Studying the response of streamwater chemistry to changes in discharge can provide valuable insights into how catchments store and release water and solutes. Previous studies have determined concentration–discharge (cQ) relationships from long-term, low-frequency data of a wide range of solutes. These analyses, however, provide little insight into the coupling of solute concentrations and flow during individual hydrologic events. Event-scale cQ relationships have rarely been investigated across a wide range of solutes and over extended periods of time, and thus little is known about differences and similarities between event-scale and long-term cQ relationships. Differences between event-scale and long-term cQ behavior may provide useful information about the processes regulating their transport through the landscape.
Here we analyze cQ relationships of 14 different solutes, ranging from major ions to trace metals, as well as electrical conductivity, in the Swiss Erlenbach catchment. From a 2-year time series of sub-hourly solute concentration data, we determined 2-year cQ relationships for each solute and compared them to cQ relationships of 30 individual events. The 2-year cQ behavior of groundwater-sourced solutes was representative of their cQ behavior during hydrologic events. Other solutes, however, exhibited very different cQ patterns at the event scale and across 2 consecutive years. This was particularly true for trace metals and atmospheric and/or biologically active solutes, many of which exhibited highly variable cQ behavior from one event to the next. Most of this inter-event variability in cQ behavior could be explained by factors such as catchment wetness, season, event size, input concentrations, and event-water contributions. We present an overview of the processes regulating different groups of solutes, depending on their origin in and pathways through the catchment. Our analysis thus provides insight into controls on solute variations at the hydrologic event scale.
The movement of water and solutes through the landscape is inherently coupled. Streamwater chemistry at a catchment outlet differs depending on the flow paths of water through the catchment and can therefore be considered a “fingerprint” of catchment transport, mixing, and reaction processes. Consequently, studying the response of streamwater chemistry to changes in discharge provides insight into how catchments store and release water and solutes. Changes in solute concentrations as functions of discharge, i.e., concentration–discharge (or cQ) relationships, have commonly been assessed using multi-year time series of low-frequency (weekly to monthly) streamwater chemistry measurements (Hall, 1970; Godsey et al., 2009, 2019; Musolff et al., 2015). At this temporal scale, cQ relationships can serve as indicators of hydrologic and biogeochemical processes. Decreasing solute concentrations with increasing flow (often referred to as “dilution behavior”, Fig. 1a) have frequently been associated with source limitations, indicating the depletion of finite solute sources in the catchment (Basu et al., 2011) or mixing with more dilute waters. Conversely, patterns of increasing solute concentrations with discharge are described as “mobilization behavior” resulting from the flushing of solutes, for example from upper soil layers (Fig. 1c). Solute concentrations that vary little across wide ranges of discharge (“chemostatic behavior”, Fig. 1b) can result from several mechanisms, including the storage and release of solutes that are not supply limited (Godsey et al., 2009; Basu et al., 2011), the overprinting of source-limitation and mobilization behavior (Cartwright et al., 2020), or from large pre-event-water contributions to storm runoff (Clow and Mast, 2010).
Time series of discharge and solute concentrations (left panels),
and corresponding cQ relationships (right panels) for a single recession,
illustrating dilution behavior (Mg, top panels), chemostatic behavior (K,
middle panels), and mobilization behavior (Mn, bottom panels). Solutes with
negative cQ slopes (dilution,
Because cQ relationships can vary between solutes and catchments, they are frequently employed as descriptors for catchment hydrological behavior. The cQ relationships obtained from long-term, low-frequency data are particularly useful for characterizing the average behavior of a catchment (Clow and Drever, 1996; Godsey et al., 2009; Godsey and Kirchner, 2014). However, these long-term cQ relationships provide limited insight into the coupling of streamwater chemistry and discharge on shorter timescales, such as during hydrologic events. A better understanding of hydrologic controls on hydrochemical processes during events requires high-frequency hydrochemical observations, ideally spanning many contrasting storms. High-frequency streamwater sampling is cost-intensive and labor-intensive, and thus most studies are limited to the characterization of individual hydrologic events (the study by Rose et al., 2018, is a rare exception). Recent technological progress in the development of in situ sensors now allows for several solutes to be monitored at sub-hourly timescales (Rode et al., 2016). Analyses of these high-frequency measurements have provided substantial insights into biogeochemical processing (Rusjan et al., 2008), solute dynamics (Evans and Davies, 1998; Schwientek et al., 2013), and the temporal evolution of source contributions over the course of individual storm events (Grimaldi et al., 2004). Event-scale studies have also highlighted a general variability in solute responses across storm events that exceeds the variability observed in weekly or monthly grab samples (Bieroza and Heathwaite, 2015; Lloyd et al., 2016). These findings suggest that the controls on solute storage and transport processes on the event scale may be fundamentally different from those that shape long-term behavior.
