Introduction
Improved understanding of groundwater dynamics is needed to answer practical
questions of water quality and quantity for groundwater discharges such as
wells and streams. Knowing groundwater travel times allows us to pinpoint
possible sources of groundwater pollution from agricultural activities,
while estimates of groundwater volumes in the subsurface are needed for
sustainable management of water resources in many countries (Granneman et
al., 2000; McMahon et al., 2010; Gusyev et al., 2011, 2012; Stewart et al., 2012;
Morgenstern et al., 2015). In Japan, there is a need for a robust and quick
approach to quantify the subsurface groundwater volume as an important
component of the water cycle due to the recently enacted Water Cycle Basic
Law in March 2014 (Tanaka, 2014). In addition, subsurface groundwater
storage plays an important role in contributing to flood river flows and
providing much of the river water during droughts. However, the complex
groundwater dynamics are often difficult to characterise on a river basin
scale due to the absence of subsurface information. Therefore, a common
practice is to utilise numerical models with simplified representations of
the complex groundwater dynamics for rainfall–runoff simulation in river
catchments. For example, a distributed hydrologic model BTOP, which has been
applied globally (Magome et al., 2015) and in many river basins for detailed
flood and drought hazard quantification (Gusyev et al., 2016;
Navarathinam et al., 2015; Nawai et al., 2015), is used to simulate
groundwater flow components using the exponential mixing model (EMM) with
mean travel distance of groundwater flow (Takeuchi et al., 2008). At the
river basin scale, a simple and yet robust tracer such as tritium (3H)
is needed to characterise the groundwater bodies as they are drained by
surface water features.
Tritium has been instrumental in providing information on hydrologic systems
and surface–groundwater interactions in river waters in the Southern
Hemisphere, but tritium tracer studies are still scarce in the Northern
Hemisphere rivers (Michel, 2004; Michel et al., 2015; Harms et al., 2016). In
the Southern Hemisphere, tritium measurements in river water have been
commonly used to understand groundwater dynamics by determining groundwater
transit time distributions and by constraining groundwater flow and
transport models (Stewart et al., 2007; Gusyev et al., 2013, 2014;
Morgenstern et al., 2010, 2015; Cartwright and Morgenstern, 2015; Duvert et
al., 2016). Tritium is a part of the water molecule and migrates through the
water cycle while being inactive except for radioactive decay. The half-life
of 12.32 years allows us to quantify water lag time in the subsurface of up to
200 years even with the natural levels of tritium concentrations in
precipitation, but requires the most sensitive equipment to detect the small
concentrations of tritium currently found in river water in the Southern
Hemisphere (Morgenstern and Taylor, 2009). Low concentrations of tritium in
precipitation are derived from cosmogenic generation in the upper atmosphere
and high tritium concentrations have been contributed by anthropogenic
point-source pollution such as atmospheric nuclear weapons testing, nuclear
fuel reprocessing, nuclear power plant accidents, and industrial
applications (Akata et al., 2011; Matsumoto et al., 2013; Tadros et al.,
2014). The high bomb-tritium contribution compared to the very low tritium
in current precipitation is expected to cause a long-lasting ambiguity in
the groundwater reservoirs for the Northern Hemisphere (Stewart et al.,
2012), especially for Japan with its low tritium concentrations due to
mainly low-tritium maritime precipitation but large contributions of bomb
tritium from atmospheric nuclear weapons testing. This ambiguity can be
resolved with time series sampling, especially for water younger than 20 years
due to the still-remaining steep gradient in the tritium output. Both the
past record of tritium concentration in precipitation and tritium
measurements in river water are required for application of tritium for
understanding river–aquifer dynamics in river basins of Japan and other
countries.
In this study, we explore the use of tritium to characterise groundwater
dynamics for the specific tritium conditions of Japan, with large
contributions of bomb tritium from the continent, but low natural tritium
concentrations from low-tritium maritime precipitation. A total of 12 headwater
catchments in Hokkaido were selected to test the methodology of subsurface
volume characterisation from estimated groundwater transit times (Małoszewski
and Zuber, 1982) using high-precision tritium analyses in Hokkaido
river water. Firstly, we examine tritium data from the global network of
isotopes in precipitation (GNIP) to determine the continuous time series of
tritium in precipitation for the Tokyo area in Japan. This time series is
scaled for Hokkaido, Japan, using inferred information about local tritium
in young infiltrating water. Secondly, river water samples from Hokkaido
were collected in the headwater catchments during surveys in June, July, and
October 2014, and analysed at the GNS Science low-level tritium laboratory in
New Zealand. Then, the estimated Hokkaido tritium record was utilised with
the river water measurements to determine groundwater transit times using
the convolution integral with the exponential–piston flow model (EPM).
Finally, the mean transit times (MTTs) are utilised with river baseflow
discharge to estimate the subsurface storage volumes of the selected
catchments. In addition, we discuss the suitability for tritium dating of
headwater catchments in Hokkaido and other Japanese river basins for the
past, present, and future from these MTTs, and suggest the
requirements for future tritium monitoring.
Approach
Subsurface water volumes are estimated by multiplying baseflow river
discharges by groundwater transit times simulated using the convolution
integral with tritium. The approach is demonstrated in a schematic diagram
of a river catchment that drains subsurface groundwater storage by a river
network (Fig. 1a). Precipitation with known tritium concentrations
infiltrates into the subsurface and recharges the subsurface reservoir that
is drained by the stream network. River water samples have mixtures of quick
runoff and groundwater flow with different travel times and hence tritium
concentrations. Baseflow dominates river discharge during dry periods when a
river water sample represents only a mixture of groundwater with different
tritium concentrations. Using the convolution integral, we can estimate
groundwater transit times by inputting the long-term record of tritium in
precipitation and comparing the output with the tritium in the river water
at baseflow. The time-dependent tritium concentration Cout(t) (TU), at
the time of sampling t, is defined at the groundwater discharge point such as
a river, stream, or spring by the convolution integral (Małoszewski and
Zuber, 1982):
Conceptual diagram of the tritium cycle in a river and subsurface
groundwater storage (a) as the tritium input in precipitation is transformed
to the tritium output in river water by passing through the subsurface.
These complex dynamics are represented by the exponential mixing model (EMM)
for the unconfined aquifer (b) and the EPM for
the partially confined aquifer (c).
