Kobayashi et al. Progress in Earth and Planetary Science (2017) 4:6
DOI 10.1186/s40645-017-0124-y

RESEARCH ARTICLE

Progress in Earth andPlanetary Science

Open Access

High-resolution mapping and time-series
measurements of 222Rn concentrations andbiogeochemical properties related tosubmarine groundwater discharge along
the coast of Obama Bay, a semi-enclosed
sea in Japan
Shiho Kobayashi1*, Ryo Sugimoto2, Hisami Honda3, Yoji Miyata4, Daisuke Tahara2, Osamu Tominaga2, Jun Shoji5,
Makoto Yamada3, Satoshi Nakada6 and Makoto Taniguchi3

Abstract
High-resolution mapping along the coast and time-series measurements of the radon-222 (222Rn) concentrations inthe shallow zone in a semi-enclosed sea, Obama Bay, Japan, were undertaken in 2013. The temporal and spatial
variations in the 222Rn concentrations were analyzed in parallel with meteorological conditions, physical–biogeochemical
characteristics, and the submarine groundwater discharge (SGD) flux measured with a seepage meter. These data indicatethat the groundwater influences the water properties of the bay and that the groundwater supply pathways are notlimited to the local SGD. The concentrations of 222Rn flowing into the bay from rivers was known to be relatively highbecause groundwater seeps from the river bed. High-222Rn water was almost always present around the river mouth, andnorthward advection of the water affected the distribution of 222Rn concentrations in the bay. The southward wind
suppressed the advection of the high-222Rn water and largely controlled the temporal variations in 222Rn concentrations
at a station located on the north side of the river mouth, whereas the local SGD affected the short-term changes in the222
Rn concentrations. The concentrations of 222Rn and chlorophyll-a, an indicator of phytoplankton biomass, show asignificant positive correlation in the surface layer along the coastline in seasons when the nutrient supply was the main
factor limiting primary productivity.
Keywords: Submarine groundwater discharge (SGD), 222Rn monitoring, Chlorophyll-a, Nutrients, Wind-driven advection,
Coastal seas

Introduction
It is challenging to obtain evidence of the effects ofsubmarine groundwater discharges (SGDs) on marine
ecosystems, partly because groundwater is an invisible
source of the freshwater supplied to the sea. Freshgroundwater discharged from the sea bed often reduces
the salinity of the bottom water and increases the* Correspondence: shihok@kais.kyoto-u.ac.jp
1
Field Science Education and Research Center, Kyoto University,
Kitashirakawaoiwake, Sakyo-ku, Kyoto 606-8502, Japan
Full list of author information is available at the end of the article

concentrations of dissolved nutrients that are essential
for primary producers. The correspondence between the
low-salinity area associated with groundwater discharge
and the high concentration of primary producers, suchas benthic algae (Bruce 1925; Sanders 1979), seagrass(Kohout and Kolipinski 1967), and salt marsh plants(Nestler 1977), has been recognized since the 1920s.
Based on those results, the ecological significance ofgroundwater discharge into coastal seas was discussed
by Johannes (1980), but the distribution of salinity alone
was an insufficient index with which to describe the© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made.

Kobayashi et al. Progress in Earth and Planetary Science (2017) 4:6

influence of groundwater (Johannes 1980) because it
does not distinguish SGD from other freshwater.
Since the 1980s, various methods have been developed
to estimate the influence of SGD on marine primary
production. The influence of SGD on primary producers
and coastal ecosystems was quantified with the nitrogenbudget method (Valiela and Costa 1988; Valiela et al.
1992). The effects of SGD on benthic microalgae andred tides were also quantified using the groundwaterflow rate, which was calculated from the hydraulic gradient of groundwater on land (Campbell and Bate 1996;
Laroche et al. 1997; Gobler and Sanudo-Wilhelmy2001). A correspondence between the distributions of
SGD and benthic microalgae was suggested with avisualization method using thermal imaging, based on
the difference between the temperature of the seawaterand that of SGD (Miller et al. 2004).
In recent years, the geochemical tracers radium andradon-222 (222Rn) have been used to trace groundwater(Moore 1996; Burnett et al. 2006). 222Rn is a short-lived
radioisotope (half-life, 3.83 days) that is present in muchhigher concentrations in groundwater than in surface
flow (Ellins et al. 1990). Using this powerful tracer, theinfluence of SGD on benthic microalgae was evaluatedin the intertidal zone of the Yellow Sea (Waska 2011;
Waska and Kim 2010). The nutrient fluxes caused by
SGD and their potential effects on primary production
have also been demonstrated in several coastal seasaround Japan using 222Rn (Shiokawa et al. 2013;
Sugimoto et al. 2016).
A method of continuously monitoring the active concentrations of 222Rn in water has been developed andused to visualize the distribution of groundwater incoastal seas (Burnett et al. 2001; Burnett and Dulaiova
2003). This method was first applied in tracking lines ofover 100 km and successfully revealed the spatial distribution of SGD (Santos et al. 2008; Stieglitz et al. 2010).
The applicability of this approach within a site of only a
few kilometers in size was confirmed in a recent study
using multiple 222Rn detectors (Hosono et al. 2012).
SGD typically displays significant spatial and temporalvariability (Burnett et al. 2006). Therefore, highresolution mapping and time-series measurements ofthe 222Rn concentrations and biogeochemical properties
in the area of interest are required to quantify the influence of SGD on primary production along the coastline.
In this study, we monitored the active concentrations of222
Rn with multiple detectors from a boat (Stieglitz et al.
2010) to determine the distribution of 222Rn concentrations in Obama Bay, Japan, within a study site of only a
few kilometers in size (Fig. 1). We also monitored 222Rn
and the physical–biogeochemical properties of a site fora month and measured SGD directly with seepage
meters (Lee 1977; Rutkowski et al. 1999; Taniguchi andPage 2 of 15

Iwakawa 2004) to investigate the source of the 222Rn.
We then compared the distributions of SGD andchlorophyll-a (Chl-a), an indicator of the phytoplankton
biomass. In Obama Bay, a correspondence between thehigh 222Rn concentrations in the bottom layer and theelevated Chl-a concentrations were observed, suggestingthat SGD influences phytoplankton growth (Honda et al.
2016). In this study, we investigated this relationship in
more detail, focusing on the phytoplankton biomass inthe shallow zone along the coastline.

