Increasing dissolved organic carbon (DOC) concentrations
and exports from headwater catchments impact the quality of downstream
waters and pose challenges to water supply. The importance of riparian zones
for DOC export from catchments in humid, temperate climates has generally
been acknowledged, but the hydrological controls and biogeochemical factors
that govern mobilization of DOC from riparian zones remain elusive. A
high-frequency dataset (15 min resolution for over 1 year) from a
headwater catchment in the Harz Mountains (Germany) was analyzed for
dominant patterns in DOC concentration (CDOC) and optical DOC quality
parameters SUVA254 and S275-295 (spectral slope between 275 and
295 nm) on event and seasonal scales. Quality parameters and CDOC
systematically changed with increasing fractions of high-frequency quick
flow (Qhf) and antecedent hydroclimatic conditions, defined by the
following metrics: aridity index (AI60) of the preceding 60 d and the
quotient of mean temperature (T30) and mean discharge (Q30) of the
preceding 30 d, which we refer to as discharge-normalized temperature
(DNT30). Selected statistical multiple linear regression models for the
complete time series (R2=0.72, 0.64 and 0.65 for
CDOC, SUVA254 and S275-295, resp.) captured DOC dynamics based on
event (Qhf and baseflow) and seasonal-scale predictors (AI60,
DNT30). The relative importance of seasonal-scale predictors allowed for
the separation of three hydroclimatic states (warm and dry, cold and wet,
and intermediate). The specific DOC quality for each state indicates a shift
in the activated source zones and highlights the importance of antecedent
conditions and their impact on DOC accumulation and mobilization in the
riparian zone. The warm and dry state results in high DOC concentrations
during events and low concentrations between events and thus can be seen as
mobilization limited, whereas the cold and wet state results in low
concentration between and during events due to limited DOC accumulation in
the riparian zone. The study demonstrates the considerable value of
continuous high-frequency measurements of DOC quality and quantity and its
(hydroclimatic) key controlling variables in quantitatively unraveling DOC
mobilization in the riparian zone. These variables can be linked to DOC
source activation by discharge events and the more seasonal control of DOC
production in riparian soils.
Introduction
Dissolved organic carbon (DOC) in streams is a significant part of the
global carbon cycle (Battin et al., 2009) and plays a vital role
as a nutrient for aquatic ecosystems. Riverine exports of DOC from
catchments can impair downstream aquatic ecology and water quality (Hruška
et al., 2009) with potential implications for the treatment of drinking water
from surface water reservoirs (Alarcon-Herrera et al., 1994).
The pivotal role of DOC for surface water quality and ecology is not only
related to the concentration (CDOC) in the water, but also to the
specific chemical composition of DOC, referred to here as DOC quality. For
example, DOC quality defines the thermodynamically available energy
(Stewart and Wetzel, 1981), which in turn affects the growth of
microorganisms (Ågren et al., 2008). Consequently, changes
in DOC quality could change the patterns of aquatic microbial metabolism
resulting in altered ecosystem functioning (Berggren and del Giorgio,
2015). For managing water quality and aquatic ecology in surface waters, it
is therefore not only important to understand the drivers and controls of
DOC concentration, but also of the associated DOC quality. This study takes
a step in this direction.
DOC concentrations in streams were found to be highly variable in time with
strong controls being discharge (Zarnetske et al., 2018), climatic conditions
(Winterdahl et al., 2016), or at longer timescales the prevailing
biogeochemical regime (Musolff et al., 2017). DOC concentration
variability is also closely linked to distinct DOC source zones in
catchments and their hydrologic connectivity to the stream network (Broder
et al., 2015; Birkel et al., 2017). In temperate humid climates most of the
riverine DOC export is typically derived from terrestrial sources at or near
the terrestrial–aquatic interface (Laudon et al., 2012; Ledesma et al.,
2018; Musolff et al., 2018; Zarnetske et al., 2018). More specifically, the
riparian zone is seen as a dominant source zone for DOC, defining potential
DOC export loads and their temporal patterns (Ledesma et al., 2015;
Musolff et al., 2018). In this zone, DOC export is strongly controlled by
lateral hydrologic transport through shallow organic-rich soil layers, thus
connecting different patches of differently processed DOC pools to the
stream. The capacity of the riparian zone to drain and produce discharge and
thus export DOC generally increases with the rise of the groundwater table
during events. This causes a nonlinear increase in the lateral
transmissivity of the riparian soil profile and the resulting subsurface
flux to the stream, which has been called the transmissivity feedback
mechanism (Bishop et al., 2004; Rodhe, 1989). However,
distinct preferential flow paths in the subsurface (Hrachowitz et
al., 2016) and at the surface (Frei et al., 2010) can also play a
considerable role. The associated DOC export to the streams was found to be
mostly transport limited (Zarnetske et al., 2018) with storm
events generally generating most of the overall loads exported from
catchments (Buffam et al., 2001; Hope et al., 1994). Daily precipitation
and amount of discharge were found to be event-scale drivers
(Bishop et al., 1990) defining magnitude and timing of DOC
export. Strohmeier et al. (2013) therefore pointed at the importance of
temporally resolved concentration measurements for accurate load estimates.
In addition to discharge and transport capacity, the biogeochemical regime in the
riparian soils, which controls the buildup, size and quality of the
exportable DOC pool, was identified as an additional important control for
DOC export from catchments (Winterdahl et al., 2016). This
buildup of exportable DOC pools in turn is strongly related to the
hydroclimatic conditions like temperature and soil moisture content prior to
an event (Birkel et al., 2017; Broder et al., 2017; Christ and David,
1996; Garcia-Pausas et al., 2008; Preston et al., 2011), which to some
degree also define the potential for hydrological connectivity and transport
during the event (Birkel et al., 2017; Köhler et al., 2009; Shang et
al., 2018). On a seasonal scale (roughly 1–3 months) hydroclimatic
variables control intra-annual variability of DOC concentration and quality
(Ågren et al., 2007; Hope et al., 1994; Köhler et al., 2009) and
are hence considered important drivers of seasonal DOC export dynamics
(Ågren et al., 2007; Birkel et al., 2014; Köhler et al., 2009;
Seibert et al., 2009). In summary, DOC concentration and quality are jointly
controlled by the hydrologic conditions during events (defining the timing
and magnitude of DOC export) and the antecedent hydroclimatic conditions
(defining size and quality of exportable DOC pools in the soil), resulting
in a highly dynamic system with processes interacting at timescales ranging
from the event scale of hours to days to timescales of seasons.
Characterizing and quantifying such a dynamic system requires measurements
of DOC concentration and quality at a sufficient temporal resolution. Yet,
most studies to date have only focused on temporally aggregated data
(Köhler et al., 2008) and the seasonal to annual timescale
with little or no consideration of the strong interaction with event-scale
variability of DOC quantity and quality (Bishop et
al., 1990; Strohmeier et al., 2013).
Recent years have seen significant advances in sensing technologies for
high-frequency in situ concentration measurements (Rode et al., 2016;
Strohmeier et al., 2013), facilitating the assessment of the highly dynamic
DOC delivery to streams (Tunaley et al., 2016). Differences in DOC
quality observed during varying runoff conditions have been used to
characterize source zone activation in smaller watersheds (Hood et al.,
2006; Sanderman et al., 2009). Hence, the combination of high-frequency
CDOC measurements with additional spectral and analytical methods to
characterize DOC quality (Herzsprung et al., 2012; Raeke et al., 2017;
Roth et al., 2013) at temporal resolutions capable of capturing the dynamics
within hydrologic events provides an opportunity to significantly improve
our mechanistic understanding of DOC mobilization, transport, and ultimately
export from catchments (Berggren and del Giorgio, 2015; Creed et al.,
2015; Köhler et al., 2009; Strohmeier et al., 2013). Broder et al. (2017) jointly evaluated DOC concentration and quality dynamics, but they
were limited to hourly event data and data once every 2 weeks between events. Here we
see great potential in the systematic analysis of high-frequency data for
improving our understanding of the delicate interplay between hydrologic
(mobilization and transport) and biogeochemical controls (buildup of
exportable DOC pools) from the event to seasonal scales that ultimately
control DOC export from catchments. This could also stimulate improvements
in the formulation of models for DOC export to streams, which are often
constrained in terms of transferability across spatiotemporal scales because
of a mismatch between the scales of observations and those of the underlying
processes (Zarnetske et al., 2018).