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Coutt=∫-∞tCinτe-λ(t-τ)gt-τdτ,
where Cin(t)(TU) is the input tritium concentration, λ
(yr-1) is the tritium decay of 0.056262 (yr-1), and g(t) (-) is
the weighting (or system response) function that is a simplified
representation of the complex groundwater pathways (see the cross-sectional
diagram of the unconfined aquifer in Fig. 1b and the partially confined
aquifer in Fig. 1c, Małoszewski and Zuber, 1982). The unconfined aquifer
(Fig. 1b) is recharged over the entire length of the aquifer and is
described by the exponential mixing model (EMM), which has only one fitting
parameter (MTT). In the partially confined aquifer, the
confined portion that does not receive recharge is represented by the piston
flow model while the unconfined part of the aquifer is described by the EMM
resulting in the EPM; see Fig. 1c. The
weighting function of the EPM is defined by Małoszewski and Zuber (1982):
gt-τ=1Tfexp(-t-τTf+1f-1)fort≥T(1-f)gt-τ=0fort<T(1-f),
where T (years) is the MTT of groundwater, and f (-) is the
ratio of the volume of the exponential component to the total volume of the
aquifer that is equal to 1 for the EMM and is close to 0 for almost piston
flow. The convolution integral was evaluated using TracerLPM (Jurgens et
al., 2012) that uses the EPM ratio, which is defined as n=1/f-1. The
mobile groundwater volume of the subsurface reservoir, Vt (m3),
at time of sampling t at baseflow (Małoszewski and Zuber,
1982) is
Vt=Qbt⋅T=Ath¯(t),
where Qbt(m3 yr-1) is the baseflow river
discharge, A(t)(m2) is the area of subsurface groundwater storage,
h¯(t) (m) is the average saturated groundwater thickness, which can be
found as volume, Vt, divided by the subsurface storage
area, A(t). The baseflow discharge can be estimated using a baseflow
separation method such as one introduced by Stewart (2015):
Qbt=Qbt-1+k+fc⋅(Qt-Qt-1)forQ(t)>Qb(t-1)+kQbt=Qt,
where Qb(t-1)(m3 s-1) is the baseflow at time t-1, fc(-)
is the constant fraction of the increase or decrease of the river discharge
during an event, k(m3 s-1 h-1) is the slope of the dividing
line, and Q(t)(m3 s-1) and Q(t-1)(m3 s-1) are river
discharges at time t and t-1, respectively. Using this estimated groundwater
volume as initial condition we can estimate changes of the subsurface
groundwater storage including low as well as high recharge conditions:
[Vt+1-Vt]/Δt=Rt-Qbt,
where V(t+1)(m3) and V(t)(m3) is the groundwater volume of
subsurface storage at time t+1 and t, Δt(s-1) is the time
interval, and R(m3 s-1) is the groundwater recharge. From Eq. (5),
the groundwater storage is depleted by river network drainage when R < Qb
during periods of little or no groundwater recharge and is
replenished during periods when R > Qb.
Characteristics of 12 investigated headwater catchments in Hokkaido,
Japan.
Sampling point
Mean flow,
Drainage
Mean
Elevation, m a.s.l.
Surface and subsurface geology
Name of
Volume,
Drainage
ID
Location
m3 s-1
area, km2
density, km-1
slope, –
Point
Mean
Highest
dam
km3
area, km2
1
Otarunai
5.34
68
1.4
0.25
397
736
1278
Propylite, lavas
Jozankei
0.082
104
(augite hypersthene andesite)
2
Takinosawa
0.76
14
1.0
0.31
398
696
1061
Propylite, lavas (augite hypersthene
andesite), intrusive rocks
3
Izariirisawa
3.30
42
1.2
0.26
496
905
1304
Lavas (augite hypersthene
Hoheikyo
0.043
159
andesite), propylite, quartz
4
Honryujoryu
2.93
65
1.4
0.19
484
788
1211
Lavas (hypersthene andesite),
propylite, quartz
5
Kouryu
2.43
40
1.6
0.25
187
567
1297
Lavas (augite hypersthene
Izarigawa
0.015
113
andesite), propylite, shale
6
Hakusen
1.95
42
1.5
0.20
202
506
1176
Lavas (augite hypersthene
andesite), propylite, sandstone
7
Okukatsura
2.72
56
1.4
0.24
187
419
1012
Sandy siltstone,
Katsurazawa
0.093
299
siltstone, sandstone
8
Ikutora
22.59
377
1.6
0.19
353
706
1865
Rhyolitic welded tuff, lavas,
Kanayama
0.151
470
metamorphic and igneous rocks
9
Rubeshinai
1.49
45
1.3
0.27
808
1034
1364
Lavas, sandstone and
Taisetsu
0.093
234
slate, conglomerate
10
Ishikaridaira
9.21
113
1.5
0.26
831
1342
2163
Lavas (hypersthene
augite andesite), slate
11
Piukenai
2.66
60
1.4
0.21
431
1252
2247
Lavas (hornblende hypersthene
Chubetsu
0.066
292
augite andesite), welded tuff
12
Tougeshita
2.34
49
2.5
0.16
22
127
712
Sandstone and sandy alternation
Rumoi
0.023
42
of sandstone and mudstone
Study area of the Hokkaido island
Climatic conditions
The 12 headwater catchments investigated in this study are located in
the western and central parts of Hokkaido island (Fig. 2). Hokkaido island
is one of four main Japanese islands with most of its population centred in
Sapporo city and is surrounded by the Sea of Japan in the west, the Sea of
Okhotsk in the north, and the Pacific Ocean in the east (Fig. 2). It has the
cool temperate climate of the Köppen climate classification due to its
location between the northern limit of the temperate climate and the
southern limit of the cool temperate climate (JMA, 2016). Hokkaido weather
patterns vary across the island with 30-year annual average precipitation in
the following cities: 1.04 m in Muroran (southwest), 1.11 m in Sapporo
(west), 1.13 m in Rumoi (northwest), 0.79 m in Abashiri (northeast), 1.04 m
in Kushiro (southeast), and 1.04 m in Asahikawa; see Fig. 2 (JMA, 2016). The
summer climate of Hokkaido island is dictated by cold polar and warm
northern Pacific air masses and does not have the distinct rainy season
typical of other locations in Japan. For example, Sapporo city with a 30-year
monthly average precipitation of 0.05 m in June, 0.08 m in July, and 0.12 m
in August, has drier summers than Tokyo with a 30-year monthly average
precipitation of 0.17 m in June, 0.15 m in July, and 0.17 m in August (JMA,
2016). In the summer season, the western climate zone of Hokkaido island has
fair weather for most of the period with daily mean temperature ranging from
15 to 20 ∘C. August is the hottest month of the year
and the daily maximum temperature can reach 30 ∘C at some inland
places in the upper Ishikari River (JMA, 2016).The weather becomes unsteady
and changeable from September due to the influence of typhoons and fronts,
which makes September the wettest month of the year with a 30-year monthly
average precipitation of 0.14 mm. Air temperature decreases gradually
towards the winter season while snowfall may occur in late September in the
mountains of the upper Ishikari River basin (Fig. 2). From late November on, the
daily average temperature stays mostly below 0 ∘C until the end of
March. Cold air masses flow eastward in winter bringing freezing
temperatures with heavy snowfalls to the central mountain ranges facing the
Sea of Japan and clear skies to areas fronting the Pacific side. For Sapporo
city, the 30-year monthly average temperature is -0.9 ∘C in December,
-3.6 ∘C in January, and -3.1 ∘C in February, resulting in the
duration of continuous snow cover from late November to early April.
February is the coldest month of the year with daily minimum temperature
reaching -20 ∘C in some inland places of the Ishikari River basin.
On the Pacific side, the daily average temperature is slightly higher,
resulting in shorter duration of continuous snow cover from December to
early March in Muroran and Kushiro. In Sapporo city, the winter
30-year monthly average precipitation of about 0.1 m in December–February
accumulates as snow on the ground, resulting in maximum snow depth of 0.05 m
in December, 0.08 m in January, and 0.1 m in February. A large volume of
snowfall results in thick snow cover on the ground, staying throughout much
of the winter season and preventing freezing of the soil (Iwata et al.,
2010). This implies that water with tritium may infiltrate into the
subsurface as groundwater recharge. From March, the 30-year daily average
temperature sometimes reaches above 0 ∘C in the plain area, and
instead of snow, rain starts to fall, initiating the snowmelt process, which
usually ends in early April in the low elevation areas and between May and
June in the mountainous areas.
Location of the Hokkaido study area with the sampling points in
the selected watersheds shown by circles.