Methods/Experimental
Study site

Obama Bay is a semi-enclosed embayment in Fukui
Prefecture, Japan (Fig. 1a). The bay is one of the tributaries of Wakasa Bay, which is connected to the Japan
Sea, and the tidal range is less than 20 cm. The surface
area, volume, and mean depth of the bay are 58.7 km2,
0.74 km3, and 13 m, respectively. The annual precipitation in the watershed of Obama Bay is over 2000 mm/
year. The discharge of two major rivers, the Kita andMinami rivers, is usually less than 10 m3/s for each river
between May and August (data on river discharge were
provided by the Ministry of Land, Infrastructure, Transport and Tourism, Japan). There are significant groundwater resources in the basin, particularly on the Obama
Plain (Sasajima and Sakamoto 1962), and there are morethan 100 flowing artesian wells on this alluvial plain(Sugimoto et al. 2016).
The SGD rates (m3/d) flowing into Obama Bay were
estimated based on the monthly 222Rn data and a steadystate mass balance model and were relatively high in spring(March–April), when snowmelt water was predominant,and in the rainy summer season (June–September)(Sugimoto et al. 2016). The annual mean concentration of
222
Rn in the river water flowing into Obama Bay was about
59.6 dpm/L, which was lower than that in the groundwater (mean 660 dpm/L) but was not negligible(Sugimoto et al. 2016).
The rainfall, snowfall, and wind velocity data, measured at the weather station designated “Obama” from20 April to 15 May 2013, were obtained from theAutomated Meteorological Data Acquisition System(AMeDAS) of the Japan Meteorological Agency. The
mean monthly river discharge data from the largest river(Kita River) for 2013 were obtained from the Ministry ofLand, Infrastructure, Transport and Tourism of Japan.
The monthly changes in the meteorological conditionsin 2013 are shown in Fig. 2. The total monthly rainfall
was relatively high in January but gradually decreased
and was lowest in May. It increased again in June andcontinued to be relatively high from June to August. It
then increased markedly in September, which is thetyphoon season. Significant snowfall was recorded inKobayashi et al. Progress in Earth and Planetary Science (2017) 4:6(a)N

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Study
site

0

2 km (b)

B

Kita
River

A

Japan Sea
Wakasa Bay

Obama Bay
Fukui Prefecture

D

C

Minami River(c)sea level logger

to radondetector

seepage meteroutsidesalinity logger

chamber
insideSGD

water intake

seabedFig. 1 a Location of the study site, Obama Bay. b Map showing the continuous 222Rn monitoring track (dotted line), the station at which the time-series
measurements of 222Rn were made, and the seepage meter (star mark). Monitoring along the coastline from a boat was conducted from A to D.
c Schematic illustration of the deployment of the seepage meters and loggers

winter (December to February). The mean monthly
river discharge was relatively high from January to
March, was lowest for the year in May, and increasedslightly in June and July. It then increased markedly
in September, when heavy rain fell in Obama City.

Fig. 2 Total monthly rainfall (mm) and snowfall (cm) and mean monthly
river discharge rate of the Kita River (m3/s) in 2013

Measurements and sampling

The distributions of the 222Rn concentrations along theeastern coast of Obama Bay were measured on 13, 15, and
16 March, 7 June, 22 July, and 12 September, 2013, withelectronic Rn detectors (RAD 7: Durridge) from a research boat belonging to Fukui Prefectural University. The
boat ran close to the coast, within 10 m of land, where thedepth of the water column was around 2 m, at a speed of
1–2 knots. The tracking line is shown in Fig. 1a. Monitoring was conducted from Tomari (A) to Seihama (D). The
total distance of the tracking line was about 15 km.
Seawater was pumped at 5–10 L/min from 0.5 m
below the surface, and the temperature and salinity of
the water were measured every 1 min using a CTD
instrument (AAQ1183: JFE-Advantech). The 222Rn in
the seawater was measured and analyzed according to
Stieglitz et al. (2010), who developed an Rn detector system which consists of three Rn detectors connected inparallel and interfaced with an air–water exchanger(Dulaiova et al. 2005). We used two sets of Rn detector

