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Tritium dating of dripwater from Villars Cave (SW-France)
P. Jean-Baptiste, D. Genty, E. Fourré, E. Régnier
To cite this version:
P. Jean-Baptiste, D. Genty, E. Fourré, E. Régnier. Tritium dating of dripwater from Villars Cave (SW- France). Applied Geochemistry, Elsevier, 2019, 107, pp.152-158. �10.1016/j.apgeochem.2019.06.005�.
�hal-02393604�
Tritium dating of dripwater from Villars Cave (SW-France)
1 2 3
P. Jean-Baptiste1, D. Genty1, E. Fourré1, E. Régnier1
4 5
1 Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, CEA- 6
Saclay, 91191 Gif-sur-Yvette, France.
7 8
Corresponding author : Philippe Jean-Baptiste (pjb@lsce.ipsl.fr) 9
10
Abstract.
11 12
The tritium content of rainwaters and dripwaters was monitored from 2008 to 2016 to 13
determine the age of the dripwaters in Villars Cave, SW-France. Dripwater was collected at 14
two stations, one in the upper gallery and the other in the lower gallery of the cave. Stable 15
isotopes have been monitored at these two stations since 1997.
16
The infiltration time of the rainwater between the surface and the cave galleries was 17
determined using a simple one-dimensional model of the infiltration of recharge waters. The 18
model was forced by monthly precipitation and the concentration of tritium in precipitation 19
reconstructed from the IAEA isotope network and our own measurements in rainwater 20
collected at the Villars site. The age of the dripwaters is 7.0±0.5 years for the upper gallery 21
(∼10 m deep) and 11.1±0.5 years for the lower gallery (∼25 m deep). For the upper gallery 22
where 14C measurements from a modern stalagmite are available, the tritium age is in 23
excellent agreement with the time lag between the start of the bomb 14C peak in the 24
atmosphere and in the stalagmite carbonate.
25
In spite of the shallow depth of Villars Cave, the infiltration time is relatively high, 26
resulting in a substantial dampening of the seasonal to annual isotopic variations. As a 27
consequence, Villars Cave speleothems will archive climate variability at the multi-decadal to 28
centennial and millennial scale.
29 30 31
Keywords: karst hydrology, groundwater dating, tritium, δ18O.
32 33 34
© 2019 published by Elsevier. This manuscript is made available under the Elsevier user license https://www.elsevier.com/open-access/userlicense/1.0/
Version of Record: https://www.sciencedirect.com/science/article/pii/S0883292719301568 Manuscript_a1fbdb8dcc6a74f452a947b4d0f5dc91
1. Introduction 35
36
Calcite speleothems precipitated in caves contain valuable proxy records of past climatic 37
and environmental conditions. Among those proxies, δ18O is a key parameter. It allows us to 38
study past changes in temperature, precipitation and past atmospheric circulation (i.e.
39
McDermott, 2004 ; Harmon et al., 2004 ; Fairchild and Baker, 2012 ; Mischel et al., 2015 ; 40
Cheng et al., 2016). However, if one wants to use speleothems quantitatively as paleoclimate 41
archives and in particular to correctly interpret the high resolution signals available in some 42
speleothems, it is very important to first investigate the link between rainfall and seepage 43
water. Long-term δ18O time series of rainwater and dripwater allow the comparison of the 44
isotopic signal with meteorological measurements and therefore the calibration of proxy 45
records. Such a study was undertaken at Villars Cave (SW-France) from a long time-series 46
of monthly δ18O in rainwater and dripwater at two different levels in the cave. The study was 47
initiated in 1997 (Genty et al., 2014) and the monthly monitoring, which is still going on, 48
started in 2000. In order to explain their results, the authors showed that it is necessary to 49
integrate weighted rainfall δ18O over long periods, implying an apparent residence time of up 50
to several years, in spite of the shallow depth of the cave (10-25 m). This surprising result 51
calls for a better understanding of the water percolation through the limestone formation 52
above the cave and a precise evaluation of the residence time of the seepage waters.