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Topography
Out of 12 selected catchments, 11 are situated in the headwaters of
the Ishikawa River basin, which is the third largest Japanese river basin
with a drainage area of 14 330 km2, and 1 is situated in the Rumoi
River basin with an area of 270 km2 (Fig. 2). The mean annual discharge of
the Ishikari River is about 500 m3 s-1 at Sapporo city, located on
the western side of the Ishikari plain, which is the largest lowland plain
of Hokkaido island (Fig. 2), The topography of the Ishikari River basin
varies from the Ishikari plain at the seashore of the Sea of Okhotsk to
2290 m above sea level (m a.s.l.) at Mt. Asahi, which is located at the centre
and is the highest point of Hokkaido island (Ikeda et al., 1998). The ridge
of Mt. Ishikari and Mt. Mikuni extends to the northeast–southwest direction and
is a surface water divide between the Ishikari, Tokachi, and Tokoro rivers
flowing to the Sea of Japan, Pacific Ocean, and the Sea of Okhotsk, respectively
(Hasegawa et al., 2011). The selected headwater catchments are surrounded by
forested areas within catchments of existing dams, except Tougeshita (Table 1).
The 11 investigated catchments in the Ishikari River basin with areas
between 31 and 377 km2 are located at altitudes between 187 and
831 m a.s.l., and have stream drainage densities from 10 to 16 km-1 and mean
slopes between 0.19 and 0.31 (Table 1). Six of the investigated catchments
share catchment boundaries and three are located in close proximity to each
other (Fig. 2). For example, Otarunai (no. 1) and Takinosawa (no. 2) are
neighbouring catchments and are located upstream of Jozankei Dam on the
south side of the ridge that recharges the alluvial aquifer of Sapporo city.
The Izariirisawa (no. 3) and Honryujoryu (no. 4) are neighbouring
catchments located upstream of Hoheikyo Dam, and Kouryu (no. 5) and Hakusen
(no. 6) neighbouring catchments located upstream of Izarigawa Dam, situated
on the western and eastern side of the same surface water divide,
respectively. The Rubeshinai (no. 9) and Ishikaridaira (no. 10) stations in
the central part of Hokkaido island are situated on two tributaries that
drain headwaters of the Ishikari River to the downstream Taisetsu Dam lake
(Fig. 2). The Piukenai (no. 11) catchment is located upstream of Chubetsu
Dam and its tributary drains the eastern side of Mt. Asahi. Tougeshita
station is located at the lowest altitude of 22 m a.s.l. in the Rumoi River
basin; its catchment has the smallest slope of 0.16 and maximum elevation of
712 m a.s.l. (Fig. 2). The outlets of these selected headwater catchments, except
Tougeshita, are located upstream of existing dams and have operational
Ministry Land Infrastructure Transport and Tourism (MLIT) river gauging
stations (Table 1). These river gauges report historical and real-time
hourly river water levels as well as inferred historical river discharges
for some years (WIS, 2016). WIS (2016) provides historical and real-time
precipitation, and some precipitation gauges also report snow depth on the
terrain surface. WIS (2016) also provides hourly precipitation, reservoir
storage, and discharge at dam offices and estimated reservoir inflows.
Geology of the study area from AIST (2012), with zoomed-in views of the
12 study watersheds (a–f). Analysed tritium concentrations are
demonstrated in colour code.
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Surface and subsurface geology
The geology of Hokkaido is divided into the eastern region (the northeast
Japan arc), western region (the Kuril arc), and central region in the
arc–arc collision (Hasegawa et al., 2011). It has three distinct active
Quaternary volcanic fields (Fig. 3, AIST, 2012): the southwest area of the
Oshima belt, central area of the Hidaka belt (Taisetsu–Tokachi–Shikaribetsu),
and eastern area of the Nemuro belt (Akan–Shiretoko) (Hasegawa et al.,
2011). In the southwest area, the irregular arrangement of plains,
mountains, and volcanoes, such as Shikotsu and Toya with large calderas and
pyroclastic plateaus, is different from the central and eastern regions
(Hasegawa et al., 2011). The Ishikari plain is located to the west of the
Yubari Mountains and is situated on top of a deep alluvial fan, which occurs
in the low-lying areas (AIST, 2012). The alluvial aquifer of the Ishikari
plain has groundwater flow oriented towards the sea with recharge from the
surrounding elevated low permeability formations that are situated south of
Sapporo city (Dim et al., 2002; Sakata and Ikeda, 2013). Following the
arc–arc collision region, Hidaka, Yubari, and Teshio mountain ranges cross
Hokkaido island from south to north (Hasegawa et al., 2011). The Teshio
Mountains consist of Cretaceous–Tertiary folded formations while the Yubari
Mountains have Jurassic–Cretaceous formations (Sorachi group consisting of
greenstone with several inclusions of basaltic pyroclastic lava,
hyaloclastite and diabase, chert, micrite limestone, and sandstone with
felsic tuff) and serpentinite in and around the main ridge (Hasegawa et al.,
2011). For the central volcanic field, Sounkyo and Taisetsu volcanoes are
located in the Taisetsu mountain range, which is comprised of over 20
mountains including Mt. Asahi (Hasegawa et al., 2011). The Taisetsu volcano
is located a few kilometres northeast of Mt. Asahi and has produced Plinian
pumice-fall and pyroclastic flow deposits with a large eruption about 35 kyr
ago resulting in the Ohachidaira caldera.
Hourly flows at Otarunai station (a), Hoheikyo (b) and Izarigawa
(c) dams, Okukatsura station (d), Ikutora station (e), Rubeshinai station
(f), Piukenai station (g), and Tougeshita station (h). The sampling times of
June, July, and October are demonstrated by vertical lines.
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The surface and subsurface geology of the 12 selected catchments is
obtained from 1 : 50 000 geological maps of the Hokkaido area as shown in
Fig. 3a–f (AIST, 2012) and summarised in Table 1. Six catchments are located
in the eastern geologic region and share similar geologic features of
Tertiary propylite and Quaternary lava formations; see Fig. 3a and 3b (AIST,
2012). In Fig. 3a, the geology of the Otarunai (no. 1) and Takinosawa (no. 2)
catchments is dominated by Tertiary propylite of the Zenibako group overlaid by
andesite lavas (Fig. 3a) and is similar to the Hakusen (no. 6) catchment with
propylite of the Izarigawa group that is overlaid by Quaternary lavas and
Tertiary sandstones (Fig. 3b). The Quaternary volcanic lavas with augite
hypersthene andesite are dominant for the Izariirisawa (no. 3), Honryujoryu
(no. 4), and Kouryu (no. 5) catchments including propylite, quartz, and shale
for Kouryu (Fig. 3b). In Fig. 3c, the Okukatsura (no. 7) catchment is located
in the Cretaceous geologic area, which is quite different from the other
catchments, and includes sandy siltstone, siltstone, and sandstone. The
Ikutora (no. 8) catchment is described as rhyolitic welded tuff overlain by
Quaternary volcanic lavas and underlain by metamorphic and igneous rocks
(Fig. 3d). In Fig. 3e, a variety of geologic material is demonstrated in the
Taisetsu mountain range with dominant Quaternary lavas in three selected
catchments including slate and sandstone of the Hidaka group for Rubeshinai
(no. 9), pre-Tertiary slate for Ishikaridaira (no. 10), and the Sounkyo
welded tuff for Piukenai (no. 11). The Tougeshita (no. 12) river catchment in
the Rumoi River basin is dominated by Neogene mudstone, mudstone with
interbedded sandstone, and Quaternary alluvial deposits near river channels
(Fig. 3f). Exploratory bores drilled prior to construction of Chubetsu,
Jozankei, and Izarigawa dams showed aquifer materials ranging from highly
permeable shallow alluvial sand and gravel materials near river channels to
low-permeability underlying formations. The observed water levels
demonstrated groundwater heads below the terrain surface in these bores.
The constructed tritium time series in Tokyo precipitation and
Hokkaido groundwater recharge using wine data. The precipitation input curve
is constructed using tritium data of Kofu wine (1952–1960), IAEA Tokyo
station (1961–1975), Chiba NIRS (1976–2007), and Chiba JCAC (2008–present). The inset shows tritium time series from 1990 to present.