We hypothesize that seasonal- and event-scale DOC quantity and quality
dynamics in headwater streams are dominantly controlled by the dynamic
interplay between event-scale hydrologic mobilization and transport
(delivery to the stream) and inter-event and seasonal biogeochemical
processing (exportable DOC pools) in the riparian zone. Furthermore we
hypothesize that continuous high-frequency measurements of CDOC and
spectral properties can be utilized to identify and quantify the key
controls of DOC quantity and quality dynamics. The objectives of this study
are (1) to use high-frequency in-stream observations of DOC quantity and
quality during different seasons to elucidate the effects of hydroclimatical
factors (which include frequency and intensity of rainfall and snowmelt
events) on mobilization and export of DOC and (2) to establish a set of key
controlling variables that captures important hydrologic, hydroclimatic and
biogeochemical characteristics of the system to allow a quantitative
assessment of stream DOC quantity and quality during different times of the
year.
To this end, a high-frequency dataset on CDOC and DOC quality from a
1st-order stream in central Germany was evaluated in terms of key
controlling variables such as discharge, temperature and antecedent wetness
conditions. The dominant drivers of seasonal- and event-scale variability of
CDOC and quality were extracted and assessed (a) by a correlation
analysis of intra-annual variations (seasonal scale ≥1 month) and (b) by an analysis of the individual discharge events throughout the year (event
scale, hours – days). In a final step (c), these drivers were
interpreted mechanistically based on a multiple linear regression analysis
covering the entire study period. The identified parameters are discussed
with respect to underlying processes and synthesized in a conceptual model
of DOC export.
Materials and methodsStudy site
Measurements were conducted in a headwater catchment of the Rappbode stream
(51∘39′22.61′′ N, 10∘41′53.98′′ E,
Fig. 1) located in the Harz Mountains, central
Germany. The Rappbode stream flows into a large drinking water reservoir.
Downstream of the reservoir it flows into the river Bode and eventually
discharges (via the rivers Saale and Elbe) into the North Sea. The
investigated part of the catchment has an area of 2.58 km2
and a drainage density of 2.91 km km-2. The catchment is mainly
forested with spruce and pine trees (77 %); the remaining area is covered
with grass (11 %) and other vegetation (12 %). Elevation ranges from 540
to 620 m above sea level; the mean topographic slope is 3.9∘. The
90th percentile of the topographic wetness index as a measure for the
extent of riparian wetlands in the catchment (Musolff et
al., 2018) is 8.53 (median 6.77). The geology at this site consists mainly
of graywacke, clay schist and diabase (Wollschläger et al., 2016).
Soils in the vicinity of the Rappbode spring are dominated by peat. Overall,
one-quarter of the catchment is characterized by groundwater-influenced
humic Gleysols and stagnic Gleysols, which are mainly found in the riparian
zones. Riparian soils were mapped next to the Rappbode stream, 2 km
downstream of the spring (Fig. 1). At this site,
topsoil layer (A horizon) thickness in a transect was 17.7 cm ± 2.4 cm
on average (n=27) up to 25 m off the stream. The study site has a
temperate climate (Kottek et al., 2006), with a long-term mean
temperature of 6.0 ∘C and mean annual precipitation of 831 mm
(Stiege weather station 12 km away from the study site, data provided by the
German Weather Service, DWD).
An overview of all variables utilized for site description and regression
modeling as well as descriptive statistics of these variables are given in
Table 1.
Descriptive statistics of DOC and hydroclimatic variables. N refers
to number of measurements; SD – standard deviation; Min – minimum of
the measurements; Max – maximum of the measurements; CV – coefficient
of variation. Class shows if the variable was utilized as response (r) or
predictor (p) in statistical models.
VariableDescriptionClassNMeanSDMinMaxMedianCVCDOC (mg L-1)DOC concentrationr42 4274.601.941.4913.054.240.42SUVA254 (L m-1 mg-C-1)Specific UV absorbance at 254 nmr42 4273.930.890.685.444.080.23S275-295 (×10-3 nm-1)Spectral slope between 275 and 295 nmr42 42113.593.762.4419.9813.420.28Qtot (m3 s-1)Total discharge–42 4270.030.070.0021.980.012.81Specific Qtot (mm)Specific total discharge–42 4271.162.710.07876.740.382.81Qhf (m3 s-1)High-frequency quick flowp39 371*0.020.070.00011.970.0024.51Qb (m3 s-1)Low-frequency baseflowp41 516*0.010.010.0020.060.0070.91P (mm d-1)Precipitation–42 4272.215.620.0055.500.002.55T (∘C)Air temperature–42 4279.206.96-11.7531.779.150.76ETP (mm d-1)Potential evapotranspiration–20 3443.014.990.0025.980.351.66AI60Aridity index of the last 60 dp17 4822.732.720.4311.331.431.00DNT30 (∘C s m-3)Discharge normalized temperature of the last 30 dp42 427921.37919.56-66.203095.86501.271.00DOC export (g s-1)DOC export–42 4270.170.670.00518.630.043.88
*N of Qb and Qhf differs from Qtot due to the
applied filtering method for baseflow separation.
Monitoring of response variables: DOC concentration and quality
We used in situ absorption spectroscopy to estimate dissolved organic matter
quantity and quality. For simplification and because carbon is the main
focus of the paper, dissolved organic matter quality will be addressed as
DOC quality in the following. DOC quality can be characterized by specific
metrics based on the light-absorbing properties of dissolved organic
compounds: SUVA254 was calculated by normalizing the spectral absorption
coefficient at 254 nm (SAC254) for the corresponding CDOC values.
SUVA254 correlates well with aromaticity of DOC and therefore can be used
as an indicator of the general chemical composition and reactivity of
organic carbon (Weishaar et al., 2003). To refine the
understanding of DOC composition, the spectral slope between 275 and 295 nm,
denoted S275-295, was estimated from the adsorption spectra and
calculated as described in Helms et al. (2008): a linear
regression model was fitted for each time step to the logarithms of the
absorption coefficients between 275 and 295 nm to derive the slope
S275-295. S275-295 can help to distinguish between autochthonous
and allochthonous DOC, molecular weights and processing (photobleaching and
microbial degradation change aromaticity) (Helms et al., 2008).
The general patterns of such DOC quality metrics can be used to infer
information on origin and properties of DOC and thus to characterize source
zones of DOC in riparian zones (Hood et al., 2006; Hutchins et al., 2017;
Sanderman et al., 2009). An UV-vis probe (Spectrolyzer, scan Messtechnik
GmbH, Austria) was installed in the stream (Fig. 1)
from April 2013 to October 2014 to measure light absorption spectra from
220 to 720 nm in 2.5 nm steps every 15 min. There is a data gap from 11 December 2013 to 14 January 2014 due to general maintenance and
recalibration of the UV-vis probe in the laboratory. Other gaps from 18 to 27 November 2013 and from 1 to 17 September 2014 were due to a probe failure; accordingly values were excluded
a priori. Overall, the UV-vis dataset comprises 42 427 measurements. For a
description of fouling correction, on-site probe maintenance and sampling
procedure, refer to S1 in the Supplement.
