The hydrological and biogeochemical response of rivers carries information
about solute sources, pathways, and transformations in the catchment. We
investigate long-term water quality data of 11 Swiss catchments with the
objective to discern the influence of major catchment characteristics and
anthropic activities on delivery of solutes in stream water. Magnitude,
trends, and seasonality of water quality samplings of different solutes are
evaluated and compared across catchments. Subsequently, the empirical
dependence between concentration and discharge is used to classify the
solute behaviors.
While the anthropogenic impacts are clearly detectable in the concentration
of certain solutes (i.e., Na+, Cl-, NO3, DRP), the influence
of single catchment characteristics such as geology (e.g., on Ca2+ and
H4SiO4), topography (e.g., on DOC, TOC, and TP), and size (e.g., on
DOC and TOC) is only sometimes visible, which is also because of the limited sample
size and the spatial heterogeneity within catchments. Solute variability in
time is generally smaller than discharge variability and the most
significant trends in time are due to temporal variations of anthropogenic
rather than natural forcing. The majority of solutes show dilution with
increasing discharge, especially geogenic species, while sediment-bonded
solutes (e.g., total phosphorous and organic carbon species) show higher
concentrations with increasing discharge. Both natural and anthropogenic
factors affect the biogeochemical response of streams, and, while the
majority of solutes show identifiable behaviors in individual catchments,
only a minority of behaviors can be generalized across the 11 catchments
that exhibit different natural, climatic, and anthropogenic features.
Introduction
Hydrological and biogeochemical responses of catchments are essential for
understanding the dynamics and fate of solutes within the catchment, as
material transported with water carries information about water sources,
residence time, and biogeochemical transformations (Abbott et al., 2016). A quantitative
description of water quality trends can also shed light on the consequences
of anthropogenic changes in the catchment as well as on the possibilities
for preventive or remedial actions (Turner and Rabalais, 1991). Concerning changes in watershed
land use or management practices, for example, the United States Geological
Survey (USGS) established the Hydrologic Benchmark Network (HBN) (Leopold, 1962), a
long-term monitoring system of dissolved concentrations in 59 differently
impacted sites across the United States with the goal of quantifying the
human influence on the ecosystems (Beisecker and Leifeste, 1975). Water quality monitoring and
assessment are also crucial for stream and catchment restoration, which has
been widely practiced in the USA and Europe for several decades and still
represents an important challenge of river basin management. However, the
system responses to restoration often contradict a priori expectations, and
the lack of adequate monitoring and assessment of basin functioning before
the application of restoration measures is considered to be one of the main
reasons for this discrepancy (Hamilton, 2011).
The relationship between observed in-stream solute concentrations and
discharge has been explored in various catchments and with different methods
in the last decades (Langbein and Dawdy, 1964; Johnson et al., 1969; Hall, 1970, 1971; White and Blum, 1995; Evans and Davies, 1998;
Calmels et al., 2011). One emerging postulate is that concentration–discharge (C–Q)
relations represent the quantitative expression of the interaction between
catchment geomorphology, land use, hydrological processes, and the solute
releases, thus reflecting in lumped form the complex mixing process taking
place along flow paths of variable lengths and residence time (Chorover et al., 2017).
Therefore, C–Q relations have been studied with reference to hydrological
variables, e.g., hydrologic connectivity and residence time (Herndon et al., 2015;
Baronas et al., 2017; Duncan et al., 2017a; Gwenzi et al., 2017; Torres et
al., 2017); biological processes (Duncan et al., 2017a); catchment
characteristics, e.g., catchment topography, land use, catchment size, and
lithological properties (Musolff et al., 2015; Baronas et al., 2017;
Diamond and Cohen, 2017; Hunsaker and Johnson, 2017; Moatar et al., 2017; Wymore et al., 2017);
and anthropic activities (Basu et al., 2010; Thompson et al., 2011;
Musolff et al., 2015; Baronas et al., 2017).
In a log(C)–log(Q) space, C–Q relations have been observed to be usually linear
(Godsey et al., 2009), so that the empirical relations can be well approximated by a
power law, C=a⋅Qb, where a and b are fitting parameters
(Godsey et al., 2009; Basu et al., 2010; Thompson et al., 2011; Moquet et al.,
2015; Moatar et al., 2017; Musolff et al., 2017). A very common metric,
relevant also for this study, is based on the value of the b exponent, the
slope of the regression in the log(C)–log(Q) plot, because it is related to
the concept of “chemostasis” (Godsey et al., 2009) or “biogeochemical stationarity”
(Basu et al., 2010). A catchment shows “chemostatic” behavior when, despite a sensible
variation in discharge, solute concentrations show a negligible variability,
i.e., b≅0. Conversely, positive slopes (i.e., increasing
concentrations with increasing discharge) would support an enrichment
behavior when the solute amount grows with discharge, and negative slopes
(i.e., decreasing concentrations with increasing discharge) support a
dilution behavior with solute mass that does not increase proportionally to
the growing discharge. A solute is typically defined as transport-limited if it
is characterized by enrichment, while it is called source-limited in cases where it
dilutes (Duncan et al., 2017a).
The exact mechanisms leading to C–Q relations are, to a large extent, an open
question, but these relations are providing insights on solute and/or
catchment behavior (Godsey et al., 2009; Moatar et al., 2017). The concept of chemostasis emerged in
studies that explored the C–Q power law with the aim of demonstrating the
similarities in the export behavior of nutrients (Basu et al., 2010, 2011) and
geogenic solutes (Godsey et al., 2009) across a range of catchments (Musolff et al., 2015). These
studies were mostly carried out in agricultural catchments, where a “legacy
storage” was supposed to exist due to antecedent intensive fertilization
practices (Basu et al., 2010, 2011; Hamilton, 2011;
Sharpley et al., 2013; Van Meter and Basu, 2015; Van Meter et al., 2016a, b). This
storage of nutrients might have long-memory effects, and it was considered to
buffer the variability in concentrations in streams, leading to the
emergence of biogeochemical stationarity (Basu et al., 2011). However, biogeochemical
stationarity has been questioned outside of agriculturally impacted
catchments (Thompson et al., 2011), and a unifying theory explaining catchment-specific
C–Q behavior is not available yet, considering that solutes can show different
behaviors in relation to landscape heterogeneity (Herndon et al., 2015) and to the spatial
and temporal scales of measurement (Gwenzi et al., 2017). Therefore, approaching the
study of solute export and C–Q relations requires the separate analysis of
several solutes in as many catchments as possible with the aim of finding, at
least, some general behavior that can be characteristic of a given region or
solute. The recent literature is moving toward this direction (Herndon et al., 2015;
Wymore et al., 2017) with the aim of sorting out the relative influence of climatic forcing,
solute properties, and catchment characteristics on solute behavior in
search for generalizations across different catchments.
This study contributes to this line of research investigating a unique
dataset of long-term water quality data in 11 catchments in Switzerland,
where multiple solutes were observed at the biweekly (every 2 weeks) scale for multiple
decades with limited gaps. We perform the analysis focusing mainly on the
temporal domain and by quantifying magnitude, temporal trends, and
seasonality of the in-stream concentrations with the goal of highlighting
the long-term behavior differences across the 11 catchments and
investigating the drivers of such differences. Specifically, we focus on the
following research objectives: (i) investigating to which extent the solute
concentrations are influenced by anthropic activities; (ii) exploring the
dependence of solute concentrations on catchment characteristics; and
(iii) generalizing, if possible, the behaviors of selected solutes across
different catchments by means of the slope in the C–Q relations.
