Transit time can be estimated thanks to natural tracers, but few of
them are usable in the 0–6-month range. The main purpose of this work is to
analyze the potential of the ratio of heavy- to light-weight organic
compounds (the humification index (HIX); Ohno, 2002; Zsolnay et al., 1999) as a natural tracer of
short transit time (Blondel et al., 2012). Critical analysis of former
studies shows that although the link between HIX and transit time seems
consistent, the whole methodological approach needs to be consolidated.
Natural organic matter fluorescence from 289 groundwater samples from four springs and 10 flow points located in the unsaturated zone of the Vaucluse
karst system is characterized by parallel factor analysis (PARAFAC) thanks
to the excitation–emission matrix (EEM), thus (i) allowing for the identification of
main fluorescent compounds of sampled groundwater and (ii) evidencing the
inadequacy of HIX 2D emission windows to characterize groundwater organic
matter. We then propose a new humification index called the Transit Time index
(TTi) based on the Ohno (2002) formula but using PARAFAC components of heavy and
light organic matter from our samples instead of 2D windows. Finally, we
evaluate TTi relevance as a transit time tracer by (i) performing a
detailed analysis of its dynamics on a selected spring (Millet) and (ii) comparing its mean value over karst springs of the Vaucluse karst system.
Principal component analysis (PCA) of TTi and other hydrochemical parameters
monitored at Millet spring put in relief the different ranges of transit
time associated with the different organic matter compounds. PCA results
also provide evidence that TTi can detect a small proportion of fast
infiltration water within a mix, while other natural tracers of transit time
provide no or less sensitive information. TTi distributions at monitored
karst springs are consistent with relative transit times expected for the
small-scale, short average transit time systems. TTi thus appears as a
relevant qualitative tracer of transit time in the 0–6-month range where
existing tracers fail and may remain applicable, even in the case of
anthropic contamination thanks to PARAFAC modeling. Transforming it into
quantitative information is a challenging task which may be possible thanks
to intensive studies of organic matter degradation kinetics in natural
waters with the help of radiogenic isotope usage or an artificial tracer test.
Introduction
Karst aquifers are essential for water supply at both global and local
scales as they provide 9.2 % of the world's drinking water and contribute
to 13 % of the total global withdrawal of groundwater (Stevanović,
2019). But karsts are also really complex and compartmentalized systems
which offer very different paths to the infiltrated water. The hierarchized
network of karst conduits allows for a fast transit of recharge which is very
specific to karst systems (White, 2002) and makes it essential to develop
natural tracers of transit on short timescales (<6 months).
Natural tracers of stored water include major element contents, isotopes,
and dissolved gas (Malík et al., 2016; Musgrove et al., 2019;
Pérotin et al., 2021; Zhang et al., 2021), but few of these tracers
allow fast infiltration to be characterized. Indeed, natural tracers of transit
time of this range must, by definition, see their contents vary at this timescale. While variations in inorganic compounds are small on this timescale,
living and organic components of water like bacteria, total organic carbon
(TOC), or natural organic matter fluorescence (Batiot et al., 2003; Lapworth
et al., 2008; Mudarra et al., 2011; Pronk et al., 2009; Sorensen et al.,
2020) are suited to this goal. Indeed, TOC represents the quantity of
organic matter present in the water, and its mineralization (or degradation)
is complete after 6 months (Batiot, 2002). Fluorescent organic matter is a
small part of the total organic matter represented by TOC and will therefore
also be completely degraded after a maximum of 6 months. Its potential as a
natural tracer of groundwater was already put in relief in Baker and
Lamont-Black (2001). Our study focuses on fluorescent organic matter and its
relation with transit time through the humification index (HIX), as initially
proposed by Blondel et al. (2012).
Fluorescent organic matter compounds are degraded in the natural
environment. The rate of this degradability is constrained by two aspects:
the type of organic matter and biological activity. The influence of the
organic matter type is well documented; complicated molecules of organic
matter have a higher emission wavelength and less digestibility (Zsolnay,
1999). For example, humic-like organic matter is less digestible than
protein-like organic matter and thus takes more time to be degraded.
The humification index (HIX) expresses the maturation level of organic
matter. Humification refers to the gradual transition of organic matter to
highly metabolized compounds (humin). HIX is defined as the ratio of humic
to non-humic compounds. Because this ratio is related to the maturation of
organic matter, it has the potential to be related to transit times in the
range 0 to 6 months (before maturation is complete).
In a study which aimed at identifying dissolved organic matter (DOM) sources
in soil and sediment waters, Zsolnay (1999) proposed a methodology to derive
HIX from fluorescence measurements. He identified the excitation wavelength
most representative of fluorescent organic matter in soil water (254 nm).
Next, he identified emissions wavelengths corresponding to light (L from 300
to 345 nm) and heavy (H from 435 to 480 nm) organic compounds in the
emission spectra with the assumption that emission wavelengths of
fluorescent molecules increase while molecules get more condensed (Ewald et
al., 1988; Zsolnay et al., 1999). HIX was then defined by the ratio H/L of the
integral under the emission curve of 2D spectra at 254 nm of excitation
wavelength. This way of calculating HIX is dependent on DOM concentration
because of the inner-filtering effect (Ohno, 2002). The inner-filtering effect
results from either the absorption of excitation light by fluorescent
molecules before it gets to the monitored zone (primary inner-filtering
effect) or the absorption of emission light coming from photons (secondary
inner-filtering effect; Tucker et al., 1992). Using the H/L ratio to calculate
HIX permits correction for the primary inner-filtering effect which affects
each wavelength equally. But the secondary inner-filtering effect still
needs to be corrected if different study sites are to be compared (Mobed et
al., 1996). Ohno (2002) thus proposed another HIX formula that corrects for
both inner-filter effects:
HIX=HL+H.
