Introduction
Dissolved nutrients, such as nitrate and soluble reactive phosphorus, control
surface water trophic status (e.g. Likens and Bormann, 1974). Therefore,
increasing concentrations of nitrate in streams and rivers of agricultural
landscapes pose a severe risk for their ecological status and drinking water resources downstream.
Local nitrate concentrations in streams and rivers
depend largely on the following two antagonistic controls: nitrate export processes from
landscapes to the stream network (e.g. Carpenter et al., 1998; Lam et al.,
2012; Schilling and Zhang, 2004; Tesoriero et al., 2013) and in-stream
removal processes (e.g. Bowes et al., 2014; Burgin and Hamilton, 2007;
Covino et al., 2012; Hill, 1996; Montreuil et al., 2010; Mulholland et al.,
2008). The stream network itself can be treated as an interface that
connects the different landscape components and determines the dynamics of
the water quality (Hunsaker and Levine, 1995). Moreover, the convolution of
water and matter fluxes from upstream to downstream can be dominated by
hydrological turnover processes (i.e. the sum of stream–groundwater
exchange fluxes) throughout the stream network (Mallard et al., 2014).
Nitrate export processes comprise various interacting processes and drivers.
Depending on present land use (Mulholland et al., 2008) and land management
(Basu et al., 2010; Marwick et al., 2014; McCarty et al., 2014), the balance
between N inputs (fertilizers, N deposition, N fixation) and N uptake by
plants is the main driver, especially in agricultural landscapes. Organic
nitrogen mineralization in soils also plays a major part, in relation to
biological activity (Bormann and Likens, 1967), climate (Mitchell et al.,
1996), hydrology (Montreuil et al., 2010), and hydrogeological and
pedological characteristics of landscapes (Schilling and Zhang, 2004).
Another important source for in-stream nitrate is direct nitrification of
ammonium in the water column (Bernhardt et al., 2002). Denitrification in
anoxic zones, and particularly the riparian zone, acts as an important sink
for nitrate (Aquilina et al., 2012; Wriedt et al., 2007). During recession
periods (e.g. summer) the connectivity between groundwater (GW) and surface
waters plays a key role (Molenat et al., 2008; Smethurst et al., 2014). In
agricultural landscapes, this is important due to dense artificial surface
and sub-surface drainage networks (Buchanan et al., 2013; Guan et al., 2011;
Lam et al., 2012), because they drain superficial GW which is known to store
N excess from multiple years.
In-stream removal summarizes various processes contributing to a decrease of
apparent nitrate concentrations within the stream channel and the adjacent
hyporheic zone or stream sediments (Ranalli and Macalady, 2010). The
intensity of in-stream removal processes is variable and depends on local
conditions and the combination of occurring removal processes. Local
streambed morphology determines available mineral and vegetation surfaces for
the development of microbial biofilms, which can decrease nitrate
concentrations by denitrification processes (Triska et al., 1989). For
example, microbial biofilm thickness is an important control for in-stream
respiration processes (Haggerty et al., 2014) and thus for denitrification
(Burgin and Hamilton, 2007). The impact of photoautotrophic nitrate
assimilation depends on incoming solar radiation and occurs mainly during the
hours of highest ecosystem productivity (e.g. Fellows et al., 2006; Hall and
Tank, 2003). Streambed permeability and the hydraulic conductivity of
underlying sediments govern hyporheic exchange fluxes depending on local
hydraulic gradients (Krause et al., 2012) and thus largely control
denitrification processes (by controlling available nitrate loads) in the
anaerobic compartments of the hyporheic zone. There is a large body of
literature studying denitrification processes in the hyporheic zone
(e.g. Briggs et al., 2013; Harvey et al., 2013; Lewandowski and Nützmann,
2010; Zarnetske et al., 2011, 2012). Without additional information, such as
isotopic data, dissolved oxygen concentration dynamics or dissolved organic
carbon concentration changes, it is difficult to distinguish biotic and
abiotic processes properly. Hence, these processes are summarized as
in-stream removal processes, which are either estimated using land use (e.g. Covino et al., 2012), water temperatures (e.g. Lomas and Glibert,
1999), water levels (e.g. Basu et al., 2011; Hensley et al., 2015; Thompson
et al., 2011) or discharge (e.g. Flewelling et al., 2014). Compared to
hydrological export processes (concentration and dilution processes),
in-stream removal processes have a smaller impact on total in-stream nitrate
concentrations but they can be responsible for nitrate removal (apparent
decrease of nitrate concentrations, excluding dilution processes) in the
range of 2–10 % at the reach scale (i.e. 100–200 m) (Harvey et al.,
2013; Hensley et al., 2015), 10–30 % for entire river networks (Dupas et
al., 2013; Windolf et al., 2011) and up to around 70 % of total exported
nitrate-nitrogen at larger scales (i.e. total retention, including retention
processes e.g. in the riparian zone or in wetlands) (Dupas et al., 2013;
Howarth et al., 1996).
