HESSHydrology and Earth System SciencesHESSHydrol. Earth Syst. Sci.1607-7938Copernicus PublicationsGöttingen, Germany10.5194/hess-21-6153-2017The potamochemical symphony: new progress in the high-frequency acquisition
of stream chemical dataFlouryPaulfloury@ipgp.frGaillardetJérômegaillardet@ipgp.frhttps://orcid.org/0000-0001-7982-1159GayerEricBouchezJulienhttps://orcid.org/0000-0003-4832-1615TallecGaëlleAnsartPatrickKochFrédéricGorgeCarolineBlanchouinArnaudRoubatyJean-LouisInstitut de Physique du Globe de Paris (IPGP), CNRS and
Université Sorbonne Paris-Cité, 1 rue Jussieu, 75238 Paris, FranceUR HBAN, Institut national de recherche en sciences et technologies
pour l'environnement et l'agriculture, Antony (IRSTEA), FranceEndress+Hauser SAS, Huningue, FrancePaul Floury (floury@ipgp.fr) and Jérôme Gaillardet (gaillardet@ipgp.fr)7December201721126153616510January201713January201712September201729October2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://hess.copernicus.org/articles/21/6153/2017/hess-21-6153-2017.htmlThe full text article is available as a PDF file from https://hess.copernicus.org/articles/21/6153/2017/hess-21-6153-2017.pdf
Our understanding of hydrological and chemical processes at the
catchment scale is limited by our capacity to record the full breadth of the
information carried by river chemistry, both in terms of sampling frequency
and precision. Here, we present a proof-of-concept study of a “lab in the
field” called the “River Lab” (RL), based on the idea of permanently
installing a suite of laboratory instruments in the field next to a river.
Housed in a small shed, this set of instruments performs analyses at a
frequency of one every 40 min for major dissolved species (Na+,
K+, Mg2+, Ca2+, Cl-, SO42-, NO3-)
through continuous sampling and filtration of the river water using
automated ion chromatographs. The RL was deployed in the Orgeval Critical
Zone Observatory, France for over a year of continuous analyses. Results
show that the RL is able to capture long-term fine chemical variations with
no drift and a precision significantly better than conventionally achieved
in the laboratory (up to ±0.5 % for all major species for over a
day and up to 1.7 % over 2 months). The RL is able to capture the
abrupt changes in dissolved species concentrations during a typical 6-day
rain event, as well as daily oscillations during a hydrological low-flow
period of summer drought. Using the measured signals as a benchmark, we
numerically assess the effects of a lower sampling frequency (typical of
conventional field sampling campaigns) and of a lower precision (typically
reached in the laboratory) on the hydrochemical signal. The high-resolution,
high-precision measurements made possible by the RL open new perspectives
for understanding critical zone hydro-bio-geochemical cycles. Finally, the
RL also offers a solution for management agencies to monitor water quality
in quasi-real time.
Introduction
Rivers are messengers from the critical zone. The chemical composition of
rivers offers a window into the multiple processes that operate among water,
organic matter, primary and secondary minerals and living organisms at the
Earth's surface (Calmels et al., 2011; Feng et al., 2004; Kirchner et al.,
2000, 2001; Neal et al., 2012, 2013).
Understanding the parameters that control the composition of river water is
not only a scientific challenge but also one of the major challenges for
humanity to access and preserve drinkable water (Bain et al., 2012; Banna et
al., 2014; Bartam and Ballance, 1996). A limit in our understanding of water
geochemistry at the Earth's surface is limited by the temporal resolution at
which sampling can be operated (Whitehead et al., 2009). As summarized by J.
Kirchner: “If we want to understand the full symphony of catchment
hydrochemical behaviour, then we need to be able to hear every note.”
(Kirchner et al., 2004, p. 1358). Yet, taking high-frequency sample sets
back to the laboratory, filtering and analysing them for several elements is
limited by the requirement of considerable human resources (Chapman et al.,
1996; Danielsen et al., 2008; Halliday et al., 2015; Neal et al., 2013;
Rozemeijer et al., 2014; Strobl and Robillard, 2008; Telci et al., 2009).
A significant number of studies have reported high-frequency chemical
measurements in watersheds. Thus far, these data have been mostly acquired
during limited periods of time such as single storm events or a day (Beck et
al., 2009; Brick and Moore, 1996; Chapman et al., 1997; Gammons et al., 2007;
Kurz et al., 2013; Liu et al., 2008; Morel et al., 2009; de Montety et al.,
2011; Neal et al., 2002; Nimick et al., 2011, 2005; Takagi, 2015; Tercier-Weaber et al., 2009). Although these studies clearly
highlighted the wealth of information provided by sampling rivers at
sub-hourly frequency, they underestimate the legacy of past hydrological
episodes (Kirchner, 2006; Jasechko et al., 2016; Rode et al., 2016) and are
of limited use when mass budgets are to be calculated for a typical
hydrological cycle.