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Sampling at selected catchments and historical tritium
We selected one location for tritium sampling in June, July, and October 2014
in each of 12 Hokkaido headwater catchments (Figs. 2 and 3). Sampling
locations were visited in the June survey during the dry period and diurnal
fluctuation of river water levels was observed due to snow melt. Water
samples were collected only at six stations where river water levels and
discharges were below mean annual flows provided in Table 1. A total of 10 river water
samples were collected, excluding Okukatsura (no. 7) and Tougeshita (no. 12),
in July 2014, but only 1 river sample was analysed due to a large rainstorm
event started during the sampling trip. A sample of the rain was also
collected at the Kogen hot spring, situated at about 1200 m a.s.l. (Fig. 2).
In October, river water samples were collected by local dam officers at the
nine locations during cross-section measurements of river profiles when river
water levels and flows were below normal. We obtained water level data in all
stations except Kouryu (no. 5) and Hakusen (no. 6), which were washed away
during an October flood, and estimated the river discharges as demonstrated
for five stations in Fig. 4. For these two stations, we
investigated the Izarigawa Dam inflow, which was about 5 m3 s-1 on
23 October and was similar to the 6 m3 s-1 inflow of Hoheikyo
Dam, which is located in the neighbouring river catchment downstream of Izariirisawa and Honryujoryu stations (Fig. 2). The
Ikutora station had an erroneous record in February–March as indicated by an
arrow in Fig. 4e. For five stations, we conducted baseflow separation using
Eq. (4) with optimum values of fc and k to estimate baseflow
during sampling (Fig. 4): 5.64 m3 s-1 in June (no. 1a) and
3.66 m3 s-1 in October (no. 1b) at Otarunai;
0.48 m3 s-1 at Okukatsura (no. 7); 10.9 m3 s-1 in
June (no. 8a) and 9.47 m3 s-1 in October (no. 8b) at Ikutora;
1.32 m3 s-1 in June (no. 9a) and 0.53 m3 s-1 in
October (no. 9b) at Rubeshinai; and 0.27 m3 s-1 at Tougeshita
(no. 12) (Fig. 4). Samples collected during below-normal river discharges
were analysed for tritium, deuterium (D), and oxygen-18 (18O) by the
tritium laboratory in New Zealand (Morgenstern and Taylor, 2009). Water
chemistry of collected samples was analysed at the laboratory of Forest
Hydrology and Erosion Control Engineering, Graduate School of Agriculture and
Life Sciences, University of Tokyo, Japan, including silica (Si) with the
molybdenum yellow method. In a follow-up sampling trip, we collected one
river water sample near Otarunai station during winter baseflow conditions on
24 February 2016. Accumulated snow layers of up to 3 m in the winter makes
the access to rivers for sampling difficult in Hokkaido headwater catchments.
The long-term tritium record of Tokyo precipitation was constructed using
the tritium records of the International Atomic Energy Agency (IAEA)
stations and Japanese stations such as the National Institute of
Radiological Sciences (NIRS) and Japan Chemical Analysis Center (JCAC). The
GNIP Tokyo station, which was originally located at 36∘ N, has a monthly
tritium record of 18 years with samples being measured by the University of
California from 1961 to 1963 and by the IAEA Vienna Laboratory from 1964 to
1979 (IAEA/WMO, 2014). The JCAC, located in Sanno near Tokyo, has recorded
monthly tritium values in precipitation at the GNIP station in Japan from
April 2007 to the present. In addition, tritium concentrations in
precipitation were inferred from wine measurements in Kofu between 1952 and
1963 (Takahashi et al., 1969) and in Hokkaido from 1970 to 1994 (Ikeda et
al., 1998), and used to estimate pre- and post-bomb period tritium in
Japanese precipitation, respectively.
Results and discussion
Tritium time series in precipitation and recharge
Figure 5 shows the reference tritium input curve of Japanese precipitation
between 1951 and 2015 developed for the Tokyo area and the scaled reference
curve developed for Hokkaido recharge. The inferred annual tritium
concentrations derived from Kofu and Hokkaido wine are indicated by triangles
and circles, respectively. The Tokyo GNIP station values show a sharp decline
from 1000 to 100 TU between 1964 and 1968, a small increase from 1969 to
1973, and a steady decline up to the end of the record in 1979. A similar
increasing pattern between 1969 and 1973 is observed in the annual tritium
data inferred from Hokkaido wine. This tritium increase may be due to the
intense open-air nuclear testing conducted in the French Polynesia islands
(Tadros et al., 2014). The Tokyo record was then scaled by a factor 2.1 to
account for the higher tritium concentrations at the higher latitude location
(black curve) of Hokkaido. Two pronounced spikes in rain and Hokkaido wine
suggest that the tritium record from wine is delayed by approximately 1
year; this can be attributed to a time delay of the recharge of shallow young
groundwater. Therefore, the tritium input was shifted and decay was corrected by
1 year. The resulting input curve aligns with the Kofu wine record and
overlaps the Tokyo and NIRS tritium records; see Fig. 5. From the year 2007,
monthly tritium values in precipitation measured by JCAC demonstrate a
declining trend with a small tritium spike in March 2011 due to the Fukushima
accident tritium release (Matsumoto et al., 2013). This indicates that the
JCAC record of the Tokyo area is relatively unimpacted by local tritium
sources at present and may be used as the master record for scaling to other
Japanese locations with local data as demonstrated in our approach for the
Hokkaido area.
Tritium and stable isotope results
The tritium and stable isotope (D and 18O) results as well as water
chemistry analysis of Hokkaido water samples are summarised in Table 2. The
tritium values in June ranged between 4.07 (± 0.07) TU at Tougeshita
(no. 12) and 5.29 (± 0.09) TU at Okukatsura (no. 7); see locations of
sampling points in Fig. 3. Otarunai (no. 1a) had a tritium concentration of
4.26 (± 0.07) TU similar to Piukenai (no. 11) with 4.37 (± 0.07)
TU and Ikutora (no. 8a) with 4.66 (± 0.07) TU. Rubeshinai (no. 9a)
had a tritium concentration of 4.91 (± 0.07) TU, similar to Okukatsura
(no. 7). The high tritium values of Okukatsura and Rubeshinai may be
explained by contributions of snowmelt water during the time of sampling at
baseflow (Fig. 4). In June, we did not analyse the tritium concentrations of
the snow pack because we have estimated the tritium concentration of the
infiltrating groundwater from the long-term records of rain data in Japan,
and from tritium in Hokkaido wine which utilised young infiltrated groundwater
during growth. However, we emphasise that tritium measurements in
precipitation are essential for local tritium studies. These tritium
precipitation measurements provide the site-specific information for scaling
of the established input function to nearby locations. For the Hokkaido area,
we have started collection of precipitation and snow core samples for tritium
analysis from the January–April 2016 winter season at several sites of the
Ishikari River basin. This information will be used to fine-tune the local
tritium input within the various Hokkaido subcatchments. Construction and
scaling of the long-term time series tritium input function using local data
will be included in a separate publication on the tritium input in Japanese
precipitation.
Tritium, stable isotope, and chemistry results for the Hokkaido
river and rain water samples.