53
Among the various methods that have been applied to constrain groundwater ages, 54
tritium dating is one of the most popular (Clark and Fritz, 1997; Aeschbach-Hertig et al., 55
1998 ; Solomon and Cook, 2000 ; Price et al., 2003 ; Larsen et al., 2003 ; Solomon et al., 56
2015 ; Battle-Aguilar et al., 2017 ; George et al., 2018 ; Vrzel et al., 2018 ; Sundal et al., 57
2019) ; although a few examples only deal with cave dripwaters (Kaufman et al., 2003 ; 58
Kluge et al., 2010 ; Gasiorowski et al., 2015). During the 1950s and 1960s, atmospheric 59
nuclear testing released substantial quantities of tritium into the atmosphere in the form of 60
tritiated water molecules (HTO). By the time the test ban treaty negotiated by the USSR and 61
the USA came into force in 1963, the atmospheric content of tritium had reached a peak up 62
to 5000 TU in the Northern Hemisphere, corresponding to more than five hundred times the 63
natural tritium background (1 TU corresponds to a T/H ratio of 10-18). This initial atmospheric 64
stock of bomb-tritium gradually diminished due to the relatively short radioactive half-life for 65
tritium (12.3 yr) and to the strong dilution of the tritiated water molecules by the hydrosphere.
66
By 2010, these levels had practically returned to their naturally occurring atmospheric 67
concentrations, corresponding, at the latitude of France (43°N – 50°N), to rainwater tritium 68
concentrations lower than 10 TU (Cauquoin et al., 2015, 2016). In the early days of the use 69
of the tritium dating method, the pronounced ‘‘bomb-peak” produced by thermonuclear bomb 70
tests provided a strong marker. However, nowadays, inter-annual variations are small 71
although pronounced seasonal variations still occur. The method thus requires a precise 72
comparison of tritium values measured in groundwater with the regional input function of 73
tritium in precipitation and therefore a good knowledge of the past history of rainwater tritium 74
concentration in the study area.
75
This study presents the results and interpretation of monitoring tritium from 2009 to 2016 76
in precipitation at the Villars site and at two drip collection sites in Villars Cave, already 77
monitored for δ18O since the year 2000 (Genty et al., 2014) : one in the upper gallery (∼10 m 78
deep) and one in the lower gallery (∼25 m deep). The objective of the study was to determine 79
the infiltration time of the rainwater using a simple one-dimensional model of the infiltration of 80
recharge waters which might fit both our present tritium data and the δ18O monitoring results 81
(Genty et al. ,2014 and ongoing monitoring results).
82 83
2. Villars site climatic and geological settings 84
85
The Villars site is located at about 150 km from the Atlantic coast (Fig. 1) in south-west 86
France (45°.30’N, 0°50’E, 175 m altitude). Its has a temperate maritime climate with mild 87
winters and relatively humid summers. Average temperatures for December /January/
88
February and June/July/August are 5.2°C and 19.3°C respectively, with corresponding 89
average monthly rainfalls of 83 mm and 64 mm, respectively. Annual rainfall is 945 mm 90
(average of the period 2000-2016) but displays a substantial interannual variability (standard 91
deviation = ±160 mm).
92
Villars Cave has developed in middle Jurassic limestone, in an area of hilly terrain 93
covered by a deciduous forest. The cave is relatively close to the surface, with two main 94
networks of galleries located 10 meters and 25 meters below the ground, respectively 95
(Genty, 2008). These galleries are interspaced by some larger chambers. The temperature in 96
the upper galleries, 12.4 ± 0.4°C, is characterised by small seasonal oscillations lagging the 97
outside temperature by about one month. The temperature in the lower galleries do not show 98
any seasonal variation, with a stable temperature of 11.6 °C. Stalactite drip rates slow down 99
markedly during the fall (the driest season), but never stops completely, showing that 100
infiltration persists throughout the year (Genty et al., 2014).