A quantitative relation between HIX and transit time was proposed by Blondel
et al. (2012) based on natural fluorescence monitoring of four flow points
collected in the unsaturated zone of the Fontaine de Vaucluse system (France)
during two hydrological cycles (2006–2007 and 2007–2008). Careful
examination of this work revealed several methodological weaknesses.
The calculated HIX used Zsolnay's formula (Zsolnay, 1999), which lacks secondary
inner-filtering effect correction and thus prevents comparison between study
sites or flows. The excitation wavelength was 260 nm instead of 254 nm as
recommended by Zsolnay (1999). In any case, Zsolnay emission windows were
calibrated for soil water; it is thus possible that they may be unsuitable
for groundwater. The relation between HIX and transit time was obtained by
considering the mean total organic carbon (TOC) value for each hydrological
cycle and each flow point: the relation between transit time and TOC proposed by
Batiot et al. (2003) allowed transit time values to be connected to HIX. However,
this relation was based on a very limited number of samples and had a high
uncertainty.
In spite of these limitations, Blondel's study results were consistent and
led to the identification of a clear link between HIX and transit time.
Based on the critical analysis of these previous studies, we first analyzed
water fluorescence on 289 groundwater samples from four springs and 10 flow
points located in the unsaturated zone of the Vaucluse karst system. The 2D
spectra of organic matter fluorescence were compared with Zsolnay emission
windows. Main organic matter fluorescent compounds in water samples were
identified based on parallel factor analysis (PARAFAC) and bibliographical
review. We then proposed a new humification index called the Transit Time index
(TTi) based on the Ohno (2002) formula but using PARAFAC components of heavy and
light organic matter compounds from our samples instead of 2D windows.
Finally, we evaluated TTi relevance as a transit time tracer by (i) performing a detailed analysis of its dynamic on a selected spring (Millet)
and (ii) comparing its mean value over karst springs of the Fontaine de Vaucluse system.
Materials and methodsStudy site
This study was carried out in the Vaucluse karst system (southeastern
France) (Fig. 1). This hydrosystem, mainly composed of outcropping marine
Cretaceous limestones, is unusual in terms of dimension and volume. Its main
outlet, Fontaine de Vaucluse spring, has a mean flow rate of 23.3 m3 s-1 (from January 1877 to June 2006; Cognard-Plancq et al., 2006), which
is one of the highest in Europe. It is also characterized by a particularly
thick unsaturated zone (∼800 m). Monitoring of flows in its
unsaturated zone at depths ranging from ∼30 m to almost 500 m
is made possible through the artificial galleries of the LSBB (https://lsbb.cnrs.fr, last access: 27 September 2022). Several outlets of less importance are also located
on the recharge area of the Vaucluse karst system, the main ones being Millet,
St Trinit, and Nesque springs (Table 1). The main karstification mechanism is
epigenetic, but there is evidence of hypogenetic karstification at the
southern edge of the Fontaine de Vaucluse system (Audra et al., 2011).
Main characteristics of monitored flow points from Batiot (2002),
Blondel (2008), Emblanch et al. (1998), Lastennet (1994), Ollivier (2019), and
field observations. UZ is the unsaturated zone.
SpringCatchment areaKarstificationUZ thicknessLithologyLand useMillet∼2.5 km2Complex karstification – anastomosesThick ∼70 mCretaceous/Barremian limestones (marine)Forest, lavendercultivationSt Trinit∼2 km2High degree, large karstic conduitThin ∼10 to 20 mCretaceous/Aptian limestones (marine)Anthropic activities, organic farming, townLa Nesque∼1 km2Low degree, conduits of centimetric scale at the outletThin ∼10 to 20 mMarly limestones, Oligocene (lacustrine)Lavender cultivationFontaine de Vaucluse∼1160 km2Variable but high on averageVery thick ∼800 mCretaceous limestones (marine)Cultivations, cities, forestsLSBB≤1 km2Variable35 à 518 mCretaceous limestones (marine)Forest and cultivation
Location of monitored flow points on a 1:50 000 geological map
(BD-CHARM) from BRGM and spring catchment delineation (based on geology and
mass balance for Millet, St Trinit, and Nesque springs; from Ollivier (2019)
for Fontaine de Vaucluse).
Sampling and fluorescence analysis methods
Bimonthly sampling of all flow points was performed during a 1-year
monitoring period (June 2020 to October 2021). Measurements of major elements, TOC, and water stable isotopes were performed by UMR 1114 EMMAH with ion chromatography on the Dionex ICS-1100, Aurora 1030 TOC analyzer, and Picarro
L2130-i, respectively. The excitation–emission matrix (EEM) and 2D spectra of organic matter
fluorescence were analyzed at HydroSciences Montpellier using a
spectrofluorometer (SHIMADZU RF-5301 PC; 150 W xenon lamp) (Serene et al., 2022). Wavelength
windows for EEMs were λex=[220;450] nm and interval = 10 nm and λem=[250;550] nm and interval = 1 nm. Wavelengths for the
2D spectrum were λex=254 nm and λem= [220;
530] nm. The temperature was stabilized at 20 ∘C in a bath with a
thermostat. Slit widths of 15 nm were used for the monochromators with a
fast default scan speed. The stability of the apparatus was checked based on
the Raman peak on fresh MilliQ water excited at 348 nm.