ID
Location
Date
Time
Flow,
3H,
±σ,
δD,
±δD,
δ18O,
±δ18O,
Cl-,
NO3-N,
SO4-S,
Na+,
NH4-N,
K+,
Mg2+,
Ca2+,
Si,
m3 s-1
TU
TU
‰
‰
‰
‰
mg L-1
mg L-1
mg L-1
mg L-1
mg L-1
mg L-1
mg L-1
mg L-1
mg L-1
1a
Otarunai
2014/06/04
10:55
8.26
4.257
0.070
-78.78
2.27
-12.63
0.48
4.36
0.23
1.37
3.67
0.00
0.45
1.18
3.50
6.16
1b
Otarunai
2014/10/24
10:20
3.82
4.184
0.063
-76.75
1.6
-12.24
0.44
5.46
0.25
2.16
4.63
0.00
0.54
1.80
4.76
7.12
2
Takinosawa
2014/10/24
11:00
0.53
4.114
0.062
-77.50
1.45
-12.13
0.45
4.61
0.28
5.02
4.91
0.00
0.56
3.04
8.34
7.09
3
Izariirisawa
2014/10/23
-
0.71
3.825
0.070
-71.41
1.26
-11.43
0.45
3.30
0.25
4.81
4.03
0.00
0.52
2.23
8.44
7.71
4
Honryujoryu
2014/10/23
-
2.31
3.926
0.061
-85.67
1.12
-13.40
0.43
3.91
0.17
3.49
3.23
0.00
0.45
1.51
5.37
6.53
5
Kouryu
2014/10/22
13:24
-
3.748
0.065
-64.65
1.56
-10.12
0.40
4.13
0.24
5.79
4.91
0.00
1.12
2.32
9.62
13.52
6
Hakusen
2014/10/22
15:45
-
4.101
0.064
-66.94
1.91
-10.47
0.44
4.58
0.24
3.76
4.94
0.00
1.21
2.04
5.74
15.14
7
Okukatsura
2014/06/04
17:30
0.48
5.290
0.086
-91.87
0.84
-14.01
0.43
3.25
0.00
6.09
6.74
0.00
1.23
1.74
15.79
3.34
8a
Ikutora
2014/06/06
14:30
12.35
4.659
0.077
-85.37
0.84
-12.64
0.40
1.86
0.25
2.36
3.21
0.00
1.04
1.13
5.39
9.27
8b
Ikutora
2014/10/03
11:00
10.99
4.449
0.065
-80.97
1.29
-12.17
0.47
2.13
0.30
2.47
3.63
0.00
1.18
1.36
6.12
9.46
9a
Rubeshinai
2014/06/05
17:03
1.66
4.911
0.072
-97.15
1.66
-13.71
0.43
1.88
0.00
1.96
3.15
0.00
0.68
1.66
7.14
7.34
9b
Rubeshinai
2014/10/02
14:20
0.53
4.816
0.071
-94.91
0.82
-13.73
0.41
2.23
0.18
2.11
3.58
0.00
0.74
2.01
8.57
8.13
10a
Ishikaridaira
2014/07/26
12:45
6.42
5.059
0.090
-94.80
1.14
-13.59
0.40
1.93
0.24
2.09
3.69
0.00
0.74
1.92
8.33
8.62
10b
Ishikaridaira
2014/10/02
15:00
3.92
4.849
0.068
-94.59
1.32
-14.01
0.47
1.40
0.15
1.72
3.67
0.00
1.36
1.30
5.06
12.90
11
Piukenai
2014/06/05
12:30
4.79
4.366
0.067
-73.06
1.11
-11.66
0.40
13.59
0.00
13.43
8.36
0.00
2.27
6.22
13.07
12.10
12
Tougeshita
2014/06/05
9:30
0.31
4.065
0.066
-68.01
1.55
-11.37
0.44
15.12
0.00
2.90
13.12
0.00
1.23
3.91
8.00
5.46
13
Kogen*
2014/07/26
14:00
–
9.159
0.140
-76.99
1.76
-10.28
0.42
0.13
0.18
0.22
0.02
0.11
0.00
0.03
0.23
0.09
* indicates rain water sample.
Only one river water sample was analysed in July due to a large rain event
that occurred in the Ishikari River basin during the sampling, with a
tritium value of 5.06 (± 0.09) TU. A rain sample was collected at the Kogen
hot spring area, which is upstream of Ishikaridaira (no. 10) station, on 26 July
2014 and had a tritium concentration of 9.16 (± 0.14) TU. In
October, the river water tritium concentrations were slightly lower than the
summer values. The Otarunai (no. 1b),
Rubeshinai (no. 9b), and Ikutora (no. 8b) had tritium concentrations of 4.18
(± 0.06) TU, 4.82 (± 0.07) TU, and 4.45 (± 0.07) TU,
respectively. For the Takinosawa (no. 2), the tritium concentration was 4.11
(± 0.06) TU and is similar to that of Otarunai (no. 1b), which is
located in the neighbouring river catchment with similar hydrogeology. This
result suggests that the two river catchments have similar groundwater
dynamics and could be draining one subsurface groundwater storage. This is
also indicated by similarity of silica concentrations (Table 2), while higher
concentrations of calcium and magnesium of Takinosawa (no. 2) are an
indication of different geological materials such as non-alkaline felsic
volcanic rocks (see Fig. 3). A similar situation could be occurring in the
other neighbouring river catchments such as Izariirisawa (no. 3) and
Honryujoryu (no. 4), which had similar tritium concentrations of 3.83
(± 0.07) TU and 3.93 (± 0.06) TU, respectively. However,
neighbouring river catchments Kouryu (no. 5) and Hakusen (no. 6) may have
different groundwater dynamics, as indicated by tritium as well as calcium and
sulphate concentrations (Table 2). The lowest tritium concentration of 3.75
(± 0.07) TU was analysed at Kouryu (no. 5), which is similar to
Izariirisawa (no. 3) and Honryujoryu (no. 4), while Hakusen (no. 6) tritium
of 4.10 (± 0.06) TU is similar to Otarunai (no. 1) and Takinosawa
(no. 2) results. For other samples, the Ishikaridaira (no. 10b), with a
tritium value of 4.85 (± 0.07) TU, is located next to Rubeshinai
(no. 9b), with 4.82 (± 0.07) TU, while having slightly different calcium
and silica concentrations.
Relationships between δD and tritium (a), and
δD and δ18O (b) in Hokkaido river waters (diamonds)
and rain (open circle). Labels refer to IDs in Table 2.
![]()
Transit time distributions of the exponential (70 %) piston flow
(30 %) model (E70 %PM) for obtained MTT solutions (a) and
relative error between simulated and analysed tritium at selected locations
for MTTs between 1 and 100 years (b).
![]()
The relationship between tritium and δD is shown in Fig. 6a, and
for δD and δ18O in Fig. 6b. Figure 6b shows that most of the
river stable isotope data plot near a local meteoric water line with an
intercept of 19, while the one rain sample plots closer to the global
meteoric water line. No significant relationship was observed between
analysed tritium and water chemistry in Table 2. Samples collected at low
elevations (no. 12, no. 5, and no. 6) had the lowest concentrations of
tritium, and the most positive δD and δ18O values. Although
the Izariirisawa (no. 3) and Honryujoryu (no. 4) samples were collected at
about
490 m a.s.l. and had similar values of tritium, the δD and
δ18O values of Honryujoryu are much lower than those of
Izariirisawa. Otarunai (no. 1b) and Takinosawa (no. 2) samples collected in
the same area at about 400 m a.s.l. had similar values of tritium,
δD, and δ18O to the Piukenai (no. 11) June sample. This discrepancy
between June and October values may be attributed to the snowmelt water
contribution that was occurring during the June sampling trip. The Rubeshinai
(no. 9a–b) and Ishikaridaira (no. 10a–b) samples, which were collected at
about 810 m a.s.l., have similar tritium, δD, and δ18O
values to the Okukatsura (no. 7) sample collected at 187 m a.s.l. These
results indicate that the tritium values in coastal rain may be diluted by
freshly evaporated ocean water. Therefore, the tritium input of the coastal
catchments was corrected by 2.5 % (equivalent of MTT of ca. 0.5 years)
towards lower values, and for the catchments with a more negative stable
isotope signature by 2.5 % towards higher values.
