
Exp. Clim and Exp. CamaF (Fig. 8). Particles in the subsurface layer tended to be transported along the edge of theKuroshio without entering its center (Fig. 8). Calculating
the arrival rate quantitatively, around 17% of particles went
offshore to zones 5, 6, and 7 (Table 1). Particles in different

zones showed a different percentage of retention between
the two experiments, suggesting the subsurface water alsochanged with the input of real-time river discharge. The
subsurface particles showed a similar variation to the nearsurface particles, i.e., particles in zone 1 were more in Exp.
CamaF than Exp. Clim, and particles in zone 3 showed
the opposite tendency, but the differences between the two
experiments were smaller in the subsurface than near surface layer.
The two particle tracking experiments (Exp. Clim andExp. CamaF) described above suggest that the enhanced
river discharges during the heavy rain period changed theshelf circulation patterns in the coastal regions. Looking
into the daily volume transport at Bungo Channel andKii Channel, the water exchange in the Seto Inland Sea
via the two channels seemed to be strengthened in Exp.
CamaF in comparison to Exp. Clim (Fig. 9), although
the difference was not significant (p ~ 0.6). The Bungo
Channel and the Kii Channel are the two major gatesconnecting the Seto Inland Sea to the open ocean. The
surface-to-bottom integrated transport at the two channels showed the inflow from Kii Channel and the outflow from Bungo Channel were both stronger in Exp.
CamaF than in Exp. Clim during the heavy rain period inearly July (Fig. 9a), indicating a stronger water exchange
between shelf seas and the open ocean. The enhanced
water exchange was also observed in the two channels
individually (Fig. 9b, c). In the Kii Channel, Exp. CamaF
showed a significantly (p ~ 0.05) stronger inflow near thesurface and outflow in the subsurface than Exp. Climin early July. The tendency in Bungo Channel reversed,
although the difference in the subsurface was not veryclear and insignificant (p ~ 0.7). Note that the in-and-out
transport summation of the two channels was not zero
values, because of the existence of an additional smaller

Fig. 8  Same as Fig. 7, but forparticles released and tracked at
a depth of 50 m

13

Ocean Dynamics
Fig. 9  Quantification of thechanges in volume transport
between Exp. Clim and CamaF.
Daily mean transport at the
Bungo Channel (solid lines withtriangles) and the Kii Channel (dasheds line with circles)(see section map in Fig. 1b)
for Exp. Clim (black) and Exp.
CamaF (red) for a surface tobottom integration b 0–10 m
integration, and c 40–50 m
integration in July 2020.
Unit = Sv = ­106m3/s, positive
represents northward transport moving into the channel,
southward transport flows out of
the channel

channel opening (Kanmon Strait) in the northwestern
Seto Inland Sea that connects to the Sea of Japan.
Differences in the channel or bay circulation between thetwo experiments could be seen in the current fields withthe change of salinity (Figs. 10, 11, and 12, Sup. A1–3). In
Bungo Channel (zone 1), the large river discharge during
the first peak of heavy rain in early July induced a clear
low salinity plume in the western Bungo Channel in Exp.
CamaF (Fig. 10b, Sup. A1), which was not seen in the Exp.
Clim (Fig. 10a). The low salinity plume induced the local
jets, which modified the currents in the western Channel
in Exp. CamaF in comparison to Exp. Clim (Fig. 10c, d).
A similar feature was also seen in Tosa Bay (Fig. 11, Sup
A2). The low salinity plumes appeared in the western Tosa
Bay (Fig. 11b, Sup. A2), where a local jet was observed