Study sites
Observations used in this study are obtained from the Swiss National River Monitoring
and Survey Program (NADUF, 2019),
which represents the Swiss long-term surface water quality monitoring
program. This database includes in total 26 monitoring stations located in
different catchments. To ensure representativity and robustness of the
analysis we focus only on those stations with at least 10 consecutive years
of water quality measurements. This restricts the database to 11 catchments,
the corresponding locations of which are shown in Fig. 1. The
resulting case studies include five main catchments (Thur, AN; Aare, BR;
Rhine, WM; Rhone, PO; and Inn, SA), three sub-catchments (Rhone, PO; Rhine,
RE; and Rhine, DI), and two small headwater catchments (Erlenbach, ER; and Lümpenenbach, LU).
Map of NADUF monitoring stations and description of the study area.
The upper panel represents the studied catchments. (a) Swiss Plateau (blue)
and the Alpine catchments (yellow); (b) the catchments spanning both
regions, hybrid catchments (light blue). The bottom panel describes the study
sites in terms of (c) macro-geological classes, (d) land
cover, and (e) anthropic pressure.
Measurements have a temporal resolution of 14 days, which is similar to the
resolution of other studies that analyzed long-term water quality data. In
the literature, the temporal resolution of water quality observations ranges
namely from weekly (Duncan et al., 2017a, b; Gwenzi et al., 2017; Moatar et al., 2017; Wymore et al., 2017) to
14-day (Hunsaker and Johnson, 2017) to monthly (Basu et al., 2010; Thompson et al., 2011;
Musolff et al., 2015; Mora et al., 2016; Moatar et al., 2017) or even coarser
resolution (Godsey et al., 2009). In fact, only very rarely higher-frequency databases
are collected and thus analyzed (e.g., Neal et al., 2012, 2013; von Freyberg et al., 2017).
Stream water is analyzed only twice per month but is collected continuously,
thus providing samples that represent a flow-proportional integral of the
preceding 14 days. River water is lifted continuously by a submersible pump
into a closed overflow container (25 L) in the station, at a flow rate of
25–75 L min-1. From the container, samples are transferred in 1 mL
portions to sampling bottles. The frequency for the transfer of 1 mL samples
is proportional to the discharge monitored continuously by the gauging
device in the same station. The discharge-proportional sampling device is
designed to collect 1–3 L of sample per bottle in each period. The sampling
mechanism also allows the simultaneous collection of up to four integrated samples.
A 14-day sampling frequency is not sufficient for an evaluation of
short-term biogeochemical and transport processes, which might involve
solute transformation (e.g., biological processes, in-stream chemical
reactions). These are simply accounted for in a lumped form in the
flow-proportional average concentrations collected in a 2-week interval.
Conversely, the dataset is especially suitable for the investigation of
long-term trends, due to the length of the time series, which spans from
11 to 42 years (Table 1). Data are collected following ISO/EN conform methods
for water analysis and are subsequently validated by means of an extensive
quality control as described in Zobrist et al. (2018). In addition, we inspected the data
to take into account possible errors deriving from fixed detection limits,
e.g., deleting the values below the detection thresholds (see Sect. S1 in the Supplement).
Description of the catchments. The selected catchments are
characterized by different size, elevation, and average yearly precipitation.
Four catchments are entirely Alpine (ER, PO, DI, SA), while the others
encompass different morphologies (Swiss Plateau and pre-Alpine areas). The
data are sourced by the catchment descriptions included in the NADUF database.
The concentrations reported in the database concern the following solute
types: (i) geogenic solutes, originating mainly form rock weathering, such
as calcium (Ca2+), magnesium (Mg2+), sodium (Na+), silicic
acid (H4SiO4), and potassium (K+); (ii) deposition-derived
solutes, such as chloride (Cl-);
(iii) nitrogen species (nitrate, NO3; and total nitrogen, TN); (iv) phosphorus species (dissolved reactive
phosphorus, DRP; and total phosphorus, TP); and (v) organic carbon species
(dissolved organic carbon, DOC; and total organic carbon, TOC). The time
series of these concentrations are used in the analyses carried out in this
study. Furthermore, the dataset includes also the average discharge,
computed as the mean value over the period between two water quality
analyses, as well as other parameters such as water temperature, hardness
(Ca2++Mg2+), alkalinity (H+), and pH.
The selected catchments cover most of the Swiss territory. This is
characterized by dissimilarities in terms of morphology, land use, and
anthropic pressure, with the latter being intended as activities
(e.g., fertilization of agricultural lands, domestic and industrial waste water
treatments, industrial sewage disposal into water), which are expected to
have an impact on the river biogeochemistry and to alter the natural
background concentrations and their seasonality. Figure 1 shows the
catchments analyzed in this study as identified by the ID reported in
Table 1. Catchments are divided into three categories depending on the
morphological zone where they are mainly located: the Swiss Plateau, a
lowland region in the north, the mountainous Alpine area in the center and
south, and a third category that includes catchments spanning both
morphologic zones. The choice of this classification criterion is discussed
in the Sect. 3.1. Geology also differs from one region to another (Fig. 1c).
The bedrock of northern Switzerland, the Jura region, is mainly
composed of calcareous rocks, while in the Alpine area crystalline silicic
rocks are dominant (Fig. 1c). The Swiss Plateau region is instead
characterized by the “molasse” sedimentary rocks (Fig. 1c), consisting in
conglomerates and sandstones of variable composition (e.g., detrital quartz,
feldspars, calcite, dolomite, and gypsum) (Kilchmann et al., 2004). The relative chemical
weathering of carbonate rock and of gypsum are respectively 12 and 40 times
higher than the weathering rate of granite or gneiss (Meybeck, 1987), thus
suggesting that it is a good proxy to consider the Swiss Plateau area as
characterized mainly by a calcareous bedrock (e.g., Zobrist et al., 2018). As the maps in
Fig. 1d and 1e show, the prevalent land use in the Swiss Plateau area is
agriculture, while the Alpine area is mainly covered by forests and
grasslands. Table 1 specifies if the share of agricultural land is
cultivated either intensively, i.e., with significant fertilizer
applications, or extensively, e.g., Alpine grasslands, bush land, and
parks, which are mostly unfertilized (Zobrist et al., 2018). The main urban centers are
concentrated in northern Switzerland, together with most of the
industrial activities, which represent potential point sources of pollution.
The agricultural activities, especially intensive agriculture, residential,
and industrial areas are referred to in this study as “anthropic pressure”,
indicating that the sources of solutes originated from these activities are
other than natural. Given the much higher presence of these anthropogenic
factors in northern Switzerland, the anthropic pressure follows a
south–north gradient, although patches of anthropic pressure are found also
within the Alpine valleys.
MethodsMagnitude, seasonality, and trends
The magnitude of a solute is evaluated through basic statistics (i.e.,
median, 25th and 75th percentiles, minimum and maximum values). These are
computed for each solute in each catchment, with the goal of highlighting
differences across catchments, which are the result of catchment
heterogeneities and natural and anthropogenic factors affecting the quantity
of a given solute.
The seasonality of discharge and of solute concentrations is analyzed and
cross-compared to highlight differences and similarities of controls that
are related to the climatic seasonality and seasonality of man-induced
impacts. For this analysis, catchments are subdivided into the three
above-mentioned categories: Swiss Plateau, Alpine, and hybrid catchments (Fig. 1).
The Swiss Plateau and Alpine catchments have substantially different
hydrological regimes (Fig. S1 in the Supplement, upper and bottom panels) and represent the
main classes of the clusterization proposed by Weingartner and Aschwanden (1992). Some of the selected
catchments with large draining area include both typologies and are
therefore defined as “hybrid catchments”. They are characterized by a
seasonality, which is intermediate between the two end-members (Fig. S1,
central panel) because the timing of the peak is similar to the one of
Alpine catchments, but the magnitude is less pronounced as in the Swiss
Plateau catchments. For this reason, they have to be treated separately from
the other two classes. The hybrid catchments have the highest percentage of
lake surface area in their domains (Table 1), although non-negligible lake
fractions are also found in the two other categories. Large lakes represent
a discontinuity in the river network, reducing the fraction of catchment
area directly (without major water mixing effects) contributing to the
observed discharge and solute dynamics. The presence of large lakes
contributes to the dampening of the hydro-chemical signal, but its exact
quantification is not straightforward. Aware of the confounding role of
large lakes, we apply this classification in order to test if the
seasonality of solutes is related to the seasonality of discharge. With such
an analysis we aim at isolating the effect of the discharge seasonality
versus the seasonality of solute concentrations. More specifically, whenever
a solute shows a seasonality different from the one imposed by climate, we
investigate the potential reasons for such a difference, with it being either
related to specific catchment characteristics or to anthropic activities.