To date, the best combination of high-frequency and long-term monitoring
ever reported for river chemistry is a 7 h frequency sampling over 18 months (Neal et al., 2012). In this study, the authors demonstrate the “act
of discovery” permitted by such sampling scheme, by showing that the high
sampling frequency of river hydrochemistry over sufficiently long time spans
reveals patterns related to hydrological and biological drivers that are
imperceptible at lower sampling frequency. Automated approaches, developed
using probes installed directly in the river (Rozemeijer et al., 2010a;
Macintosh et al., 2011; Cassidy and Jordan 2011; Dåbakk et al., 1999;
Glasgow et al., 2004; Zhu et al., 2010; Yang et al., 2008), or using online
instrumental devices in which continuously pumped water is injected
(Rozemeijer et al., 2010b; Zabiegala et al., 2010; Jordan and Cassidy, 2011),
are alternatives to sampling methods requiring human intervention. Several
papers have been published over the last decade reporting existing devices
mostly focused on monitoring dissolved N or P and organic matter (Clough et
al., 2007; Kunz et al., 2012; Aubert et al.,
2013b;
Escoffier et al., 2016). A recent overview of the potential of available
conductivity, dissolved oxygen and carbon dioxide, nutrients, dissolved
organic matter and chlorophyll in situ probes is given by Rode et al. (2016). A
new solution for high-frequency measurement of river chemistry is offered by
bringing the laboratory's measuring devices to the field (the “lab in the
field” concept). A Swiss group has recently developed such a system (von
Freyberg et al., 2017) by installing ionic chromatography devices in a hut
next to a stream. In this paper, we present a parallel initiative named the
River Lab (RL) and funded by the French programme CRITEX: “Innovative sensors
for the temporal and spatial EXploration of the CRITical Zone at the
catchment scale” (https://www.critex.fr). This approach, like the
previously published one, overcomes traditional limitations on the number of
samples and avoids several issues related to sample transport, filtration
and storage. The RL is able to perform a complete chemical analysis of all
inorganic major anionic and cationic species in the dissolved load of river
water using ion chromatography (IC), with a frequency of up to one complete
measurement every 40 min.
This article is a proof-of-concept paper that describes the analytical
design of the RL and its performance by evaluating the precision,
reproducibility and accuracy of concentration measurements. The first
results from the RL reveal a significant improvement in reproducibility
compared to conventional sampling and analysis techniques. Leveraging these
optimal analytical conditions, the RL is able to reveal temporal patterns of
river chemistry, such as daily concentration variations. The RL opens thus
new opportunities in the field of river chemistry research and environmental
monitoring.
Monitoring site
The RL was installed in the Orgeval Critical Zone Observatory, located 70 km
eastward from Paris, France (https://gisoracle.irstea.fr/), a
temperate agricultural catchment, within the Seine River watershed, and part
of the French Critical Zone Research Infrastructure OZCAR (“Observatoires
de la Zone Critique, Applications et Recherche”). The Orgeval catchment is
one of the most instrumented and documented river observatories in France,
with 50 years of hydrological data (Garnier et al., 2014). Catchment
hydrologic data are available on the ORACLE website
(https://bdoh.irstea.fr/ORACLE/).
The RL is installed at the outlet of the Avenelles River, a sub-catchment in
the Orgeval watershed. The Avenelles River drains an area of 45 km2.
The climate is temperate and oceanic, with cool winters (mean temperature
3 ∘C), warm summers (20 ∘C on average) and an annual
precipitation rate of ∼ 650 mm on average. The Avenelles
sub-catchment sits within the sedimentary carbonate-dominated Paris Basin.
The river is perennial, supplied by groundwater from the Brie aquifer, with
water chemistry dominated by Ca2+, SO42-, HCO32-
and NO3- ions. The water level at the Avenelles gauging station
shows an average daily volumetric flow rate of 0.2 m3 s-1 (from 1962 to
2016), with low water period in summer (0.1 m3 s-1) and flash flood events
reaching 10.4 m3 s-1 in spring.
Design of the River Lab
The concept of the RL is to pump river water and feed it to a set of
physico-chemical probes and ion chromatography (IC) instruments for a
complete analysis of major dissolved species continuously at high frequency
(40 min is needed for a complete analysis). All the instruments of the
RL fit into an isolated bungalow of 4 m length by 2.5 m width, kept at
24 ∘C ± 2 ∘C. The RL was designed by IPGP
(Institut de Physique du Globe de Paris, France) and IRSTEA (Institut
National de Recherche en Sciences et Technologies pour l'Environnement et
l'Agriculture, France) and assembled by Endress & Hauser
(E+H®) (Fig. 1). A technical diagram is
available in the Supplement (Fig. S1).
Diagram of the Orgeval River Lab. Bold blue arrows indicate the
primary circuit of unfiltered water. Dashed arrows indicate filtered water
supplied to IC instruments. (1) The inlet of the primary circuit samples the
river at a constant 20 cm depth maintained by buoys. Water is first filtered
through a < 2 mm pore size strainer. The distance between the mouth
and the pump is 6 m. The primary circuit assembly is almost entirely
composed of polyvinyl chloride (PVC) pipes. (2) The electric pump runs
continuously at a constant power, leading to a rate of 700 L per hour.