After fouling correction, UV-vis measurements were used to derive
CDOC, SUVA254 and S275-295. For validation and calibration of
CDOC and SUVA254, 28 grab samples were used that have been taken
once every 2 weeks from the stream to measure the specific absorption coefficient at
254 nm (SAC254 (UVT P200, Real Tech Inc., Canada). Subsequently,
CDOC was measured in the laboratory by thermo-catalytic oxidation at
900 ∘C with nondispersive infrared (NDIR) detection (DIMATOC® 2000, Dimatec
Analysentechnik GmbH, Germany). A continuous time series of CDOC from
the UV-vis spectra was created using partial least-squares regression (PLSR)
to the 28 concentration values via the R package pls (Mevik and
Wehrens, 2007). The PLSR proved to robustly work with a large number of
predicting variables and strong collinearities (Musolff et al., 2015;
Vaughan et al., 2017). The procedure generally followed the method described
in Etheridge et al. (2014) using all
turbidity-compensated spectra within a single regression model, chosen by
10-fold cross validation of the training dataset. Through this method,
CDOC was defined by a local combination of several wavelengths that
proved to yield better results than the predefined global settings provided
by the probe (Vaughan et al., 2017).
SUVA254 was calculated by dividing the spectral absorption coefficient at
254 nm (SAC254) by the PLSR-derived CDOC values. The resulting
SUVA254 values were then validated (but not calibrated) by the 28
SUVA254 values derived from the manual SAC254 measurements in the
field and the associated lab CDOC measurements (see Sect. 3.1). As a second
quality metric S275-295 was estimated from the fouling-corrected
adsorption spectra as described above and in Helms et al. (2008). There are no laboratory values available to verify S275-295 calculations, so calculated values were verified by comparison to the
literature.
Predictor variables: stream level and discharge, evapotranspiration,
and antecedent wetness condition
Discharge Qtot was calculated from a stage–discharge relationship, which
was established based on the 15 min stage readings from a barometrically
compensated pressure transducer (Solinst Levelogger, Canada) and
manual discharge measurements once every 2 weeks using an electromagnetic flow meter (n=42;
MF pro, Ott, Germany).
Manually measured discharge maximum was 0.39 m3 s-1 at
a water level of 83.8 cm. Ungauged water levels above this value and the
associated discharges were extrapolated from the stage–discharge
relationship and found to be within a valid range when comparing to modeled
discharge from the mesoscale hydrological model (mHM; Mueller et al.,
2016; Samaniego et al., 2010). A hydrograph separation into event and
baseflow components was applied following the method described by
Gustard and Demuth (2009). Total discharge Qtot was partitioned
into a high-frequency quick flow (Qhf) component, active during events,
and a low-frequency component representing baseflow (Qb). To derive the
baseflow hydrograph, local flow minima of non-overlapping 5 d
periods were selected and linearly connected to each other using the lfstat
package (Koffler et al., 2016) in R (R-Core-Team, 2017). If
the baseflow hydrograph exceeded the actual flow, it was constrained to
equal the observed hydrograph of Qtot. Consequently, subtracting the
baseflow hydrograph (Qb) from the total hydrograph of Qtot yields
the hydrograph of Qhf, which has positive values during events
(Qtot > Qb) and zero values during non-event periods
(when Qtot=Qb). All consecutive positive values between two
non-event periods (zero values) were considered one event and extracted
from the complete dataset for further processing.
To characterize ambient weather conditions, a weather station (WS-GP1,
Delta-T, UK) placed about 250 m northwest of the UV-vis probe
provided data on air temperature (T), air humidity, wind direction and speed,
solar radiation, and rainfall (P) at 30 min intervals. Measurements of the
weather station started at 21 May 2013 until 26 November 2014. Measurements
were at an hourly interval for the first 5 d, until 26 June 2013.
Potential evapotranspiration (ETP) was calculated on an hourly basis from
the weather data after the Penman–Monteith method (Allen et al., 1998). The
antecedent aridity index (AIt) gives an estimate of the water balance in
the last t days and equals the aridity index for longer time periods given by
Barrow (1992). Accordingly, AI60 was derived for the
measurement period by dividing the cumulative sum of precipitation over the
last 60 d (P60) by the cumulative sum of ETP of the last 60 d
(ETP60). As a consequence, time series of lumped variables start t days
after the actual begin of the field observations.
The discharge-normalized temperature of the preceding 30 d (DNT30) was
calculated by dividing the mean air temperature of the preceding 30 d by
the mean discharge of the preceding 30 d. DNT30 gives an
estimate of the ratio between temperature (which controls soil DOC
production; e.g., Christ and David, 1996) and discharge (which
controls DOC export; e.g., Hope et al., 1994) in the last 30 d and therefore can potentially be related to the state of DOC storage in
top soils. We chose AI60 and DNT30 as these variables turned out to
work best in terms of variance inflation and interaction for the statistical
modeling.
In order to obtain an analogous dataset, time series of all variables were
constrained by excluding such observations that fell into the data gaps of
the UV-vis probe (see Sect. 2.2.1).
Statistical analysis
Evaluation of the variable's predictive power was carried out for the entire
dataset as well as for separated discharge events. Descriptive statistical
tools were applied using the software R (R-Core-Team, 2017). Spearman's
rank correlation was used to look for significant relations of
CDOC and DOC quality with potential controlling variables since
concentration, discharge and solute loads in river systems usually have
lognormal probability distributions while C–Q relationships can be described
by power-law functions (Jawitz and Mitchell, 2011; Köhler et al.,
2009; Rodríguez-Iturbe et al., 1992; Seibert et al., 2009).
Event-scale analysis
Consequently, concentration–discharge (C–Q) relationships were characterized
and quantified in log-log space for the event analysis. Since metrics of DOC
quality are typically normally distributed (Guarch-Ribot and Butturini,
2016; Sanderman et al., 2009), relationships between quality and Qtot
were analyzed in semi-log space. Corresponding C–Q and quality–Q relationships
for each runoff event (n=38, extracted with the method explained in
Sect. 2.2.2) were represented by combinations of multiple linear regression models
with Qtot, Qb and Qhf and their log transformations as
predictors. As recommended by Marquardt (1970) and Menard (2001), multicollinearity of predictors was taken into account based on the
variance inflation factor (VIF; R package car, Fox and Weisberg,
2011):
VIFi=11-Ri2>10,
where VIFi is the variance inflation factor for every predictor
variable i in the complete model, predicted by multiple linear regression
from the remaining predictor variables of the complete model. Ri2 is
the corresponding coefficient of determination. Predictor variables were
excluded from the model if Eq. (1) holds for predictor variable i.
The best overall combination of two variables for the prediction of events
was chosen according to the best mean R2 of all 38 single
models. Hence, independent variable log(CDOC) is best predicted by a
combination of both discharge components (log(Qhf) and Qb) during
single discharge events. Subsequently, the 38 triplets of intercepts and
regression coefficients of these single models were extracted for further
analysis. Note that the hysteresis loop size did not significantly bias
regression coefficients obtained from this method (S2, Fig. S1 in the Supplement).
Seasonal-scale analysis
To explain seasonal variations in the event analysis, the 38 regression
coefficient triplets were correlated with seasonal-scale antecedent key
controlling variables. Variables which showed strong correlations were added
in different combinations to the existing event model as potential
predictors for seasonal variations in addition to the event-scale variance.
Here, models of the dependent variables (CDOC, SUVA254 and
S275-295) always used the same predictor variables. The
interaction between two predictor variables was generally used for
modeling. This implies that the measured hydroclimatic variables influence
each other and thus cause a non-additive effect on the dependent variable.
Here, we write interaction terms as the product between the two interacting
variables (variable1 × variable2). Again, predictors (variables and
interaction terms) were tested for multicollinearity and excluded from the
complete model if Eq. (1) holds for variable i.
Akaike's information criterion (AIC) and R2 were used for
model selection and validation. The 5-fold cross validation was applied to
estimate the prediction error. Once the most valid model was selected, the
predictive power of the chosen predictors for the different models of
CDOC and DOC quality was tested. Partial models were built by stepwise
dropping the least influencing predictors according to AIC and by comparing
the subset of event-scale predictors with the subset of seasonal-scale
predictors.