The comparison between the seasonality of solute and discharge is made
through an index of variability defined as the ratio between the mean
monthly deviations from the mean of solute concentration and discharge
respectively, where the deviation is determined as the average
difference between the monthly means and the annual average value, resulting
in the following equation:
Indexofvariability=1∑nNormalizedmeanofmonthlydeviationsofconcentrationNormalizedmeanofmonthlydeviationsofdischargen=∑n∑i=112CiC‾-1∑i=112QiQ‾-1n,
where i represents the month of the year, from 1 to 12, and n is the number of
the catchments belonging to the specific catchment class for which the index
of variability is computed. In other words, an index of variability larger
than 1 suggests that the seasonality of the solute is more pronounced than
that of discharge, and vice versa for an index of variability smaller than 1.
Finally, we evaluated the occurrence of trends in the long-term
concentration time series at monthly and annual scale using the monthly
average concentration of each solute in each catchment and each year for the
entire period. The statistical significance of trends was tested with the
Mann–Kendall test modified to account for the effect of autocorrelation
(Hamed and Rao, 1998; Kendall, 1975; Mann, 1945), fixing a significance level of 0.05. Trends are
investigated and compared across catchments in order to understand if they
are consistent across Switzerland, thus suggesting the presence of clear
drivers underlying the trend, or if they are just occurring in a subset of
catchments. The time series span different periods of time, so the results
might be impacted by the natural variability in discharge over the different
years. This might be a potential issue, but we observed that in cases with a trend in
discharge present (e.g., in the CH catchment, not shown) the
patterns of concentrations do not show any different behavior compared to
those observed in other catchments, which our analysis attributes to
external forcing (e.g., anthropic activities).
Concentration–discharge relations
The empirical relation between solute concentration and discharge C=a⋅Qb
was explored separately for each solute and for each
catchment with the objective of investigating solute behaviors across
catchments and whether this behavior can be generalized. The two variables
are expected to exhibit a linear relation in a log–log scale, expressed by
means of the two regression parameters a, the intercept with the same
dimensions of the concentration, and b, the dimensionless exponent
representing the slope of the interpolating line. We focus our attention on
the latter, which determines the behavior of the solute. The Student's
t test was applied to verify the statistical significance of having a b
exponent different from zero. The level of significance α was set at 0.05.
When the p value was lower than α, the slope identifying the
log-linear C–Q relation was considered significant and quantified by b,
otherwise the slope was considered indistinguishable from zero, thus
suggesting no evidence of a dependence of concentration on discharge.
In each catchment, the time series of discharge were divided into two
subsets using the median daily discharge q50 to separate flow below the
median (low flows) and flows above the median (high flows). Hourly discharge
time series were available from the Swiss Federal Office for the Environment (FOEN)
at the same river sections and for the same period of the time series
of water quality provided by the NADUF monitoring program. The median daily
discharge was computed from the hourly series, which were aggregated to
obtain daily resolution.
Determining the C–Q relations separately for high and low flows allows a finer
classification of the solute behavior into different categories (Moatar et al., 2017)
than considering only the dependence on the entire range of discharge. The
three main behaviors – enrichment or removal (i.e., positive slope),
chemostatic (i.e., near-zero slope), and dilution (i.e., negative
slope) – can indeed be the result of mechanisms controlling the runoff
formation and the transport mechanism. Accordingly, we have in total nine
different combinations characterizing the C–Q relation across
high- and low-flow regimes, which allow assigning distinct behaviors to a given solute.
For solutes that showed long-term trends over the monitoring period, we also
investigated the evolution of the b exponent in time. In this case, the
concentration and discharge time series were divided into decades and the
C–Q relations over all discharge values were computed separately for each
decade. The behavioral classification is performed on a single b (i.e., not
divided into low- and high-flow b), since, differently from the previous
analysis of C–Q relations, the focus is on the detection of long-term trends
in solute behavior rather than on the understanding of the processes leading
to differences between high and low flows.
ResultsMagnitude
Among the geogenic solutes, Ca2+ is the most abundant, most likely due
to the composition of the bedrock present in most of the catchments
(calcite, dolomite, and anhydrite/gypsum; Rodríguez-Murillo et al., 2014). In absolute terms,
geogenic solutes and Cl- have the highest concentrations
(≈10–50 mg L-1), while phosphorus species concentrations
(≈0.01–0.1 mg L-1) are on average 1 to 2 orders of magnitude less abundant than
nitrogen species (≈0.5–1.5 mg L-1) and organic carbon (≈1.5–5 mg L-1).
Some solutes are constituents of other species, like in the case of
nutrients NO3 of TN and DRP of TP. NO3 is often introduced in
catchments as an inorganic fertilizer, such as DRP, which represents a readily
available nutrient for crops. We computed the ratio between the solute and
its component for the two couples (NO3/TN, DRP/TP) and observed their
pattern across the catchments (Fig. 2). We take as reference values the
ratios in the ER catchment, since, due to limited anthropogenic pressure, it
represents the background concentrations of nutrients (Zobrist, 2010). Variations
compared to ER values might provide an indication of the ratio of nutrients
coming from anthropic activities. NO3 is the major constituent of TN, since
it is about 85 % of TN, while DRP contributes much less to TP, being only 35 % of TP.
Both have a decreasing pattern with decreasing catchment
anthropogenic disturbances, although in DRP/TP this pattern is more evident.
DRP/TP spans from a maximum of 65 % in WM to a minimum of 22 % in ER,
while NO3/TN has a maximum of 93 % in AN and it is 63 % in ER.
Ratios of DRP/TP (red) and NO3/TN (green) across
catchments computed on the period 2005–2015. Both patterns show a decreasing
trend from more to less anthropogenically affected catchments (left to right of
x axes). This pattern is more evident for phosphorus. Background colors refer
to the catchment classification explained in Sect. 3.1.
Effects of catchment characteristics and human activities on the observed
stream solute concentrations can be seen for certain solutes as shown by
Fig. 3, where each box shows the measured concentrations in the
11 catchments and the last box on the right refers to all the catchments
grouped together. The catchments, expressed by the corresponding acronym
(see Table 1), are ordered, from left to right, from the most impacted by
human activity – i.e., higher percentage of catchment area used for
intensive agriculture – to the least impacted, which is almost equivalent to
considering a south-to-north gradient. The most evident effect of catchment
characteristics refers to the presence of Ca2+ and H4SiO4 in
the stream water (Fig. 3a). Despite the lower solubility of silicic rocks
compared to the calcareous rocks, H4SiO4 concentrations in the
southern Alpine catchments of Inn (SA), Rhine (DI), and Rhone (PO) are
significantly higher than the median value across catchments. The impact of
human activities, instead, is more evident in Na+ and Cl-
concentrations. These are showing, basically, the same pattern across
catchments (Fig. 3b), indicating that they are most likely influenced by
the same driver, which is the spreading of salt on roads during winter
months for deicing purposes. We consider the spreading of deicing salt an
anthropic activity related to the presence of inhabitants in a catchment.