Identification of natural organic matter components in our samples was
performed by both manual and automatic procedures. Manual peak picking was
performed on raw EEMs of over 10 % of representative samples of the
dataset, leading to the identification of about 80 fluorophores
corresponding to seven different components. EEMs were also treated thanks to
R software and the staRdom package (Pucher et al., 2019). Each of the 289 EEMs
was corrected with blank subtraction, Raman normalization (Lawaetz and
Stedmon, 2009), and scattering removal (Lakowicz, 2006; Murphy et al.,
2013). Removed scatter was interpolated with spline interpolation (Lee et
al., 1997). PARAFAC modeling was then performed to extract organic matter
components thanks to the same software and package using non-negative
constraints for all modes, following the method described by Andersen and Bro
(2003).
Transit Time index (TTi) definition
Based on the fluorescence analysis of our samples, we propose the Transit
Time index (TTi) which derives from the HIX definition of Ohno (2002) but
differs by the analytic method of organic matter (2D vs 3D). TTi is thus the
ratio of heavy organic matter (high-emission wavelengths, humic-like organic
matter) to heavy and light organic matter (low-emission wavelengths,
protein-like organic matter):
TTi=humic-likehumic-like+(protein-like),
where humic-like and protein-like parameters correspond to the sum of all
compound weights of each type from the PARAFAC model. Unlike HIX, TTi
considers the totality of fluorescent organic matter compounds in groundwater.
TTi value close to 1 means that organic matter is composed of low digestible
organic matter (humic-like), which indicates a relatively long transit time. On
the other hand, TTi close to 0 means that organic matter is composed of
highly digestible organic matter (protein-like), which indicates a very short
transit time.
Results and discussionIdentification of organic matter compounds2D spectra results
The emission wavelengths of windows used to compute HIX have been compared
to 2D spectra of natural fluorescence of representative samples from our
dataset (Fig. 2). As compared to the proposed windows, (i) the protein peak
of the Millet spring sample only partially fits inside, and (ii) the 370 nm
protein peak of the St Trinit spring sample is shifted towards longer
wavelengths. In both cases, emission of fluorescence of protein organic
matter components is not correctly considered by HIX calculation. Proposed
fluorescence windows are thus not appropriate to characterize organic matter
in groundwater. We hypothesize that this mismatch may be related to the fact
that groundwater's organic matter is more digested than that of its own
source, which is the soil.
The 2D spectra at excitation wavelength 254 nm for two representative
samples (Millet on 3 May 2021 and St Trinit on 1 February 2021). Comparison with Ohno
(2002) H and L windows.
PARAFAC model results
The resulting PARAFAC model managed with non-negative constraints contains four
different components of organic matter. It was chosen for its explained
variance of 0.9825 and its core consistency of 92.7 %, and it was checked
thanks to split-half analysis, Tucker's congruency, plotting of
components, and random initialization (Andersen and Bro, 2003). Three of the
four identified components contain two close but distinguishable compounds
(components 1, 2, and 4). PARAFAC components are in good agreement with
manual peak picking performed on raw EEMs, compound 3 apart, which is
affected by harmonics. Comparison with excitation–emission windows from the
literature allows organic matter compounds represented by each
component to be identified (Fig. 3).
Component 1 is typical of heavy compounds belonging to humic-like organic
matter type (Blondel, 2008; Quiers et al., 2014). Component 2 is composed of
two tryptophan-like compounds (Trp). Indeed, the compound with the longer excitation
wavelength closely matches Trp 1 from Birdwell and Engel (2010), and the compound
with the shorter excitation wavelength appears to be another declination of
tryptophan-like organic matter, different from Trp 2 from Birdwell and Engel
(2010). Component 3 is consistent with P1 observation from Quiers et al. (2014). This compound lies close to Trp 2, but its emission wavelength is too
high for it to belong to tryptophan-like organic matter. Quite surprisingly,
it is far from P1 observation of Blondel (2008). We hypothesize that P1 was
mistaken for tryptophan-like organic matter by Blondel (2008). Component 3
also lies far from the hand peak picking window of P1, probably because
hand peak picking was performed on raw EEMs not yet corrected from harmonics.
Component 4 contains one main compound which we assume to be tyrosine-like
organic matter (Tyr) because it lies really close to the Tyr 1 observation of Mudarra
et al. (2011). A second component with lower intensity may correspond to Tyr
2 of Mudarra et al. (2011).
The four components identified by PARAFAC modeling can thus be gathered
into humic-like organic matter (component 1) and protein-like organic matter
(components 2, 3, and 4). We also note that 2D spectra at 254 nm may
accurately illustrate the maximum intensity of Trp2 and H2 but miss
the maximum intensity of H1. Use of EEM instead of 2D spectra thus appears
necessary to achieve the characterization of all the humic-like and
protein-like compounds required for humification index calculation.
Comparison of organic matter component location in EEMs in
literature and our study.