Widely available chemical data from in situ sensors are limited to a handful of solutes, including nitrate, orthophosphate, and dissolved organic matter. While these solutes provide interesting insights into different aspects of catchment processes (Carey et al., 2014; Dupas et al., 2016; Koenig et al., 2017), they are unlikely to characterize all relevant processes regarding solute mobilization from different parts of the catchment. Furthermore, event-scale cQ behavior is usually not placed into the context of long-term cQ behavior because streamwater chemistry has rarely been measured at high temporal resolution over long periods. For example, solute concentrations during successive events in wetter years have been shown to be lower than in drier years (Biron et al., 1999), and events in wetter conditions may result in stronger surface water acidification than events following drier conditions (Wellington and Driscoll, 2004). However, because these studies measured solute concentrations only during individual events, we have no information on the long-term cQ behavior at these sites. Thus it remains challenging to identify controls of cQ behavior on both the event scale and longer timescales.
In this study we used high-frequency measurements of 14 different solutes ranging from major ions to trace metals that we obtained from an automated field laboratory at the outlet of a pre-Alpine catchment (von Freyberg et al., 2017). We quantified 2-year cQ relationships from the snow-free periods of a 2-year dataset, and compared them to event-scale cQ relationships of 30 hydrologic events that differed in size, antecedent wetness conditions, and seasonality indicators. Our study aims to explore questions such as (1) how 2-year cQ behavior differs from cQ behavior observed on the event scale; (2) how variable cQ relationships between individual events are; and (3) if inter-event variability in cQ relationships can be explained by specific environmental controls.
The Erlenbach catchment is a small (0.7 km
Streamwater and precipitation chemistry were analyzed semicontinuously at an automated field laboratory located at the Erlenbach catchment outlet (von Freyberg et al., 2017, 2018). Streamwater was pumped continuously from the stream to the field laboratory, and precipitation was collected with an open 45 cm diameter funnel. During periods without rain, the field laboratory received only streamwater for analysis. The field laboratory analyzed precipitation whenever more than 50 mL of precipitation accumulated in the rain sampler (corresponding to roughly 0.3 mm of precipitation), and the previously analyzed sample was streamwater. A new sampling and analysis cycle was initiated every 30 min; thus, during rainfall, streamwater and precipitation samples were each analyzed hourly. A drift correction standard was analyzed instead of streamwater every 4 h (every 6 h after 12 March 2018).
Before analysis, each precipitation or streamwater sample passed through a
0.2
In addition to the on-site analysis of major ions and stable water isotopes,
aliquots of filtered rainwater (every sample) and streamwater (one sample
per hour) were automatically collected into vials. Each vial contained 1 mL
of ultrapure
Streamwater electrical conductivity (EC) was measured at 5 min resolution (s::can condu::lyser, S::CAN Messtechnik GmbH, Vienna, Austria). For the purpose of this study, only every second EC measurement was used to match the 10 min measurement frequency of river discharge (see below).
River discharge was measured at a concrete flume installed at the Erlenbach catchment outlet (Hegg et al., 2006). Precipitation rates were recorded with a heated tipping-bucket rain gauge (Joss-Tognini 15183, LAMBRECHT meteo GmbH, Göttingen, Germany) at a meteorological station located at 1216 m above sea level in the Erlenbach catchment. At the meteorological station, air temperature was measured with a ventilated thermometer (VT3, Meteolabor AG, Wetzikon, Switzerland), and groundwater level fluctuations were recorded in a fully screened well (these readings were relative rather than absolute because they have not been calibrated against manual water level measurements). Discharge, precipitation, air temperature, and groundwater levels were recorded at 10 min resolution. These data were aggregated to 30 min or 1 h intervals to match the frequency of the solute data, except for the analysis of the EC data, for which the 10 min resolution was used. For this purpose, we extracted those data that were closest to the sampling times of the streamwater samples from the 10 min discharge, air temperature, and groundwater level time series. For the precipitation samples, associated precipitation amounts were calculated as cumulative sums from the 10 min tipping-bucket recordings.