101 102
3. Water sampling and tritium measurements 103
104
Rainwater was collected in a rain-gauge according to the procedure recommended by 105
the IAEA for the Global Network of Isotopes in Precipitation (at https://nucleus.iaea.org/wiser 106
/gnip.php). The rainwater samples were collected in 125 mL Pyrex bottles previously baked 107
and filled with argon. Tritium was monitored at two drip stations already used for collecting 108
stable isotope data : station #10A located in the upper level and station #1A located in the 109
lower level of the cave (Genty, 2008 ; Genty et al., 2014). Each drip station consists of a thin 110
stalactite (3–5 cm in length, 1 cm diameter) directly connected to the gallery roof. Dripwater 111
was also collected in 125 mL Pyrex bottles placed under each stalactite. The time needed to 112
collect the 50 grams of dripwater needed for the tritium analysis was from a few minutes to a 113
few hours depending on the drip rate.
114
Tritium was measured in our Saclay laboratory by 3He mass spectrometry (Jean- 115
Baptiste et al., 2010) with a typical uncertainty of ± 0.2 TU.
116 117
4. Results 118
119
4.1. Tritium in rainwater 120
121
The tritium content of rainwater at the Villars site was monitored from November 2008 to 122
March 2011 and again from July 2014 to February 2016 (Table 1). Tritium concentrations 123
measured during these two periods show a marked seasonal variability, with values ranging 124
between 2.1 TU and 8.9 TU. This seasonal pattern is similar to the one observed by the 125
IAEA tritium monitoring station in Orléans, located some 300 km to the north of the Villars 126
site (see Fig. 1), and can be described by a sine function with a weak long-term decreasing 127
trend (Fig. 2).
128 129
4.2. Reconstruction of the past history of tritium in rainwater at Villars Cave 130
131
The past history of tritium concentration in precipitation at the Villars site was 132
constructed from the IAEA Global Network of Isotopes in Precipitation database (at 133
https://nucleus.iaea.org/wiser/gnip.php). When it was available we used the nearest IAEA 134
station located in Orléans; however data from Orléans are discontinuous, with a first monthly 135
time-series from 1967 to 1979 and a second one covering the period 1996-2003. For these 136
two periods however the comparison with the results from the Thonon-les-Bains station, 137
located further east on Lake Geneva (see Fig. 1), is quite satisfactory (Fig. 3). Therefore, the 138
Thonon results were used before 1967 and to fill the gap between the two Orléans time- 139
series. Beyond 2003, the reconstruction of the tritium concentration in rainwater was based 140
on our own monitoring at the Villars site (2008-2011 and 2014-2016), with gaps in the data 141
being filled by interpolation using the sine function shown in Fig. 2. This sine function was 142
fitted to the data of Fig. 2 by simple visual agreement. It is the sum of two components (see 143
equation below) : the first one represents the exponential decrease of the mean value of 144
tritium in precipitation through time; the second one describes the annual (sinusoidal) 145
variability of tritium in precipitation, the amplitude of which also decreases with time, leading 146
to the following equation : 147
148
TU = 9.0 exp[-0.035×(t-1996)] + [6.0 - 0.113 (t-1996)] sin[2π(t-1996.2)] exp[-0.02×(t-1996)]
149 150
4.3. Tritium in dripwater 151
152
The tritium concentrations of the two sampled drip sites are reported in Table 2. Tritium 153
values in the upper gallery (station #10A) are between 3.2 TU and 5.1 TU (average value : 154
4.2 TU). At station #1A located in the lower gallery, tritium values are ∼1 TU lower (average 155
value : 3.3 TU), ranging between 2.7 TU and 4.3 TU. As shown in Fig. 4, the variability of 156
tritium in dripwater is much less than in rainwater, showing that the karstic porous medium 157
through which rainwater percolates essentially acts as a low-pass filter. This is in agreement 158
with the stable isotope results of Genty et al. (2014) for the same drip sites, which also show 159
that the δ18O seasonal isotopic signal is strongly dampened during the infiltration process.