DOC and TOC concentrations are very high in the Lümpenenbach (LU) and
Erlenbach (ER) catchments (Fig. 3c), which are the smallest catchments
with the highest average yearly precipitation rate and very low anthropic
presence. The Thur (AN) and Aare (BR) catchments also show DOC and TOC
concentrations higher than the average, but in these catchments the presence
of wastewater treatment plants can influence TOC concentrations. Finally,
nutrients, such as nitrogen species and phosphorus species, which are
connected with anthropic activities (fertilization, wastewater treatment
plants), show a relatively clear decreasing median concentrations from the
most to the least impacted catchment (Fig. 3d). Indeed, regressing median
solute concentration with the percentage of intensive agricultural land and
the inhabitant density (Table S1a in the Supplement) gives a statistically significant
dependence for some nutrients (i.e., NO3, TN, DRP). Because the
catchments that are mostly impacted by agricultural activities are mainly
located in the Swiss Plateau, a significant positive correlation between
nutrients and the percentage of the Swiss Plateau area of the catchment exists;
conversely, we observe a significant negative correlation with the
percentage of the Alpine area. One should note, however, that the
correlation is performed on 11 catchments only, so that a lack of significance
should be interpreted with care. Indeed, if we extend the correlation
analysis to the b exponent derived from the C–Q relations analysis – thus
implicitly accounting for the complex interactions between catchment
geomorphology, land use, hydrological processes, and solute releases – with
the same catchment characteristics (e.g., Moatar et al., 2017) the correlation becomes
weaker and, basically, not significant for any solute (Table S1b and c).
Box plot of measured concentrations across catchments. The grey box on
the right of each subplot refers to the concentrations computed from all the
observations of all the catchments. The black horizontal dashed line represents
the median of all the measurements across all the catchments. (a) shows
the effect of bedrock geological composition on Ca2+ and
H4SiO4 concentrations. (b) shows the pattern of Na+
and Cl- concentrations across catchments. (c) shows the DOC
and TOC concentrations. (d) shows the decreasing trend of the
nutrients' median concentrations. The catchments are ordered by increasing percentage of
land used for intensive agriculture, as shown in the bottom table, and the
background colors refer to the catchment classes: Swiss Plateau (blue), hybrid
(light blue), and Alpine (yellow) catchments.
Seasonality
Different climates and catchment topographies determine various hydrological
responses, as we can observe in Fig. S1 from the analysis of discharge
seasonality across the 11 catchments, expressed through the monthly
average streamflow normalized by its long-term average. We present the
results with the catchments divided into three groups as previously explained. The
partition into these classes helps in highlighting the effects of
topography, climatic gradient, and somehow also the impact of anthropic
activities since it follows a similar south to north gradient. The
seasonality of streamflow in Swiss Plateau catchments is determined by a
combination of precipitation and snowmelt. The peak flow is typically
observed in spring and is not much higher than the average in the other
months. Alpine catchments, instead, show stronger seasonality induced by
snowmelt and ice melt in spring and summer, which generates higher streamflows
than in the other months. Hybrid catchments exhibit flow peaks in
June–August similarly to the Alpine ones, but the deviation from the average
value is less pronounced.
The deviations of discharge and concentration are compared using the index
of variability (Sect. 3.1) for each morphological class of catchments
(Fig. 4). Only few solutes show a value of the index higher than 1. This
indicates that seasonality of solute concentrations is generally lower or
much lower than the seasonality of streamflow. This is especially true for
the Alpine catchments, where the marked seasonality of streamflow seems to
dominate the variability in concentrations. For TP the index of variability is higher
than 1 in Alpine catchments and also the highest compared to the other two
typologies. In Swiss Plateau and hybrid catchments, instead, only solutes
impacted by human activity (Na+, Cl-, nitrogen species, and DRP)
show a ratio close or even higher than 1.
Bar plot of the index of variability. Each bar represents the average
monthly variability in
concentration relative to discharge variability
per catchment class. The colors of the bars differentiate catchment morphologies:
blue for Swiss Plateau, aqua-green for hybrid, and yellow for Alpine catchments.
A–C represent the observable patterns of the index of variability across
the three classes. Type A is the result of the different seasonality of discharge
dominating the response. Type B refers to those solutes with an index of
variability much lower in the hybrid catchments than in the others. Type C
represents solutes with the index of variability lower in Alpine
catchments than in the other classes.
DOC and TOC concentrations are characterized by low indexes of variability,
especially in the hybrid catchments. The patterns of the index of
variability across different morphologies can be classified into three
categories, represented by the symbols A–C in Fig. 4. The monotonic
line in type A refers to those solutes, the variability index of which
changes across morphologies solely as a result of the seasonality of
streamflow (Ca2+, Na2+, K+, and Cl-). The type B solute
(Mg2+, TP, DOC, and TOC) response shows a lower
variability index in hybrid catchments compared to the other catchments and suggests that, among the
factors controlling the seasonality of the biogeochemical response, there are
factors that are specific to the Alpine environment, which are discussed in
Sect. 5.2. The type C pattern, instead, refers to solutes related to
fertilization (NO3, TN, and DRP) and to H4SiO4, which is a
product of weathering and only minimally involved in biological processes.
These solutes are characterized by a much lower variability index in Alpine
catchments than in hybrid and Swiss Plateau catchments. Differences in their
regime are further discussed in Sect. 4.
The analyzed solutes show different intra-annual dynamics. For instance,
despite the quite pronounced streamflow seasonality of the Rhine river at
Rekingen (hybrid catchment used as a representative example), solute
concentration patterns show different seasonal cycles (Fig. S2).
Ca2+, Mg2+, Na+, K+, Cl-, NO3, and
TN concentrations peak in February–March and have lower values during
the spring–summer period, showing a pattern opposite to that of streamflow.
H4SiO4, instead, has a shifted seasonality compared to the other
solutes, peaking in December–January. Phosphorus species together with
organic carbon species do not show any consistent seasonality over the year.
Trends
Long-term trends in the concentration time series are investigated with
respect to the seasonal cycle for each year separately (Fig. S2). One
catchment (Rhine – Rekingen) is taken as an example for illustration
purposes, but the generality of trend results is discussed in the following.
Focusing on the long-term horizon, different dynamics can be observed across
various solutes. Some of them show visible trends: for instance Cl- has
increased from the 1970s to 2015, while phosphorus species have decreased
considerably. Some solutes have different trends across different
catchments. A generalization of long-term patterns is shown in Fig. 5 for
the three main detected behaviors. The upper panel represents the occurrence
of an evident trend, either increasing (as in the example of Cl-) or
decreasing (e.g., TP). Na+, Cl-, DRP, and TP belong to this
category. While Na+ and Cl- have increased in time, DRP and TP have
decreased in the monitoring period, as the monthly trends in Table S1a show
(see Fig. 6 for DRP only).
Three example long-term patterns of solute concentrations.
(a) represents a clear increasing trend, (b) a non-monotonic
trend (firstly increasing and then decreasing), and (c) shows the
absence of any trend. The patterns are shown for the station of Aare – Brugg
as an example case.
Observed DRP concentrations in three catchments characterized by
different classes (i.e., Thur, AN; Rhine, WM; and Rhone, PO). The blue line
represents the mean until 1986, whereas the red line represents the mean
after 1986 and until the end of the monitoring period. After the introduction
of the phosphate ban in 1986, the DRP concentrations have shown an evident decrease.
The middle panel shows a non-monotonic trend. This is typical of Mg2+,
which first increased in most catchments (1970s–1990s) and then decreased (1990s–2015).
K+, TN and TOC also show this type of trend in most
catchments. Finally, the lower panel of Fig. 5 shows a number of solutes
(Ca2+, H4SiO4, NO3 and DOC) that do not exhibit any
long-term trend, although analysis on a monthly base revealed some
significant trends (Table S1c).
Results of the C–Q relations analysis. The symbols “+”,
“-”, and “=” refer to the possible behavior combinations described in Fig. 7,
while the numbers indicate how many catchments exhibit a specific behavior for
each solute. The solutes are classified as reported in the first column.