Kobayashi et al. Progress in Earth and Planetary Science (2017) 4:6

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systems, with a measurement interval for each system of
10 min, so the average values and standard deviations of
the 222Rn concentrations were obtained every 5 min.
Seawater samples for analyses of nutrient concentrations
and Chl-a were collected every 10 min and every
20 min, respectively. However, seawater sampling foranalyses of Chl-a failed at some points in March.
At a station on the tracking line (star in Fig. 1b), we
measured the temporal variations in the 222Rn concentrations in addition to the SGD fluxes, using a seepage
meter. The water depth at the station was around 1 m.
Divers deployed the seepage chamber (base radius,
15 cm) on the seabed at the station on 18 April 2013. A
diagram of the seepage chambers is shown in Fig. 1c.
Salinity and temperature loggers (Infinity-CT or MDSCT, JFE-Advantec) were set inside and outside the chambers, whereas sea-level loggers (DIK-613A, Daikirika) were
set only on the outsides of the chambers. The details of theseepage meters have been described by Taniguchi andIwakawa (2004). We calibrated the seepage meters in thelaboratory with a Coriolis flowmeter (FD-S, Keyence) before
and after the field measurements were made. The seepage
fluxes were determined in mL/min from each calibration
curve and then converted to cm/d using the area of thechambers (cm2). Seawater was pumped from the sea bottom at 5–10 L/min next to the chamber (Fig. 1c) from 20
April to 15 May in 2013. The 222Rn concentrations in theseawater were measured every 10 min using a 222Rn
detector (RAD 7: Durridge) interfaced with an air–waterexchanger located on land. The seawater samples for nutrient analysis were collected once a day from 20 April to 12
May in 2013. The times of water sampling are shown withthe data on nutrient concentrations (Fig. 10).

For the data analysis, we used the 222Rn concentrations in the river water flowing into the bay, which had
been measured monthly in 2013 by Sugimoto et al.
(2016). Because approximately 80% of the riverine 222Rn
entering the bay is contributed by the two major rivers(Kita and Minami rivers), the mean 222Rn concentrationsin the two rivers were assumed to represent the value
for the surface river waters (Sugimoto et al. 2016). We
also used the 222Rn concentrations and salinity in theterrestrial groundwater measured in March 2013 by
Sugimoto et al. (2016).
The temporal changes in the tidal level from 20 April
to 15 May 2013 were also obtained from the Japan
Meteorological Agency. We used fast Fourier transforms(FFTs), providing power spectra, to evaluate the fluctuation cycles in the time series for wind velocity, sea level,
SGD flux, and 222Rn concentration.
We evaluated the factors limiting primary productivity
using the equations of Steel (1962) for temperature (FT)and nutrients (FN), as follows:
F T ¼ T =T opt  exp 1−T =T opt
ð1Þ

Samples and data analysis

Results and Discussion

The seawater samples were filtered through GF/F
0.7 μm glass filters, frozen, and stored in the freezer.
The concentrations of nitrate (NO3), nitrite (NO2),
phosphate (PO4), and dissolved silicate (DSi) were measured with an auto-analyzer (QuAAtro, BL-Tech). The
concentration of ammonium (NH4) was measured witha fluorometer (Trilogy, Turner Design) with a CDOM/
NH4 module (Model 7200-041, Turner Design), using
the ortho-phthaldialdehyde (OPA) method (Holmes etal. 1999). In this study, we defined dissolved inorganic
nitrogen (DIN) as the sum of NO3 and NO2 because almost all the concentrations of NH4 at the surface along
the coastline were below the detection limit (0.1 μM).
Dissolved inorganic phosphorous (DIP) is defined as
PO4. The concentration of Chl-a on the GF/F filters wasquantified with a calibrated fluorometer (Trilogy, Turner
Design). The analytical errors were within 10%, suggesting that the values analyzed were sufficiently accurate to
draw conclusions from the data.

Distributions of the concentrations of 222Rn, nutrients,
and Chl-a along the coastline

F N ¼ MIN ðDIN=ðK N þ DINÞ; DIP=ðK P þ DIPÞÞ

ð2Þ

where Topt (25 °C) is the optimum temperature forphytoplankton growth, and KN (1.7 μM) and KP(0.19 μM) are the half-saturation constants for DIN and
DIP, respectively. These parameters were obtained fromstudies of the Japanese coast (Yanagi and Onitsuka
1999). We set T, DIN, and DIP to the maximum andminimum values extracted from the temperatures, DIN
concentrations, and DIP concentrations observed ineach month, respectively.

High-resolution mapping of 222Rn was performed in
March, June, July, and September of 2013. The concentrations of 222Rn, salinity, and Chl-a along the coastline
in each month are shown in Figs. 3, 4, and 5, respectively. For the purpose of illustration, the tracking line
was divided into three zones by four points (A–D,shown in Fig. 1b). The results of 222Rn mapping showcommon patterns in the spatial distributions during
June, July, and September: the concentrations were relatively high in the zone between B and D but low in thezone between A and B (Fig. 3). The concentration in thezone between C and D was highest in July. The average
concentrations of 222Rn on the tracking line (from A toD) in June, July, and September were nearly equal, at
2.8, 3.3, and 2.6 dpm/L, respectively. The average concentration of 222Rn in March was 9.6 dpm/L, muchhigher than in the other 3 months. The distribution ofKobayashi et al. Progress in Earth and Planetary Science (2017) 4:6

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Fig. 3 Distributions of 222Rn (dpm/L) in a March, b June, c July, and d September, determined by monitoring the coastline from a boat. All the data
in March are shown in the same panel, although each datum was obtained on a different day

the 222Rn concentration in March differed from that in
the other months. The 222Rn concentration was highest
around point B but relatively low in the zone between B
and C.
In June, July, and September, salinity was relatively low
in the zone between B and D but high in the zone between A and B (Fig. 4). The distributions of low-salinity
water corresponded strongly to the zones in which the222
Rn concentrations were relatively high in all surveys.
The distribution of Chl-a was constant throughout thesurvey, including in March. The Chl-a concentrations
were relatively high in the zone between B and D but
low in the zone between A and B (Fig. 5). The distribution of high-Chl-a water corresponded strongly to
the zones in which the 222Rn concentration was relatively high.
The correspondence between the distributions of222
Rn and Chl-a is shown in Fig. 6. The distributions ofboth parameters corresponded well in June, July, andSeptember, but not in March. The locations of the peakvalues for the two parameters differed in March. The
correlation coefficients indicated significant correlations
between the concentrations of 222Rn and Chl-a in June,
July, and September. Significant correlations were also