TTi application at Millet springHydrodynamic and hydrochemical functioning of Millet spring
Descriptive statistics of major ions, TOC, electrical conductivity,
humic-like and protein-like organic matter, TTi, standard deviation, and
coefficient of variation are available in Table 2 and represented as time
series in Fig. 4. These parameters were chosen for their ability to improve
recharge and transit time knowledge on flows and springs of the Fontaine de
Vaucluse system (Garry, 2007; Barbel-Périneau, 2013; Blondel, 2008).
Silica and magnesium are two elements classically used as markers of the
reserve and therefore are associated with a long transit time because of
their slow solution kinetics (Lastennet and Mudry, 1997).
Discharge at Millet spring reacts sharply to rainfall events (Fig. 4) which
indicates that the karst network is mature. Conversely, natural tracers
such as δ18O and major elements are not well correlated with discharge
and have low-amplitude variations, highlighting the high mixing ability of
the Millet system. It is particularly the case of δ18O, whose
variations are close to detection limits. Such global hydrochemical
stability suggests the presence of a storage zone in which the mixing of
waters is particularly effective, which we relate to the structure of its
karstification (anastomose).
Electrical conductivity correlates well with discharge, and the highest
conductivities are reached during high discharge with a 2 to 4 d delay.
This positive correlation between electrical conductivity and discharge is
less usual, but it has also been observed in other karst springs of the
Fontaine de Vaucluse system (Notre-Dame-des-Anges spring in Emblanch et al.,
2006), other Mediterranean karst systems such as the Lez spring (Bicalho et
al., 2012), or in Europe at Podstenjšek spring, Slovenia (Ravbar et al.,
2011). This phenomenon may result from dilution of deep flows with recent
water like in the Lez spring (Bicalho et al., 2012), a change in the
catchment delineation that captures old water stored outside the usual
catchment area (Ravbar et al., 2011), or a supply of water stored in the
unsaturated zone (Emblanch et al., 2006).
At Millet spring, electrical conductivity is mainly carried by Ca2+ and
HCO3- contents. Its increase at the beginning of flood events is
caused by HCO3- increase, which indicates the arrival of water
characterized by higher pCO2 (see Fig. 4). pCO2 has higher content
in soil water because of biological respiration and organic matter
decomposition. A pCO2 increase in spring involves (i) a stronger
influence of soil water which may be stored in the unsaturated zone or
epikarst or (ii) a very fast infiltration supply. Case (i) is the most
likely because case (ii) involves high organic matter content (TOC), while
the increase of TOC corresponds to less than 0.6 mg L-1.
Arrival of water associated with a short residence time is usually evidenced
by TOC (Batiot et al., 2003). TOC is a natural tracer of fast infiltration,
which decreases with increasing transit time. From a hydrodynamic point of
view, Millet spring is a fast-reacting karstic system, and TOC is thus
expected to increase sharply during flood events. Measured TOC does
correlate with discharge, but it varies little and does not exceed 2 mg L-1, showing evidence of a mix of different water types. Stored water
may have a lower TOC content in comparison with fresh water supply because
of natural organic matter degradation. It thus appears that as TOC provides
evidence of arrival of water with short transit times, its limits of
sensitivity also seem to be reached.
Descriptive statistics at Millet spring of major ions, TOC,
electrical conductivity, humic-like and protein-like organic matter,
and TTi. SD is standard deviation, and CoV is coefficient of variation, over the
period June 2020 to October 2021 (29 samples).
Millet spring time series of rain, discharge (Mazzilli et al., 2022), TTi, humic-like
(component 1) and protein-like (sum of components 2, 3 and 4) fluorescent
organic matter, continuous and punctual electrical conductivity,
δ18O, pCO2, and magnesium, silica, chloride, nitrate, and sulfate contents over the period from June 2020 to October 2021. Colors above
the discharge plot and numbers on the TTi curve correspond to Fig. 6b.
Relation between TTi components and other variables
The correlation matrix presented in Fig. 5a shows a positive correlation
between TTi and component 1 and anticorrelation with components 2, 3, and 4,
which stems from the TTi formula. The anticorrelation is stronger with tyrosine
due to its high digestibility, leading to a higher variability of its
concentrations. Indeed, fluorescent organic matter digestibility decreases
with increasing emission wavelength, and tyrosine has the lower one (Fig. 3).
Components 2 and 3 are strongly correlated, caused by the similarity of
their emission wavelength and thus of their degradation kinetics. The
highest correlation of the first (humic-like) component is found with
electrical conductivity and discharge. The humic-like component has the longest lifetime
of all fluorescent organic matter because of its low digestibility, and it
thus has the highest emission wavelength (Fig. 3). Humic-like organic matter
is thus always present in the system as seen in Fig. 4 and thus may vary
at the same low frequency as electrical conductivity and discharge, while
protein-like organic matter, because of its short life duration, may vary
at high frequency. TTi correlation with magnesium was expected because this
tracer has increasing values with transit time like TTi. But TTi is
surprisingly anticorrelated with SiO2. This anticorrelation is mainly
supported by tyrosine variation. Usually, rock dissolution is the main source
of dissolved silica, inducing increasing contents with transit time. If so,
SiO2 would be correlated with TTi, magnesium, and electrical
conductivity. At Millet, SiO2 is anticorrelated with these elements and
correlated with tyrosine, which has decreasing contents with increasing
transit time, thus indicating similar SiO2 and tyrosine kinetics, both
coming from an organic source (soil). This hypothesis is validated by the
high content of SiO2 in Millet soil water (around 10 mg L-1 in
November 2000; Batiot, 2002).