In agricultural landscapes, nitrate export is a type of diffuse pollution even if nitrate fluxes can have
distinct locations of inflow into the stream network via subcatchments and
related drainage network outlets. Groundwater might enter streams and rivers
at spatially distinct locations, due to topography, local heterogeneity of
streambeds and hydrogeological settings (Binley et al., 2013; Krause et al.,
2012). Hence, changes in total water and nitrate fluxes occur frequently all
along the stream network. This is mainly true for first-order stream
networks. Considering that a major part of the regional stream and river
network consists of first-order streams (e.g. 48 % for the contiguous US;
Poff et al., 2006), nitrate export and turnover processes in first-order
stream networks can have a large impact on total catchment nitrate export
even on larger scales.
In this study, we define the following different subcatchments and stream reaches
where nitrate fluxes can vary as nitrate sinks or sources: nitrate sources
are tributaries which cause an increase in stream nitrate loads; nitrate
sinks are stream sections where nitrate load is decreasing. A nitrate source
does not necessarily result in an increase of in-stream nitrate
concentration, but does always increase the total nitrate load.
The temporal variations of hydrological and nitrate export processes along
different spatial scales have been reproduced by varying modelling approaches
(e.g. Donner et al., 2002; Huang et al., 2014; Johnes, 1996; Smethurst et
al., 2014; Wagenschein and Rode, 2008; Wriedt and Rode, 2006). Nevertheless,
there is still a lack of knowledge on how the spatial patterns of in-stream
nitrate concentrations evolve throughout stream networks and whether these
patterns are constant over time or vary in time. We analyse this complex
interplay of different processes by investigating two main research questions:
Can we quantify the spatio-temporal impact of distinct nitrate sinks and
sources on nitrate dynamics in a first-order stream network?
Can we determine underlying processes and drivers?
Answering these questions is relevant for a future improvement of water-quality
threshold compliances in agricultural landscapes, ecological water
quality management e.g. planning of river restoration and the implementation
of environmental guidelines, such as the European Water Framework Directive.
Topographical map of the Löchernbach catchment. The sharp
elevation steps in the map represent the vineyard terraces within the
catchment. Locations of active drainpipes and stream reaches are marked
(dashed lines) with the names referred to throughout the paper.
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In this study, we use a set of discharge and water quality data gathered
during 10 snapshot sampling campaigns along the main stream of a small
agricultural headwater catchment. A dense artificial drainage network and a
predominantly impervious streambed allowed for detecting distinct
groundwater inflow locations. This unique setting allowed us to quantify and
model the dynamics of nitrate sinks and sources in a first-order stream
network during the summer period. Thus, we can distinguish and quantify the
interaction of conservative mixing and dilution processes and biogeochemical
in-stream processes on the (first-order) network scale.
Methods
Sampling methods and water quality data
Sampling campaigns were carried out during base flow periods from June to
August 2012. Two types of campaigns were conducted (Table 1). We sampled
(a) a 100 m stream reach (Reach 1, Fig. 1) at five positions during five
campaigns for water temperatures (T), electrical conductivity (EC) and
major anion concentrations (chloride, nitrate, sulfate), and (b) the main
stream upstream, downstream and inside all active drainpipes/tributaries
(Fig. 1) during 10 campaigns for T and EC, and during 2 campaigns (No. 1,
No. 10) for major anion concentrations (chloride, nitrate, sulfate). During
each campaign, discharge was determined with salt dilution gauging (slug
injection) at the catchment outlet and at several locations (0–4) throughout
the stream network (Fig. 1).
For T, absolute measurement uncertainty was 0.2 K and the relative
accuracy for EC was 0.5 % of the measurement (WTW LF92). Water samples were
taken with 100 mL brown glass bottles, which were stored in a refrigerator
and analysed for major anions (chloride, nitrate, sulfate) within 2 to 4
weeks after sampling with ion chromatography (Dionex DX-500). Measurement
uncertainty was 0.1 mg L-1 for major anions. Climate data (air
temperatures (Tair), relative humidity, global radiation, wind
speed) were taken from a nearby climate station (1.3 km to the south).