MTTs estimated using E70 %PM described in the
text. One, two, or three possible MTTs are obtained using relative error (RE)
between analysed and simulated tritium.
Sample information
Analysed
Scaling
1st solution
2nd solution
3rd solution
ID
Location
Date
3H, TU
±σ, TU
factor
MTT, years
RE, –
MTT, years
RE, –
MTT, years
RE, –
1a
Otarunai
2014/06/04
4.257
0.070
2.10
14
0.003
–
–
–
–
1b
Otarunai
2014/10/24
4.184
0.063
2.10
14
0.003
–
–
–
–
2
Takinosawa
2014/10/24
4.114
0.062
2.10
13
0.003
–
–
–
–
3
Izariirisawa
2014/10/23
3.825
0.070
2.05
13
0.048
–
–
–
–
4
Honryujoryu
2014/10/23
3.926
0.061
2.10
13
0.046
–
–
–
–
5
Kouryu
2014/10/22
3.748
0.065
2.05
13
0.043
–
–
–
–
6
Hakusen
2014/10/22
4.101
0.064
2.05
14
0.000
–
–
–
–
7
Okukatsura
2014/06/04
5.290
0.086
2.15
1
0.008
4
0.041
23
0.002
8a
Ikutora
2014/06/06
4.659
0.077
2.10
7
0.013
19
0.010
–
–
8b
Ikutora
2014/10/03
4.449
0.065
2.10
8
0.002
17
0.009
–
–
9a
Rubeshinai
2014/06/05
4.911
0.072
2.15
7
0.016
20
0.004
–
–
9b
Rubeshinai
2014/10/02
4.816
0.071
2.15
7
0.001
20
0.003
–
–
10a
Ishikaridaira
2014/07/26
5.059
0.090
2.15
6
0.019
22
0.007
–
–
10b
Ishikaridaira
2014/10/02
4.849
0.068
2.15
5
0.019
22
0.004
–
–
11
Piukenai
2014/06/05
4.366
0.067
2.10
10
0.009
16
0.009
–
–
12
Tougeshita
2014/06/05
4.065
0.066
2.05
11
0.003
13
0.009
–
–
Simulated groundwater transit times
Table 3 summarises the estimated groundwater MTTs from measured tritium river
concentrations using several scaling factors and the exponential (70 %)
piston flow (30 %) model (E70 %PM) (Fig. 7). In Table 3, MTTs are
selected from the MTT range between 1 and 100 years with relevant scaling
factors of tritium input, which was done by automated simulation with a
developed Visual Basic module in TracerLPM (Jurgens et al., 2012). The
scaling factor of the Hokkaido tritium curve was re-adjusted using the stable
isotope composition of each sample and ranges between 2.05 and 2.15
(Table 3). In regards to the choice of the EPM, we selected this model based
on the hydrogeological similarity of Hokkaido to New Zealand settings from
Morgenstern et al. (2010). Morgenstern et al. (2010) found that the piston
flow component is greater than 20 % purely due to flow through the
unsaturated zone in the headwater catchment. An EPM with 30 %
contribution of piston flow within the total flow volume therefore seems
appropriate in this study. In Fig. 7a, we demonstrate several groundwater
transit time distributions for estimated MTTs with E70 %PM in Table 3.
Each cumulative distribution function describes the proportion of water with
transit times up to the specific transit time and MTTs are at about 0.63 of
total flow volume (Fig. 7a). The horizontal initial parts of these transit
time distributions on the x axis represent the 30 % piston flow
sections and range from 0.3 to 7 years; see Fig. 7a. In Table 3, the good
correspondence between analysed and simulated tritium values is demonstrated
by small relative error values, which are equivalent to 1σ (TU) error
of tritium analysis with values of about 1.5 %. These relative errors are
plotted between 1 and 100 years for selected unique and non-unique cases in
Fig. 7b. The serrated pattern of relative errors is transferred from
simulated tritium concentrations and is due to the monthly time step in
tritium input of the TracerLPM. The smallest relative errors of Otarunai (no.
1a and no. 1b) and Kouryu (no. 5) demonstrate one MTT solution estimated with
E70 %PM (Fig. 7b). The similar pattern is reported for six study
catchments in Table 3. Despite either the youngest MTTs of ca. 0.1 years or
the oldest MTTs above 100 years being excluded as improbable, we find several
equally good fits for some stations indicating that the MTT solution is
non-unique (i.e. water with different MTTs can have similar tritium
concentrations) (Table 3). For example, we have two solutions for groundwater
transit times at Ikutora (no. 8a–b), Rubeshinai (no. 9a–b), Ishikaridaira
(no. 10a–b), Piukenai (no. 11), and Tougeshita (no. 12) while Okukatsura
(no. 7) has three solutions: very young (e.g. Okukatsura MTT of 1 year),
young (e.g. Okukatsura MTT of 4 years), and old (MTT of 23 years). This is
due to the interference by the bomb tritium that is still present in Hokkaido
groundwater and will take a number of years to decay and flush out. Having
tritium-series measurements with 3-year intervals will enable us to choose
either the young or old MTT value and therefore to reduce the ambiguity of
the simulated transit times.
Simulated tritium concentrations in Hokkaido river water for the
years 2000, 2010, 2015, 2020, 2025, and 2030 vs. groundwater MTT. A factor
of 2.0 was used to scale the Tokyo input with E70%PM.
The inset shows 2015 tritium concentrations with a unique MTT indicated in
blue and non-unique MTTs (two possibilities) indicated in purple.
![]()
To illustrate this point, historical and future tritium concentrations at
baseflow are demonstrated for the full range of MTTs in Fig. 8, where tritium
concentrations at baseflow are simulated with E70 %PM. This model used
the Hokkaido recharge from 1950 to 2015 established here and forecasted
monthly long-term average tritium values from 2015 to 2030 with the
assumption of stable tritium concentrations in rain similar to those of the
last 5 years. From the simulated tritium concentrations, tritium in river
water will reach levels similar to those analysed in the Southern Hemisphere
in the next decade as also demonstrated by Stewart and Morgenstern (2016).
This implies that one tritium river water sample may then be sufficient to
estimate unique groundwater MTTs and therefore robust storage volumes for
most of the Japanese catchments, if assumptions about transit time
distributions are made. Despite the present ambiguity, we can attempt to
utilise river water chemistry in Table 2 for selecting young or old MTT and
estimating groundwater storage volume. For these locations with non-unique
MTTs, we evaluate the change of chemical composition between two sampling
dates and assume an increase of silica and other ion concentrations
with MTT (Morgenstern et al., 2010, 2015). For the locations with one
collected sample, the lowest silica concentrations are observed for
Okukatsura (no. 7) with 3.34 mg L-1 and Tougeshita (no. 12) with
5.46 mg L-1, compared to the other collected samples of Hokkaido study
catchments, indicating higher likelihood of the younger MTTs or differences
in dissolution rates. From this assumption, we select MTTs of 1 and 4 years
for Okukatsura (no. 7) and 11 years for Tougeshita (no. 12). Following the
same pattern, October samples of Ikutora (no. 8b) and Rubeshinai (no. 9b)
have slightly higher ion concentrations, including silica, compared to June
samples while demonstrating decrease in analysed tritium concentrations. This
may indicate older MTTs in October leading to MTTs of 17 years for Ikutora
(no. 8b) and 20 years for Rubeshinai (no. 9b).