The Erlenbach stream generally shows a fast and flashy response to rainfall events, resulting in very short durations of the rising limb of the storm hydrograph, in particular during short and intense events. Consequently, few samples were collected during the rising limb of each storm in spite of the high sampling frequency of the field laboratory. The falling limb of the streamflow hydrograph, on the other hand, was generally well captured. For this reason, we excluded all samples collected during periods of increasing discharge from our analysis (in Sect. 3.3 below, we show that excluding these periods has negligible effects on the 2-year cQ behavior). The relationship between solute concentrations and streamflow during hydrograph recession is informative in particular on outflow processes from shallow and deeper groundwater, as well as riparian water (Inamdar et al., 2006).
We excluded the winter months from the dataset to avoid ambiguities arising
from rain-on-snow and snowfall events in our analysis, because solute
responses to these events may differ from those during rain events in
snow-free periods. Consequently, only data points between 1 May and
15 November in both 2017 and 2018 were analyzed (hereafter referred to as the “snow-free periods”). During these snow-free periods, we identified individual events based on the following criteria: (1) events had to have a substantial increase in discharge, i.e., peak discharge at least 20 L s
For each event, we analyzed the solute concentration and discharge data during the hydrograph recession, starting at the main discharge peak of an event until 95 % hydrograph recession to baseflow, or until the event was cut off by the start of a subsequent event (if discharge had receded by at least 75 %). We estimated cQ relationships for solute/event combinations for which at least five data points were available on the recession limb.
To identify likely streamwater sources, we quantified average solute concentrations of different compartments in the Erlenbach catchment: median solute concentrations in streamwater, precipitation, and their lower and upper quartiles were calculated from the snow-free periods in the time series. Solute concentrations in groundwater were obtained from sampling two pumping wells in the upper part of the catchment at three different instances between 2016 and 2017. Although these groundwater chemistry data may not provide a comprehensive picture of Erlenbach groundwater, they can nonetheless indicate which solutes are likely to be dominant therein. Concentrations in soil water of the neighboring Studibach catchment were obtained from sampling 18 suction lysimeters during eight baseflow snapshot campaigns in the snow-free season in 2016 and 2017 (detailed description of the campaigns are given in Kiewiet et al., 2019). We aggregated the data from six sites (three forested, three non-forested), spread over three elevations in the catchment (1361, 1502 and 1611 m above sea level), at which suction lysimeters were installed at 15, 30 and 50 cm depth. The lysimeters were emptied and set to a tension of 50 mbar the day prior to sampling.
We characterized the source-sink behavior of the Erlenbach catchment for
individual solutes using a dimensionless solute flux index calculated from
the high-frequency data of the snow-free periods. The solute flux index
We estimated both the 2-year cQ behavior (i.e., using all recession data
from the snow-free periods in 2017 and 2018) and the individual event-scale
cQ behavior by fitting power-law relationships between concentration
We calculated the relative standard errors of the event-scale slopes and
intercepts, in order to exclude events for which the cQ relationships could
not be well constrained. The cQ relationships were excluded from our analysis if
the relative standard error of either the cQ slope
Given that our samples represent changes in chemistry during hydrograph recession, the
meaning of the obtained cQ slopes can be interpreted as follows: a cQ slope
of
The 30 hydrologic events span wide ranges of storm durations, intensities, antecedent wetness conditions, and other potential controls, thus facilitating an investigation into how these environmental controls may influence the slopes and intercepts of the event-based cQ relationships. To this end, we quantified 15 different parameters for each event from the following five categories: (1) temperature and proximity to midyear as seasonality indicators; (2) relative input concentrations, which quantify the ratio between the volume-weighted average precipitation concentration during the event to the streamwater solute concentrations during pre-event baseflow; (3) groundwater levels, baseflow discharges, and antecedent precipitation as indicators of antecedent wetness conditions; (4) several measures of event magnitude and intensity; and (5) event and pre-event-water contributions determined from isotope hydrograph separation (following the approach presented by von Freyberg et al., 2018). Table 1 presents an overview of these 15 environmental controls, as well as their ranges in our dataset.