Concentration–discharge relations were computed for all the solutes across
all the catchments as summarized in Table 2. For each solute, we computed
the number of catchments showing a given specific behavior, which we denoted
with the combination of the symbols “+” (i.e., enrichment/removal),
“-” (i.e., dilution), and “=” (i.e., chemostatic behavior) for discharge above
and below the median.
Geogenic solutes are mostly characterized by dilution. The only exception is
H4SiO4, which shows six different behaviors across the
11 catchments, making it impossible to identify the most representative behavior
for this solute. This is the case also of other species (nitrogen species,
TP, and organic carbon species), which show at least three different
behaviors across catchments. Silicon is mainly generated through rock
weathering, but it is also involved in biological processes, which might
influence its behavior across catchments.
Overall, dilution is dominant for all solutes in both low- and high-flow
conditions, as it occurs respectively in 65 % and 57 % of the
catchments. Therefore, even in low-flow conditions, the solute transport is
mainly source-limited across catchments. Only sediment-related solutes
(i.e., TP, TOC) show a marked transport-limited behavior. The label
“sediment-related solutes” comes from the fact that phosphorus and organic
carbon are bonded to soil particles and, when soil is eroded, carbon- and
phosphorus-rich soil particles are mobilized by flowing water. In such
conditions, soil erosion becomes one of the main contributors to the
phosphorus and organic carbon load into the rivers. We investigated also
C–Q relations for suspended sediment concentrations and they show increasing
slope across all the catchments, indicating, as expected, higher erosion
rates in the presence of high-flow conditions. Only 29 % of the
catchment–solute combinations have different behaviors between low- and
high-flow conditions, and therefore the C–Q relations are represented by
bent lines, having different slopes between low- and high-flow conditions.
NO3 and DOC represent a conspicuous component of TN and TOC
respectively, but NO3 shows almost the same behaviors of TN, in spite
of a different distribution across catchments, while DOC and TOC behave
differently. Phosphorus species also show different behaviors, which is
consistent with the fact that DRP represents only a small fraction of TP.
Since in the trend analysis we identified four species (Na+, Cl-,
DRP, and TP) that are characterized by remarkable long-term trends, we
investigated if such a significant change in magnitude has an effect on the
C–Q relation analyzing the temporal changes in the b exponent. The changes
in the value of b across all catchments with record length longer than
30 years during different decades are shown in Fig. 8a–d,
whereas Fig. 8e–h
show an example of the variation of the TP
C–Q relations across decades for the human-impacted catchment of Aare
(BR) and the Alpine catchment of Rhone (PO). Although the observed concentrations
of all four solutes – Na+, Cl-, DRP, and TP – are characterized by
the presence of evident trends in time, the behaviors in the C–Q relation
differ. Na+ and Cl- have a constant b exponent across decades,
while phosphorous species show increasing b, which, in some catchments,
leads to a switch from a behavior of dilution to one of enrichment.
DiscussionInfluences of human activities on solute concentrations
The cause–effect relation between the observed in-stream concentrations and
the anthropic activities is sometimes evident in the concentration
magnitude, seasonality, and long-term trends. Phosphorus and nitrogen are
the main nutrients applied for agricultural fertilization, and a decreasing
pattern of their magnitude from mostly intensive agricultural catchments to
forested catchments is observed (Fig. 3d). Indeed, taking the
concentrations of NO3 and DRP registered at ER as reference background
of natural concentrations (Zobrist, 2010), corresponding to 0.20 mg L-1 of NO3,
0.38 mg L-1 of TN, 0.002 mg L-1 of DRP, and 0.02 mg L-1 of TP, the concentrations
in all the other catchments are significantly higher. For example, the most
impacted AN catchment recorded median concentrations of 2.50 mg L-1 of
NO3, 3.03 mg L-1 of TN, 0.06 mg L-1 of DRP, and 0.15 mg L-1 of TP. Following
the stoichiometric composition of plants, nitrogen species concentrations
are 1 order of magnitude higher than phosphorus species concentrations
(Fig. 3d). Nitrogen is the main nutrient required for crop growth
(Addiscott, 2005; Bothe, 2007; Galloway et al., 2004; Zhang, 2017), and indeed NO3 is one of the main
components of fertilizers applied in agriculture. NO3 represents a
large fraction of TN (Fig. 2). The variability in the ratio between
average NO3 and TN concentrations across the different catchments is
comparable with that estimated by Zobrist and Reichert (2006), who observed a variation from
55 % in Alpine rivers to 90 % for rivers in the Swiss Plateau. Both
the NO3-to-TN ratio and the DRP-to-TP ratio show a decreasing trend from more to
less anthropic-impacted catchments, with the range of variability being, however,
higher for phosphorus species (from about 0.6 in the Thur river to about 0.2 in
the Inn river). The DRP/TP ratios across catchments can be explained as the
result of the cumulative effect of two main factors: the lower DRP input due
to less intensive agricultural activity in the Alpine zone and the higher
share of phosphorus sourced by suspended sediments contributing to TP in
Alpine catchments due to generally higher erosion rates.
Anthropic activities affect also the seasonality of certain solutes. In
Fig. 4, we assigned the pattern “C” to those solutes (i.e.,
H4SiO4, NO3, TN and DRP) characterized by a much lower index
of variability in Alpine catchments than in hybrid and Swiss Plateau
catchments. For those solute concentrations, the variability in Swiss Plateau
and hybrid catchments is comparable to or higher than streamflow variability,
while in Alpine catchments streamflow seasonality is much stronger than
solute seasonality. A non-negligible fraction of these solutes is introduced
through agricultural practices or by means of other human activities. Their
input is characterized by its own seasonality, which influences the solute
dynamics and makes it comparable to or larger than the discharge seasonality – a
behavior that is not observable for most geogenic solutes (Fig. 4). Additional
evidence supporting this result is represented by the patterns of the
average monthly discharge and solute load (computed as the product between
concentration and discharge) normalized by the respective average value.
This representation is made for Ca2+, originating from rock weathering,
and NO3, mainly of anthropic origin (Fig. S3a and b). The plot,
inspired by the analysis of Hari and Zobrist (2003), shows how the seasonality of
Ca2+ load follows the seasonality of discharge across all catchments well, while
NO3 load has its own seasonality in the catchments with the largest
agriculture extent, especially in the first part of the year. Indeed, in the
case of NO3, there is no correspondence between the seasonality of
discharge and load (e.g., the time of maximum discharge does not coincide
with the time of maximum or minimum load), thus suggesting that the input is
characterized by an independent seasonality.
Anthropic activities do not only influence the average solute concentrations
and the seasonality, but also the long-term dynamics. Na+ and
Cl- show a clear positive trend in time (Table S1a), largely because of the
increasing application of deicing salt (NaCl) (Gianini et al., 2012; Novotny et al., 2008; Zobrist and Reichert, 2006). A
clue of the cause–effect relation between deicing salt application and
increased Na+ and Cl- concentrations in stream water comes from
stoichiometry. The molar ratio between Na+ and Cl- in salt
is 1:1; therefore, the closer the ratio computed on observed in-stream
concentrations is to 1, the more likely it is that the deicing salt may be the driver. Figure S4
shows the box plot of the Na:Cl molar ratio across catchments, and it is clear
that catchments with higher population density show values closer to 1.
However, the Erlenbach (ER) and Lümpenenbach (LU) catchments, which do
not show any increasing long-term trend neither in Na+ nor in
Cl- concentrations, show Na:Cl values higher than 1, which is
consistent with the catchments with a low population density (i.e., Rhone, PO; Rhine, DI; and
Inn, SA). In this respect, Müller and Gächter (2011) analyzed the phenomenon of increasing
Cl- concentrations in Lake Geneva basing their analysis on the NADUF
data at the Rhine – Diepoldsau (DI) station. The concentrations detected by
the water quality monitoring station are much lower than the amount of the
input of salt declared by the cantonal authorities, and the increasing trend
characterizes the whole year and not only the winter months. These two
factors suggest that an accumulation effect with a long memory in the system
might exist. The salt could be stored somewhere in the soil or in the
groundwater and could be progressively delivered to the streams over years.