ResultsMonitoring of DOC and hydroclimatic parameters
The basic statistics of UV-vis-derived CDOC and DOC quality as well as
hydroclimatic variables throughout the 1.5-year measurement period are given
in Table 1.
The amount of precipitation during 2013 (665 mm) and 2014 (682 mm) was close
to the long-term annual mean at the nearest weather station. Discharge shows
event-type variability but in general followed the hydrological year, with the
lowest values in late summer and highest values in spring
(Fig. 2a). The highest discharge was 1.98 m1 s-1 during snowmelt on 27 April 2014. With a
coefficient of variation (CV) much higher than 1, the discharge regime can
be described as erratic (Botter et al., 2013), indicating the
importance of the quick flow component for discharge in the Rappbode
catchment. Consequently, the variability of Qhf mostly follows
Qtot, but without the seasonal baseflow trends. A total of 38
discharge events have been separated by discharge partitioning, yielding an
average frequency of 0.086 d-1 (2.58 month-1) at an average
duration of 134 h per discharge event. A dry period occurred from 14 June
to 23 July 2013, which resulted in a steady decline in discharge during
that time (Fig. 2).
(a) Precipitation and discharge, (b) antecedent hydrometeorological conditions, (c)CDOC, (d)SUVA254 and (e)S275-295 over the entire
measurement period. CDOC in (c) was
fitted with PLSR to the measured grab samples (red dots). Grab samples (red
dots) in the SUVA254 values (d) were just used for
validation.
Air temperature exhibited strong seasonal patterns and was comparable to the
seasonal mean at the nearest station. Daily sums of ETP peaked in summer
whereas ETP in autumn and winter reached the minimum. The general pattern
follows a typical seasonal sinusoidal shape (not shown).
The aridity index AI60 (median = 1.43) indicates a general wet climate
with higher precipitation than potential evapotranspiration. AI60 peaked
in winter whereas minimum values occurred in summer during the drought and
in winter during the freezing period (Fig. 2b).
Summer precipitation has only a small impact on AI60. With a CV of 0.74,
ETP60 generally has more influence on the variability of
AI60 than P60 (CV = 0.53).
DNT30 peaked in summer whereas minimum values occurred in winter
(Fig. 2b). Generally,
Q30 (CV = 0.89) has more influence on the variability of
DNT30 than T30 (CV = 0.53). Precipitation events in cold
periods have only a small impact on DNT30, and peaks due to precipitation
are barely detectable.
CDOC based on the PLS regression fits well to the DOC concentration
measured in the lab (R2=0.97, residual standard error:
0.68 mg L-1) (Fig. 2c).
The maximum deviation of PLS-based CDOC from lab-measured CDOC was
1.7 mg L-1 on 24 July 2013. We argue that the PLSR predicts the average
characteristic composition of DOC rather well but hardly accounts for
changes in DOC quality and thus spectral properties due to extreme
situations like droughts and floods, which can strongly differ in DOC source
area mobilization in comparison to average events
(Vaughan et al., 2017). Accordingly, CDOC and
hence calculated SUVA254 values match the manual measurements to a
lesser extend during such situations, leading to an overall
R2 of 0.5 for SUVA254 values, but removing three
measurements taken during longer dry periods (9 July, 4 September 2013, 23 July 2014) increases overall R2 to 0.73.
There are no laboratory values available to verify S275-295 calculations, but calculated values are of the same magnitude as reported
in the literature (Helms et al., 2008; Spencer et al., 2012).
CDOC, SUVA254 and S275-295 exhibit pronounced event-type
variability over the entire year
(Fig. 2c–e). In winter
months, DOC was low in concentration, but had a distinct quality signature
with high S275-295 and SUVA254 values
(Fig. 2c–e). Furthermore,
only small fluctuations of concentration and quality were observed in
winter. Summer months showed minimum CDOC, SUVA254 and
S275-295 values in both years during baseflow but also the most
distinct CDOC and quality variations during discharge events. Late
summer and autumn CDOC values were different between 2013 and 2014 with a
pronounced temporal variability in 2014 compared to rather small
fluctuations in 2013. DOC quality characteristics were similar in autumns of
both years, exhibiting an average range compared to the entire measurement
period. During events in spring and autumn, S275-295 and
SUVA254 remained at a constant level, indicating the export of DOC
of similar composition.
Exported DOC loads (Table 1)
peaked during high-discharge events during spring and autumn and closely
follow the hydrograph (Fig. S2). Accordingly, the CV of the load is closer
to that of the discharge than to the CV of DOC
(Table 1). Maximum DOC export
was found during the discharge event on 27 April 2014 with rates of up to
18.6 g s-1. Although events in drier summer months show stronger
concentration fluctuations, exported loads remain low.
Correlation analysis
Table 2 gives an overview regarding correlations in
the entire dataset. We used Spearman's rank correlation coefficient (rs) correlation to
determine the direction and strength of relationships between variables.
CDOC correlates strongest with SUVA254, but rs between CDOC
and S275-295 and between S275-295 and SUVA254 is markedly
smaller.
Spearman's rank correlation coefficient (rs) of possible controlling variables over the
entire observation period. Only complete cases were used (n=17082). All
correlations are highly significant (p<0.001) because of the large
sample size, rs with absolute values larger than 0.6 printed in bold for
better readability. Numerical subscripts of T, Q, AI and DNT indicate how many
preceding days were aggregated.
Correlations of Qtot with S275-295 are stronger than
Qtot with SUVA254 and CDOC. In comparison to
Qtot, correlations with Qhf are markedly higher for CDOC and
SUVA254, but lower for S275-295. On the other hand, when relating
CDOC and metrics of quality to the baseflow fraction of discharge
(Qb), rs is close to 0 for CDOC and SUVA254, but 0.61 for
S275-295. CDOC and quality further correlate with antecedent
discharge, temperature, discharge-normalized temperature (DNT30) and
aridity index (AI6,14,60). CDOC and
SUVA254 correlate best with AI6, whereas S275-295 correlates
with T30, Q15, Q30, DNT30 and AI60.
R2, intercept z, and regression coefficients a and b of
the model predictors log (Qhf) and Qb in Eq. (2) of all 38 events
plotted against time. The headings in the top of the figure indicate which
variable was represented by Y in Eq. (2). Blue lines indicate the locally
weighted scatterplot smoothing (LOWESS), background colors indicate seasons
(grey: winter; red: summer; white: autumn and spring). Note the
different scales of the y axes.
Event-scale analysis
High coefficients of determination (R2) between CDOC and
DOC quality metrics with Qhf and in the case of S275-295 with
Qb underline the prominent role of discharge and its different timescales in DOC variability. Consequently, quantifying DOC mobilization for a
range of individual events may provide information for better understanding
direction, shape and strength of C–Q relationships. The analysis of the
response of CDOC and DOC quality to discharge events covers 44 % of
the entire time series. The relationship between CDOC and
Qtot during events resembles a segmented slope in log-log space
(Fig. S3a), similar to the C–Q behavior described by Moatar et
al. (2017), which inhibits a proper parameterization by the usually applied
simple power-law regression. However, when detrending the discharge by
baseflow subtraction, the resulting CDOC–Qhf relationship is more
linear in log-log space (Fig. S3b). This behavior occurs for the event-scale
discharge variability of the entire dataset. For DOC quality metrics
SUVA254 and S275-295, we applied a similar model to predict the
non-transformed independent variables:
Y=alog(Qhf)+bQb+z,
where Y is log(CDOC), SUVA254 or S275-295, and a and b are regression
coefficients and z is the intercept.