Correlation matrix of variables (a) and variable contributions to
the three principal components of PCA (b).
To characterize the source of the TTi signal, principal component analysis (PCA)
was performed on 27 Millet spring samples with TTi, TTi components (Tyr, P1,
H1, and H2), and other variables related to transit time (electrical
conductivity, discharge, magnesium, and silica contents). Component 2 Trp was
omitted because of its strong correlation with P1 (0.76, Fig. 5b) and its
lower intensity, as seen is Fig. 3. The three principal axes of PCA explain
about 73.2 % of the total variance. The first principal component (Dim. 1)
represents 36.7 % of total variance, the second (Dim. 2) 25.2 %, and
the third (Dim. 3) 11.3 %. They are carried by different variables as
seen in Fig. 5b. PCA results are provided in Fig. 6.
The first dimension is negatively scored with tyrosine and SiO2 and
positively with Mg2+ and TTi. These variables have high-frequency variation in common. Indeed, tyrosine has the shorter lifetime duration of
tested variables and is linked to silica. Its degradation kinetics is
therefore very short, implying strong variations over time. TTi and
magnesium are in the opposite direction. For TTi, it is because of its
opposition to protein-like organic matter compounds, caused by its
construction. For magnesium, this opposition is caused by the dilution of
stored water by freshwater supply. Dim. 1 thus corresponds to high-frequency
variations led by rain events (daily scale) bringing fresh organic matter
rich in tyrosine and SiO2 and diluting stored water, implying magnesium
decrease.
The second dimension is positively scored with electrical conductivity,
discharge, and humic-like organic matter (component 1). These variables
evolve at low frequency (monthly to seasonal scale), mainly due to the
alternance of low- and high-flow periods. Positive correlation between humic-like
organic matter, electrical conductivity, and discharge, while unexpected, may
stem from the fact that as with TOC, humic-like organic matter results from a mix
of stored and fresh water. Increase in humic-like organic matter can be
caused by the arrival of (i) fresh water with high content of all types of
organic matter and thus TOC content or of (ii) stored water with high
relative humic-like organic matter content compared to the other organic
compounds. Case (ii) seems the most likely because at Millet, the increase
of humic-like is associated with a steady TOC content. This second dimension
therefore seems to indicate a seasonal variation of humic-like organic
matter content due to seasonal storage dynamics, which may induce a seasonal
variation of TTi.
The third dimension is negatively scored with SiO2 and positively with
P1 and magnesium. In comparison with other variables, these two variables
have intermediate-frequency variations. Indeed, P1 emission wavelength is
between the emission wavelengths of humic-like organic matter and tyrosine (Fig. 3) and
thus has an intermediate lifetime duration (from weeks to months). Magnesium
is little explained by Dim. 3 (10.6 %), suggesting that a little part of
magnesium has an organic source (soil). SiO2 is in the opposite
direction, which shows a part of its content coming from rock dissolution or
mineralization.
Variation frequency can easily be linked with transit time because natural
tracers of a precise range, must, by definition, vary in this range. Dim. 1
is thus susceptible to tracing a short transit time at daily scale, Dim. 2 a long
transit time at monthly/seasonal scale, and Dim. 3 an intermediate transit time
at weekly scale.
Projection of samples on the factor plane is consistent with the
different ranges of transit time associated with PCA dimensions (Fig. 6b, d). Samples from the 2020 low-water period are mainly expressed by
Dim. 2 and Dim. 3, corresponding to intermediate- to low-variation frequency.
It is consistent because during low-flow periods, the expected transit time is
long and may correspond to months. These samples are also, to a lesser
extent, expressed by Dim. 1. For example, sample 5 has the lowest Dim. 1
score and corresponds to the reaction to a rain, whereas sample 7, which has
the higher score, corresponds to a dry period during the low-flow period.
The 2021 low-water period has more frequent rainfall events. As compared to
the 2020 low-water period, spread of samples from the 2021 low-water period
is higher on factor planes 1 and 3. Samples from this period are mainly
explained by Dim. 1 and 3, corresponding to intermediate and short transit
time, in agreement with Millet's well karstified system inducing fast
reactions. Flood event samples are expressed by Dim. 1 and Dim. 2 – high- and
low-variation frequency. Samples close to Dim. 2 reflect the flushing out of
stored water (piston effect – samples 12, 16, and 20); they correspond to the
beginning of floods. Sample 1 is aligned with Dim. 1. It corresponds to
freshwater arrival at the end of a flood, thus with short transit time (see
time series in Fig. 4).
Short low-water period samples appear in no particular dimension because
water age after a flood event may differ depending on intensity of previous
floods and piston effect duration and also contain very recent water as seen
in the case of the long-duration, 2021 rainy low water period.
Observation of sample projection on PCA results thus validates the accuracy
of the identified dimensions and the viability of TTi components to
illustrate different ranges of transit time at Millet karstic system. TTi is
therefore able to provide more information about the functioning of complex
karstic systems, even in highly mixed systems like Millet.