However, this is difficult to assert conclusively since the salt input is
uncertain. Indeed, estimating the input of salt used for deicing purposes is
not trivial, due to the lack of reliable data (Müller and Gächter, 2011). Official sources
(EAWAG, 2011) state that improved technologies have enabled a sensible decrease
in the specific amount of spread salt (from 40 g m-2 in the 1960s to
10–15 g m-2 today), but the total amount of salt still shows an increasing
trend, likely because it is spread more often and on wider surfaces. The
recent study of Zobrist et al. (2018) uses as a proxy for salt consumption the salt
production by Swiss salt refineries and claims an increase from 360 Gg NaCl yr-1
in the 1980s to 560 Gg NaCl yr-1 in the present, thus
supporting the observed positive trend.
A positive cause–effect relation between anthropic activity and solute
concentration in terms of trend is also shown for phosphorus species, which
have decreased consistently since 1986 (Fig. 6), when the phosphate ban in
laundry detergents was introduced in Switzerland (Jakob et al., 2002;
Rodríguez-Murillo et al., 2014; Prasuhn and Sieber, 2005; Zobrist and Reichert, 2006; Zobrist, 2010).
A non-monotonic trend emerged from the analysis of long-term data for
Mg2+, K+, TN, and TOC (Fig. 5). Considering for example
Mg2+, Zobrist (2010) focuses the trend analysis over the period 1975–1996 on
Alpine catchments and observes a similar non-monotonic increasing–decreasing
pattern. Zobrist (2010) attributes this pattern to an increase in water temperature,
which is evident for the Rhine and Rhone rivers. For the Rhine and Rhone rivers,
our results support the conclusion of Zobrist (2010) because a reverse
increasing–decreasing trend in Ca2+ corresponds to
the decreasing–increasing trend of Mg2+. This is consistent with the
temperature dependence in calcite solubility. However, in the Thur catchment
(AN and HA catchments) which is mainly agricultural, the non-monotonic trend
of Mg2+ does not correspond to a trend in Ca2+. Since
Mg2+ can cumulate through fertilizer applications and carbonate weathering
(i.e., Mg2+ production) can be affected by N fertilizers and manure
application (Hamilton et al., 2007; Brunet et al., 2011), we hypothesize that fertilizers might also
have an impact on the Mg2+ long-term dynamic. In this respect, the
analysis of monthly trends of Mg2+ (Table S1b) shows a more evident
increasing trend for agricultural than for non-agricultural catchments. For
K+ the difference across the gradient of agricultural pressure is not
as remarkable as for Mg2+. Monthly trends of TN and DOC revealed
an increasing tendency in the first months of the year (January–April) and
decreasing ones in the last part of the year (August–December), thus
suggesting that they are induced either by streamflow trends (Birsan et al., 2005) or by
biogeochemical processes, which have a pronounced seasonality related to
temperature and moisture controls rather than to human activities.
In summary, the anthropogenic signature is clearly detectable in the water
quality of catchments with an important fraction of intensive agriculture
and relatively high population density, especially in the magnitude of
concentrations of nutrients (i.e., nitrogen and phosphorous species); in the
increasing long-term trends of Na+ and Cl-; and, a positive outcome
of environmental regulations, in the decreasing long-term trends of
phosphorous species. Moreover, the seasonality of nutrients differs
considerably from the seasonality of naturally originated solutes (e.g., geogenic solutes).
Influence of catchment characteristics on magnitude and trends of solute concentrations
A statistically robust link between catchment characteristics and river
biogeochemical signatures is not straightforward, because the spatial
heterogeneity in river catchments and the limited sample size make the
search for cause–effect relations between catchment characteristics and
in-stream concentrations challenging. However, catchment characteristics
play a role for certain solutes, and we found evidence of their impact
especially in the magnitude and seasonality of solute concentrations. First,
the geological composition of the bedrock influences the weathering
products, increasing Ca2+ concentrations in mostly calcareous
catchments (northern Switzerland) and of H4SiO4 in silicic
catchments (Alpine catchments in central and southern Switzerland). The
catchments DI, PO, and SA, which are entirely located in the Alpine area
(Table 1) and mainly lie on crystalline bedrock (Fig. 1c), have a higher
concentration of silicic acid (Fig. 3a) along with a lower concentration
of Ca2+ in comparison to the other catchments, with the AN in the
Swiss Plateau area (Table 1) being an exception, which is characterized by a
concentration of silicic acid that is comparable to that of Alpine
catchments. The influence of lithology was identified before in the literature,
with, for instance, high Ca2+ concentrations in one of the tributaries
of the Amazon River attributed to the presence of a more-carbonate-rich lithology
in the corresponding catchment (Baronas et al., 2017; Rue et al., 2017; Torres et al., 2017).
In the seasonality analysis, the classification of catchments into classes
helps in highlighting the impact of the topography on the solute variability.
In the Alpine catchments, discharge seasonality generally dominates the
seasonality of solute concentrations, except for TP, which is related to the
presence of suspended sediments in the streamflow caused by higher erosion
rates (Haggard and Sharpley, 2007). Indeed, suspended sediment concentrations, coming from
erosion, are much higher in Alpine catchments, excluding the two small
headwater catchments LU and ER, than in the others (Fig. S5). Furthermore,
erosion represents a source also for DOC and TOC (Schlesinger and Melack, 1981). TP, DOC, and TOC
together with Mg2+ have been classified as solutes belonging to the “B”
class (Fig. 4); i.e., their concentration patterns show lower variability
in hybrid catchments than across other classes.
The driver of Mg2+ variability is, however, less clear than for the others. The higher
variability in its concentrations in Alpine catchments in comparison to
other catchments might be due to the presence of glaciers. The Rhone, Rhine, and
Inn rivers include considerable glaciated areas in their catchments, and this
might have an effect on magnesium concentration in stream water. The
chemistry of glacier water is generally characterized by low water–rock
contact times because the volume of water and the flow rate are high that
the time water molecules interact with sediments is relatively short
(Wimpenny et al., 2010b). Therefore,
water sourced by glacier melt can have a dilution
effect in terms of Mg2+, and this explains why Mg2+ concentrations
are significantly higher during low-flow periods than during high-flow
periods. This is also consistent with the observations of other studies,
e.g., Ward et al. (1999) and Wimpenny et al. (2010a, b). Weathering processes in Alpine
environments are also studied using isotope data (e.g., Tipper et al., 2012; von Strandmann et al., 2008).
Results underlie the uncertainty on the processes determining
weathering products as Mg2+. Besides the contribution of
glacier-sourced water to streamflow and biological processes affecting
Mg2+ concentrations (Wimpenny et al., 2010b), the dissolution of bedrock that is not proportional
to its composition (Kober et al., 2007), which is likely to take place in the presence of
carbonate-poor glacial sediments (McGillen and Fairchild, 2005), might also play a role. Carbonate
rocks might dissolve with preferential release of Mg2+, which therefore
contributes strongly to solute fluxes in rivers. This phenomenon has been
observed also in the Swiss Alps (Haut Glacier d'Arolla), where carbonate
contents of sediments are of the order of 1 % (Brown et al., 1996; Fairchild et al., 1999), but their
contribution to solute fluxes is much higher (McGillen and Fairchild, 2005).