13(Fig. 11d, Sup. A2). The large low salinity region shown inthe center of the Bay also appeared as a local strengthening current along the edge of the low salinity plume where
the salinity gradient was strong (Fig. 11b, d). In Kii Channel, low salinity water flowed along the western and eastern
channels, and a few small plumes appeared outside of thewestern Channel in Exp. CamaF (Fig. 12, Sup. A3). The
isohalines were tilted across the Kii Channel, which showed
different types of the buoyant plume in the west and eastof the channel (Figs. 13 and 14). Basically, the contrast
between the west and east sides of the channel was causedby the advection of the strong ambient currents (Fig. 12c, d).
Two different types of plumes, a bottom-advected plume anda surface-advected plume (Yankovsky and Chapman 1997)
were seen in the Kii Channel. In the inner part of the westernOcean Dynamics
Fig. 10  Daily mean surface
salinity and surface currents at
the Bungo Channel (zone 1) on
6 July. a, c: Exp.Clim and b, d:
Exp.CamaF. Color shading inc, d represents the curent speed(m/s).Vectors in different colors
show the speeds in different
ranges for better vizualization: < 0.05 m/s (green),
0.05–0.4 m/s (black), > 0.4 m/s(white)channel, the isohaline crossed the bottom and was parallel tothe coast, which was characterized as the bottom-advected
plume (Fig. 13c, d). More fresh water in Exp. CamaF deepened the isohaline, and strengthened the bottom-advected
plume on 10 July. In the eastern Channel, the isohalines
distribution were mostly parallel to the depth, which wasconsidered to be a surface-advected plume (Fig. 13e, f). The
heavy rain resulted in fresher water appearing near the surface, and strengthened the surface-advected plume in Exp.
CamaF in comparison to Exp. Clim on 10 July (Fig. 13e, f).
As the rain continued and accumulated for a few more days,
the tilted isohalines across the channel further deepened on
the western channel (Fig. 14a, b). In the western channel,
the plume had mostly changed into being surface-advected,
Exp. CamaF showed a stronger stratification than Exp. Clim

(Fig. 14c, d), and the fresher water was further advected
offshore. In the eastern channel, the plume remained surfaceadvected, with a stronger plume shown in Exp. CamaF thanin Exp. Clim (Fig. 14e, f).

8 Conclusion and discussion
The present study explored the influence of changes inriver discharge on the variation of coastal salinity and currents based on ocean model simulations. The experimentwith real-time river discharge (Exp. CamaF) showed alower RMSE and better fit to observation compared to thecase with monthly climatology rivers (Exp. Clim).

13

Ocean Dynamics
Fig. 11  Same as Fig. 10, but for
Tosa Bay (zone 2)

The real-time river discharge (Exp. CamaF) led to distinct freshening in the heavy rain region of southwesternJapan. The heavy rain not only influenced the nearshore area,
but the freshwater also went out of the coastal areas andentered the Kuroshio in some areas and arrived hundreds ofkilometers offshore. The fresher water modified the Kuroshio speed, weakened the relevant vorticity owing to theadditional buoyancy inputs from the nearshore side against
the ambient buoyancy condition in the offshore side associated with the Kuroshio, and the weakening of the vorticity
resulted in less trapping of particles inside the Kuroshio
meander jet.
The fresher water modified the coastal currents,
resulting in different coastal circulation and shelfexchange shown in the passive particle tracking and the13flow field. The resulting change in the near-shore circulations caused by input of the real-time river discharge
suggests that the extreme freshwater input did not simply
lead to strengthening circulation or enhanced water mass
exchange between the shelf and offshore regions. For
example, our simulations demonstrated more retention of
the particles in some shelf regions in Exp. CamaF thanin Exp. Clim (Table 2). The effects of the extreme riverdischarges on the coastal circulations were also affectedby the local topography around the river mouths and thecoastal regions, the ambient density/geostrophic current
structures, and the other external forcing factors suchas local wind and precipitation. Further development ofocean modeling would be required for elucidating moredetailed views of the underlying mechanisms. In theOcean Dynamics
Fig. 12  The same as Fig. 10,
but for Kii Channel (zone 3).
The sections shown in a wereused for showing vertical profiles in Figs. 13 and 14