Time variability of Transit Time index (TTi)
Lowest TTi values occur in low-flow periods: TTi is 0.3 on average during
2021 low-flow period and 0.5 on average during the 2020 low-flow period
(Fig. 4). It is consistent with the relative transit times expected over
these two periods based on magnesium contents (∼1.3 in 2020
vs ∼0.8 mg L-1 in 2021) and hydrometeorological
conditions as rainfall is more uniformly distributed in time in the 2021
low-flow period than in the 2020 period.
TTi behavior during flood period is complex as it may correlate either (i) negatively (e.g., samples 12 and 15) or (ii) positively (e.g., samples 8–9 and 20)
with discharge. Case (i) is expected when infiltrated fresh water, which is
rich in organic matter (protein-like compounds), is dominant and thus
yields a decrease in TTi. Case (ii) is unusual except at the early stage
of high water when infiltrated water flushes out old water (piston effect).
At later stages it may be related to the ratio of stored to fresh water being too high to
induce a decrease in TTi, for example, in the case of short floods.
It thus appears that TTi is able to identify piston effects and also, as
TOC, to identify a slight proportion of freshwater in a mix. However, TTi is
more sensitive as seen by its punctual correlation with discharge (case (ii)) and its punctual uncorrelation with TOC as in samples 5 to 8 and 14 to
17 (Fig. 4).
Analysis of time variability of TTi at Millet spring thus (i) reinforces the
consistency of TTi variations and (ii) indicates a better sensitivity of this
marker than TOC to freshwater arrivals. Thanks to its higher sensitivity,
TTi also allows for a better understanding of the Millet karstic system where
other natural tracers fail.
PCA performed with Millet spring samples thanks to the following
variables: TTi components, TTi, SiO2, Mg2+, electrical
conductivity, and discharge. Dimensions 1 and 2 are presented in (a) and (b) and
dimensions 2 and 3 in (c) and (d). Panels (a) and (c) show variables. Panels (b) and (d) show individuals, where point color corresponds to hydrodynamic periods (see Fig. 4) and point label to sample number (see
Fig. 4), and the confidence ellipse is 70 %.
Comparison of average TTi values of Vaucluse karst springs
Visual comparison of electrical conductivity, chloride, magnesium, nitrates,
TOC, and TTi distributions from our dataset is provided in Fig. 7. TTi
variability is higher than that of other elements at each monitored spring,
which suggests that TTi is more sensitive. The TTi median value increases from
St Trinit to Nesque springs via Millet spring.
The spring with the lowest TTi values (St Trinit) has the highest karstification
level, thinnest unsaturated zone, and highest nitrate and chloride contents,
which are compatible with shorter median transit times. As compared to St
Trinit, Millet has lower and less variable chloride and nitrate contents
and less variable TOC. The anastomose karst network is assumed to provide a
mixing effect of infiltrated water. We thus suppose that this system is less
affected by fast infiltration than St Trinit. The highest TTi values are found
at the Nesque spring, which is the less karstified system. The relative
distribution of TTi values at St Trinit, Millet, and Nesque springs is
therefore consistent with the expected behavior of a transit time indicator,
depending on their karstification type and thus on their hydrodynamical and
hydrochemical responses. The median value of TTi at Fontaine de Vaucluse spring
is similar to that of Nesque spring. However, the mean water transit time at
Fontaine de Vaucluse spring is expected to be significantly higher than that
of Nesque spring because it is the outlet of a wider system with thicker
saturated and unsaturated zones. This inconsistency is related to the
relatively short timescale of transit times covered by TTi. Maturation of
organic matter components constituting TTi is almost complete after 6 months
(TOC degradation; Batiot, 2002), while the water flowing from the Fontaine de
Vaucluse spring is a mixture of water with a long residence time (several
years) and freshwater coming from rapid infiltrations through the shortcuts
existing in the underground infiltration network (Margrita et al., 1970).
Transit times of most Fontaine de Vaucluse samples are thus probably out of
the range of relevance of TTi to quantitatively identify transit time
values. However, TTi as it is more sensitive may identify freshwater
arrivals in the mix of waters flowing at the Fontaine de Vaucluse spring
during flood events.
Box plots of electrical conductivity, chlorides, magnesium,
nitrates, TOC, and TTi of Fontaine de Vaucluse (FV, 25 samples), Millet (27
samples), Nesque (S. Nesque, 29 samples), and St Trinit (Trinit, 29 samples)
springs.
Way forward towards a transferable qualitative tracer and a quantitative approach
As demonstrated in Sect. 3.2 and 3.3, TTi is a new tool which seems to have a
real potential to be a qualitative natural tracer of transit time. To make
TTi a quantitative natural tracer of transit time, several avenues have to
be explored, such as an artificial tracer test, the use of radiogenic isotopes, or
the study of organic matter degradation kinetics.