Catchment size or precipitation might also influence river solute
concentrations. This is evident from the behavior of the Lümpenenbach (LU)
and Erlenbach (ER) catchments, which are 3 orders of magnitude
smaller than the other catchments considered in the study and show median
concentrations lower than those of the other catchments. This is true for
all solutes, except DOC and TOC, the concentrations of which are the highest
in the Erlenbach (ER) and Lümpenenbach (LU) rivers. These catchments are
situated in the Alptal valley, which is characterized by more humid climate
(double annual precipitation) compared to other catchments. Recently, Von Freyberg
et al. (2018) analyzed isotope data of 22 catchments across Switzerland, including
LU and ER; computed the young water fraction (i.e., the proportion of
catchment outflow younger than approximately 2–3 months) across 22 Swiss
catchments; and tested its correlation with a wide range of landscape and
hydro-climatic indices. They inferred that hydrological transport in LU
and ER is dominated by fast runoff flow paths, given the humid conditions and
low storage capacity when compared to other catchments. DOC exports have
typically been associated with near-surface hydrologic flow paths (Boyer et al.,
1997; Tunaley et al., 2016; Zimmer and McGlynn, 2018), thus offering a possible explanation for the higher
concentration of DOC and TOC in these catchments.
In summary, the comparison among catchments highlighted differences in
magnitude of silicic acid and calcium, likely due to the different underlying
lithology. Steeper morphologies show higher sediment transport in surface
water, which is consistent with the observation of the pronounced seasonality of
sediment-binding solutes (i.e., TOC and TP) in the Alpine catchments. The
headwater catchments ER and LU, which are smaller and wetter than the other
case studies, show a peculiar behavior with enhanced DOC and TOC
concentrations, likely as a consequence of humid conditions, near-surface
and/or surface flow, and low storage capacity.
Solute behavior classification in the log(C)–log(Q) space. The
definitions are derived from the classification of Moatar et al. (2017),
which is based on the value of b, the slope of the regression line in the
log(C)–log(Q) space. The discharge time series is divided into low-flow and
high-flow events based on q50, the median daily discharge. Red areas
represent hydrological dilution behavior, yellow areas represent biogeochemical
removal for low flows, and green areas represent hydrological export behavior.
The grey horizontal line crossing the axes origin represents the near-zero slope
area, i.e., it is representative of biogeochemical stationarity. The colorless
solutes outside these areas do not show any dominant behavior. The dimension of
circles represents the percentage of catchments in which the dominant behavior
is observed (from 60 %to 100 %).
Consistency of solute behaviors across catchments
This study showed that concentration–discharge relations reveal nearly
chemostatic behavior for most of the considered solutes across catchments;
i.e., analyzed solute concentrations vary a few orders of magnitude less than
discharge (Fig. S6). This outcome agrees with other studies (e.g., Godsey et al., 2009;
Diamond and Cohen, 2017; Kim et al., 2017; McIntosh et al., 2017). We found that the in-stream biogeochemical signal
is highly dampened, which is consistent with other studies (Kirchner et al., 2000; Kirchner and Neal, 2013), but
different behaviors of solutes could be nonetheless detected in the
log(C)–log(Q) space, thus allowing a partition into four categories, as
suggested by Moatar et al. (2017). A representation of such partitioning is offered in
Fig. 7, where the space between the negative-slope line and the
near-horizontal line represents the dilution behavior, and the space
delimited by the positive-slope line and the near-horizontal line represents
the enrichment or removal behavior. In fact for low-flow conditions
(i.e., q<q50) this is typically associated with biogeochemical
processes of solute removal (e.g., nitrification), while for high-flow
conditions (i.e., q>q50) it is generally associated with the
capacity of the flow to entrain particles containing the solute. Such a
description provides a different point of view of C–Q relations compared to
the existing literature since the subdivision between low- and high-flow
conditions allows for a more detailed investigation of the processes potentially
determining the observed solute behaviors. However, the 14-day frequency
sampling does not allow a direct detection of short-scale processes and
especially fast flood waves. This limitation could contribute to the low
percentage, only 29 %, of cases in which a solute switches the behavior
between low-flow and high-flow conditions. The additional uncertainty is due to
the choice of the median daily discharge as a breaking point for the curves.
However, in a recent study, Diamond and Cohen (2017) tested various breaking points for the
C–Q relations of different solutes with most of the breaking points centered
on approximately the median flow supporting our choice. In search for
generalizations, we assigned a solute to each specific class if the same
behavior was observed in at least 60 % of the analyzed catchments.
Geogenic solutes are grouped in a single circle since almost all of them
show a dilution behavior. Only H4SiO4 does not show a clear
signal, probably because it is involved, although to a minor extent, in
complex dynamics related to biological processes (Tubaña and Heckman, 2015), which can affect
its behavior. The diluting behavior of geogenic solutes is a quite well
consolidated fact in the literature (Godsey et al., 2009; Thompson et al., 2011; Baronas et al., 2017; Diamond and Cohen, 2017;
Hunsaker and Johnson, 2017; Kim et al., 2017; Moatar et al., 2017; Winnick et al., 2017; Wymore et al., 2017),
and this study contributes to this
body of knowledge confirming this behavior. Residence time is a fundamental
hydrological variable for weathering products, since it is related to the
weathering rates and therefore to the resulting solute concentration
(Maher, 2010). Catchments that show chemostatic behavior (e.g., BR for
Ca2+ or WM for H4SiO4) likely have average water residence times that
exceed the time required to reach chemical equilibrium, while a dilution
behavior is expected when residence times are generally shorter than
required to approach chemical equilibrium (Maher, 2011). Our results suggest that
the concentrations of geogenic solutes across the catchments are far from
the equilibrium, which is likely due to the relatively fast hydrological
response of Alpine and subalpine catchments also associated with
substantial precipitation amounts. However, it is very likely that the residence time
and the flow pathways are highly heterogeneous in Alpine catchments, with
water from different sources having different biogeochemical characteristics
(Torres et al., 2017; Baronas et al., 2017).
Therefore, flow paths with a sufficiently long residence
time for reaching chemical equilibration must exist, but they do not leave a
major signature on the examined geogenic solutes. In conclusion, there is a
quite-high confidence in claiming that geogenic solutes are characterized by
a dilution behavior.
The Cl- solute is also clearly characterized by dilution and our
results are in agreement with other studies (Thompson et al., 2011; Hoagland
et al., 2017; Hunsaker and Johnson, 2017).
NO3 relations with discharge are less clear (Aguilera and Melack, 2018; Butturini et al., 2008; Diamond and Cohen, 2017;
Hunsaker and Johnson, 2017), but this study highlighted a dilution behavior also for NO3 in
the majority of catchments for both low-flow and high-flow conditions. This
result partially agrees with the observations of Wymore et al. (2017), who claimed that
NO3 shows variable responses to increasing discharge. In fact, we
observed that while dilution is evident in 80 % of the catchments for
low-flow conditions, this percentage drops to 63 % for high-flow
conditions. Although NO3 is one of the main components of TN
(Fig. 2), TN does not show the same behavior. For low flows, TN is also
characterized by dilution, but for high flows TN shows chemostatic behavior
in about 70 % of catchments.
The behavior of phosphorus and its compounds is not clear. For
low flows, DRP behaves chemostatically in about 40 % of catchments but
dilutes in about 60 % of catchments. TP behavior could not be classified
due to its variability across catchments for low flows, whereas for
high flows it clearly shows hydrological export in 90 % of catchments,
because of increased suspended sediment concentration. In-stream sediments
can be, however, both source and sink for phosphorus (Haggard and Sharpley, 2007), as high
suspended sediment concentrations in rivers favor the sorption of phosphorus
to particles, thus lowering DRP concentrations (Zobrist et al., 2010). For high-flow
conditions, we observed various DRP behaviors across catchments (about
45 % of dilution, 45 % chemostatic, and 10 % enrichment), so that a
clear classification is not possible. The weak correlation between DRP and
suspended sediment concentration suggests that the sorption of phosphorus
to particles is not the only and most influencing factor of the DRP dynamic.