We applied Eq. (2) to 38 individual discharge events. The mean
R2 of all log(CDOC) models (one model for each discharge
event) is 0.84 (±0.15). Respective mean R2 values for
SUVA254 and S275-295 were 0.83 (±0.14) and 0.64 (±0.26). Performance of the models is always better than a simple linear
regression with log(Qtot) (mean R2 for log(CDOC),
SUVA254 and S275-295 is 0.76 (±0.16), 0.70 (±0.15) and 0.50 (±0.26), respectively). R2 of the
models from Eq. (2) varies over time
(Fig. 3). Dependent variables
log(CDOC) and SUVA254 show a similar behavior with maximum
R2 in autumn and winter and minimal R2 values
in spring and summer (Fig. 3a, b). R2 values of the S275-295 models show a different and
less consistent pattern with higher variability between events than
CDOC and SUVA254 models
(Fig. 3c). In comparison to
CDOC and SUVA254, S275-295 values in winter and spring
events have a systematically lower R2.
Coefficients of CDOC and DOC quality models vary between the events
(Fig. 3a–c). Coefficient
a (regression coefficient of log(Qhf)) shows low but more systematic
variations over time, represented by a smaller CV in comparison to z and b
(mean CVa=0.76, mean CVz=2.58, mean CVb=5.30 of the
CDOC, SUVA254 and S275-295 models). High a values indicate a
stronger increase in CDOC and change in quality of DOC with an increase
in Qhf, whereas small a values indicate only little change with
increasing Qhf. All three models show a distinct change in a from dry
summer to autumn 2013. The summer months generally show the strongest
variability in model coefficient, meaning that CDOC and DOC quality
reacted strongly and more variable to the comparable small discharge events.
Winter months in contrast show the least variability in model coefficient a,
indicating a more homogeneous reaction to discharge in this time of the
year. Baseflow and intercept model coefficients b and z have a similar, less
distinct pattern for all three models with higher parameter variability in
summer compared to the other months.
Seasonal-scale analysis
A correlation analysis of the model coefficients a, b and intercept z was
performed to identify the variables that explain their temporal dynamics
(Table 3). More specifically, we aim to predict a, b
and z by hydroclimatic conditions before and during the event represented by
the medians of DNT30 and different temporal aggregations of AI, T and
Q. Again, we rely on Spearman's rank correlation to characterize and quantify
the relationships more independent of their shape. Intercept z as well as
coefficient b (related to Qb) do not show any correlation at p<0.001. Regression coefficient a (related to Qhf) shows good correlations
(p<0.01) with T15, T30, Q30, AI60 and DNT30 for
all models. But median values of DNT30 and AI60 are the only variables
which show highly significant correlations (p<0.001) with
coefficient a for CDOC as well as for the quality metrics models.
The strongest increase in CDOC within an event (high a) occurs when
AI60 is low and DNT30 is high, which translates into events during warm
and dry low flow situations. On the other hand, during cold and wet high-flow periods (AI60 and Qb high, DNT30 low), large events (high
Qhf) produce a smaller increase in CDOC. This situation typically
occurs during winter.
Spearman's rank correlation coefficient (rs) of the 38
CDOC,
SUVA254 and
S275-295 model coefficients with
hydroclimatic variables. Asterisks indicate p values (∗∗∗: < 0.001; ∗∗:
< 0.01; ∗: < 0.05).
rs with absolute values larger than 0.6 are printed in bold.
Based on the highest rs values in the correlation analysis for the
event scale (Table 3), we
selected DNT30 and AI60 as variables to explain seasonal
variations in regression coefficient a. The results were used to build a
regression model for all available data of CDOC, SUVA254 and
S275-295. We added to the model of Eq. (2) the seasonal-scale
AI60 and DNT30. In addition we added those interactions for which VIF < 10 (Eq. 1): log(Qhf) ×Qb, AI60×DNT30 and DNT30×Qb. These two additions allow the model to
account for temporal changes in the relationships of CDOC and DOC
quality with discharge. Note that we, again, rely on power-law behavior of
CDOC but logarithmic (semi-log) behavior for SUVA254 and
S275-295 (above):
Y=z+alog(Qhf)+bQb+cAI60+dDNT30+i,
where Y represents one of the three dependent variables log(CDOC),
SUVA254 and S275-295. a, b, c and d are regression coefficients, and z is the
intercept. i indicates valid interaction terms (VIF < 10, Eq. 1)
log(Qhf) ×Qb, AI60×DNT30 and
DNT30×Qb.
The results of the modeling are depicted in Table 4
and Fig. 4. A basic overview of all regression
parameters and model statistics is given in Table S1. The CDOC model
performs best, explaining most of the overall variance (R2=0.72±0.04 5-fold cross-validation prediction error), compared
to the mean R2 of 0.84 for modeling single events only.
SUVA254 and S275-295 models explain similar parts (0.64±0.2 and 0.65±0.0) of the overall variance compared to the
mean R2 for the events of 0.83 and 0.64, respectively. All
models generally explain both seasonal- and event-scale variability
(Fig. 4, R2;
see Table S2), but towards small values residuals of the DOC quality models
tend to overestimate, whereas residuals of the CDOC model
increase with increasing concentration (Fig. S4). Yet, 95 % of the
residuals lie within a range of 1.08 to -0.90 mg L-1,
±0.44 L m-1 mg-C-1 and ±2.2×10-3 nm-1 for the CDOC, SUVA254 and S275-295 models, respectively.
Evaluation of the whole dataset model by dropping the least
influencing variable according to AIC, starting from the complete models
(Eq. 3).
Comparison between measured (black) and multiple regression models
of the complete predictors (green) as given by Eq. (3), only seasonal
predictors AI60 and DNT30 plus their interaction (red) and only
discharge predictors log(Qhf) and Qb plus their interaction (purple)
for (a)CDOC, (b)SUVA254 and (c)S275-295 values. Complete and
discharge only model were smoothed (5-hourly) for better visualization.
Inspection of models taking only event-scale predictors (log(Qhf),
Qb and interaction) or only seasonal-scale predictors (AI60,
DNT30 plus their interaction) into account reveals that both sets of
variables can explain a comparable part of the total variance
(R2 event scale: 0.40, 0.36 and 0.47; R2 seasonal
scale: 0.42, 0.36 and 0.48 for the CDOC, SUVA254 and S275-295 models, respectively). Yet, when only using seasonal-scale drivers
(AI60 and DNT30 plus their interaction), the general trend but no
event-type variability is reproduced in the model
(Fig. 4). On the other hand,
the pure discharge model does not reproduce baseflow and peak height well
during the seasons.
For the complete CDOC and SUVA254 model, seasonal-scale drivers
AI60 and DNT30 plus their interaction DNT30×AI60 and
event-scale driver log(Qhf) alone are the most important predictors,
able to explain 68 % of the total variance for CDOC and 54 % for
SUVA254 compared to 72 % and 64 % of the respective complete models
(Table 4). In contrast to the
CDOC and SUVA254 models, the interaction of seasonal-scale drivers
(DNT30×AI60) barely influences the R2 of the
S275-295 model, but it is rather DNT30 plus the interaction of
DNT30×Qb and event-scale hydrological drivers
log(Qhf) and Qb that alone can explain 54 % of the variance
compared to 65 % of the complete model.
Interactions between AI60 and DNT30 play a crucial role in the
CDOC and SUVA254 models. There is a small negative effect of increasing
soil wetness during low DNT30 values and a small negative DNT30 effect
for dry soils. However, if exposed to increasing AI60 values, the effect
of medium and high DNT30 values changes towards a positive interaction.
Hence, when AI60 is low and DNT30 is high, which typically occurs during
the summer months (Fig. 2b) or
vice versa in winter, interaction leads to the lowest mean CDOC and
SUVA254 values during non-precipitation periods (Fig. S5a, b). With medium
AI60 and DNT30 values around autumn and spring, the interaction (Fig. S5c) has a more positive influence on CDOC and SUVA254 values, resulting
in higher baseflow CDOC and SUVA254 values. This interaction can
thus represent the change of regression coefficient a that was observed in
the event analysis (Fig. 3).
In comparison to the CDOC and SUVA254 models, for the
S275-295 model the interaction of log(Qhf) with Qb has
direct influence on the time-variant regression coefficient a and thus more
influence on the R2
(Table 4).