In our tritium interpretation, we also found only one MTT solution of
groundwater transit times for seven river samples in six catchments
(Table 3): Otarunai (no. 1a–b), Takinosawa (no. 2), Izariirisawa (no. 3),
Honryujoryu (no. 4), Kouryu (no. 5), and Hakusen (no. 6). To validate this
finding, we sampled river water near Otarunai station on 24 February 2016, to
investigate tritium concentrations in winter baseflow conditions in the
Sapporo area of Hokkaido. If this river sample gives the same MTTs, it will
confirm that one tritium sample is sufficient to estimate unique MTTs in these
and possibly other Japanese river catchments. Moreover, the result of similar
tritium concentrations and MTTs for the neighbouring river catchments
indicates similar groundwater flow and drainage patterns, which are
controlled by hydrogeological settings. These six river catchments are
situated in similar Quaternary lavas and Tertiary propylite formations
(Fig. 3) while having different river catchment features, such as mean annual
flows, drainage areas, terrain slopes, etc. (Table 1). The chemical
concentrations may also support this hypothesis of similar groundwater flow
and drainage patterns in these catchments. For example, Mg/Ca ion ratio
estimated from meq L-1 concentrations is about 0.6 for Otarunai
(no. 1), Takinosawa (no. 2), and Hakusen (no. 6) river catchments and is about
0.43 for Izariirisawa (no. 3), Honryujoryu (no. 4), and Kouryu (no. 5) river
catchments. It may also be possible that the neighbouring river catchments
have only one subsurface groundwater storage supporting river baseflows at
different river catchments. It is known that the groundwater systems can have
different boundaries than river catchments and one subsurface groundwater
storage can be drained by neighbouring river catchments (Grannemann et al.,
2000; Gusyev et al., 2014). In that case, a subsurface groundwater storage
shared by the Otarunai (no. 1) and Takinosawa (no. 2) river catchments
contributes inflow to the Jozankei Dam and regional groundwater recharge to
the Sapporo alluvial aquifer (Dim et al., 2002), indicating important implications
for water availability for dam inflows and groundwater abstraction at Sapporo
city (Sakata and Ikeda, 2013). However, a detailed hydrogeologic study is
required to further investigate this hypothesis.
Vulnerability of stable isotope-based MTT to aggregation error has been
recently discussed by Kirchner (2016a, b). Kirchner (2016a) demonstrated the
MTT aggregation error of 18O using hypothetical transit times at two
neighbouring headwater catchments and indicated a need for similar evaluation
for tritium-inferred ages. It seems that tritium and MTT data of our
neighbouring catchments in Hokkaido can be used to evaluate the MTT
aggregation error between real and apparent MTTs. For this evaluation, we
select Otarunai (no. 1) with an area of 68 km2 and Takinosawa (no. 2)
with an area of 14 km2, which are situated in similar hydrogeological
settings in neighbouring catchments (Figs. 2 and 3). On 24 October,
Otarunai (no. 1b) had tritium of 4.18 TU at baseflow of
3.66 m3 s-1 and Takinosawa (no. 2) had 4.11 TU at
0.53 m3 s-1. The simulated MTTs with E70 %PM are 14 and
13 years for Otarunai (no. 1b) and Takinosawa (no. 2), respectively
(Table 3). The combined discharge for these two locations is
4.19 m3 s-1, leading to the tritium concentration of 4.12 TU and
MTT of 13.9 years. From this tritium concentration of 4.12 TU, we use
E70 %PM with the same scaling factor of 2.1 to simulate an apparent MTT
of 13.6 years, which is very close to the combined MTT of 13.9 years. As a
result, the baseflow MTT of these two catchments has very low MTT aggregation
error (about 2 %) demonstrating a good match between the two MTTs. It is
also important to note that the apparent MTT of 13.6 years remains unique
(i.e. it is the only best-fit solution in the range of MTTs between 1 and
100 years). This point is illustrated in the inset in Fig. 8 with the
unique MTT solution shown in blue and non-unique solutions shown in purple (the
detailed discussion is provided by Stewart and Morgenstern, 2016). From this
example, we find that neighbouring catchments with topographic heterogeneity
have low MTT aggregation error under the following conditions: (1) similar
MTTs and tritium concentrations at baseflow; (2) unique MTT solutions (no
interference of bomb-peak tritium), and (3) similar transit time
distributions of groundwater flow (due to hydrogeologic similarity). Once
these conditions are violated, the MTT aggregation error of neighbouring
catchments may be significant. This preliminary finding should be further
investigated for other tritium cases in light of the discussion by
Kirchner (2016a, b).
Simulated groundwater storage and saturated thickness
We estimate ranges of groundwater storage volumes between 0.013 and
5.07 km3 and saturated water thicknesses between 0.2 and 24 m by using
Eq. (3). These values of saturated water thickness are smaller than the
recent estimates of groundwater storage thickness of 180 m by Gleeson et
al. (2016) and much larger than the 0.055 m saturated water thickness of
young (MTT of 0.2 years) terrestrial water identified by Jasechko et
al. (2016). For the Otarunai (no. 1) and Takinosawa (no. 2), we used MTTs of
14 and 13 years with baseflow values of 3.66 and 0.53 m3 s-1 to
find groundwater storages of 1.62 and 0.22 km3, respectively. Dividing
these two volumes by the respective drainage areas of 68 and 14 km2
(Table 1) we find saturated thicknesses of water of 23.8 m for Otarunai and
15.7 m for Takinosawa. For nearby catchments, the saturated water thickness
of the Izariirisawa (no. 3) with a catchment area of 42 km2 is 6.9 m
(estimated from 0.29 km3 storage based on MTT of 13 years and
0.71 m3 s-1 baseflow). The Honryujoryu (no. 4) has 14.6 m
saturated water thickness (estimated from 0.95 km3 storage obtained at
2.3 m3 s-1 baseflow and catchment area of 65 km2). The
Ikutora (no. 8) has the largest drainage area of 377 km2 and saturated
water thickness of 13.4 m (estimated from 5.07 km3 storage using MTT
of 17 years at 9.5 m3 s-1 baseflow). The Rubeshinai (no. 9) has
7.3 m saturated thickness of water (estimated from 0.33 km3 storage
using MTT of 20 years at 0.53 m3 s-1 baseflow and catchment area
of 45 km2), while the saturated water thickness of Ishikaridaira
(no. 10) is about 24 m (estimated from 2.72 km3 storage obtained using
MTT of 22 years at 3.92 m3 s-1 baseflow and catchment area of
113 km2). The Tougeshita (no. 12) has the saturated thickness of water
of 1.8 m (from catchment area of 49 km2 and 0.094 km3 of storage
with MTT of 11 years). From Eq. (3) with young MTTs, we estimate
groundwater volume of 0.013 km3 with MTT of 1 year and 0.052 km3
with MTT of 4 years for Okukatsura (no. 7) and find the saturated water
thickness of 0.2 and 0.9 m with the catchment area of 56 km2, and 0.013
and 0.052 km3 volumes, respectively.
We indicate the importance of groundwater storage characterisation with
tritium river water samples at baseflow by a comparison of stable isotopes
and tritium-simulated MTTs. Out of 16 tritium samples, only 3 samples have
MTTs below 5 years at baseflow while modelled MTTs of 12 samples range
between 6 and 23 years (Table 3). For these 12 samples, only tritium analysis
allows us to characterise groundwater storage with long transit times from
years to decades due to the limitation of 18O and 2H stable
isotopes for identifying MTTs older than 5 years (McGuire and McDonnell,
2006). This order-of-magnitude difference in sensitivity between the stable
isotope and the tritium methods will naturally result in the stable isotope
method being preferably applied to short transit time and low volume systems,
and the tritium method to long transit time and large volume systems.