160 161
4.4. δ18O data and model input 162
δ18O in dripwater data are taken from Genty et al. (2014) and ongoing monitoring. After 163
2000, δ18O in precipitation data used in the model are also taken from the work of Genty et 164
al. (2014). Prior to the start of the monitoring of precipitation at the Villars site in 2000, the 165
δ18O of rainwater and monthly precipitation rate are taken as being the same as during the 166
decade 2000-2010 (see discussion at the end of §5).
167 168
In the following, we construct a simple one-dimensional model of the infiltration of 169
recharge waters forced by monthly precipitation and constrained by tritium and δ18O data, to 170
determine the infiltration time of the rainwater.
171 172
5. Description of the infiltration model 173
174
The complex permeability structure of karstic formations can result in infiltration times 175
between the surface and the drip site being highly variable: diffuse flow through micro- 176
fractures and interconnected porosity will lead to dampened seasonal variations while 177
preferential flow through larger fractures will be more sensitive to rainfall events and seasons 178
(Ford and Williams, 2007 ; Fairchild and Baker, 2012). Because of the high heterogeneity of 179
karstic systems, several types of water flow can coexist and interact. Describing water 180
movement through this heterogeneous medium is a complex problem. Our approach here is 181
to envision the simplest possible physical model that might explain the measured tritium 182
concentrations at the drip sites #10A and #1A, as well as the results of the still ongoing δ18O 183
monitoring at the same locations.
184
δ18O has been monitored in Villars Cave (Genty et al., 2014) at four points : #10A and 185
#10B in the upper gallery, and #1A and #1B in the lower gallery. Among the many results 186
based on this monitoring (Baker et al., 2000 ; Genty, 2008 ; Genty et al., 2014), three 187
observations are particularly important for the model design : 188
1) All four stations display a quite stable δ18O composition, with a substantial dampening 189
of the seasonal variations. This suggests that the residence time and mixing of the water 190
feeding the dripping stalactites are high enough to filter out high frequency (seasonal) 191
signals.
192
2) The lower gallery has a mean δ18O value 0.2 permil lower than that of the upper 193
gallery. A first hypothesis put forward by Genty et al . (2014) is that this difference could be 194
due to the contribution of an older reservoir consisting of rainwater that precipitated several 195
decades ago when the averaged outside temperature was lower (by about 2–3 °C) yielding a 196
lower precipitation δ18O. However, the tritium value of this old water reservoir would likely be 197
higher than in present-day precipitation due to the large bomb-tritium peak found in earlier 198
decades ; this is inconsistent with the tritium values at #1A which are low. Therefore, a 199
second more likely explanation put forward by Genty et al. (2014), is that during the high 200
winter recharge periods, when the rainfall δ18O is lower, water from the upper level overflows 201
to the lower level. This second hypothesis is that retained for the construction of our model.
202
3) Although upper gallery stations #10A and #10B are only a few meters apart, their 203
hydraulic behavior is different : station #10A shows a higher sensitivity of the dripping rate to 204
rainfall events than #10B and also displays a more variable δ18O isotopic composition (Genty 205
et al., 2014) and chemical and luminescent properties (Baker et al., 2000). This is likely the 206
consequence of the heterogeneity of the fissure network superimposed on the bulk 207
percolation. These fissures act as a by-pass connecting some dripping stalactites more 208
directly to the surface.
209
Based on the above observations, our model consists of two reservoirs : a small (10 210
meters) reservoir extending from the surface to the upper gallery and a larger reservoir (25 211
meters) extending from the surface down to the lower level (Fig. 5). The upper parts of the 212
two reservoirs are connected to allow water to overflow from the small upper gallery reservoir 213
to the second larger one during high recharge periods (see above point 2). The cut-off value 214
(i.e. the value above which the overflow from the upper reservoir to the lower reservoir is 215
allowed to occur) is tuned as to obtain the right value for the δ18O of dripwater in the lower 216
gallery (see §6 below). Both reservoirs consist of a network of interconnected pores (void 217
fraction = ω) through which rainwater can percolate. The downward advection rate is defined 218
by the fraction β of the rainfall rate R which percolates through the reservoir. The remaining 219
fraction, (1-β) is considered either as surface runoff or water which drains through large 220
fissures and does not participate in the dripping system. In addition, each drip site has a 221
direct connection to the surface to allow a small fraction of rainwater to mix with the dripwater 222
exiting from each reservoir (see above point 3). These direct contributions to dripwaters of 223
the upper and lower galleries are determined as to fit their observed δ18O variability (see §6 224
below).