obtained between the concentrations of 222Rn and PO4
in June and between salinity and NO3 in March, June,and September (Table 1).
We created the zones shown in Fig. 1b using points
A–D to investigate the differences in the biogeochemical
properties of each zone along the coastline. The datasets
obtained from all the observations made in March, June,
July, and September were integrated, and the average
concentrations of 222Rn, salinity, NO3, and PO4 in eachzone are shown in Fig. 7. In the zone between A and B,
salinity was relatively high and the concentrations of222
Rn, NO3, and PO4 were low. Salinity was lowest andthe concentration of NO3 was highest in the zone between B and C, whereas PO4 was not high in this zone.
The concentrations of 222Rn and PO4 were highest in
the zone between C and D.
Temporal changes in the concentration of 222Rn, SGD
flux, and biogeochemical properties

Figure 8 shows temporal variations in the 222Rn concentration measured with a Radon detector; the SGD flux
measured with a seepage meter, the wind velocity (a parameter of the physical environment), and salinity levels
inside and outside the seepage chamber at the point at

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Fig. 4 Distributions of salinity in a March, b June, c July, and d September, determined by monitoring the coastline from a boat. All the data in
March are shown in the same panel, although each datum was obtained on a different day

which the 222Rn concentration was relatively high (star;
location shown in Fig. 1b). The sea level varied from 0.7
to 1.2 m. The concentrations of 222Rn increased and decreased over a 5–6 day cycle, although the SGD flux
showed no such cycle (Fig. 8a). The minimum values of
the cycle were nearly stable from April to May, whereas
the SGD flux dropped sharply to almost zero in May.
Figure 9 shows the FFT power spectra for the SGD flux,
the sea level at the station, the concentration of 222Rn,
and the wind velocity and salinity inside and outside thechamber. The power spectrum for the SGD flux shows
variations with diurnal (25 h) and semidiurnal (12.5 h)cycles, with characteristics similar to those of the power
spectrum for sea level. The temporal variations in the222
Rn concentrations throughout the monitoring period
showed a 5–6 day cycle and were coincident with thosein the wind velocity; 222Rn increased when the northward wind became strong but decreased when thesouthward wind became dominant (Fig. 8b). The resultsof the FFT analysis also revealed that 222Rn and windvelocity peaked with a periodicity of ~100 h (Fig. 9). As
for the shorter cycles, the power spectra for wind, salinity, and the concentration of 222Rn displayed peaks withperiodicities of 24, 20–24, and 20 h, respectively.

Figure 8c shows the temporal variations in salinity inside and outside the chamber. The salinity outside thechamber shows negative peaks, with the period of low
salinity ranging from 1 to 7 h. Peaks were observed almost daily, whereas no peaks occurred between 24 and
26 April, from 30 April to 3 May, or between 6 and 9
May. Several peaks coalesced into a single peak on
26–27 April. The salinity inside the chamber also
showed negative peaks 6–12 h after the negative peaksin the outside salinity, although the magnitude of the declines was much smaller than the declines in the outside
salinity. The inside salinity also displayed a declining
trend in April and an increasing trend in May.
The temporal variations in salinity outside the chamber are presented with the wind velocity data (Fig. 8d).
The northward wind showed peaks over a 5–6 day cycle.
The negative peaks in the outside salinity appeared
5–6 h after the northward wind strengthened. The outside salinity remained high during periods when thesouthward wind was dominant.
Temporal variations in the concentrations of nutrients(NO3, PO4, DSi) and associated biogeochemical properties at the station described above are shown in Fig. 10.
The plots in Fig. 10a indicate the times of waterKobayashi et al. Progress in Earth and Planetary Science (2017) 4:6

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Fig. 5 Distributions of chlorophyll-a (μg/L) in a March, b June, c July, and d September, determined by monitoring the coastline from a boat.
All the data in March are shown in the same panel, although each datum was obtained on a different day

Fig. 6 Line plots of 222Rn (dpm/L) and chlorophyll-a (μg/L) in a March, b June, c July, and d September, observed by monitoring the coastline
from a boat. Letters A to D in c and d correspond to those in Fig. 1b

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Table 1 Correlation coefficients for salinity and nutrients, 222Rn and nutrients, and 222Rn and chlorophyll-a
222

Rn vs. NO3

222

Rn vs. PO4

222

Sal. vs. NO3

Sal. vs. PO4

March, 2013–0.76**

0.12

0.37*

0.18

Rn vs. Chl-a–0.17

June, 2013

–0.66**

–0.15

0.14

0.54**

0.91**

July, 2013

–0.35

–0.11

–0.12

0.37

0.79**

September, 2013–0.82**

0.10

0.38

0.29

0.52*

Significant correlations are marked with asterisks**Significant (p < 0.01) *significant (p < 0.05) no mark not significant
Sal. salinity, vs. versus

sampling for nutrient analysis. The 222Rn concentrations at
the daily sampling times were extracted from Fig. 8a andare plotted in Fig. 10b. The tide level shows diurnal (25 h)and semidiurnal (12.5 h) cycles, but the timing of water
sampling was not related to the tidal cycle. The concentrations of PO4 varied between 0.1 and 0.2 μM and increased
and decreased over a 5–6 day cycle. The concentration of
DSi ranged between 0.0 and 6.4 μM and showed several
relatively large peaks that lasted for 1–2 days during themonitoring period. The concentrations of NO3 ranged between 0.0 and 1.0 μM and peaked on the days when DSi increased. The temporal variations in the concentrations of
PO4 throughout the monitoring period showed a weak relationship with the daily average concentrations of 222Rn (r =0.27, p = 0.1), whereas the changes in NO3 and DSi showedno relationship to the changes in 222Rn.