There is a positive effect of increasing Qb at low and medium
log(Qhf) values and a positive log(Qhf) effect during low Qb.
However, the effect of log(Qhf) changes towards a negative interaction
if exposed to increasing Qb so that log(Qhf) barely increases
S275-295 values during high Qb situations.
DiscussionPerformance of event-scale and complete models
Within 1 year, DOC concentration and quality dynamics fluctuate on event
and seasonal scales. The regression models revealed that discharge had a
different impact on observed DOC concentration than on observed DOC quality
in the Rappbode stream at the seasonal scale
(Fig. 3). We found that
during summer initial CDOC was low during baseflow while large amounts
of DOC were available to be exported from the riparian soils to the stream
during events, leading to high model coefficient a (Fig. 3). Contrarily, the increase in concentration in winter is less pronounced
(low model coefficient a, Fig. 3) because there is less DOC available to be washed out. Although the
largest amounts of exportable DOC are to be expected at the end of the
summer and in early autumn (Clark et al., 2005),
CDOC and DOC quality changed most distinctly with the discharge
components Qhf and Qb in the summer
(Fig. 3). Unfortunately,
there were no DOC measurements of the riparian soil water available, which
could further elucidate this discrepancy.
The regression models across the entire observed time series (Sect. 3.2.2)
utilize event-scale drivers log(Qhf) and Qb as well as more
seasonally driven variables AI60, DNT30 and their interactions to
explain DOC concentrations and quality variations. We are aware that
predictions based on statistical relationships between predictors and DOC
responses, which are outside the range of the calibration data (e.g., during
extreme droughts and flooding), have to be treated with care. Furthermore,
validity and sensitivity of the statistical relationships to the
predictors do not account for long-term changes in biogeochemical and
hydroclimatical factors but can influence DOC export behavior on its own.
Other influences not regarded in this model are the occurrence of chemical
compounds like nitrogen (Garcia-Pausas et al., 2008), sulfate,
chloride or acid deposition (Futter and de Wit,
2008), which all can impact the available forms, stability and mineralization
of carbon in soils. Studying the interactions of DOC with other elements
could therefore be useful to add understanding to the actual mobilization
and processing mechanisms. But since we measured DOC in the stream, we view
DOC as an integrated response signal, already carrying all the information
from processing and transformation up to abiotic removal in the riparian
zone. Thus, we argue that hydroclimatic and discharge dynamics as chosen
here are a 1st-order controls of the DOC dynamics in the stream,
represented by a high correlation coefficient (rs) between hydroclimatic
variables and DOC quantity and quality (Table 3) as well as an R2 of 0.72 for the complete CDOC
model. Also, the complete CDOC model represented the observed
cumulative DOC export well with a Nash–Sutcliffe efficiency (NSE) of 0.998
throughout the year. Taken by themselves, seasonal-scale drivers
(DNT30+AI60+DNT30×AI60) were able to explain the
same amount of CDOC variability as hydrological event-scale drivers
(Qhf+Qb+Qhf×Qb). But with an NSE of
0.979 cumulative modeled DOC export from event-scale drivers resembled
actual cumulative DOC export much better than seasonal-scale drivers alone
(NSE = 0.783), indicating that predictors based on low-frequency
measurements alone are not able to explain DOC export as accurately as those
derived from higher-frequency measurements. The different export behavior
obtained from DOC export modeling based on low- versus high-frequency
measurements is most pronounced during events (Fig. S6), which again
highlights the importance of high-frequency measurements.
We used an hourly resolution for modeling CDOC and DOC quality
(∼17 000 values in ∼1 year). In a low-frequency study, Köhler et al. (2009) took 470 stream
water samples in 14 years (based on Köhler et al., 2008).
Consequently the DOC concentration variance, which needed to be
explained, shifted from a focus on seasonal-scale and inter-annual
variations in Köhler et al. (2009) towards highly frequent
fluctuations on top of the seasonal-scale shifts and thus a more holistic
perspective in the present study. In addition, Köhler et
al. (2009) did not analyze the processes which are responsible for the
shifts between the models, which had been independently set up for snow-covered, melting and snow-free periods.
Other studies took higher observational frequency into account and added DOC
source characterization to better understand the mobilization dynamics: e.g.,
Broder et al. (2017) and Tunaley et al. (2016) examined event-driven changes in DOC export in a headwater stream,
based on highly resolved (15 min to 3 h frequency) events. Like in the
present study, both found that antecedent wetness conditions and seasonality
are related to DOC dynamics in streams. Both studies provided a qualitative
and descriptive assessment only and concluded that a more specific
understanding of how DOC gets exported from catchments (Tunaley et
al., 2016) might become even more important with respect to future changes
in the hydrologic regime due to climate change
(Broder et al., 2017). We argue that we need a better
quantitative understanding of hydrological and biogeochemical mechanisms and
interactions based on time series of different key controlling variables
covering all relevant process scales in terms of resolution and length.
Several authors identified seasonality as an important driver for DOC
dynamics (Ågren et al., 2007; Broder et al., 2017; Tunaley et al.,
2016). However, the term “seasonality” is rather vague and often not
clearly defined in terms of its impact on DOC export. This makes its use for
a quantitative comparison between catchments and different climates
difficult. Therefore we used a set of more easily identifiable, quantitative
hydroclimatic variables instead, which reflect the general seasonal dynamics
(Table 3) and at the same time allow for a better
assessment of the dominant processes for DOC concentration and quality
variations.
In summary, we used high-frequency measurements of hydroclimatic variables
and their interactions as a proxy representation for seasonality, which
allows a more quantitative comparison to other catchments and a more in-depth evaluation of the system.
Hydroclimatic classification
To estimate how event-scale and seasonal controls interact to produce the
observed nonlinear responses of DOC concentrations and quality in our study
catchment, we can separate the observation period into three distinct
hydroclimatic states. These three discrete system states were chosen to
highlight certain typical scenarios out of a continuum of hydroclimatical
conditions, which are based on the seasonal-scale predictors of the complete
regression models (Fig. 5): (1) high DNT30 and low
AI60, representing warm and dry situations mainly found in summer; (2) moderate DNT30 and AI60, representing intermediate warm and wet
situations, mainly found in spring and autumn; and (3) low DNT30 and high
AI60, representing cold and wet situations mainly found in winter. To
synthesize our modeling results in terms of potential underlying
mobilization processes, these three states were compared by looking at both
event and non-event responses of DOC concentrations and quality during those
states.
Box plots of hydroclimatic variables (controlling factors) and DOC
quantity and quality metrics (response) classified into three hydroclimatic
states: (1) warm and dry, (2) intermediate, (3) cold and wet. Red
indicates non-event situations, and purple indicates event situations during the
according states. Variables were rescaled for better illustration.
Particular median CDOC values during non-event situations were
4.13, 3.72 and 3.16 mg L-1 for the warm and
dry, intermediate, and cold and wet states, respectively. Both warm and dry
and intermediate states differ highly significantly (Kruskal–Wallis test, p<0.001) from the cold and wet state.
Daily mean CDOC, SUVA254 and S275-295 values of 1.49 mg L-1, 0.68 L m-1 mg-C-1 and 5.0×10-3 nm-1 were minimal at the end of the drought in August 2013, when
baseflow levels were low, whereas values of 4.14 mg L-1, 4.05 L m-1 mg-C-1 and 15.8×10-3 nm-1 were measured during phases with higher baseflow levels in the cold and wet
state. CDOC, SUVA254 and S275-295 values showed the strongest
increase during warm and dry situations
(Fig. 5) also indicated by
the highest slopes of regression coefficient a (event-scale models,
Fig. 3). Events during the
intermediate state also showed elevated CDOC, SUVA254 and
S275-295 values, but in comparison to summer events at a decreased
variance and range (Fig. 5).
Changes due to events in cold and wet situations were small in range and
variance. Variance and mean of S275-295 were generally lower during
warm and dry situations than during intermediate and cold and wet phases.