225
The time-evolution of the tritium and 18O vertical distributions across each reservoir is 226
described by the following classic mass balance equation (1), which is solved numerically 227
with vertical steps ∆z = 1 m and using a time step ∆t = 1 month : 228
C(z,t+∆t) = (C(z,t) + R(β/ω)(∆t/∆z) [C(z-∆z,t)- C(z,t)] ) exp(-λ∆t) (1) 229
where C(z,t) is the concentration (in mol.m-3) of tritium : C=(106/18)×2×TU×10-18 (with the 230
decay constant λ=0.0563 s-1) or 18O : C= (106/18)×(2000×10-6)×(1+δ18O/1000.) (with λ=0) at 231
depth z and time t and R is the monthly precipitation rate. Since the above numerical scheme 232
is implicitly diffusive, no additional explicit diffusion is considered here (see section 6 below).
233
Although the parameters β and ω are not known, they only appear as their ratio, as 234
shown in equation (1). As a consequence, the void fraction ω is set arbitrarily to a plausible 235
value of 0.3 and the value of β is optimized to obtain the best fit between the tritium data and 236
the model simulation.
237
The simulation starts in 1965. As indicated in §4.4, after 2000 δ18O in precipitation data 238
used in the model are taken from the work of Genty et al. (2014) and ongoing monthly 239
monitoring. Prior to the start of the monitoring of precipitation at the Villars site in 2000, the 240
δ18O of rainwater and monthly precipitation rate are taken as being the same as during the 241
decade 2000-2010. This is an approximation since in the δ18O of rainwater in the past may 242
have been slighty lower due to the somewhat colder climate in the 60’s and 70’s (Genty et 243
al., 2014). However, based on our tritium results, the model shows that the transit time of the 244
dripping water is on the order of 10 years (see below). Therefore the uncertainty in the δ18O 245
composition of rainwater in the past decades (1965 –1990) has no influence on the simulated 246
δ18O in dripwater after 2000.
247 248
6. Model results and discussion 249
250
The results of the simulation for the upper and lower galleries are shown in Fig. 6a and 251
Fig. 6b, respectively.
252
Because ∆z is not infinitesimally small, the numerical scheme used in the simulation 253
(see equation 1) is implicitly diffusive since at each time step the concentration in each layer 254
∆z is well mixed. Using a spike of numerical tracer, we infer an implicit hydrodynamic 255
dispersion coefficient DL= 2.5×10-8 m2 s-1 by comparing the actual widening of the spike at 256
the exit of the reservoir with what would be obtained from a purely diffusive process (Ferziger 257
and Peric, 2002). For a pore water velocity βR/ω ∼5×10-8 m.s-1, this corresponds to a 258
longitudinal dispersivity (i.e., in the direction of the flow) αL∼ DL/v = 0.5 m (Fried and 259
Combarnous, 1971 ; Sahimi, 1995). This value is well within the range of typical longitudinal 260
dispersivities observed for porous media and fractured rocks (Gelhar et al., 1992; Schulze- 261
Makuch, 2005). Therefore, no additional explicit diffusion was considered here.
262
The agreement with the data is quite satisfactory considering the simplicity of the model 263
physics, the uncertainties in the precipitation rate and δ18O of rainwater prior to 2000 (see 264
above), and the uncertainty in the reconstruction of the tritium input function. The main 265
contribution of the tritium data is to allow us to determine the fraction β of the rainwater that 266
actually infiltrates through the karstic formation, which in turn determines the transit time of 267
the water. On the other hand, δ18O data are most useful to determine the value above which 268
the overflow from the upper reservoir to the lower reservoir is allowed to occur (cut-off value) 269
as well as the direct contributions of rainwater to the dripwater in the upper and lower 270
galleries.