We assessed 15 environmental controls grouped into five different categories: seasonality indicators, relative input concentrations, antecedent wetness conditions, event characteristics, and event-water contributions. Minimum and maximum values indicate the ranges observed in the dataset. Relative input concentrations are specific for every solute (and not available for all solutes and events), whereas all other controls do not differ between individual solutes. Event-water contributions were only assessed for 22 out of 30 events. Groundwater levels are expressed as negative values so that the maximum corresponds to the wettest conditions, consistent with the other wetness indicators.
As a seasonality indicator, the proximity to midyear is calculated as a
summer–winter index:
We used weighted rank correlation coefficients to quantify the dependence of
event-scale cQ slopes and intercepts on the 15 environmental controls. The
weights were the inverses of the standard errors of the individual cQ slopes
and intercepts, to prevent highly uncertain points from substantially
influencing the results. The statistical significance of these correlation
coefficients (their
We extracted 30 events from the time series of 2017 and 2018 that fulfilled the criteria outlined in Sect. 2.3. While IC measurements were available for all 30 hydrologic events, not all events had the required five sample points for all of the cations analyzed by ICP-MS (i.e., boron, barium, iron, manganese, chromium, strontium, and copper). Furthermore, some events had to be excluded from further analysis for individual solutes due to high relative standard errors of cQ slopes and/or intercepts (see Sect. 2.5). This resulted in 24 to 30 usable events for major ions measured with the IC. More than 20 events were evaluated for all other solutes, except manganese and copper. The concentrations of these two solutes were low and variable, resulting in no clear power-law relationship with discharge for many events. Consequently, only data from 17 and 11 events were usable for manganese and copper, respectively.
We quantified most environmental controls for all 30 events. However, meaningful event-water contributions could only be calculated for 22 events from stable water isotope measurements. Ratios of precipitation concentration to the pre-event baseflow solute concentration were not available for all events and all solutes due to sporadic problems with the rain collector. Also, these ratios were not assessed for EC, because EC was not measured in precipitation. Table 1 provides an overview of the range of environmental controls covered by the selected events. Some small events lasted only a few hours, whereas other events were extended, multi-day storms, and antecedent wetness conditions ranged from relatively dry to very wet. Although all events took place between May and November, they spanned a wide range of air temperatures and weather conditions.
Streamwater concentrations at Erlenbach were dominated by calcium, sulfate,
magnesium, and sodium, with median concentrations between 2 and 48 mg L
Median solute concentrations and their upper and lower quartiles in streamwater, groundwater,
and precipitation in the Erlenbach catchment and in soil water in the adjacent Studibach catchment. Groundwater solute data were collected at one to three different sampling times at two pumping wells located in the upper part of the catchment. These are probably not representative of groundwater concentrations throughout the catchment, but still provide a rough indication of which solutes dominate groundwater. Concentrations in precipitation and streamwater were obtained from the time series recorded at the Erlenbach outlet, excluding months with snow.
We can group different solutes based on their most important sources in the Erlenbach catchment as determined from their concentration ranges in groundwater, streamwater, and precipitation, as well as output–input flux indices (Table 2). The concentrations of calcium, strontium, barium, and boron in the groundwater samples were similar to, or higher than, those in streamwater. Concentrations of magnesium, sodium, and sulfate were higher in streamwater than in the groundwater samples, suggesting that other, unsampled, groundwaters with higher concentrations also contribute to streamflow. In general, groundwater concentrations of weathering products are likely to be highly heterogeneous due to spatially variable contributions from the geochemically complex flysch bedrock (Fischer et al., 2015; Kiewiet et al., 2019). Precipitation concentrations of weathering products are low, and flux indices close to 1, implying that they are primarily derived from within-catchment processes (Table 2). In this paper, we will refer to these solutes as groundwater-sourced.
The output–input flux index of chloride at Erlenbach was 0.33 for the
snow-free periods, but was
Nitrate concentrations in Erlenbach streamwater can also be mainly
attributed to atmospheric inputs as well as manure inputs from grazing. The
output–input flux index of nitrate was
Iron was the most abundant trace metal in the Erlenbach streamwater, with
median concentrations of around 5
We calculated 2-year cQ relationships based on the snow-free recession periods of the 2-year time series and event-scale cQ behavior from the recessions of the 30 extracted events. The 2-year cQ relationships were relatively narrow and well-defined for most solutes (Fig. 2), resulting in low uncertainties of 2-year slopes and intercepts (Table S1 in the Supplement). Furthermore, 2-year slopes and intercepts calculated for the whole time series were broadly similar to those determined from only hydrograph recession periods (Table S1), largely because recessions comprised nearly all of the 2-year data. Thus, our analysis can straightforwardly compare cQ behavior during individual recessions with the 2-year cQ behavior across 2 years of recessions.