(3) Almost all the river water just flows through the pipe and remains
unfiltered. A fraction is filtered through a 2 µm tangential
stainless steel filtration unit, then filtered through a 0.2 µm
cellulose acetate frontal filter prior to being delivered to IC
instruments at a flow rate of 1 L per hour. (4) A multiport valve before
introduction to the IC instruments allows for switching between filtered
river water and standard or blank solutions. (5) All probes are deployed in
an overflow tank of 5 L of unfiltered river water. (6) The outlet of the
primary circuit is downstream in the river.
The RL has been designed around a primary circuit, which pumps the river
water at 700 L per hour. First, the unfiltered river water sampled in
the middle of the stream (Fig. 1) continuously supplies an overflow tank
where six parameters are measured: pH, conductivity, dissolved O2,
dissolved organic carbon (DOC), turbidity and temperature. The water is then
released into the river downstream from the RL. The turnover time of water
in this primary circuit is 2 min. The turbidity probe is installed
upstream of the overflow tank in a pipe perpendicular to the flow to provide
more accurate measurements. The turbidity and DOC probes benefit from an
automatic self-cleaning every 5 min using compressed air. For all
probes, the frequency of acquisition is one measurement per minute. The tank
and each probe are hand-cleaned weekly. All probes are developed and
provided by Endress & Hauser (E+H®).
Second, a fraction of water pumped through the primary circuit feeds another
circuit directed toward two IC instruments for the measurement of major
dissolved species concentrations. A filtration system is deployed between
the primary circuit and the IC instruments, consisting of a tangential
filter with a 2 µm pore size, followed by a 0.2 µm frontal
filtration system through cellulose acetate filters (Fig. 1), crucial for the
IC instruments. Cation and anion chromatographs, connected in series, are
fed simultaneously every 40 min from the filtered water circuit through
a injection valve. Between two injections, the water in the filtered circuit
is constantly renewed (1 L per hour). Our tests show that the frequency for
a complete and uncontaminated analysis of cation and anion is actually
limited by the filtration device (see Sect. 4.3).
Assessment of the RL accuracy and instrumental drift based on
concentration measurements made after several injections of the standard
solution “River ×1”. The uncertainty on the calibration solution is the
quadratic sum of the uncertainty on the standard solutions (provided by the
manufacturer) and the overall uncertainty for weighing during solution
preparation. Measurement errors over 1 week and over 2 months are
expressed as the relative standard deviation (RSD) calculated for repeated
injections of the solution “River ×1” directly into the IC instruments via
the multiport valve (see Fig. 1).
Mg2+K+Ca2+Na+SO42-NO3-Cl-Calibration concentration10.03.0130.010.070.060.040.0Uncertainty (mg L-1)0.030.010.390.030.840.840.28Uncertainty (%)0.30.450.30.31.21.40.7One measurement (injection of “River ×1” solution 4 times successively) Number of measurements(4)(4)(4)(4)(4)(4)(4)Average (mg L-1)10.083.00129.869.9870.2660.3140.32SD (mg L-1)0.020.010.160.020.690.630.27RSD (%)0.160.270.120.210.860.740.33One week (injection of “River ×1” solution every 8 h) Number of measurements(19)(19)(19)(19)(19)(19)(19)Average (mg L-1)10.133.02130.6410.0170.5460.6340.44SD (mg L-1)0.030.010.390.020.670.440.22RSD (%)0.280.320.300.220.960.720.54Two months (injection of “River ×1” solution every 2 days) Number of measurements(28)(28)(28)(28)(25)(25)(25)Average (mg L-1)10.333.14134.3410.0570.0562.3340.57SD (mg L-1)0.060.040.800.051.170.550.43RSD (%)0.541.340.590.501.680.921.07
The IC analysis is performed using two Dionex®
ICS-2100 (Thermo Fisher Scientific®) instruments
using eluent produced with concentrated eluent cartridges and ultra-pure
water (Fig. 1). The cationic species measured are Na+, K+,
Mg2+ and Ca2+, and anionic species are Cl-, NO3-
and SO42-. The chosen analysis time is 30 min (40 min if
Sr2+ concentration measurements are included; see details in Supplement “Ion
Chromatographs characteristics”). The multiport valve installed upstream of
the ICs allows us to check the drift of the instruments and the background
signal by regular introduction of calibration solutions and pure distilled
water (see Sect. 4). Pure distilled water is regularly (every 2 weeks)
introduced to check the residual noise. Both cationic and anionic
chromatographs are calibrated every 2 months using synthetic solutions
mimicking the river chemistry, made from 1000 ppm mono-elemental standard
solutions. Two sets of calibration solutions are prepared, one for anions
and the second for cations. The first solution (called “River ×1”) is
prepared based on concentrations of the river water during summer, i.e. with
the highest measured concentrations for most species. In the second
solution, these concentrations are doubled (called “River ×2”). Further
solutions are produced out of River ×1 and ×2 through dilution by up to
tenfold to achieve lower concentrations (“River ×0.5;×0.25;×0.1”). The
resulting five calibration solutions cover the entire range of possible
natural variability of each species observed for the Orgeval River,
including flood events.