271
With a cut-off value of 150 mm per month (i.e. the value above which the overflow from 272
the upper reservoir to the lower reservoir is allowed to occur), the model reproduces well the 273
average δ18O values observed both in the upper and the lower galleries (note, however, that, 274
in the range 130-170 mm, the fit is rather insensitive to the choice of this cut-off value). For 275
the upper gallery (drip station #10A), a direct contribution of surface rainwater to the 276
dripwater of 15% is necessary to account for the observed variability in the δ18O data 277
(Fig. 6a). For the lower gallery (station #1A), this direct contribution is less than 5% (Fig. 6b).
278
The amount of rainwater that percolates through both reservoirs is about half the total 279
precipitation (β=40% for the upper reservoir and β=60% for the lower one). This β parameter, 280
adjusted as to fit the tritium data, is the one that sets the transit time of the infiltrating waters 281
between the surface and the drip sites.
282 283
This transit time, determined using of a spike of numerical tracer, is 7.0 years for the 284
upper gallery and 11.1 years for the lower gallery. The estimated uncertainty on the age 285
determination is ±0.5 yr. For the upper gallery, the transit time of 7.0 years is in excellent 286
agreement with 14C measurements in a young stalagmite which indicate that the time lag 287
between the start of the bomb 14C peak in the atmosphere and in the stalagmite carbonate is 288
7.0±0.5 years (see Fig. 23 in Genty, 2008).
289
Our results complement and precise the previous findings of Genty et al. (2014) based 290
on the study of δ18O only : 291
- Our model shows that the hypothesis of an overflow from the upper to the lower 292
reservoir explains well the observed δ18O shift of -0.2 permil between the upper and lower 293
galleries.
294
- The observation that the isotopic variability of cave dripwaters was substantiallly 295
dampened compared to that of monthly precipitation led Genty et al. (2014) to suggest that 296
Villars cave drip waters were a mixing of rainwaters corresponding to an integratetion of 297
weighed rainfalls over several years. This "mixing time" has to be clearly distinguished from 298
the transit time of the dripwaters across the karstic formation inferred from tritium data in the 299
present study. The mixing time deduced from studies using only δ18O data (Genty et al., 300
2014 ; Mischel et al., 2015) relies on the oversimplified assumption of a well-mixed reservoir.
301
In our piston flow-type model on the contrary, the reservoir is not well-mixed but during its 302
transit time through the karstic formation, water get mixed on its way down due to 303
hydrodynamic dispersion, thus explaining the substantial dampening of the initial isotopic 304
variability. Therefore, the relationship between the two notions of "mixing time" and "transit 305
time" is not straightforward because the initial hypothesis concerning the structure of the 306
reservoir are chiefly different.
307
Tracer studies reported in the literature (Yonge et al., 1985; Chapman et al., 1992;
308
Kaufman et al., 2003; Kluge et al., 2010; Oster et al., 2012 ; Gasiorowski et al., 2015) show 309
that the delay between rainfall and the discharge of the dripwater is extremely variable from 310
one cave to another, ranging from weeks to years and even decades. At sites with rather fast 311
percolation, seasonal variations and meteorological events such as droughts may be 312
recorded in speleothems. At the Villars site, in spite of the shallow depth of the cave, the 313
infiltration time is relatively long. Therefore, because of the resulting strong dampening of the 314
seasonal to multi-annual variations, the Villars Cave speleothems will likely archive climate 315
variability on the multi-decadal to millennial scale, as illustrated by the study of millennial 316
climatic variability during the paleoclimatic stages OIS3 and OIS4 (Genty et al., 2003 ; 2010) 317
and longer climatic cycles (Wainer et al., 2011).