The 2-year cQ relationships from the entire recession time series (data points are shown in light blue and the dark blue line indicates the 2-year power-law fit), compared to the cQ behavior of individual events (colored lines; up to 20 events are shown for better visibility). Discharge is normalized by the average discharge of the time series. Most solutes exhibit a 2-year dilution pattern, whereas the trace metals iron, manganese, and chromium exhibit a 2-year mobilization pattern. The cQ relationships vary much more from event to event for some solutes (e.g., potassium, chloride, and nitrate) than for others (e.g., calcium, magnesium, sodium, and EC).
Figure 2 shows that major ions and EC exhibited dilution behavior (slopes
Event-scale cQ relationships at Erlenbach were much more variable than the 2-year behavior. A comparison between different events revealed substantial inter-event variability in both slopes and intercepts for a number of solutes (colored lines in Fig. 2 and blue circles in Fig. 3). The extent of this variability differed dramatically among the solutes. Individual events followed the 2-year trend for calcium, magnesium, sodium, strontium, and EC, for example, whereas they deviated substantially from the 2-year trend for potassium, chloride, and nitrate.
A comparison of event-scale and 2-year cQ intercepts
For most solutes, the averages of all event-scale intercepts (light-blue diamonds in Fig. 3a) were similar to the 2-year intercepts (red bars in Fig. 3a), because discharge was normalized by its mean value (see Sect. 2.5). The inter-event variability in intercept values was particularly high for chloride, nitrate, and some of the trace metals, possibly suggesting changes in sources, flow paths or reaction rates of these constituents between events.
Conversely, the slopes of the event-scale cQ relationships differed
substantially from the 2-year behavior for some of the elements (Figs. 2
and 3). This was true in particular for three solutes with significant
atmospheric inputs (chloride, potassium, and nitrate), which exhibited
dilution behavior in the 2-year dataset (2-year slope
None of the solutes exhibited stronger dilution behavior, on average, during events (blue diamonds in Fig. 3b) than over the 2-year period (red bars in Fig. 3b). In the case of potassium, chloride, nitrate, and manganese, most individual events (blue circles in Fig. 3b) exhibited substantially weaker dilution, or greater mobilization, than the 2-year cQ relationships (red bars in Fig. 3b). Considered together, these results indicate that dilution processes were equally important, or more important, over the long term than during individual events.
Solutes with similar sources and similar chemical properties tend to exhibit similar 2-year cQ behavior and similar degrees of inter-event variability in slopes and intercepts. For example, calcium, magnesium, sodium, strontium, barium, boron, sulfate, and EC all exhibited well-defined 2-year dilution behavior and little variability in both cQ slopes and intercepts at the event scale. All solutes exhibiting this behavior were previously identified as groundwater-sourced in the Erlenbach catchment (Sect. 3.2). Average event slopes of these solutes were approximated well by the 2-year behavior (Fig. 3b), resulting in event patterns that fan out around the high-discharge section of the 2-year cQ trend (Fig. 2). This similarity between 2-year and event-scale behavior indicates that similar mechanisms controlled solute and water mobilization both during individual events and over the long term for these solutes.
Conversely, potassium, chloride, and nitrate have substantial atmospheric inputs at Erlenbach. These three solutes were characterized by 2-year dilution behavior, and high variability in event slopes ranging from negative to positive values, along with some inter-event variability in intercept values. These solutes exhibit event cQ patterns that fan out and stack up around the 2-year relationship. This large inter-event variability in cQ slopes may plausibly arise from varying degrees of atmospheric deposition, evapotranspiration, and dry deposition, resulting in temporally and spatially variable concentrations of chloride, potassium, and nitrate in shallow soil layers. A large variability in soil water concentrations was indeed observed in the neighboring Studibach catchment (Table 2). In the case of nitrate and potassium, the inter-event variability may also be affected by biological uptake and reaction processes. In summary, chloride, nitrate, and potassium showed a much more diverse behavior on the event scale and also – at least to some extent – on longer timescales than the purely groundwater-sourced solutes.