Data from probes and ICs are collected, merged and updated in a single
database in real time. Data from the gauging station (flow discharge and
precipitation level) are automatically added to the database. Several
parameters of the RL can be remotely monitored such as pump activity,
pressure, flow and temperature in the primary circuit; activation of the
tangential filtration cleaning system; instrument connection; and
temperature in the bungalow. A set of alarms and sensors controls each key
point of the system. An email is automatically sent in case of dysfunction.
Under normal operating conditions, the RL needs human intervention only once
a week.
Precision on concentration measurements of the whole RL system
calculated as the relative standard deviation (RSD) of concentration
measurements made over three 24 h closed-loop experiments, during which
the inlet and the outlet of the primary circuit are connected through a
300 L tank of river water.
DateNumber ofMg2+K+Ca2+Na+SO42-NO3-Cl-measurementsRSD (%)20 July 2015(22)0.170.900.210.220.390.470.2428 August 2015(20)0.320.630.310.360.200.250.1917 April 2016(35)0.381.200.170.310.310.380.30Analytical performances of the River Lab
RL data acquisition started on 12 June 2015. The reliability
of the system was assessed through five different tests involving IC
measurements and the sampling procedure (accuracy, drift, precision of the
whole system, cross-contamination and reproducibility). We refer to the
third edition of JCGM 200-2012 (Joint Committee for Guides in Metrology)
(JCGM, 2012) for the terminology used in assessing the performance criteria.
Accuracy and instrumental drift
The aim of the RL is to achieve very high-frequency measurements of river
chemistry over long periods of time (pluriannual). To compensate for any
long-term drift in the IC calibration, instruments are calibrated with a new
set of solutions every 2 months or after each maintenance operation on the
IC instruments. However, calibration drift can occur over timescales shorter
than 2 months, resulting in systematic and/or random errors in
concentration measurements. We evaluated this effect using a set of
injections of the “River ×1” solutions, over 1 week and over 2 months
(Table 1). For all species measured, no systematic variation was observed in
the measured concentration of the solution “River ×1”, showing that at the
two timescales, instrumental drift does not induce any systematic bias on
concentration measurements, and that most of the error is of random nature.
Therefore, the standard deviation of the concentration measurements of a
given solution can be used as a reliable measure of the error due to
instrumental drift. The measurement error over 1 week is calculated as the
standard deviation of concentration measurements over 19 injections of
solution “River ×1” performed every 8 h during 1 week (from
5 to 12 November 2015). The measurement error over 2 months is calculated as the standard deviation of concentration measurements
over a series of injections performed every 2 days during 2 months (from
28 December 2015 to 26 February 2016). These error
estimates are lower than 1 % over 1 week and lower than 1.7 % over
2 months (Table 1). The agreement between the calculated concentrations of
the “River ×1” solution and the RL measurements also demonstrates the
accuracy of the prototype (Table 1).
Precision of the whole system
In order to estimate the precision of the whole system (IC instruments
combined with the sampling device including the primary circuit, the pump
and the filtration units), we performed a “closed-loop experiment” over the
course of 1 day by connecting the inlet and the outlet of the primary
circuit to a 300 L tank containing river water. The test was performed 3 times over two different seasons (on 20 July 2015, 28 August 2015 and 17 April 2016). The conductivity probe
(one measurement every minute) was used to check the stability of the water
chemistry during the course of the experiment (Fig. S2). Our results show
that a lapse of 2 h at least is necessary for the system to stabilize,
corresponding to the homogenization time of the water within the closed loop
(Fig. 2). After 2 h, major anion and cation concentrations show a
remarkable stability, indicating the absence of drift over of 24 h time
lapse despite the temperature variations in the river water, and allowing us
to estimate the precision of the whole system over 1 day using the
standard deviation of the measurements performed during the test. The
results of the test are presented in Table 2. The precision reached is better
than 0.5 % for all species except for potassium, for which it is better
than 1.2 %.
Assessment of the precision (in deviation from the mean for four
dissolved species) of the whole RL system including the primary circuit,
filtration systems and IC instruments (17 April 2016). A closed
system is established on the primary circuit of the RL by connecting the
inlet and the outlet through a 300 L tank of river water. The system is then
run for a period of 24 h. The time between two IC analyses is 40 min. The purple curve represents data of temperature of the water in the
tank. We do not consider the first 2 h (three first measurements),
corresponding to the homogenization of water in the circuit and tank (see
conductivity measurements in Fig. S2) for the calculation of precision.