Distribution of 222Rn concentrations and its source

The concentrations of 222Rn at the northern end of the
tracking line, the point closest to the outside of the bay,
were less than 1.5 dpm/L throughout the sampling
period (Fig. 3), confirming that the influence of SGD
was infrequent. In contrast, high 222Rn concentrations(up to 15 dpm/L) were observed inside the bay throughout the monitoring period, suggesting the influence of
SGD in Obama Bay.
The average 222Rn concentration was highest in March(9.6 ± 2.9 dpm/L), whereas the values in June, July, andSeptember (2.8 ± 1.3, 3.3 ± 2.0, and 2.6 ± 1.6 dpm/L, respectively) were relatively low. The average salinity in
March was 29.0 ± 2.7, much lower than outside the bay
at the end of February (33.3; Sugimoto et al. 2016). The
average salinity was lowest in September (26.5 ± 2.6) inFig. 7 Box plots of a 222Rn (dpm/L), b salinity, c phosphorous (μM), and d nitrate (μM), showing the minimum and maximum values, 25th and75th percentiles, and median values, from data obtained by monitoring the coastline from a boat in March, June, July, and September

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Fig. 8 Time series of a 222Rn (dpm/L) and SGD fluxes measured with a seepage meter (cm/d), b 222Rn (dpm/L) and northward wind (m/s), c salinity
inside and outside the chamber, and d northward wind (m/s) and salinity outside the chamber, observed at a fixed station (star, shown in Fig. 1b)from 20 April to 15 May, 2013. The wind velocity data were obtained from AMeDAS

response to heavy typhoon rain. The river discharge wasrelatively high and largely stable from January to March,suggesting the influence of snowmelt water after significant snowfall in January and February (Fig. 2). These results are consistent with those of Sugimoto et al. (2016),
who reported that the SGD rates flowing into Obama
Bay, estimated from monthly 222Rn data with a steadystate mass balance model, were relatively high in spring(March–April) when snowmelt water was the highest.
The 222Rn concentration was measured along a few
kilometers of the coastline every 5 min in this study, using
two sets of a three-radon-detector system at a boat speedof 1–2 knots. The spatial resolution was slightly higher thanthat in previous pioneering works (Stieglitz et al. 2010;

Hosono et al. 2012), and the distribution of the 222Rn concentration along the coast of the semi-enclosed bay was determined in detail. Similar distributions were observed in
June, July, and September, when the 222Rn concentration
was highest around the mouths of the Kita and Minami rivers, and the high-222Rn water dispersed along the eastern
coast toward the north (Fig. 3).
The first candidate source of high-222Rn water is theriver water. The 222Rn concentrations in the river watercollected at the river mouths in Obama Bay were relatively high (maximum, 234 dpm/L; Sugimoto et al. 2016)
because groundwater seeps from the river bed and thenflows rapidly down to the river mouth. These concentrations are consistent with those at other river mouths inKobayashi et al. Progress in Earth and Planetary Science (2017) 4:6

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Fig. 9 Spectra obtained with fast Fourier transforms (FFTs) for the time series of 222Rn, SGD flux, sea level, and salinity outside and inside the chamber,
observed at a fixed station (star in Fig. 1b) from 20 April to 15 May 2013. The northward wind data were obtained from AMeDAS

Japan (e.g., Hosono et al. 2012; Shiokawa et al. 2013),
whereas those at the mouths of continental rivers aremuch lower (e.g., Cable et al. 1996). The total river discharge was large enough (approximately 1.0–2.0 ×
106 m3/day; Sugimoto et al. 2016), and the axis of thebay was small enough (<4 km) for the river water to disperse throughout the bay within the period of 222Rn degradation (half-life, 3.83 days).
The second candidate source of high-222Rn water is therelatively large SGD offshore from the mouths of the Kita
and Minami rivers, as has been mentioned in other surveys conducted by Obama City and models of the flow
streams of terrestrial groundwater in the watersheds.
Interviews with local residents confirmed the presence offresh SGD at the point. However, we could not access thesites at the mouths of the Kita and Minami rivers to deploy seepage meters at that time. The concentrations of
PO4 and 222Rn were both highest along the coastline andcorrelated with one another (Fig. 7; Table 1) around thissite (in the zone between C and D, see Fig. 1b), indicating
the presence of significant SGD.

Temporal changes in 222Rn concentration and SGD flux

We continuously monitored the concentration of222
Rn and measured the SGD flux using a seepage
meter at a specific station (location shown in Fig. 1b)
for a month, to determine the major sources of222
Rn. The seepage meter located on the sea bottom
indicated the presence of local SGD at the station(Fig. 8a). SGD includes the submarine fresh groundwater discharge (SFGD), originating from the freshwater on land, and the recirculated submarine
groundwater discharge (RSGD), originating from theseawater that infiltrates the seabed under tidal influences (Taniguchi 2002a; Taniguchi et al. 2002b). The
SFGD and RSGD generally show large differences insalinity, so they can be separated. We calculated themusing the salinity inside and outside the chamber, as
shown in Fig. 8c. The contribution rate of fresh
SGD (SFGD/SGD × 100 [%]) was calculated as follows (Ishitobi et al. 2007):
SGD ¼ SFGD þ RSGD