The trace metals iron, manganese, and chromium were mobilized during events, as reflected in positive event and 2-year cQ slopes. Interestingly, this mobilization behavior was more pronounced (i.e., slopes were steeper) on the event scale than on longer timescales. In contrast to the other metals, copper concentrations in streamwater suggested chemostatic behavior over long timescales, whereas some mobilization behavior was evident on the event scale (Fig. 2). These findings suggest that the trace metals were mainly sourced from the soil layers, and the observed cQ behavior of manganese and iron confirms common assumptions about them being co-located in the soil layers where they are bound to organic material or present as oxides.
The grouping of solutes based on cQ behavior aligns well with the previously identified source areas in the Erlenbach catchment (Sect. 3.2). These alignments were most obvious for the purely groundwater-sourced solutes because they could be clearly attributed to one main source. For most other solutes, the combination of different sources and the co-occurrence of reaction and mixing processes resulted in less clear patterns. A scatterplot of event slopes against event intercept values (Fig. 4) supports the grouping of solutes and illustrates the different degrees of inter-event variability. The low uncertainty of the calculated slopes and intercepts furthermore confirms that the observed variability among events is real-world behavior rather than noise.
Scatterplots of event cQ slopes and intercepts of the 14 different solutes and EC (error bars indicate one standard error). Solutes
from different dominant sources cluster and exhibit similar ranges of
variability.
Other likely modulators of the variability observed for different solutes are their ionic form and their possible occurrence as nanoparticulates. Cations are known to undergo exchange buffering through electrostatic binding to negatively charged sites in soils (Helling et al., 1964; Rhoades, 1982). This likely resulted in less variable cQ behavior on the event scale for the cationic solutes calcium, magnesium, sodium, strontium, and barium compared to the anionic solutes sulfate and boron. These anions are less buffered by ion exchange and showed a far more variable cQ behavior. Two other anions, chloride and nitrate, also exhibited highly variable cQ behavior at the event scale. It consequently seems likely that exchange buffering processes modulate the degree of observed inter-event variability. Nevertheless, the importance of cation exchange buffering on the variability of solute behavior cannot be well constrained with our dataset because most analyzed cations are groundwater-sourced species, while two of the three solutes with substantial atmospheric inputs, namely chloride and nitrate, are anions. The only cation with important atmospheric input (potassium) is biologically very active, potentially obscuring the effects of cation exchange buffering. With respect to the trace metals, a large part of their total concentration can occur as natural nanoparticulates (Gottselig et al., 2017), which may undergo different transport and adsorption processes compared to their dissolved forms. This likely had a strong effect on their cQ behavior at Erlenbach.
We used weighted rank correlation coefficients to assess how variations in cQ slopes and intercepts from event to event were related to seasonality indicators, relative input concentrations, antecedent wetness conditions, event characteristics, and event-water contributions. A heatmap (Fig. 5) illustrates how event-scale cQ slopes and intercepts depended on these different environmental controls. Individual examples of these relationships are shown in Fig. S2 in the Supplement for different solutes and drivers.
Weighted rank correlation coefficients expressing the dependence
of event-scale cQ intercepts
The event-scale cQ intercepts for all solutes, regardless of their dominant source, generally responded similarly to each control (with the exception of chloride; Fig. 5a). Seasonality indicators and antecedent wetness conditions were the most important environmental controls on cQ intercepts for all solutes, with drier and warmer conditions resulting in higher concentrations in streamwater. By contrast, although the effects of each control on event-scale cQ slopes were broadly similar within groups of solutes that shared the same dominant source (precipitation, soil water, and groundwater), they often differed between these groups (Fig. 5b). In the following, we therefore discuss the behavior of the event-scale cQ slopes of these groups of solutes together, rather than discussing individual solutes.
We found that the event-scale cQ slopes of groundwater-sourced solutes were positively correlated with seasonality indicators, with weaker solute dilution (i.e., less negative cQ slopes) during warmer conditions. Antecedent wetness conditions and event characteristics were other important factors controlling the event-scale cQ behavior of most groundwater-sourced solutes. We found stronger dilution (i.e., more negative cQ slopes) during larger events associated with wetter antecedent conditions. cQ slopes were also more negative when event-water contributions were larger, consistent with stronger dilution of groundwater-sourced solutes by larger volumes of recent rainfall (i.e., event water). Most of the observed relationships were less clear for boron (which is primarily sourced from groundwater, but does not occur as a cation, but instead as either undissociated boric acid or as the borate anion, depending on pH), supporting the hypothesis that differences in ionic charge modulate event-scale cQ behavior. By contrast, however, sulfate (an anion) showed very similar dependencies as the cations in groundwater, contradicting this hypothesis. Streamwater electrical conductivity (EC) at Erlenbach is dominated by the two most abundant solutes, calcium and magnesium (along with their counter-ion, bicarbonate), which are groundwater-sourced. Consequently, the event-scale cQ behavior of EC depended on similar factors as that of the groundwater-sourced solutes.