Therefore we conclude that seasonal-scale hydroclimatic variance controls
the overall variance of S275-295, whereas CDOC and
SUVA254 are driven through event-type variance.
Conceptual model of DOC mobilization from the riparian zone
The relationship between AI60 and DNT30 in combination with differences
in DOC concentration and quality of the three states is of particular
interest to support a mechanistic explanation for differing DOC export
during events. Hence, these metrics can be utilized for conceptualizing DOC
mobilization dynamics of seasonal-scale variations in CDOC and the
observed quality–discharge dependencies
(Fig. 6).
Conceptual model of riparian DOC export from precipitation during
the three hydroclimatic states: warm and dry, intermediate, cold and wet.
Depth of the soil column is around 0.5 m. Seasonal-scale variations in
CDOC in the soil solutions (summer vs. winter) were discussed in
Kalbitz et al. (2000). Changing combinations between SUVA254 and
S275-295 values are described as more groundwater-influenced (black)
and more riparian-influenced (green) DOC quality. Arrows indicate the export
of DOC; colors of the arrows refer to the respective DOC quality. Panels in
the middle row show the relation between CDOC and Qhf during
the three representative situations. Dashed lines indicate the
“dispersion” of the point cloud (according R2) during the
events. Panels in the bottom line indicate the change of CDOC during an
event. Corresponding changes of colors indicate more groundwater-influenced
(black) and more riparian-influenced (green) DOC quality. Baseflow levels
under cold and wet conditions are usually higher than baseflow levels
during the warm and dry phase (see Fig. 5). Thus,
during the cold and wet situation, higher layers of soil, more enriched in
DOC, are activated, but at the same time, there is also a tradeoff between
amount of water and available DOC in the respective soil layers which can
account for lower overall DOC concentrations.
Warm and dry situations
Warm and dry situations are hydroclimatically defined by high temperatures
and low mean discharge (high DNT30), relatively dry soil conditions (low
AI60), and low baseflow levels, as typically found in summer when
the Rappbode is fed mainly by deeper riparian groundwater. During baseflow
conditions highly processed DOC enters the stream via the deeper groundwater
flow paths (Broder et al., 2017). DOC in deeper
groundwater has usually passed through multiple soil layers, and its amount and
its composition has been altered by sorption and biogeochemical processes
(Inamdar et al., 2011; Kaiser and Kalbitz, 2012; Shen et al., 2015). Low
S275-295 values indicate high molecular weight of DOC with a dominance
of terrestrial waters (Helms et al., 2008; Spencer et al., 2012) entering
the stream during that time. Precipitation events can get buffered and
retarded in the soils (low Qhf) (state warm and dry,
Fig. 6). Due to the soil type
and generally high groundwater tables in our catchment, soil moisture can
remain high, even when there was no rainfall for some time. Yet, lower water
contents can increase the mineralization rate compared to (oxygen-free)
water-logged soils. However, Kalbitz et al. (2000) and citations therein
report a positive correlation between mineralization rate and DOC
concentration of the soil solutions. In consequence, DOC production can be
higher than mineralization in the unsaturated riparian zone environment
(Kalbitz et al., 2000; Luke et al., 2007) leading to a net
production of DOC. Hence, favorable conditions for the accumulation of DOC
during non-event periods exist in the subsurface due to the lack of moving
water in the topsoil, where the high temperatures allow for (microbially
driven) riparian DOC net production. To account for the positive balance
between DOC removal mechanisms (mineralization, degradation) and DOC
production in the riparian soil, we will use the term net production in the following.
We argue that the increase in CDOC and change of DOC quality with
discharge events is due to the addition of a new, distinct DOC source,
located in the shallow riparian soils and connected via transmissivity
feedback and preferential flow paths (Fig. S7). Since CDOC during
non-event situations was very low
(Fig. 5), higher DOC
concentrations exported from the topsoils with different quality were able
to override the low-flow DOC signal towards a riparian zone signal.
DOC quality during events changed markedly towards higher
SUVA254 values typical for higher aromaticity of the organic matter and
associated with processed DOC (Hansen et al., 2016; Helms et al., 2008) and
higher S275-295 (but not as high as in cold and wet situation) indicating a
relative increase in low-molecular-weight components in comparison to the low flow
signal.
The (de)activation of an additional DOC source with changes in discharge
could also explain the observed lower R2 values in the event
analysis during summer (Fig. 3) because in this situation CDOC is not only driven by discharge but
an addition of a differing DOC source that is not explained by the
hydrological drivers of the event-scale models. The extent of this
additional DOC source is determined by antecedent hydroclimatical conditions
which favor DOC net production and thus indicate a sensitivity to
biogeochemistry-driven DOC export as found by Winterdahl et al. (2016) on top of a general-transport-limited system
(Zarnetske et al., 2018). Accordingly, event analysis showed
the highest CDOC and DOC quality peaks and revealed the steepest
CDOC–Qhf and quality–Qhf relations in summer. After the event,
CDOC and DOC quality metrics gradually drop back to the baseflow signal.
In contrast to our findings, Raeke et al. (2017) found higher-molecular-weight molecules at elevated discharge in three temperate catchments
(including the one studied here). However, they used grab samples from
different hydroclimatic situations and streams, thus potentially masking the
event-scale dynamics of DOC mobilization as revealed in the current study.
Also, the comparability between spectrophotometry and high-resolution mass
spectrometry is questionable for DOC in general
(Chen et al., 2016). But also the magnitude of
in-stream processing and biodegradation could further influence DOC
composition and hence SUVA254 and S275-295 measurements in
stream water (Bernal et al., 2018; Hansen et al., 2016). However,
Creed et al. (2015) and Nimick et al. (2011) stated that headwaters in general are dominated by allochthonous
carbon with the role of in-stream processing increasing with stream order.
Also, the role of in-stream processing at mean residence times below 1 d
(which holds for our study site, 2 km downstream of the spring) was found to
be minor (Kaplan et al., 2008; Köhler et al., 2002). Note that the
wide riparian zone (several tens of meters) in our catchment consists of
large parts of a flood plain, leaving only little possibility for leaf
litter falling directly into the stream. Therefore, in-stream decomposition
and leaf litter in the stream are likely to be of minor importance on our
experimental site.
Intermediate state
Intermediate DNT30 and AI60 conditions are defined by moderate
temperatures and discharge (medium DNT30), precipitation, and
evapotranspiration (medium AI60), which results in higher baseflow levels compared to warm and dry conditions. Strong precipitation events
translate into a distinct discharge signal (high Qhf) (intermediate state, Fig. 6).
Conditions for the accumulation of DOC during non-event periods are less
favorable due to colder temperatures than warm and dry periods, decreasing the
riparian DOC net production. During baseflow conditions some of the riparian
DOC pools are already activated due to a higher groundwater table. This
mixing of riparian and deeper groundwater DOC pools translates into
intermediate values of concentration and quality parameters, even under
non-event conditions.
In case precipitation increases discharge, the DOC signal changes both
concentration and quality. This process happens faster than during the warm
and dry situation since antecedent wet conditions facilitate DOC
mobilization from riparian soils. Hence the temporal shift between DOC and
discharge peak diminishes, resulting in higher R2 values
during events (Fig. 3). There was no exhaustion of
the exportable DOC by consecutive events, although there is less DOC
production paired with more effective export mechanisms, highlighting the
large store of DOC in the comparably small riparian zone (Ledesma et al.,
2015). The intermediate situation averages multiple situations (transition
states in autumn and spring) and thus does not have the character and
clarity of the end-members. Similar quality signals indicate the same process
and location of source zone activation in autumn 2013 and 2014. However,
concentration peaks developed differently, suggesting that the conditions
for antecedent DOC storage and export during preceding phases were
different. For example, there were only small mobilization and storage limitations
during intermediate DNT30 and AI60 levels in spring 2014, which
translated into pronounced DOC loads exported during events. However, DOC
quality, especially S275-295, barely changed during these events.