318 319
7. Conclusion 320
321
We have monitored the tritium content of rainwaters and dripwaters at the Villars site 322
from November 2008 to March 2011 and again from July 2014 to February 2016 in order to 323
determine the transit time of the infiltrating waters between the surface and the drip 324
observation sites. Two drip stations, already used for the monitoring of stable isotopes, were 325
selected : one station located in the upper gallery (∼10 m deep)and one station located in 326
the lower gallery (∼25 m deep). The main results of our study are as follows : 327
Tritium concentration in rainwater shows a marked seasonal variability, with values 328
ranging between 2.1 TU and 8.9 TU. This seasonal pattern is similar to the one observed at 329
the IAEA tritium monitoring station in Orléans, located some 300 km to the north of the Villars 330
site.
331
Tritium values in the upper gallery are between 3.2 TU and 5.1 TU (average value : 332
4.2 TU) while in the lower gallery they are on average ∼1 TU lower (average value : 3.3 TU).
333
The seasonal variability of tritium in dripwater is almost completely dampened compared to 334
that in rainwater, showing that the karstic porous medium through which the rainwater 335
percolates essentially acts as a low-pass filter, in agreement with the stable isotope results of 336
Genty et al. (2014) for the same drip sites.
337
The infiltration time of the rainwater was determined using a simple one-dimensional 338
model of the infiltration of recharge waters, forced by monthly precipitation and tritium 339
concentrations in precipitation reconstructed from the IAEA isotope network and our own 340
measurements in rainwater at the Villars site. The transit time of the dripwater across the 341
karstic formation was found to be 7.0 years for the upper gallery and 11.1 years for the lower 342
gallery, with an uncertainty estimated at ±0.5 yr.
343
For the upper gallery where 14C measurements in a young stalagmite are available, the 344
transit time determined using tritium is in excellent agreement with the time lag between the 345
start of the bomb 14C peak in the atmosphere and in the stalagmite carbonate.
346 347
Acknowledgments 348
349
This research was funded by the french CNRS (Centre National de la Recherche 350
Scientifique).
351 352
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454
455
Figure captions 456
Fig. 1 : Villars Cave location.
457
Fig. 2 : Decreasing sine function used to interpolate between the IAEA tritium data (Orléans 458
station, in red) and our own measurements at the Villars site (in blue).
459
Fig. 3 : Comparison of the IAEA rainwater tritium time-series in Thonon-les-Bains (in black) 460
and Orléans (in red).
461
Fig. 4: Tritium measurements in Villars Cave. Blue squares and red circles correspond to the 462
upper gallery (drip station #10A) and lower gallery (drip station #1A), respectively. The black 463
curve is the reconstructed tritium concentration in precipitation at the Villars site.
464
Fig. 5 : Sketch of the model (see §5 for details).
465
Fig. 6a : Comparison between model simulation and δ18O and tritium data at drip station 466
#10A (upper gallery). The smooth curves represent the model result without the additional 467
direct contribution from surface waters. δ18O data come from the ongoing monitoring of stable 468
isotopes in Villars Cave (Genty et al., 2014).
469
Fig. 6b : Comparison between model simulation and δ18O and tritium data at drip station #1A 470
(lower gallery). The smooth curves represent the model result without the additional direct 471
contribution from surface waters. δ18O data come from the ongoing monitoring of stable 472
isotopes in Villars Cave (Genty et al., 2014).
473 474 475
Table captions 476
Table 1 : Tritium results for rainwater at the Villars site.
477
Table 2 : Tritium results for dripwater in Villars Cave.