Cross-contamination
The ability of the RL to detect rapid variations in river chemistry
(typically expected during storm events) depends on (1) the response time of
the RL to a perturbation in the river and (2) the potential cross-contamination from one sample to the next one. We assessed these two effects
by a tracer injection experiment. After establishing a closed-loop
experiment (on 29 August 2015) and allowing for the period of
stabilization, we introduced a known amount of NaCl (200 g previously
dissolved in a small amount of river water) into the 300 L tank of river
water in order to simulate a “spike” in the river chemistry. The
monitoring of conductivity in the primary circuit allowed us to follow the
propagation of the spike injection into the primary circuit while Cl-
concentrations measured by the IC every 40 min allowed us to follow its
propagation through the filtration devices and IC instruments (Fig. 3). The
conductivity probe shows that the salinity spike is detected very quickly
and stabilized after 5 min. This indicates that the water in the primary
circuit is quickly homogenized (in agreement with the high flow rate of the
primary circuit: 700 L h-1). Conversely, the Cl- and Na+
concentrations only reach the expected concentration at the second IC
measurement, i.e. after 80 min.
Cross-contamination assessment and response time of the RL system
after a spike injection of 200 g of NaCl. A closed system is established on
the primary circuit of the RL by connecting the inlet and outlet through a
300 L tank of river water prior to the injection. The conductivity
measurement frequency is 1 per minute, whereas the time between two
measurements of chloride concentration is 40 min. Error bars for
conductivity and Cl- concentration measurements are within symbols'
size. Results are normalized to the difference between the minimum value,
before the tracer injection (0 %), and the maximum value, at the end of the
experiment (100 %).
The first IC measurement following the spike injection indicates that only
93 % of the final steady-state concentration is reached, revealing a
contamination of the (n)th sample by 7 % of the (n-1)th sample.
In practice, such a contamination will only be significant if the
instantaneous derivative of river concentration with time is important. In
the case of the Orgeval River, where the RL is deployed, the relative
derivative of the concentration with respect to time is lower than 1 % per
hour for 90 % of the time for all species. In this case, the
cross-contamination induces an error of 0.07 % compared to the true
concentration, which means that the effect of cross-contamination is
negligible compared to the precision of the RL (see Sect. 4.2). However,
in the case of flood events, when the stream flow increases quickly, the
derivative of concentration can change by more than 10 % per hour. In such
cases, cross-contamination will induce an error of 1 % or more. The
injection test shows that the time resolution of the RL is limited by the
transfer time of the water between sampling and injection into the IC
instruments. This transfer time of the water in the RL is mainly due to the
design of the filtration system, which may be improved in the future.
Reproducibility: RL vs. laboratory
As a final test for assessing the ability of the RL to record fine natural
variations of river chemistry in comparison to conventional techniques of
filtration and analyses in the laboratory, we focused on 2 days in the
summer of 2015 following long periods without rain (21 July 2015
for cations and 19 April 2016 for anions), which showed very high
resolution diurnal variations (< 5 % relative) in chemical
composition of the Orgeval River. In addition to the analyses made by the RL
every 40 min, we conducted hourly sampling of the river by collecting 5 L of water and filtering it immediately using a
Teflon® frontal filtration unit
(Sartorius®) with 0.2 µm porosity
polysulfonether filters. Bottles of acidified (at pH = 2) and unacidified
river water were transported to the laboratory at IPGP for measurement of
major cations and anions, respectively, using IC devices similar to those
installed in the RL (Thermo Fisher® ICS-2100).
In the laboratory, measurements were performed using a Thermo
Fisher® ICS-5000 for cation measurements and a
Dionex® 120 from Thermo
Fisher® for anion measurements. The
calibration procedure in both laboratory and RL is the same, using the same
set of calibration solutions. The error measurement reached in the
laboratory is estimated at 1 % through repeated injections of the standard
solution “River ×1” (every five samples). Comparison between the RL and the
laboratory for the seven measured species are shown in Fig. 4. First, the
measurements made by the RL are more precise than those performed in the
laboratory, a feature that can be primarily attributed to the greater
stability of the continuously working injection system of the RL. Second,
the fine variations measured by the RL are reproduced in the laboratory,
validating the observed diurnal variations and supporting the reliability of
the RL to detect changes of the order of a percent within a day. The third
observation is that small yet systematic offsets between the two sets of
data exist, up to 3 % for Mg. One possible explanation for this difference
is that the filtration procedures differed between the RL and the manual
sampling, which may have led to a discrepancy in the concentration
measurements related to the potential for some elements to be hosted in the
colloidal phase (Dupré et al., 1999). In addition, the most accurate
measurements were obtained with the RL rather than with the laboratory
equipment because the RL is continuously processing solutions with a similar
matrix, thereby minimizing memory effects and cross-contamination that can
compromise measurements if widely differing samples are run successively on
the same instrument. These features of the measurement protocol,
representative of most laboratory workflows for hydrochemical measurements,
are likely to lead to inaccuracies. Regardless of the observed discrepancy
between the two sets of measurements, we note that variations in
concentration recorded by the RL and measured at the IPGP laboratory have
the same amplitudes and are synchronous.