Analysis of temporal variations of the b exponent. (a–d) represent
the values of the b exponent of the C–Q empirical relation (C=aQb)
of (a)Na+, (b)Cl-, (c) DRP, and
(d) TP across four decades from 1974 to 2013 – (i) 1974–1983,
(ii) 1984–1993, (iii) 1994–2003, and (iv) 2004–2013 – across all the
catchments with monitoring period longer than 30 years. The dashed red line
represents the zero threshold (i.e., biogeochemical stationarity).
(e–h) represents two examples of how the C–Q relations vary across
the decades (e) 1974–1983, (f) 1984–1993,
(g) 1994–2003, and (h) 2004–2013. The C–Q relations refer to the catchments
BR (Swiss Plateau, in blue) and PO (Alpine, in yellow) for the total phosphorus.
TOC is the only solute characterized by enrichment in both low-flow and
high-flow conditions. DOC was proved by a set of studies to exhibit an
enrichment behavior (e.g., Boyer et al., 1996, 1997; Butturini et al., 2008;
Hornberger et al., 1994; McGlynn and McDonnell, 2003; Pedrial et al., 2014;
Wymore et al., 2017), but our results are in this respect highly uncertain for low flows
and suggest a chemostatic behavior for high flows. Wymore et al. (2017), for instance,
analyzed the biogeochemical response in the Luquillo catchment in Puerto
Rico and detected an enrichment behavior. This catchment is mainly covered
by the tropical forest and characterized by very wet conditions
(≈4500 mm yr-1 of rainfall). This is the likely reason leading to higher DOC
concentration with increasing streamflow. The underlying mechanism could be
that of a larger share of streamflow coming in wet conditions from shallower
soil pathways (von Freyberg et al., 2018), which are generally more organic-rich than the deeper
horizons hosting lower DOC quantities (Evans et al., 2005). Our study seems to confirm
this hypothesis, as the wettest catchments analyzed in this study (Erlenbach,
ER; and Lümpenenbach, LU) show enrichment of DOC at least for low-flow
conditions. These are likely mainly dominated by subsurface flow, thus
confirming the impact of soil wetness in the unsaturated zone on DOC
behavior for undisturbed catchments characterized by wet conditions.
The results of this study also showed that the variability in solute
magnitude in the long term can play a role in the definition of the solute
behavior. Na+ and Cl- show dilution during the entire monitoring
period, despite the increasing concentrations through time (Fig. 8).
However, DRP and TP switch from the highly negative b exponent of the C–Q
power law relation to the even positive b (Fig. 8), after the time when the
measures to reduce the phosphate input were introduced (Fig. 6). Such
measures (Zobrist and Reichert, 2006) lead to a conspicuous decrease in DRP concentration and
partially also in TP. Therefore, the fraction of DRP in TP decreased in time
(Fig. S7) and the other TP components became more important than DRP in
the definition of TP behavior. Among these, the component carried with
sediments might be responsible for the switch, which took place in all the
analyzed catchments, from dilution to enrichment across the last four
decades. DRP also shows an increasing trend of the b exponent of the C–Q
relations across decades, but the behavior only switches from
dilution to enrichment in two catchments (AN, WM). This means that when DRP inputs were
higher, the transport was not source-limited, while decreasing the input
forced DRP to have a more chemostatic behavior, probably because the input
became so low that the phosphorus transport was controlled by a legacy of
phosphorus stored in the soil, which was accumulated during the years of
undisciplined agricultural practices (Sharpley et al., 2013; Powers et al., 2016;
Van Meter et al., 2016a).
Conclusions
The long-term water quality data analysis of this study was designed for
understanding the signature of catchment characteristics and the influence
of anthropic activities on solute concentrations observed in Swiss rivers.
The analysis of magnitude, seasonality, and temporal trends revealed clear
cause–effect relation between human activities and certain solute
concentrations (i.e., Na+, Cl-, NO3, DRP). Indeed, changes in
the anthropic forcing (e.g., phosphate ban or increased deicing salt)
overwhelm the natural climatic variability and are clearly reflected by
changes in magnitude of solutes like DRP, TP, Na+, and Cl-. The
seasonality of anthropogenic-related solutes (i.e., NO3, TN, DRP,
and TP) in the catchments in the Swiss Plateau, more impacted by human
activities,
is clearly altered compared to the seasonality of Alpine catchments.
The detection of the signature of catchment characteristics is less
straightforward and can only be captured in a quantitative but not
statistically significant way due to the spatial heterogeneity of catchment
characteristics and the relatively small sample size (11 catchments).
Although the solute export is the result of multiple complex processes,
catchment topography, geology, and size are expected to have a role in
determining solute concentrations, especially of weathering solutes, whose
concentrations are influenced by the bedrock composition, and
sediment-binding substances (i.e., TP, TOC, and DOC) which have an enrichment
behavior in catchments characterized by steeper morphologies and higher
erosion rates. While we see evidence for a role of catchment
characteristics, these influences are relatively minor in our analysis.
The analysis of the empirical C–Q power laws was used to investigate and
possibly obtain a generalizable classification of solute behaviors.
Repeating the analysis for low-flow and high-flow conditions provides a more
detailed description of solute behaviors in comparison to most of the
previous literature. The variability in solute concentration is generally
much smaller than that of streamflow, which, as a first step of the analysis,
would support a chemostatic behavior. However, the overall dominant behavior
across solutes and catchments is dilution. For many solutes, this result is
consistent with other studies (i.e., geogenic solutes and Cl-).
Sediment-binding substances (TP, DOC, and TOC) show, however, an enrichment
during high-flow events, while for other solutes it is not possible to
define a clear behavior (e.g., DRP).
Finally, we observed that anthropic activities affect not only the magnitude
of concentrations of solutes in rivers, but also their seasonality and
long-term dynamics. Remarkable variation in long-term dynamics, moreover,
might also determine changes in solute behavior in time, as we demonstrated
for DRP and TP. This time-varying perspective of solute behaviors represents
a novelty in the literature and gives clear quantitative evidence that
anthropic activities might also influence the C–Q relations. Together with the
small sample size, one of the main limitations of the study is the coarse
temporal resolution of the water quality data that prevents the direct
analysis of (solute) fast response times associated with flood dynamics.
Luckily, the advancement of technologies in high-resolution concentration
measurement research (von Freyberg et al., 2017) will alleviate this limitation in the
future. Despite these limitations, the above results reinforce and
extend the current knowledge on the biogeochemical responses of rivers,
demonstrating that long-term observations allow identifying various aspects
of anthropic activities on the solute inputs to rivers.
Data availability
The NADUF database can be downloaded
from https://www.eawag.ch/en/department/wut/main-focus/chemistry-of-water-resources/naduf/ (EAWAG, 2016).
The supplement related to this article is available online at: https://doi.org/10.5194/hess-23-1885-2019-supplement.
Author contributions
SF and MB conceived the original idea, and the discussion
with PB contributed to its development. MB developed the analysis code and
performed the analysis. SF and PB contributed to the interpretation of the
results and shaping the research and discussed results. MB wrote the paper with
the contributions of all the co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We acknowledge Ursula Schoenenberger for providing the database used for
making Fig. 1 and Stephan Hug for the information about the NADUF program.
River discharge and water quality data were kindly provided by the Swiss
River Survey Programme (NADUF; http://www.naduf.ch, last access: October 2016). We acknowledge
Marius Floriancic for providing the macro-geology classes map and for the fruitful
scientific discussion. This study was supported by the DAFNE project
(https://dafne.ethz.ch/, last access: March 2019), funded by the Horizon 2020 programme WATER 2015 of
the European Union (grant agreement no. 690268).
Review statement
This paper was edited by Laurent Pfister and reviewed by two anonymous referees.
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