For the solutes with significant atmospheric inputs (chloride, potassium, and nitrate), the effects of possible environmental controls on cQ slopes were less clear, but still indicated similar behavior among the solutes of this group. We found that the event-scale cQ slopes of chloride and nitrate were influenced by antecedent wetness conditions, with dilution behavior during wetter conditions and mobilization behavior during drier conditions (Fig. 5b). This can potentially be explained by stronger evapoconcentration of these solutes in the soil under drier conditions. We also observed a tendency toward more positive slopes with higher relative input concentrations for all three solutes, and during events in which the event-water contribution was larger. The observed dependencies of nitrate and potassium differed somewhat from those of chloride, likely due to the overprinting with reaction processes acting on these solutes, and due to overlapping patterns from atmospheric and groundwater sources in the case of potassium.
Among the trace metals, iron often exhibited contrasting behavior compared to the others (Fig. 5b). While manganese, chromium, and copper showed a tendency toward stronger mobilization under colder and wetter conditions, iron tended to be mobilized more during warmer and drier conditions. Given that both iron and manganese are found predominantly in wetter soils (in the neighboring Studibach catchment; Kiewiet et al., 2019), we would have expected these two metals to depend on similar drivers. However, Kiewiet et al. (2019) also observed higher concentrations of manganese in riparian areas compared to iron. We would expect these riparian zones to be activated first during a rain event, whereas soils farther from the stream would likely only start to contribute to streamflow later. This sequence of contributions from source areas dominated by different trace metals could potentially explain why the cQ slopes of the trace metals varied differently with seasonality, event characteristics, and event-water contributions. The high concentrations of manganese in riparian areas may also explain the very high cQ slopes observed for this element. As the relative contribution from riparian areas decreases later in the recession, manganese concentrations in streamwater drop rapidly, decreasing more than proportionally compared to discharge. As metal complexation and mobilization is known to depend on various factors such as pH and redox conditions in the soil layer (Gotoh and Patrick, 1972, 1974), further field measurements are necessary to better understand the mobilization of trace metals from soil layers during hydrologic events.
Previous studies have shown that hydrological controls on streamwater chemistry are mostly invariant on timescales of weeks to months; for example, Gwenzi et al. (2017) found similar cQ behavior for weekly to monthly sampling frequencies, and Godsey et al. (2019) found similar cQ behavior in weekly/monthly grab samples and in year-to-year variations in annual average concentrations. Other studies have also shown that long-term average cQ behavior is relatively independent of the sampling frequency (e.g., Bieroza et al., 2018). Our analysis of event-scale cQ patterns, as defined by high-frequency sampling within and between individual events, provides a new perspective on cQ relationships. Our results indicate that solute responses to discharge variations can be fundamentally different during individual events compared to longer timescales. These differences in cQ behavior reveal the effects of the dominant sources, reaction processes, and ionic forms of the different solutes.
Figure 6 provides an overview of the different cQ patterns observed at Erlenbach and the role of various environmental controls in shaping these patterns during events and over the long term. For groundwater-sourced solutes such as calcium, magnesium, and sodium, we found that 2-year cQ relationships were relatively good approximations of event-scale cQ patterns. These solutes exhibited little inter-event variability, and their cQ relationships on both the event scale and longer timescales were dominated by dilution. The inter-event variability of cQ slopes of groundwater-sourced solutes was mainly controlled by season, event size, and event-water contributions.
Schematic overview of cQ behavior based on dominant solute sources and environmental controls at the Erlenbach catchment. Solutes originating from similar sources generally behave similarly, and their cQ patterns are mostly sensitive to the same environmental controls. The light-blue patches in cQ behavior show the expected variability in long-term cQ data, with average long-term cQ relationships indicated by the blue lines. Red lines show cQ relationships for individual events and indicate the degree of inter-event variability.