ð3Þ

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Fig. 10 As in Fig. 8 but for a tidal level (m) and the concentrations of b 222Rn (dpm/L), phosphorous (μM), nitrate (μM), and silicate (μM). Values
on the X-axes of the plots in a indicate the time (h) of water sampling for nutrient analysis, and those on the Y-axes of the plots are the tide levels at
those times SGD  C sgd ¼ SFGD  C sfgd þ RSGD  C rsgd

ð4Þ

where SGD is the seepage flux determined with a seepage meter, Csgd is the salinity inside the chamber, Crsgd isthe salinity outside the chamber, and Csfgd is the salinityof the fresh groundwater on land. In this study, Csfgd,
which is the salinity (calculated from conductivity measurements) of the terrestrial groundwater calculated by
Sugimoto et al. (2016), was assumed to be zero. The
level of SGD flux changed sharply on 1 May, so we
divided the datasets for SGD flux and salinity into theperiods 20 April–1 May and 2–15 May. Crsgd was set tothe salinity averaged over each period, and Csgd was set
to the salinity averaged over the 1 h before the end ofeach period. We then solved the system of equations to
calculate SFGD and RSGD. When Csgd was larger than
Crsgd, the contribution of SFGD was assumed to be zero.
The contribution rates of SFGD (%) varied among
months at the station: 1.4% in April but 0.0% in May
2013. The average SGD flux was 4.6 cm/day in April
and 0.8 cm/day in May, with minimum and maximum
values for the whole monitoring period of 0.1 and18.9 cm/day, respectively. The average SGD fluxes were
smaller than the results in the Chokai area (average SGD
flux = 38.9 cm/day; Hosono et al. 2012), where the tidal
range is as small as that in Obama Bay, but large SFGD
fluxes were observed. The average SFGD flux, 0.41 cm/
day, was consistent with that obtained with the 222Rn
mass balance model throughout the bay (0.62 cm/day;
Sugimoto et al. 2016).

Temporal changes in the SGD flux and sea level
showed variations with diurnal (25 h) and semidiurnal(12.5 h) cycles (Fig. 9), suggesting the influences of diurnal and semidiurnal tides. This indicates that the SGD at
the site consisted mainly of tidally influenced RSGD.
The observed SGD flux dropped sharply to almost zero,and the salinity inside the chamber increased in May,
while the seasonal change in the SGD flux was consistent with that obtained with the 222Rn mass balance
model (Sugimoto et al. 2016). These trends indicatedthat the reduced SGD flux in May was partly attributable
to the disappearance of the snowmelt water and partly
to the rise in sea level from May, when SGD was suppressed. The seasonal changes in snowmelt water andsea level are mainly attributed to the seasonal change in
air temperature, which varies little from year to year.
These factors may explain the reduction in SGD
observed in this study.
The minimum values of the 5–6 day cycle of the 222Rn
concentration were almost constant from April to May,
whereas the local SGD flux observed at the station
dropped sharply in May (Fig. 8), suggesting the influenceof the advection of high-222Rn water. The observed distribution of the 222Rn concentration along the coast suggested that high-222Rn water was present around themouths of the Kita and Minami rivers during the observation period (Fig. 3). The results of the FFT analysis(i.e., that both the 222Rn concentration and wind velocity
showed peaked with a periodicity of ~100 h; Fig. 9) indicated that wind played an important role in the advection of high-222Rn water.

Kobayashi et al. Progress in Earth and Planetary Science (2017) 4:6

Page 12 of 15

We estimated the time (Ti) required for the high-222Rn
water near the mouths of the Kita and Minami rivers toreach the station. As an example, for the northward
wind velocity of ~2 m/s that prevailed for 6 h daily between 2 and 13 May (Fig. 8), the velocity of the winddriven flow, V (m/s), was calculated as follows:
V ¼ ro a  Cd  W y 

pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
W x2 þ W y 2 =ro s  dt=dz

ð5Þ

where ro_a is the density of air, Cd is the friction coefficient at the sea surface, ro_s is the density of seawater,and W_x and W_y are the wind velocities along the Xand Y-axes, respectively. We set the Y- and X-axes tonorth–south and west–east, respectively; dt is the timescale on which the wind blows in a particular direction
and dz is the friction depth. The friction depth (D) was
defined as follows (Officer 1976):
pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
D ¼ π 2  N z =fð6Þ
where π is 3.14, f is the Coriolis coefficient (7.3 × 10−5)at latitude 35° north, and Nz is the eddy viscosity coefficient. D ranges from 5 to 50 m, assuming Nz ranges
from 1 to 100 cm2/s. The minimum value of D exceeds
the water depth of the coastal zone of the bay, so we set
dz to equal the water depth of 1–5 m. We assumed thefollowing values: ro_a, 1.205 kg/m3; Cd, 0.0013; ro_s,
1022 kg/m3; W_x, zero; W_y, 2 m/s; and dt, 6 × 3600 s.
Using equation (5), the resulting V value is 0.03–
0.13 m/s. We would expect the actual flow speed to behigher than this value because of the additional influenceof the density-driven flow. Because the distances between the river mouths and the station were ~2 km, Ti isless than 1.0 day, which is much shorter than the halflife of 222Rn (3.83 days). Therefore, the high-222Rn water
near the mouths of the Kita and Minami rivers is a potential source of 222Rn in the area of the station.
Influence of the advection of high-222Rn water on 222Rn
concentrations