Reproducibility assessment of IC measurements made by the RL every
40 min (blue), compared with concentration measurements made in the
laboratory after conventional hourly river sampling (orange). Tests were
performed on 21 July 2015 and 19 April 2016 for the cationic
and ionic species respectively. For measurements performed in the
laboratory, the error measurement is 1 % (except for K+ at 2 %),
calculated as the standard deviation over repeated injection of the standard
solutions “River ×1”. For RL measurements the error is given in Table 2.
DiscussionWhat are the benefits of bringing the lab into the field?
The RL presented above allows us to record continuously, at a high frequency
and over long spans of time, the concentration of seven major dissolved species
in a river system. Although this is beyond the scope of the present paper,
the RL presented here opens new possibilities for the exploration of the
fine structure of hydrochemical evolution at the catchment scale and for
improved understanding of the associated hydrological, geochemical and
biological processes. From a technical point of view, our study shows that
deploying the conventional laboratory measurement techniques in the field
adds significant value. The tests performed and reported above clearly
demonstrate an improvement in precision compared to the analysis of bottled
samples taken back to the lab. We see three main reasons for this
improvement.
In a given river, dissolved concentrations typically vary by less than
1 order of magnitude when water discharge changes by several orders of
magnitude (Godsey et al., 2009). This constancy allows us to select a
relatively narrow range of concentration for establishing specific
calibration curves of the IC instruments, a condition which is rarely
possible in the laboratory, where different kinds of samples are analysed.
While in the laboratory samples are injected discretely, in the RL river
water samples are injected as a continuous flow. Thus, the primary circuit
and the filtration system operate continuously at a constant pressure, which
supports stable and accurate analyses.
The third factor is the experimental conditions in the bungalow. The
temperature is maintained at 24 ∘C ± 2∘ (in
addition to the 40 ∘C thermostatically controlled temperature in
the column, precolumn and detection device of the ICs) allowing for better
stability of the IC measurements. Moreover, the RL IC instruments are never
stopped, which favours stability.
What is revealed by a higher sampling frequency?
To our knowledge, the high frequency of measurements (one measurement every
40 min) reached by the RL installed on the Orgeval River is the highest
ever reported for stream chemistry over several months. To highlight the
corresponding improvement in the recorded concentration signal, we tested
the effect of sampling frequency on the concentration signal. First, we
artificially subsampled the RL original signal at two lower sampling
frequencies: every 7 h (starting 5 October 2015 at 10 pm) and
every 24 h. The 7 h frequency was chosen to reproduce the sampling
frequency of Neal et al. (2012) made in the Plynlimon watershed, Wales. The
daily sampling frequency is typically what is achievable on the long term by
“human grab-sampling” in the field. Second, we calculated the probability
density function (PDF) of concentration measurements over a given time
interval. The use of PDFs allows us to explore the structure of
concentration signals beyond the mean concentration, which constitutes an
important metric for river solute budget, but lacks any insight into the
variations in concentrations that can be used to retrieve information on
catchment processes. We describe the PDF by three statistical parameters: mean,
standard deviation and skewness. Skewness indicates the distribution
asymmetry, both in magnitude and direction (a positive skewness means that
most values are higher than the mean). Altogether, the three parameters
account, at first order, for the structure of a concentration signal. We
compared these three parameters for the computed PDFs to quantify the signal
degradation induced by artificial subsampling.
We applied this statistical approach to two representative periods of the
hydrological cycle of the Orgeval Critical Zone Observatory: a typical 6-day
rain event caused by the arrival of a wet, Atlantic meteorological front (in
October 2015) and a dry summer low water stage period (July 2015), where the
stream is essentially sustained by groundwater, during an apparently steady
hydrological period. We first present the behaviour of calcium and sulfate
concentrations as an example during the two considered periods (Figs. 5 and
6), before generalizing to all measured species (Supplement
and Figs. S3, S5 and S6).
(a) Calcium concentration and stream flow in the Orgeval River during
a rain event (from 1 to 25 October 2015), sampled every 40 min (RL
original signal at 40 min frequency) and artificially subsampled every
7 h and every day at 10:00. Black dots represent data during the rain
event strictly (from 5 to 10 October 2015 at 10:00), over which
probability density functions (PDFs) of concentration are calculated and
represented as histograms (b). For each PDF, the following
statistical parameters are calculated: average (Ave.), standard deviation
(SD), and skewness (Skew.). Grey dots represent concentration values
outside of the rain event and are not considered in the corresponding PDF.
The two statistical parameters standard deviation (SD) and skewness
(Skew.) are not calculated for the daily subsampling because of the too
small number of points.
(a) Sulfate concentration in the Orgeval River during a summer event
(from 7 to 19 July 2015) sampled every 40 min (RL original signal)
and artificially subsampled every 7 h, and every day at 14:00.
Probability density functions (PDFs) of concentration are represented as
histograms (b). For each PDF, the following statistical
parameters are calculated: average (Ave.), standard deviation (SD), and
skewness (Skew.).