The artificial tracer test consists in injecting a tracer in a place known for
its strong and rapid connectivity with the hydrosystem and in monitoring
its restitution at a presumed outlet. It thus informs about the existence of
a path between the injection point and the outlet and provides an estimate
of the transit time between the two. A set of several artificial tracer
tests may provide enough transit time values to quantitatively connect TTi
with transit time. However, to compare TTi with artificial tracer tests, it
is necessary to check that they provide the same information. An artificial
tracer test with uranine, tryptophan, and humic-like organic matter performed
by Frank et al. (2020) in a karst system shows that uranine has the same
transport properties as tryptophan but not as humic-like organic matter. It
therefore seems that artificial tracer tests may not correctly illustrate
the behavior of all the organic matter compounds involved in TTi. Moreover,
not all artificial tracers may be compatible with simultaneous analysis of
natural fluorescence of organic matter. For example, widely used uranine may
overlay the natural fluorescence of protein-like compounds (P1,
tyrosine-like). Indeed, the quantification of artificial tracers in a sample
is performed by spectrofluorescence, exactly like the quantification of
fluorescent organic matter compounds needed to calculate TTi. Some
artificial tracers have emission and excitation wavelengths in the same area
of the EEM of some organic compounds and may thus hide the natural signal
of organic matter. Use of such tracers would not be compatible with
quantification of TTi. Furthermore, some organic matter compounds have the
ability to adsorb themselves to artificial tracers molecules, which would
interfere with the TTi signal. Selection of an artificial tracer in such an
experiment will therefore have to be taken with caution. A more fundamental
remark is that TTi is related to residence time of a mix of water
originating from different paths within the aquifer, while artificial
tracers only trace the fastest circulations due to injection through
well-connected conduits. This observation raises the question of the
comparability of transit time evidenced by both methods. In addition, the
presence of possible injection points is not guaranteed for all
hydrosystems. For example, no possible injection point could be identified
at Millet spring for now. As a conclusion, artificial tracing may be a good
candidate to identify fast infiltration, as long as no interference is
possible (fluorescein usage), and the transit time obtained is not mistaken for
residence time.
A second approach to establish a quantitative link between TTi and residence
time is the use of radiogenic isotopes like beryllium-7, radium, or
radon-222. Price apart, the use of radiogenic isotopes may be
problematic because of the volume of water needed for analysis which reaches
several hundred of litters (e.g 500 L for beryllium-7; Frey et al., 2011),
which reserves its use for water flow points with sufficient discharge for
sampling to be performed within a sensible timescale. The sampling time for
radiogenic isotopes from some flow points in our study can be several days.
Even quite important springs may reach low discharges that prevent such
analysis during low flows. Nevertheless, radiogenic isotopes can be relevant
for linking TTi to transit time values for samples from springs and flows
with a sufficient discharge.
The study of the degradation rate of organic matter is also necessary to
transform TTi as a quantitative natural tracer because it may help to
estimate the duration life of each kind of organic matter in the natural
environment and therefore inform on transit time. In soil, the
biodegradation of labile organic matter was estimated from 2 to 5 d, while
the stable organic matter ranges from 0.2 to 8.6 years (half-life from
Kalbitz et al., 2003). But the biological activity is more important in
soil than in groundwater. Therefore, these lifetimes obtained in soil are
probably shorter than those in groundwater. A recent paper based on the
improvement of the understanding of dissolved organic matter degradation in
groundwater pointed out the lack of knowledge on this subject (McDonough et
al., 2022). As little is known about the behavior of DOM in natural waters,
even less is known about fluorescent organic matter, which is a small part
of the DOM. The paper cited previously therefore discusses a very long transit
time (several years), while we are considering a very short one (weeks to
months). Nevertheless, interesting studies were performed about fluorescent
organic matter degradation kinetics in soils, or in wastewater, testing
different water treatment (De Willigen et al., 2008; Conant et al., 2011;
Baldock et al., 2021; Choi et al., 2017; Guo et al., 2020). These studies
are difficult to perform, but they could be adapted to natural water in order to
improve fluorescent organic matter natural degradation and therefore help in the development of TTi.
The transferability of TTi in different pedoclimatic and anthropogenic contexts
may also be questioned. Indeed, anthropic activities and seasonality may
affect TTi through organic matter production and degradation:
Fluorescent organic matter mainly comes from vegetation related to the plant
cycle. As the vegetation changes with the seasons, the organic matter supply
changes as well, at least in terms of quantity. Seasonality does not
significantly affect organic matter composition, as demonstrated in Musadji et al. (2019). Anthropic activities such as land use or wastewater infiltration
within the hydrosystem can affect both quantity and types of organic matter
compounds because they involve input of external organic matter to the
system. The influence of anthropic activity on the type of organic matter
compounds may be significant and may vary over time. Moreover, the anthropic
activities and the vegetation may vary from one site to another due to
different pedoclimatic conditions and complicate the transposition of a
quantitative TTi.
As TTi is a ratio of different organic matter compounds, it is calculated
independent of the absolute amount of organic matter. Possible bias may
appear when very low input of a specific type of organic matter results in
DOM content below the detection limits. In this case, degradation is
overestimated, which may impact the quantitative relation between TTi and
transit time. Overall, we expect TTi to be little affected by seasonal
variations of productivity, even if it is quantitatively linked to residence
times. As TTi is a ratio, a regular and constant supply of anthropic organic
matter may not impact its variation. But in contrast, a punctual supply of
humic or protein-like organic matter may result in an over- or
underestimation of TTi.
The degradation of organic matter involves interactions with biocenose.
Degradation occurs at different rates depending on the type of organic
matter compounds and on the biodiversity and microbial activity of the soil.
The latter may vary throughout seasons and with anthropic activities because
of varying factors such as sunlight duration, moisture rate, temperature,
climate, or pesticide use.