Atmospherically derived solutes also exhibited 2-year dilution behavior, but their event-scale behavior ranged from dilution to mobilization, with event-scale cQ patterns stacking up and fanning out around the 2-year cQ relationship (Figs. 2 and 6). Their event-scale cQ slopes were usually more positive than their 2-year cQ slopes, indicating a stronger importance of chemostatic and mobilization mechanisms on the event scale. The controls on event-scale cQ slopes were less clear for nitrate and potassium than for chloride, likely due to reaction processes (in the case of potassium and nitrate) or overprinting of contributions from different sources (in the case of potassium). Nevertheless, we observed stronger mobilization following drier antecedent conditions and during events with larger event-water contributions, suggesting that evapoconcentration of atmospherically deposited solutes plays an important role.
Trace metals showed mixed behavior in our analysis, likely due to different patterns of distribution in soils and groundwaters, and possibly also due to their presence as nanoparticulates (Fig. 6). In our study, the different trace metals often responded differently to environmental controls, possibly reflecting differences in their relative abundance in the riparian zone, in their complexation mechanisms, in their redox sensitivities, and in their biological cycling (Herndon et al., 2015; Koger et al., 2018).
Our analysis of 30 events extracted from a 2-year time series of sub-hourly streamwater solute measurements demonstrated that concentration–discharge (cQ) relationships at the hydrologic event scale can differ substantially from those over the long term. In addition, cQ relationships varied greatly from event to event for some solutes (e.g., potassium, chloride, and nitrate), but varied much less for others (e.g., calcium, magnesium, sodium, and EC). The variability in cQ relationships among different hydrologic events (and solutes) could be linked to a range of environmental controls.
Our analysis would not have been possible if we had analyzed only a few solutes or collected data only during a handful of hydrologic events, as is common practice in catchment hydrochemistry studies (Rode et al., 2016). To understand the complex mechanisms governing solute storage and release from different parts of the catchment, it is necessary to analyze many different solutes and to sample multiple hydrologic events at high enough frequency to capture event behavior. However, we are aware that sampling and analysis systems like the one that we employed at Erlenbach are resource-intensive and thus difficult to deploy in many field situations. Our analysis suggests, however, that a viable alternative may be to analyze one solute (or proxy thereof) from every major store and streamwater source in the catchment. For instance, EC in Erlenbach streamwater exhibited very similar cQ relationships as calcium, magnesium, and sodium, making it a suitable tracer for groundwater-sourced solutes (in other catchments, EC may be a better proxy for solutes from other compartments; Benettin and Van Breukelen, 2017). Automated sensors for nitrate and phosphorous are available and can provide high-frequency information on biogeochemically active solutes. The analysis of trace metals is not available through automated sensors, but in many cases dissolved organic carbon can be a suitable proxy for iron and some other trace metals (Nierop et al., 2002; Grybos et al., 2007). Measurements of pH may also provide helpful insights into the mobilization of trace metals from soil layers. All in all, with sufficient knowledge about the possible sources that contribute to streamflow in a catchment, much simpler measurement systems may still provide meaningful insight into solute storage and release processes.
The grouping of solutes based on their dominant source, cQ behavior, ionic character, and dependence on environmental controls is illustrated in a schematic overview in Fig. 6. This overview highlights similarities and differences among solutes, summarizes their expected range of cQ behavior on the event scale, and indicates their sensitivities to environmental controls. Analyses of event-scale cQ patterns may help in identifying the vulnerability of different catchment compartments to changes in land use and climate and may benefit monitoring and management strategies. For example, if the climate warms and summers become drier in this area of Switzerland, our data suggest that the event slopes of chloride, nitrate, and potassium will become more positive, leading to enhanced flushing of these solutes during hydrologic events. Evaluating the generality of the results presented here will require further studies in catchments of contrasting climate, geology, and land use.
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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JLAK and JWK conceptualized the study. JLAK, JF, and BS collected and analyzed the solute data, LK collected and analyzed the soil moisture data from Studibach, JLAK analyzed the dataset, and JLAK prepared the manuscript with contributions from all co-authors.
The authors declare that they have no conflict of interest.
The authors thank the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) for facilitating this research project in the Erlenbach catchment and for sharing hydro-climatic data. We are also particularly grateful to Ilja van Meerveld for helpful discussions. Julia L. A. Knapp was funded through an ETH Zurich Postdoctoral Fellowship.
This research has been supported by the ETH Zurich (ETH Zurich Postdoctoral Fellowship).
This paper was edited by Thom Bogaard and reviewed by two anonymous referees.