1-month integration, the particle distribution difference
among the study regions between Exp. Clim and Exp.
CamaF was close to significant (p ~ 0.2). The difference
would likely grow with longer integration because theriver discharge differed not only during heavy rain periods, but also on general days. Worth noticing is that thetransport difference was also 15observed during the dry
period in the 1-month simulation, indicating the realtime discharge was not limited to its influence in theheavy rain period.
The ability to simulate salinity in the near-surface
water compared to observations was improved with the
input of real-time river discharge. However, the errors
remained nonnegligible, especially at the sea surface. JCOPE-T DA applied terrain-following verticalcoordinates in the near-surface layers, and its first layer
was below the sea surface. In such a case, the model
had limitations in simulating the exact surface salinity. During the extraordinary precipitation when buoy
stations may even observe the nearly zero salinity due
to the continuous heavy rainfall and the direct rainfall
over the sea surface, the model would have difficulty
in reproducing such a salinity reduction. In addition to
rainwater coming from rivers, there was also the rainthat directly fell onto the sea, as well as the freshwater
input directly from the coast without a river mouth thatboth led to biases of model simulation. In this study, theexperiments were run freely without data assimilation
for isolating the effect of river discharge. In the operational hindcast of the forecast system, the surface salt

13

Ocean Dynamics
Fig. 13  Isohaline a, b across,c, d along the west side, and e,
f along the east side of the Kii
Channel on 10 July for (left)
Exp. Clim and (right) Exp.
CamaF. The sections weremarked in Fig. 12aflux was assimilated into the model that could improve
the salinity bias.
Particles released in the Seto Inland Sea (zone 4) showed
the least offshore movement of particles in near-surface andsubsurface waters. The large percentage of particles thatstayed in the Seto Inland Sea zone 4 could be caused by its
inland topography. The tidal effect was considered to affectthe shelf seawater exchange (Lin et al. 2020), which could
alter the retention of particles inside the Seto Inland Sea.

13

To examine the sensitivity on the time interval of data,
we repeated the particle tracking using the daily averaged ocean currents. The daily particle distributions wererather different from the hourly tracking (Figs. 13 and 7).
The current filed was different from hourly output, muchless particles in the daily experiment arrived at the Kuroshio extension region (Fig. 15) during the same integration period. Not only the distribution was changed, but anincrease or decrease in percentage of particles occurredfrom Exp. Clim to Exp. CamaF could also be different inOcean Dynamics
Fig. 14  Same as Fig. 13, but for
14 Julysome areas, i.e., zones 3, 4, and 7 at depth of 5 m, andzones 1, 3, and 6 at a depth of 50 m (Table 1). The daily
averaged data filtered out large percentage of the tidal
signal, which may be the main reason responsible for thedifference. A previous study suggested the exclusion of
the tidal effect could accelerate the shelf exchange (Linet al. 2020). The enhancement of shelf exchange could also
be seen in the daily experiments, which show a greater
percentage of particles moving offshore near the surface
compared to in the hourly experiments (Table 1). Although

the daily output remains as the primary option for most of
the reanalysis dataset owing to its benefit in saving storage
space, its missing information and therefore potential bias
in interpretation should be noted.
The real-time river discharge improved the coastal
salinity during the selected heavy rain period. To confirm
the change from Exp. Clim to Exp. CamaF is robust but
not random, the additional ensemble experiments wereconducted by perturbing the initial condition of Exp.
CamaF. The ensemble runs and Exp. CamaF both showed13

Ocean Dynamics
Fig. 15  Same as Fig. 7 for theparticles released and tracked at
depth of 5 m, but based on daily
current fields

Fig. 16  The shelf(depth ≤ 200 m) avergaed salinity in July 2020 around Japanfor Exp. Clim (black), Exp.
CamaF (red), and the ensemble runs (magenta) and mean
(green) by perturbing the initialcondition of Exp. CamaF

smaller standard deviations than their differences with
Exp. Clim, indicating the differences between Exp. Climand Exp. CamaF were robust (Fig. 16). We also noticed
that the ensemble spread gradually reduced with time,suggesting the results converged. Besides, we repeatedthe comparison against the observations described earlier (Figs. 4 and 5). The ensemble runs shown the similar
results as Exp. CamaF, the difference between ensemble
runs and Exp. Clim were significant at Ise Bay and Ariake
Sea (p < 0.01, i.e., Fig. 4), and was nearly significant at
Tosa Bay (p = 0.2, i.e., Fig. 5a), but was insignificant in
the offshore region (p > 0.5, i.e., Fig. 5c). Whether the
improvement remains for a normal or dry season is thenext step for examination. The real-time river discharge