Therefore, the difference in stable-isotope-derived and tritium-derived water
storages is driven by the difference in MTTs. In addition, the aggregation
error proposed by Kirchner (2016a, b) may cause stable-isotope-derived MTTs
to underestimate storage. It has been demonstrated that the use of stable
isotopes enables MTT simulation in the range of a few months up to about 5
years (McGuire and McDonnell, 2006) for groundwater storage volume estimates
(Małoszewski et al., 1992; Leopoldo et al., 1998; McGuire et al., 2002;
Jasechko et al., 2016). Leopoldo et al. (1998) simulated MTTs of about 0.4
years with 18O values in two Brazilian agricultural watersheds of
1.6 km2 and 3.3 km2 and obtained groundwater volume of
0.0001 km3 with 0.06 m saturated thickness of water for the Bufalos
watershed and 0.00037 km3 with 0.11 m saturated thickness of water for
the Paraiso watershed. In cases when simulated MTTs from stable isotopes and
tritium have similar values, the groundwater storage volumes do not differ
much. For example, Małoszewski et al. (1992) reported similar estimated
MTTs of about 4.1 years with 18O and tritium in the Wimbachtal watershed
of 33 km2 and computed subsurface water volume of about 0.22 km3
with 6.6 m of saturated thickness of water. MTTs obtained with stable
isotope and tritium tracers in many catchments have been summarised by
Stewart et al. (2010). Following Kirchner (2016a, b) the vulnerabilities of
tritium-based MTTs to aggregation error needs to be investigated further.
The change of Otarunai groundwater storage estimated using tritium
groundwater volume at baseflow. The groundwater storage is recharged by
precipitation and snowmelt and is drained by the baseflow component of river
discharge.
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From these findings, we suggest that the changes of subsurface groundwater
storage, which supplies the majority of baseflow especially during winter and
dry summer conditions in Hokkaido, need to be accounted for in the management
of water resources in our study catchments. The importance of the subsurface
groundwater storages is emphasized by comparing them with the normalized
storages of the five dams (i.e. water storage in the reservoir divided by the
corresponding catchment area) (Table 1). For these five dams, this average
saturated thickness of water ranges between 0.1 and 0.8 m and is much
smaller than storage in the study headwater catchments, which have saturated
thicknesses of water between 0.2 and 24 m. To demonstrate groundwater storage
changes, we simulate the hourly change of estimated groundwater volume at
Otarunai station from June 2014 to February 2016 (Fig. 9). The tritium
sampling times are shown by vertical lines; see Fig. 9. In this simple
approach, the lumped numerical model does not include any sophisticated
calculations such as energy balance, delay in recharge, soil types, etc., and
only simulates the changes of saturated groundwater storage that receives
recharge from infiltrated soil water and contributes to the baseflow
discharge. In our simulation, the groundwater storage is recharged by
20 % of precipitation and 80 % of snow melt water, which is estimated
from hourly snow depth using a snow water equivalent of 0.4, and is drained by
baseflow, which was estimated from hourly river discharge data (Fig. 4a). We
obtained these recharge rates from a range of field values reported by Iwata
et al. (2010) for the Tokachi site in Hokkaido. Iwata et al. (2010)
investigated water infiltration rates at 0.2 and 1.05 m soil depth from 2002
to 2006 and reported that the largest rates of soil water infiltration of
between 79 and 85 % occurred during the spring snow melt season compared
to the summer–fall water infiltration rates of 20–25 % in 2002. From Eq.
(5), with the estimated volume of about 1.62 km3, we find a groundwater
volume of 1.65 km3 and saturated water thickness of 24.3 m on 4 June 2014
using Eq. (3). This simulation demonstrates a decline of
groundwater volume by 0.03 km3 and of saturated water thickness by
0.5 m from June to October, while having some small spikes during periods of
high groundwater recharge in August and September 2014. From 24 October,
the groundwater volume declines over December–February reaching
the smallest volume of 1.59 km3, which is equivalent to saturated water
thickness of 23.4 m. Once the snowmelt season starts in mid-March 2015, the
accumulated snow layer of up to 3.1 m melts and snow melt water replenishes
groundwater storage until the end of snowmelt season; see Fig. 9. As a
result, the groundwater volume of subsurface storage equals 1.64 km3
and saturated water thickness 24.1 m on 14 May 2015. From June, the
subsurface storage is again drained by baseflow, resulting in 1.63 km3
of groundwater volume and 24.0 m of saturated water thickness on 4 June 2015.
This difference of groundwater volume between 4 June 2014 and
2015 is due to drier weather conditions in the year 2014 with annual
precipitation of 0.86 m compared to annual precipitation of 1.00 m in the year
2015. The groundwater volume continues to gradually decline due to drainage
by baseflow while receiving groundwater recharge from precipitation until
October 2015. Once the winter season starts, the groundwater storage is again
drained by winter season baseflow of about 1.8 m3s-1, reaching
1.58 km3 of groundwater volume and 23.2 m of saturated water thickness
on 24 February 2016 (Fig. 9). After the melting of snow starts in mid-March,
the snow melt water of accumulated snow layer recharges the subsurface
storage and the groundwater volume is again replenished (not shown). This
result indicates two important points: (1) the role of snow hydrology in
groundwater dynamics demonstrating the impact of a dry winter with little
snow on the drought conditions in Hokkaido, and (2) the large groundwater
volumes of subsurface storage in the Hokkaido headwater catchments
potentially available to maintain baseflow during prolonged droughts.
Concluding remarks
We demonstrated the application of tritium by estimating the groundwater mean
transit times (MTTs) and subsurface volumes in headwater catchments of
Hokkaido, Japan, from tritium data of river water and precipitation. The 16
river water samples in Hokkaido were collected in June, July, and October 2014
at 12 locations. These locations drain areas between 14 and 377 km2
and are all located upstream of MLIT dams, except Tougeshita station. The
collected water samples were analysed by the Tritium Laboratory, New Zealand,
and resulting tritium concentrations ranged between 4.07 TU (± 0.07)
and 5.29 TU (± 0.09) in June, and 3.75 TU (± 0.07) and 4.85 TU
(± 0.07) in October 2014. One river sample had 5.06 (± 0.09) TU
and one rain sample had 9.16 (± 0.14) TU in July 2014.
The tritium record in precipitation was reconstructed from GNIP stations for
the Tokyo area and scaled to the Hokkaido area using local data. To estimate
MTTs we applied the exponential (70 %) piston flow (30 %) model
(E70 %PM) to the reconstructed tritium record for Hokkaido and obtained
non-unique fits of very young, young, and old groundwater transit times due to
the interference by bomb-peak tritium that is still present in Japanese
waters. Having tritium-series measurements with 3-year intervals would enable
us to choose either the young or old MTT value and therefore to reduce the
ambiguity of the simulated transit times. Eventually, tritium in groundwater
will reach natural levels and one tritium river water sample will be
sufficient to estimate a robust groundwater storage volume as well as
saturated thickness of water in the subsurface. However, we also simulated
unique MTT values in six river catchments located near Sapporo city assuming
that the system response function (E70 %PM) describes catchment flow
conditions there. This finding led to two important conclusions: (1) one
tritium sample is sufficient to estimate MTT for most of our watersheds, and
(2) the similar tritium and MTTs of baseflow in adjacent river
catchments are controlled by hydrogeological settings resulting in similar
groundwater flow and drainage patterns. The unique MTT shown by some of the
river watersheds allows us to estimate unambiguous groundwater storage
volumes as demonstrated for the Otarunai catchment. For example, the
groundwater storage ranges between 0.013 and 5.07 km3, with saturated
water thickness from 0.2 and 24 m. Knowledge of groundwater storage volume
enables us to investigate changes of groundwater volumes with time and
provide useful information for the improvement of numerical models and water
resources management especially during droughts. As a result, the adopted
approach may be a cost-effective method of characterising groundwater transit
times and volumes of subsurface storage and could be used to improve
simulated groundwater dynamics by rainfall–runoff models in future studies.