Rain event. The Ca concentration time series recorded at a 40 min
frequency shows that minimum Ca concentrations are recorded at maximum water
discharge, but this relationship is invisible at lower sampling frequency
(Fig. 5). Narrow peaks during the maximum of the stream flow are unresolved
at a daily or 7 h frequency. The comparison of the calculated PDFs
shows that a bimodal character is captured at all frequencies. The average
and standard deviation are not significantly affected by the sampling
frequency, with a relative difference of less than 2 % for the values of
these parameters between the three distributions. However, the skewness
values vary among the different records. From the 40 min frequency to
the daily frequency signals, the skewness is weaker, which means that even
if the overall concentration variability is well captured at the lower
sampling frequencies, the concentration signal is clearly degraded. This
degradation is particularly intense during the middle of the rain event,
where the concentration signal evolves quickly.
Summer event. Despite the absence of rain events during the 2015 summer, the
River Lab recorded high-frequency variations revealing a diurnal structure
with 7 % relative variations between day and night. Each element exhibits
its own type of daily variation in terms of amplitude and regularity.
Figure 6 shows that the structure of this signal is altered when the
sampling frequency decreases. While these daily variations are still
captured when sampling occurs every 7 h, their amplitude is somewhat
altered (5 %) compared to the 40 min sampling frequency (8 %). The
daily variability of the signal is absent on the daily sampling frequency.
While the mean remains the same over the range of sampling frequency, the
variability quantified by the relative standard deviation decreases with
lower sampling frequency, by up to 50 % for the daily frequency compared
to the 40 min frequency signal, indicating a significant loss of
information. The skewness of the concentration distribution recorded at a
subsampled daily frequency has a value that is opposite in sign compared to
the other two frequencies, indicating that there is an inversion of the
measured asymmetry of the PDF at lower sampling frequencies. Therefore, too
coarse a sampling frequency can yield a strongly altered signal compared
to higher frequencies, resulting in a biased shape of the distribution of
the concentrations.
Generalization. The resampling approach applied above is generalized and
expanded to other elements for both the summer and rain events. The
generalization to all species measured is presented in the Supplement. In Figs. 5 and 6, we arbitrarily chose the hour of sampling
(10:00 and 14:00 for Figs. 5 and 6, respectively). In Figs. S3, S5
and S6, the subsampling is performed at each of the possible sampling
hours. This statistical analysis quantitatively demonstrates that such high-frequency measurements are able to capture the day–night chemical cycles of
the Orgeval River. Given the amplitude and duration of typical rain events
in the catchment, the alteration of the signal by lowering the sampling
frequency is less critical but still significant during these periods
(Supplement; Figs. S3, S5 and S6).
What is revealed by better analytical precision?
(a) Calcium concentration and stream flow in the Orgeval River during
a rain event (from 1 to 25 October 2015), as recorded by RL and for two
artificially degraded signals using a normally distributed noise with
standard deviation of 2 and 4 %, to reflect the effect of decreased
analytical precision. Black dots represent data during the rain event
strictly from 5 (12:00) to 10 October 2015. The probability density
functions (PDFs) of concentration are calculated and represented as
histograms (b). For each PDF, the following statistical
parameters are calculated: average (Ave.), standard deviation (SD) and
skewness (Skew.). Grey dots represent concentration values outside of the
rain event, which are not considered for the analysis presented in (b).
(a) Sulfate concentration in the Orgeval River recorded by the RL
during 2 weeks in summer (7 to 19 July 2015), and for two artificially
degraded signals, using a normally distributed noise with a standard
deviation of 2 and 4 %, to reflect the effect of degraded analytical
precision. The probability density functions (PDFs) of concentration are
calculated and represented as histograms (b). The average (Ave.),
standard deviation (SD), and skewness (Skew.) are calculated for each
PDF.
As shown above, the Orgeval RL not only achieves high-frequency measurements
but also results in improved precision compared to conventional lab analysis
following manual sampling. Therefore, any sampling procedure, even at a high
frequency, involving conventional lab analysis induces a loss of precision.
We demonstrate this effect through a numerically generated artificial
degradation of the precision. Using the original RL concentration signal as
a reference, we artificially degraded the signals by adding a normally
distributed noise onto the concentration signals recorded by the RL. Noise
levels of 4 and 2 % were tested; they are representative of the
“standard” analytical precision reported for most laboratory IC devices.
The same representative periods as in the previous section (summer and rain
events) were utilized for these tests. In this section we present the
example of one element for each characteristic period (Ca2+ for rain
event Fig. 7 and SO42+ for summer event Fig. 8. The generalization
for all elements is detailed in the Supplement
(see Figs. S4, S7 and S8).
Rain event. Figure 7 illustrates the concentration PDF obtained after
degradation of the analytical precision for the Ca concentration. The narrow
peaks recorded during the maximum of the stream flow are virtually invisible
in the signal at a 4 % precision, and strongly smoothed in the signal at a
2 % precision. The original bimodal characteristic of the PDF is still
visible in the 2 % precision signal but no longer in the 4 % precision
signal. The mean and standard deviation appear to be insensitive to these
changes in analytical precision, while the skewness is strongly impacted,
reflecting significant alteration of the concentration PDF at lower
precision.