The results of the FFT analysis indicated that both the222
Rn concentration and the wind velocity peaked with aperiodicity of ~100 h (Fig. 9), suggesting that wind wasstrongly related to the long-term temporal changes in
the 222Rn concentration at the observation site. In thissection, we examined the influences of the advection ofhigh-222Rn water, caused by wind, on temporal changes
in the concentrations of 222Rn in more detail.
In the short term, the temporal changes in the wind,salinity, and 222Rn concentration displayed variations
with periods of around 24, 20–24, and 20 h, respectively(Fig. 9). Therefore, the cycle of the 222Rn concentration
was not perfectly coincident with the variations in windvelocity. This is considered to be partly because the advection of the low-salinity high-222Rn water within a day

was controlled not only by the wind but also by the tidal
currents, the density flow (such as the estuarine circulation), vertical mixing, and the interactions of all thesefactors, and partly by the technological limitations of the
FFT analysis, which were attributed to the spike-likechanges in salinity and the 222Rn concentration. Therefore, the temporal variation in salinity was mainly controlled by wind, in addition to many physical factors,
and the temporal variation in the 222Rn concentration
could be synchronized to that in salinity.
The salinity at the station increased during the periods
when the southward wind was dominant (Fig. 8d), suggesting that the northward dispersion of the low-salinity
high-222Rn water flowing from the mouths of Kita andMinami rivers was suppressed. The 1 h averaged 222Rn
concentrations, measured every 10 min at the station(location indicated by a star in Fig. 1b), are plotted versus salinity outside the chamber in Fig. 11, to allow a
discussion of the influence of wind.
The northward wind was dominant on 30 April andthen weakened on the next day. The southward wind became strong on 2 May (Fig. 8d), so we used the datasets
for the 222Rn concentration and salinity in the period 30
April–2 May. The endmembers and mixing curves areshown in Fig. 11a, and the data for the 3 days in thisperiod, which were measured from noon on 1 day tonoon on the next day, are plotted in Fig. 11b, along witha part of each mixing curve. The 222Rn concentrationand salinity of seawater outside the bay, measured on 26
April 2013, were 0.9 dpm/L and 34.0, respectively. The
222
Rn concentrations measured on the same date in thetwo major rivers (Kita and Minami rivers) were 72.5 and73.6 dpm/L, respectively, so we used the mean value of
73.1 dpm/L as the endmember for river water. The
mean concentrations of 222Rn in the terrestrial groundwater and the seawater outside the bay were 660 dpm/L.
The salinity of the river water and terrestrial groundwater was assumed to be zero, all according to Sugimoto
et al. (2016). The 222Rn concentration and salinity at
0.9 m under the seabed at the station were determined
to be 54.2 ± 23.5 dpm/L and 31.3 by direct measurement(Sugimoto et al. 2017, unpublished data), so we used avalue of 54 dpm/L as the endmember for RSGD.
Half the plots in Fig. 11b are located above the mixing
curve between seawater outside the bay (SW) and riverwater (RW), suggesting that the local SGD contributed
to the 222Rn concentration, whereas the major component of SGD, either terrestrial groundwater (tGW) or
recirculated submarine groundwater (RSGD), could not
be determined. In contrast, the daily changes in the222
Rn concentration were in response to changes in winddirection. The concentration of 222Rn was relatively highon 30 April–1 May, the period in which northward wind
was dominant, whereas the 222Rn concentration graduallyKobayashi et al. Progress in Earth and Planetary Science (2017) 4:6

Page 13 of 15

The results of our various observations, including
those made with seepage meters, the continuous monitoring of 222Rn concentrations at a single station, andthe monitoring survey of 222Rn concentrations from aboat, together with measurements of the biogeochemical
properties, suggest that the advection of high-222Rn
water plays an important role in controlling the temporal changes in the 222Rn concentration at the stationin the zone between B and C, whereas the local SGD
might be another source of 222Rn in the period whensignificant SGD flux was observed. The mass balance
model (Burnett et al. 2008), which estimates SGD fluxes
using 222Rn records from monitoring surveys, could not
be applied because the temporal changes in the advection of high-222Rn water were significant.
Influence of SGD on the biogeochemical environment
along the coastline

Fig. 11 a 222Rn concentration (dpm/L) and salinity of the endmembers
for seawater (SW), river water (RW), terrestrial groundwater (tGW), andrecirculated submarine groundwater (RSGD). The solid line, dotted line,
and bold line are the mixing curves between SW and RW, SW and tGW,
and SW and RSGD, respectively. b Average 222Rn concentration (dpm/L)
for 1 h in each day plotted against salinity outside the chamber. The
symbols RW, tGW, and RSGD indicate a part of each mixing curve

decreased on 1–2 May and became close to the endmember of SW on 2–3 May, the period in which thesouthward wind was dominant (Fig. 11b). These results provide evidence of the advection of high-222Rn
water, which was controlled mainly by the wind during the observation period. Moreover, as shown in
Fig. 7, high-222Rn and high-PO4 seawater was present
around the mouths of the Kita and Minami rivers(the zone between C and D, shown in Fig. 1b). Thiswater moved northward when the northern wind became strong, supplying 222Rn and PO4 to the station
at which the temporal variations in these parameters
were measured.

The results of the monitoring survey along the coastline
suggest that groundwater, which is characterized by high222
Rn, contributes to the nutrient supply in the bay. The
N:P:Si molar ratio of the fresh groundwater on land wasapproximately 26:1:27, whereas that in the river water
was 82:1:150 (Sugimoto et al. 2016), suggesting the
groundwater is rich in PO4. The concentrations of PO4
and 222Rn were both highest along the coastline in thezone between C and D (Fig. 7) and varied simultaneously (Fig. 10). This mutual correlation suggests thatnutrients are supplied by SGD to the surface layer along
the coastline.
The factors limiting primary productivity are listed inTable 2. The limitation factors for the maximum andminimum temperatures at each observation point (FT)
were nearly 1.0 in June, July, and September, suggestingthat temperature was not a limiting factor in these seasons. FT in March ranged from 0.64 to 0.81, indicatingthat temperature was one of the factors limiting primary
productivity. Of the factors limiting primary productivity
Table 2 Maximum and minimum temperatures and concentrationsof nutrients along the monitoring line shown by the dotted line in
Fig. 1b in each month and limiting factors (FT, TN) for primary
productivity
March

June

July

SeptemberTemp. (°C)

FT

NO2+3 (μM)

DIP (μM)

FN

Max.