Variation in space and time in organic matter composition and degradation
rate may thus stem from either anthropic or natural factors. Influence of
anthropic compounds can be circumvented by careful identification and
separation of PARAFAC components. Variation in organic matter composition
and degradation kinetics in different pedoclimatic contexts is not an
obstacle to the qualitative use of TTi but may be a serious limitation to
the transferability of a quantitative link between TTi and residence time.
Variation in organic matter degradation kinetics with time on the same
hydrosystem throughout the year is questionable, but it is beginning to be studied,
as shown by McDonough et al. (2022). A detailed study of the composition of
organic matter source in soil and of its future in groundwater through lab
tests may provide valuable elements to estimate the lifetime of fluorescent
compounds in hydrosystems and thus to quantitatively link TTi with transit
time.
Conclusions
Groundwater from karst aquifers is an important resource for drinking water
supply in the world (Stevanović, 2019). Soils from carbonate aquifers
are generally poorly developed, which, combined with the rapidity of
groundwater fluxes within karst conduits, explains the vulnerability of
these aquifers to contamination. To face the challenge of the protection of
karst water resources, several specific hydrogeochemical tracers have been
developed by the community to characterize the different types of fluxes and
recharge. One of the main current challenges is to develop natural tracers
able to estimate water transit times for short time ranges of the order of
0 to 6 months. The main purpose of this work was to study the potential of
the ratio of heavy- to light-weight organic compounds (HIX) as a natural
tracer of short transit time in karst systems with a strong fast
infiltration component in order to characterize the vulnerability of the
aquifer.
Critical analysis of former studies showed that although the link between
HIX and transit time seems consistent, the whole methodological approach
needed to be consolidated. Natural fluorescence from 289 groundwater samples
from four springs and 10 flow points located in the unsaturated zone of the
Vaucluse karst system was characterized by parallel factor analysis
(PARAFAC) of the EEM, thus (i) allowing for the identification of main
fluorescent compounds of sampled groundwater and (ii) evidencing the
inadequacy of HIX emission windows to characterize groundwater organic
matter. We then proposed a new humification index called the Transit Time index
(TTi) based on the Ohno (2002) formula but using 3D PARAFAC components of heavy
and light organic matter instead of 2D windows.
Finally, we evaluated TTi relevance as a potential transit time tracer by
(i) performing a detailed analysis of its dynamics on a selected spring
(Millet spring) and (ii) comparing its mean value over karst springs of the Fontaine de Vaucluse system. Principal component analysis (PCA) of TTi, TTi
components, and other hydrochemical parameters monitored at Millet spring put
in relief the timescales of variability associated with the different
organic matter compounds, which we relate to their digestibility. PCA
results also provided evidence that TTi can detect a small proportion of
fast infiltration water within a mix, while other natural tracers of transit
time provide no or less sensitive information. Relative distribution of TTi
at monitored karst springs is also consistent with relative transit times
expected for small-scale, short average transit time systems. TTi is
therefore consistent with other natural tracers of transit time and provides
qualitative complementary results. This qualitative approach of transit time
based on TTi is transferable to other karst sites, even in the case of
anthropic contamination, thanks to PARAFAC modeling.
To be a quantitative tracer of water transit time, TTi needs to be linked
with other tools providing quantitative approaches such as radiogenic
isotopes, artificial tracer tests, or experimental studies of the degradation
kinetics of organic matter. Transferability of the quantitative relation
between TTi and transit time from one karst system to another may however be
challenging because of organic matter supply variability, which depends on
the hydro-pedoclimatic context and anthropic activities.
Code availability
The code is not publicly accessible as it was built following the steps of the package developer available here: https://cran.r-project.org/web/packages/staRdom/vignettes/PARAFAC_analysis_of_EEM.html
(last access: 27 October 2021).
Data availability
Discharge time series of Millet spring used in this study are available at 10.15148/bbae7eab-8abd-40d9-834e-9a0683e59da5 (Mazzilli et al., 2022).
For all the springs, chemical parameters are available at 10.15148/7b94438a-7bb2-4382-bb25-a4a0c3fdc5d7 (SNO KARST, 2021), and the raw excitation–emission matrix of organic matter fluorescence is available at 10.15148/8d6104e1-ae78-4b4e-8e50-198ccc5b19c9 (Serene et al., 2022).
Author contributions
MB and the SMBS (https://www.lasorgue.fr/, last access: 27 September 2022) took
water samples that were analyzed for major elements, TOC, and water stable
isotopes by MB, JD, and RS and for fluorescence
of organic matter by LS. Formal data analysis was
performed by LS and NM. CBG, CE, NM, and LS
provided critical feedback and helped to shape the research and the
analysis. GM acquired funding. LS
prepared the manuscript with contributions from all co-authors.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
This work was performed within the framework of the FDV/LSBB observation
site, which is part of OZCAR (French network of Critical Zone
observatories), SNO KARST (French observatory network, https://sokarst.org/, last access: 27 September 2022) initiative of INSU/CNRS, which seeks to support knowledge
sharing and promote cross-disciplinary research on karst systems, and of the
H+ observatory network. The authors would like to express their gratitude to the LSBB team for their
technical and logistic help. A special acknowledgement is given to SIAEPA from the
Sault region and Veolia for giving us access to Nesque spring.
Financial support
This research has been supported by Total (grant no. 187737) and the Montpellier Université d’Excellence
(GAIA competition 2019).
Review statement
This paper was edited by Zhongbo Yu and reviewed by Weiquan Dong and one anonymous referee.
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