478
Orléans
Villars cave
Thonon- Les-Bains ATLANTIC
OCEAN
Figure 1
100 km
YEAR
TRITIUM IN RAIN WATER (TU)
Fig. 2
2000 2005 2010 2015
0 4 8 12 16
1960 1970 1980 1990 2000 2010 1
10 100 1000 10000
YEAR
TRITIUM IN RAIN WATER (TU)
Fig. 3
2000 2004 2008 2012 2016 2020 0
5 10 15
YEAR
TRITIUM (TU)
Fig. 4
Overflow R(1-β2) R = Monthly precipitation rate
R(1-β1) Rβ1
Rβ2
Upper gallery drip site
Lower gallery drip site
Direct contribution from rainwater
Direct contribution from rainwater
Figure 5
YEAR TRITIUM (TU)δ18 O (permil)
Fig.6a
-7 -6 -5
2000 2004 2008 2012 2016 2020
2
4
6
8
10
YEAR TRITIUM (TU)δ18 O (permil)
Fig.6b
-7 -6 -5
2000 2004 2008 2012 2016 2020
2
4
6
8
10
Sampling date Tritium (TU) 19/10/2008 - 28/11/2008 3,93 20/03/2009 - 24/05/2009 6,02 24/05/2009 - 16/06/2009 7,65 16/06/2009 - 17/07/2009 5,09 17/07/2009 - 04/08/2009 5,36 04/08/2009 - 07/09/2009 7,04 07/09/2009 - 21/10/2009 4,53 21/10/2009 - 04/11/2009 2,12 04/11/2009 - 17/11/2009 2,51 17/11/2009 - 27/11/2009 2,33 27/11/2009 - 14/01/2010 3,47 14/01/2010 - 19/01/2010 3,57 19/01/2010 - 22/02/2010 3,50 22/02/2010 - 04/03/2010 4,48 04/03/2010 - 13/04/2010 5,77 13/04/2010 - 26/04/2010 8,91 26/04/2010 - 04/06/2010 5,29 27/09/2010 - 03/11/2010 3,36 03/11/2010 - 30/11/2010 2,16 30/11/2010 - 27/01/2011 3,87 27/01/2011 - 31/01/2011 5,72 31/01/2011 - 22/02/2011 3,27 22/02/2011 - 02/03/2011 3,49 21/05/2014 - 02/07/2014 6,91 16/08/2014 - 10/10/2014 2,99 10/10/2014 - 29/10/2014 2,97 23/11/2014 - 27/01/2015 3,56 27/03/2015 - 29/04/2015 3,62 29/04/2015 - 03/06/2015 2,81 03/06/2015 - 27/07/2015 6,90 27/07/2015 - 10/09/2015 5,33 10/09/2015 - 05/10/2015 3,69 05/10/2015 - 23/11/2015 2,28 21/01/2016 - 25/02/2016 2,51
Table 1
Villars 10#A Villars 1#A
Sampling date Tritium (TU) Sampling date Tritium (TU)
29/01/2009 4,38 29/01/2009 3,92
19/03/2009 4,67 19/03/2009 4,16
22/06/2009 4,36 29/11/2010 4,30
02/07/2009 4,37 30/11/2010 4,22
17/07/2009 4,15 26/01/2011 3,88
01/08/2009 4,38 15/03/2011 3,77
17/08/2009 4,18 01/07/2014 3,44
09/09/2009 4,40 10/10/2014 3,53
22/09/2009 4,67 29/10/2014 3,80
25/10/2009 4,74 27/01/2015 3,39
03/11/2009 4,40 27/01/2015 3,35
18/11/2009 4,55 28/03/2015 3,17
29/11/2009 4,54 29/04/2015 3,15
11/01/2010 4,10 03/06/2015 3,26
19/01/2010 4,25 03/06/2015 3,03
02/02/2010 4,21 27/07/2015 2,96
22/02/2010 4,29 10/09/2015 3,02
04/03/2010 4,34 05/10/2015 2,76
13/04/2010 4,03 19/01/2016 2,88
26/04/2010 4,22 25/02/2016 2,79
11/05/2010 4,32 18/03/2016 2,68
31/05/2010 4,23 23/06/2016 2,70
05/07/2010 4,42 23/09/2016 2,93
30/08/2010 4,29 12/09/2010 4,23 15/09/2010 4,17 04/10/2010 4,24 19/10/2010 4,53 03/11/2010 5,06 29/11/2010 4,14 23/01/2011 3,98 26/01/2011 4,33 31/01/2011 4,23 15/03/2011 4,17 01/07/2014 3,83 27/01/2015 4,28 05/10/2015 3,84 19/01/2016 3,44 25/02/2016 3,57 23/06/2016 3,83 23/09/2016 3,21
Table 2