11.9

0.80

20.7

0.18

0.49

Min.

8.1

0.64

0.4

0.00

0.00

Max.

23.3

1.00

1.9

0.21

0.53

Min.

20.6

0.98

0.1

0.00

0.00

Max.

29.8

0.98

8.3

0.23

0.55

Min.

27.5

1.00

0.1

0.08

0.05

Max.

28.9

0.99

3.8

0.18

0.49

Min.

26.3

1.00

0.0

0.00

0.00

Max. maximum, Min. minimum, Temp. temperatureKobayashi et al. Progress in Earth and Planetary Science (2017) 4:6

in the sea, light intensity was not considered in thisstudy because of a lack of data. However, light intensity
was assumed to rarely limit primary production at thesurface, as inferred from the limitation factor for lightmeasured at the bottom in the shallow zone of Obama
Bay (Sugimoto et al. 2017). FN values for the minimum
concentrations of nutrients at each observation point
were all nearly 0.0, and FN values for the maximum concentrations were around 0.5, suggesting that nutrientswere a major factor limiting primary production at all
the observation points.
The correspondence and correlation between the concentrations of 222Rn and Chl-a at the surface in June,
July, and September (Fig. 6, Table 1) suggest that the nutrients supplied by SGD influenced the phytoplankton
biomass. The correlation of these factors in March was
not significant, mainly because not only nutrients limited primary production but also temperature and probably light intensity. Of course, it is important to consider
the influence of the seawater residence time on the concentrations of both 222Rn and Chl-a. Although the links
among 222Rn, nutrient supply, and primary production
must be more fully clarified in future studies, the resultsof this survey provide persuasive evidence that SGD influences the phytoplankton biomass in Obama Bay,
whereas the nutrient supply pathways are not limited to
the local SGD but are also influenced by its advection.

Conclusions
In this study, we conducted high-resolution mappingand made time-series measurements of 222Rn and thebiogeochemical properties along the coastline, combinedwith SGD measurements made with seepage meters, toexamine the influence of SGD on the biogeochemical
environment in Obama Bay. Our results show that (1)the observed concentrations of 222Rn along the coast of
the bay indicate that groundwater affected the biogeochemical properties of the bay and was not limited to
the local SGD; (2) 222Rn flowing into the bay from rivers(in which groundwater seeps from the river bed) had astrong effect on the distribution of 222Rn concentrations
along the coast; (3) high-222Rn water was always present
around the river mouths, and the northward advectionof the water affected the distribution of 222Rn concentrations; and (4) the southward wind suppressed the advection of high-222Rn water and was the main control on
temporal variations in the 222Rn concentration at a station located to the north of the Kita and Minami rivers.
Although the effect of the residence time of the seawateron the correlation between the concentrations of 222Rn
and Chl-a must be clarified in a future study, the resultsof this study provide persuasive evidence that the nutrients supplied by SGD influence the phytoplankton biomass in Obama Bay.

Page 14 of 15

Abbreviations
AMeDAS: Automated Meteorological Data Acquisition System; Chl-a: Chlorophyll-a;
SGD: Submarine groundwater discharge
Acknowledgements
The observations made with seepage meters along a line extending fromonshore to offshore were made in collaboration with Wakasa High School.
We are indebted to the members of the diving club and their teachers, Dr.
Yasuyuki Kosaka and Mr. Hiroaki Hirayama, and the headmaster of Wakasa
High School for their assistance. We sincerely thank Mr. Teruhiko Nakajima
and Mr. Tomohiro Kawashiro of Fukui Prefectural University for supporting
the development and deployment of the seepage chambers and the 222Rn
monitoring at the fixed stations. We are grateful to the journal editors andthree anonymous reviewers for their helpful comments and suggestions.
Funding
This work was performed with the support of the Research Project Human–
Environmental Security in Asia-Pacific Ring of Fire: Water–Energy–Food Nexus(R-08-Init) at the Research Institute for Humanity and Nature (RHIN).
Authors’ contributions
SK, RS, DT, OT, and JS conceived and designed the study. HH, YM, and MY
performed the field observations and data analysis. SN collaborated with thecorresponding author in the data analysis and discussion. MT collaborated
with the authors in the planning of the field observations. All the authors
have read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.
Author details
1
Field Science Education and Research Center, Kyoto University,
Kitashirakawaoiwake, Sakyo-ku, Kyoto 606-8502, Japan. 2Research Center for
Marine Bioresources, Fukui Prefectural University, 49-8-2, Katsumi, Obama,
Fukui 917-0116, Japan. 3Research Institute for Humanity and Nature, 457-4,
Kamigamo Motoyama, Kita-ku, Kyoto 603-8047, Japan. 4Idea Consultants Co.,
2-2-2, Hayabuchi, Tsuzuki-ku, Yokohama, Kanagawa 224-0025, Japan.
5
Graduate School of Biosphere Science, Hiroshima University, 1-4-4
Kagamiyama, Higashi-hiroshima, Hiroshima 739-8528, Japan. 6Graduate
School of Maritime Sciences, Kobe University, 5-1-1 Fukae-minami,
Higashi-nada-ku, Kobe 658-0022, Japan.
Received: 30 May 2016 Accepted: 9 March 2017

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