13

is now planned to replace the default monthly climatology rivers for JCOPE-T DA for better simulating thecoastal salinity and currents, especially during extreme
conditions.
Supplementary Information The online version contains supplementary material available at https://d​ oi.o​ rg/1​ 0.1​ 007/s​ 10236-0​ 23-0​ 1551-1.
Acknowledgements X. Guo thanks the support of a Grant-inAid for Scientific Research (MEXT KAKENHI, grant number:
22H05206). Research product of Today's Earth that was used in thispaper was supplied by Japan Aerospace Exploration Agency and Institute of Industrial Science, The University of Tokyo.
Data availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on
reasonable request.

Ocean Dynamics

Declarations
Conflict of interest The authors declare no competing interests.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as longas you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article areincluded in the article's Creative Commons licence, unless indicatedotherwise in a credit line to the material. If material is not included in
the article's Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you willneed to obtain permission directly from the copyright holder. To view acopy of this licence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/.

References
Blumberg AF, Mellor GL (1987) A description of a three-dimensional
coastal ocean circulation model. Three-Dimensional Coastal
Ocean Models, vol. 4, N. Heaps, Ed., American GeophysicalUnion pp 208
Carniel S, Warner JC, Chiggiato J, Sclavo M (2009) Investigating theimpact of surface wave breaking on modeling the trajectories ofdrifters in the northern Adriatic Sea during a wind-storm event.
Ocean Model 30(2):225–239
Chassignet E et al (2009) US GODAE: global ocean prediction with
the HYbrid coordinate ocean model (HYCOM). Oceanogr
22(2):64–75
Chelton DB, Schlax MG, Samelson RM (2011) Global observationsof nonlinear mesoscale eddies. Prog Oceanogr 91(2):167–216
Clayson CA, Edson JB, Paget A, Graham R, Greenwood B (2019)
Effects of rainfall on the atmosphere and the ocean during
spuRS-2. Oceanogr 32(2):86–97
Dai A, Qian T, Trenberth KE, Milliman JD (2009) Changes incontinental freshwater discharge from 1948 to 2004. J Clim
22(10):2773–2792
Egbert GD, Erofeeva SY (2002) Efficient inverse modeling of barotropic ocean tides. J Atmos Oceanic Tech 19(2):183–204
Flierl GR (1981) Particle motions in large-amplitude wave fields.
Geophys Astrophys Fluid Dyn 18:39–74
Garvine RW (1987) Estuary plumes and fronts in shelf waters: a layer
model. J Phys Oceanogr 17(11):1877–1896
Geyer WR, MacCready P (2014) The estuarine circulation. Annu Rev
Fluid Mech 46(1):175–197
Heki K, Arief S (2022) Crustal response to heavy rains in Southwest
Japan 2017–2020. Earth Planet Sci Lett 578:117325
Horner-Devine AR, Hetland RD, MacDonald DG (2015) Mixing
and transport in coastal river plumes. Annu Rev Fluid Mech
47(1):569–594
Imasato N, Qiu B (1987) An event in water exchange between continental shelf and the Kuroshio off Southern Japan: Lagrangian
tracking of a low-salinity water mass on the Kuroshio. J Phys
Oceanogr 17(7):953–968
Kawase H, Imada Y, Tsuguti H, Nakaegawa T, Seino N, Murata
A, Takayabu I (2020) The heavy rain event of July 2018 in
Japan enhanced by historical warming. Bull Am Meteor Soc
101(1):S109–S114