Summer event. Figure 8 shows how the sulfate concentration signal is
affected when the precision is degraded. Day–night variations are only
visible in the original RL signal because of its high analytical precision.
The effect of degraded precision on the PDFs is more important than for the
rain event (Fig. 7). While the mean value is robust, the standard deviation
is altered (+150 % from the RL signal to the 4 % precision signal).
The skewness decreases (but keeps the same sign) by up to 90 % for the
signal at 4 % precision compared to the original signal and 74 % for the
signal at 2 % precision, indicating that the original RL signal asymmetry
is lost as precision is worsened. These changes in the parameters of the
concentration PDF show that the structure of the concentration signal in the
Orgeval River would be significantly altered if the measurements were made
with analytical precision lower than that of the RL prototype.
Generalization. This approach has been expanded to other elements for both
the summer and rain events, as shown in the Supplement,
confirming that concentration PDFs are strongly sensitive to the analytical
precision for all species (Figs. S4, S7 and S8).
Conclusions
This paper demonstrates the feasibility of deploying conventional laboratory
instruments in the field to measure the concentration of major dissolved
anions and cations in rivers (Na+, K+, Mg2+, Ca2+,
Cl-, SO42-, NO3-) at a high frequency (one
measurement every 40 min) and at a high analytical precision (better
than 1 %) over several months. The River Lab prototype was installed in
the Avenelles stream at the Orgeval Critical Zone Observatory, France. The
RL features physico-chemical probes, an online 0.2 µm pore size
filtration system, and two ionic chromatographic devices, all installed in a
closed, air-conditioned bungalow. The RL is autonomous, remotely operable,
and data can be transmitted automatically. Human intervention is required
only once a week. Therefore, the RL allows for an efficient attribution
of human resources, as well as considerable saving of consumables.
A suite of tests performed on the RL to assess quality measurement and to
compare with more conventional “grab sampling” followed by laboratory
measurements revealed only a minor drift in the instrument calibration,
leading to improved precision. This precision is not easily achieved in the
laboratory under standard analysis conditions, showing the benefit of
transporting the laboratory devices to the field. The analytical
capabilities of the RL for major dissolved elements could theoretically be
extended to other elements separable by ion chromatography. Preliminary
tests demonstrate that species present in trace amounts in river water (down
to ppb, such as strontium or lithium) could be measured with the same
gain in precision.
For this particular prototype, the measurement frequency (every 40 min)
appears to be limited by the turnover time of water in the filtered water
circuit, which is itself imposed by the filtration unit. However, the high
frequency and high precision of the RL enabled precise and accurate
observations on the fine structure in hydrochemical time series. Their
interpretation is beyond the scope of the present proof-of-concept paper but
the RL is able to capture the abrupt changes in dissolved species
concentrations during a typical 6-day rain event, as well as daily
oscillations during a hydrological steady period of summer drought.
Using the high-frequency RL signal as a benchmark, it is possible to
artificially alter the sample frequency and the analytical precision and
study the resulting effect on the hydrochemical distribution obtained for
characteristic hydrological events. This analysis shows that in order to
retrieve the fine structure of the hydrochemical signal, high sampling
frequency and improved analytical precision are both necessary conditions.
To paraphrase James Kirchner's quote: “If we want to understand the full
symphony of catchment hydrochemical behaviour, then we need to be able to
hear every note” (Kirchner et al., 2004). The improvements made possible by
the RL here or concomitantly by von Freyberg et al. (2017) allow us to
consider hearing the full potamological symphony.
Future work will explore the relationships between the desired measurement
frequency and the timescales characterizing the complex interactions between
primary and secondary minerals, biotic processes and hydrological processes
in catchments. Recording such fine stream hydrochemical variations has the
potential to offer a new perspective in critical zone science development.
Data will be available in a dedicated database website after a contract
accepted on behalf of all institutes.
The Supplement related to this article is available online at https://doi.org/10.5194/hess-21-6153-2017-supplement.
The authors declare that they have no conflict of interest.
Acknowledgements
This work was supported by the EQUIPEX CRITEX programme (grant no.
ANR-11-EQPX-0011, PIs J. Gaillardet and L. Longuevergne) and funding from
IRSTEA (Institut national de Recherche en Sciences et Technologies
pour l'Environnement et l'Agriculture). We thank Magadalena Niska for
administrative help.
We would like to thank Jérôme Laurent, Xu Zhang, Quentin Charbonnier, Damien Calmels, Pascale Louvat, James Kirchner, Jenny Druhan, Susan Brantley,
Bill McDowell and Jon Chorover for their help
in the field and helpful comments. Alain Guerin (IRSTEA), Sylvain Losa (Thermo
Fisher), Cedric Fagot, Patrick Reignier and Matthieu Bauer from
Endress+Hauser (colleagues of Frédéric Koch)
are thanked for technical assistance. Paul Floury benefited from a doctorate grant
from MESR, France. The Orgeval CZO River basin belongs to the French
National Infrastructure OZCAR (Observatoires de la Zone Critique,
Applications et Recherche).Edited by: Laurent Pfister
Reviewed by: two anonymous referees
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