Elevated temperatures during this period cause a warming of riparian
topsoil, which is rich in organic matter, and hence an increase in
biological processing and DOC production. Declining, still high baseflow
levels and soil moisture lead to increased DOC production and export during
these events.
Cold and wet situations
Cold and wet situations, mainly found in winter, are defined by low
temperatures and high mean discharge (low DNT30), humid conditions (high
AI60), and high baseflow levels (cold and
wet state, Fig. 6). Generally low
CDOC values indicate that less DOC mass is available in relation to the
generated runoff in the riparian zone in comparison to the warm and dry
situations. Unfavorable conditions for the net production of DOC during
non-event periods exist in the topsoil, where the low temperature impairs
riparian DOC production. Accordingly, low SUVA254 and high S275-295 values were observed during that period, indicating a relatively higher
amount of low-molecular-weight compounds due to reduced DOC processing.
Furthermore, high baseflow levels lead to a good hydrological connectivity
of DOC sources to the stream during non-event situations.
Precipitation events result in small slopes of the CDOC and
quality–Qhf relationships. Dilution due to the impermeability of the
frozen soil surface (Laudon et al., 2007) is likely to occur
under prolonged periods of temperatures below zero. Since riparian DOC pools
are already connected to the stream, we attribute the small shift in DOC
quality and CDOC during events to a shift of the contribution
(hydrological connection) of DOC source areas with similar DOC quality,
rather than to the activation of new, differing DOC pools. The 1st-order
hydrological forcing under largely saturated soil conditions could thus
explain the high R2 but low regression coefficient a of the
event-scale models of CDOC and SUVA254
(Fig. 3) in the cold and wet
state. On the other hand, a dominance of hydrological forcing also implies
little influence of antecedent biogeochemical conditions during this state
(Winterdahl et al., 2016). In contrast to CDOC and
SUVA254, R2 of S275-295 drops during the cold and wet
situation, indicating a decoupling from hydrologic forcing. The dominant
hydrological state could be able to leach differing DOC from the riparian
zone by shifts in physicochemical equilibria (Shen et al., 2015),
thereby forming the corresponding quality. However, this finding needs
further research. The same observations of CDOC and quality interaction during winter and
spring (low DOC variance in winter, still low quality variance but strong
CDOC fluctuations in spring) were made in 2013. But due to the lack of
weather data (the weather station was deployed 2 months after the sensor
deployment, which inhibited derivation of AI and DNT for this period), no further
statements can be made for this period
(Fig. 2).
Conclusions
Seasonal- and event-scale DOC quantity and quality dynamics in headwater
streams are dominantly controlled by the dynamic interplay between
event-scale hydrological mobilization and transport (delivery to the stream)
and inter-event and seasonal biogeochemical processing (exportable DOC
pools) in the riparian zone. Observing DOC concentration and quality,
together with hydroclimatical factors, at high frequency resolves dynamics
at the temporal scale of the underlying hydrological and biogeochemical
processes, which is unattainable with standard grab-sample monitoring. This
allows for an improved, in-depth assessment of DOC export mechanisms as
joint measurements of DOC quantity and quality give additional insights into
source locations in the riparian zone, DOC processing and mobilization.
Observed DOC concentration, SUVA254 and S275-295 averaged at 4.06, 3.93 L m-1 mg-C-1 and 13.59×10-3 nm-1, respectively, but were found to be highly variable in
time. The analysis of event-scale variability revealed clear seasonal-scale
shifts of the role of discharge in shaping DOC quantity and quality.
Overall, the temporal dynamics of DOC concentration and quality can be
explained by a few key controlling hydrological variables, which
characterize instantaneous discharge, and hydroclimatic metrics, which
define the conditions prior to the event.
The hydrological variables (Qhf and Qb) were able to explain 40 %,
36 % and 47 % of the overall variability of CDOC, SUVA254 and
S275-295 and play a crucial role in modeling DOC export. In comparison,
seasonal-scale variables (AI60 and DNT30) alone are able to explain
similar percentages (42 %, 36 % and 48 % for CDOC, SUVA254 and
S275-295) of the overall variability of DOC quantity and quality but
lack in adequately predicting exported DOC loads. Combining both sets of
variables, as done in this study, significantly increases the predictive
capacity of the overall models (72 %, 64 % and 65 % for CDOC,
SUVA254 and S275-295). Evaluation of the developed statistical models also
highlights the importance of interactions between the seasonal-scale
antecedent predictors AI60 and DNT30 for DOC concentration and quality
dynamics. AI60 describes the potential for mobilizing DOC in riparian
soils, whereas DNT30 describes the changes in DOC storage by looking at
the relationship of DOC production and prior mean export from riparian
soils. Hence, the relationship between AI60 and DNT30 describes the
potential for exporting DOC from riparian soils and allows us to conceptualize
DOC exports under differing hydroclimatical conditions. We found that cold
and wet situations (AI60 high, DNT30 low) are not mobilization limited
(high mobilization potential due to wet soils and high baseflow levels) but
limited in production and processing (due to low temperatures). High
hydrological connectivity leads to low CDOC when the DOC net production
is low compared to the DOC export. Here, events do not change the quality
signature of the DOC in the stream since all riparian DOC sources had
already been connected to the stream before. In contrast, we interpret warm
and dry conditions (AI60 low, DNT30 high) as mainly
mobilization-limited situations (dryer soils, low baseflow levels). High DOC
net production rates (high temperatures) and low hydrological connectivity
lead to an accumulation of DOC in the upper soil layers of the riparian zone
during non-event situations. Under those baseflow conditions low
concentrations of highly processed DOC are exported from deeper soil layers
to the stream. Overall, DOC quality varies the most during such warmer dry
periods because events change the signature of DOC quality in the stream
water by adding freshly processed DOC from upper riparian DOC sources to the
older more intensely processed DOC from the underlying baseflow signature.
The findings reported and analyzed here provide a mechanistic explanation of
the seasonally changing characteristics of DOC–discharge relationships and
therefore can be utilized to infer the spatiotemporal dynamics of DOC
origin in riparian zones from the DOC dynamics of headwater streams.
Our interpretation is based on the integrated signal of DOC concentration
and quality measured in the stream. Accordingly, it remains partially
unresolved, and the explicit processes in the riparian zone are responsible
for the measured and conceptualized DOC dynamics in the Rappbode stream.
Further research in the riparian zone with its shallow groundwater dynamics
is necessary to fully mechanistically explain the explicit spatiotemporal
mobilization patterns as well as to identify appropriate molecular markers
that can be used to trace DOC from riparian source zones into the stream in
order to better understand DOC mobilization processes.
The study demonstrates the considerable value of continuous high-frequency
measurements of DOC quality and quantity and their key controlling variables
in quantitatively unraveling DOC mobilization in the riparian zone. We
believe our approach allows long-term DOC monitoring with a manageable
allocation of time and resources as well as a better comparability between
catchments of different seasonal characteristics. This study highlights the
dependency of DOC export on hydroclimatic factors. Potential impacts of
climate change on the amount and quality of exported DOC are therefore
likely and should be further investigated.
Data availability
All datasets used in this synthesis are publicly available via the following link:
10.4211/hs.e0e6fbc0571149b79b1e75fa44d5c4ab (Werner, 2019)
The supplement related to this article is available online at: https://doi.org/10.5194/bg-16-4497-2019-supplement.
Author contributions
JHF, OJL, AM and GHdR planned and designed the research. MRO carried out parts
of the field work and conducted a first version of data processing and
analysis. BJW performed the statistical analysis and wrote the paper with
contributions from all co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
Special thanks to Toralf Keller for excellent and steady field work as well
as to Wolf von Tümpling for the support in the laboratory.
Financial support
This research has been supported by the Federal Ministry of Education and Research Germany (grant no. BMBF, 02WT1290A).The article processing charges for this open-access publication were covered by a Research Centre of the Helmholtz Association.
Review statement
This paper was edited by Tom J. Battin and reviewed by four anonymous referees.
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