Lin L, Liu D, Guo X, Luo C, Cheng Y (2020) Tidal effect on water
export rate in the eastern shelf seas of China. J Geophys Res:
Oceans 125:e2019JC015863
Ma W, Ishitsuka Y, Takeshima A, Hibino K, Yamazaki D, Yamamoto K, Kachi M, Oki R, Oki T, Yoshimura K (2021) Applicability of anationwide flood forecasting system for Typhoon
Hagibis 2019. Sci Rep 11:10213. https:// ​ d oi. ​ o rg/ ​ 1 0. ​ 1 038/​
s41598-​021-​89522-8
Miller JR, Russell GL, Caliri G (1994) Continental-scale river flow inclimate models. J Clim 7(6):914–928
Miyazawa Y, Zhang R, Guo X, Tamura H, Ambe D, Lee J-S, Okuno A,
Yoshinari H, Setou T, Komatsu K (2009) Water mass variability
in the western North Pacific detected in a 15-year eddy resolving
ocean reanalysis. J Oceanogr 65(6):737–756
Miyazawa Y, Varlamov SM, Miyama T et al. (2017) Assimilation ofhigh-resolution sea surface temperature data into an operational
nowcast/forecast system around Japan using a multi-scale threedimensional variational scheme. Ocean Dynamics 67:713–728.
https://​doi.​org/​10.​1007/​s10236-​017-​1056-1
Miyazawa Y, Varlamov SM, Miyama T, Kurihara Y, Murakami H,
Kachi M (2021) A nowcast/forecast system for Japan’s coasts
using daily assimilation of remote sensing and in situ data.
Remote Sens 13(13):2431
Morimoto A, Dong M, Kameda M, Shibakawa T, Hirai M, Takejiri
K, Guo X and Takeoka H (2022) Enhanced cross-shelf exchange
between the pacific ocean and the bungo channel. Japan related
to a heavy rain event. Front Mar Sci 9:869285. https://​doi.​org/​10.​3389/​fmars.​2022.​869285
Press WH, Flannery BP, Teukolsky SA, Vetterling WT (1992) Numerical recipes in FORTRAN 77: The Art of Scientific Computing.
Cambridge University Press
Sasaki H, Kida S, Furue R, Aiki H, Komori N, Masumoto Y, Miyama T, Nonaka M, Sasai Y, Taguchi B (2020) A global eddying hindcast ocean simulation with OFES2. Geosci Model Dev
13(7):3319–3336
Trenberth KE, Smith L, Qian T, Dai A, Fasullo J (2007) Estimates of
the global water budget and its annual cycle using observationaland model data. J Hydrometeorol 8(4):758–769
Troselj J, Sayama T, Varlamov SM, Sasaki T, Racault M-F, Takara K,
Miyazawa Y, Kuroki R, Yamagata T, Yamashiki Y (2017) Modeling of extreme freshwater outflow from the north-eastern Japanese river basins to western Pacific Ocean. J Hydrol 555:956–970
Urakawa LS, Kurogi M, Yoshimura K, Hasumi H (2015) Modeling
low salinity waters along the coast around Japan using a highresolution river discharge dataset. J Oceanogr 71(6):715–739
Varlamov SM, Guo X, Miyama T, Ichikawa K, Waseda T, Miyazawa Y(2015) M2 baroclinic tide variability modulated by the ocean circulation south of Japan. J Geophys Res: Oceans 120(5):3681–3710
Yankovsky AE, Chapman DC (1997) A simple theory for the fate ofbuoyant coastal discharges. J Phys Oceanogr 27(7):1386–1401
Yoshimura K, Sakimura T, Oki T, Kanae S, Seto S (2008) Towardfloodrisk prediction: a statistical approach using a 29-year riverdischarge simulation over Japan. Hydrol Res Let 2:22–26
Yamazaki D, Kanae S, Kim H, Oki T (2011) A physically baseddescription of floodplain inundation dynamics in a global river
routingmodel. Water Resour Res 47:W04501. https://​doi.​org/​10.​1029/​2010W​R0097​26.
Zhao N, Manda A, Guo X, Kikuchi K, Nasuno T, Nakano M, Zhang
Y, Wang B (2021) A Lagrangian view of moisture transport
related to the heavy rainfall of July 2020 in Japan: importanceof the moistening over the subtropical regions. Geophys Res Lett
48(5):e2020GL091441

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