Channel geomorphology and streambed structural characteristics, such as
channel widths and depths, rock outcrops, and vegetation at the stream banks
and in the streambed, were mapped once at 23 random locations distributed
throughout the stream network.
Stream network discharge patterns
Patterns of relative stream network discharges are determined by the
successive application of mixing equations on EC data (and T, chloride or
sulfate data at reaches where two active drainpipes were found) obtained
upstream, downstream and inside all active drainpipes from the catchment
outlet up to the main spring. Fractions (f) of reach drain water discharge
(fdi) relative to downstream stream discharge (Qi) are
calculated after Genereux (1998) based on the conservative mixing equations
for two or three endmembers (EC and T, or alternatively chloride and
sulfate, when available – the majority (66 %) of the reaches have only one
active drainpipe; thus the equations are reduced to two endmembers which can
be solved using one parameter only – EC):
Qi=Qdi1+Qdi2+Qi-1,
1=fdi1+fdi2+fi-1,ECi=fdi1ECi1+fdi2ECdi2+fi-1ECi-1,
and
Ti=fdi1Ti1+fdi2Tdi2+fi-1Ti-1,
where the subscript i represents the total number of upstream stream
reaches (i.e. the number of the actual reach of interest) with i = 0
at the stream network main source and the subsubscripts 1 and 2 stand for the
drainpipes leading to the stream at the upstream end of reach i. Resulting
fractional drainpipe water contributions are then used to calculate relative
discharge patterns throughout the stream network for all sampling campaigns
with following equations:
fnet,di=fnet,i⋅fdi
and
fnet,i-1=fnet,i-fnet,di1-fnet,di2,
where the subscript “net” stands for fractional water fluxes of all stream
reaches (and drainpipes) relative to the discharges at the catchment outlet.
This simple conceptual stream-source model was possible due to the
disconnection of the saturated zone to the stream, the visual exclusion
(thermal imaging (e.g. Schuetz and Weiler, 2011)) of other groundwater
sources and the assumption of negligible water losses to the
(anthropogenically restructured) colluvium. Absolute stream network discharge
patterns and drainpipe discharges are then derived by combining absolute
discharge measurements from the catchment outlet (Qi=9,obs)
with the fractional results of the stream-source model (Eq. 7) for each
stream reach (Qi) and each drainpipe, respectively in the following
form:
Qdi=fnet,di⋅Qi=9,obs.
Measurement errors and associated uncertainties of calculated stream network
discharges and drainpipe discharges are propagated by applying the equations
given in Genereux (1998) for mixing equations with two and three components,
respectively. Stream network discharges (Qi,obs) observed with
salt dilution gauging (with an approximated error of 10 %; e.g. Moore,
2005) are then used to validate derived stream network discharge patterns.
Nitrate source concentrations
Nitrate concentrations measured inside all active drainpipes
(Cdi,obs) during sampling campaigns No. 1 and No. 10 are used to
assess nitrate source concentrations for the whole study period: assuming a
groundwater system with slow seasonal nitrate dynamics, drainpipe nitrate
concentrations for all sampling campaigns (campaigns No. 2 to No. 9) are
derived by linearly interpolating between the observed nitrate concentrations
from the first and the last sampling campaign (sampling campaigns No. 1 and
No. 10). This assumption is in line with observations made in the following
summer (results not shown).
In-stream nitrate removal
The sum of all nitrate removal processes in surface waters (i.e. in-stream
removal) under stationary conditions regarding discharge input and
conservation (i.e. change in concentration equals change in load) is
commonly simulated with a kinetic first-order removal model following an
exponential function (e.g. Stream Solute Workshop, 1990)
Ci,obsτi=Ci,obs(0)⋅exp-kiτi,
where Ci,obs(0) stands for the nitrate concentration observed at the
beginning of a stream reach i and Ci,obs(τi) stands for the
nitrate concentration observed at the end of stream reach i. k stands for the
removal rate (T-1) and τ stands for the stream reach residence
time (T). τ is determined by
τ=lv,
where l stands for the reach length (L) and v for the mean flow velocity
(L T-1). v can be approximated with the ratio of discharge to the wetted
stream cross section A (L2)
v=QA.
For a trapezoidal stream bed with a known stream bank angle α (∘), streambed width b (L) and mean water depth h (L), A can be estimated with
A=b⋅h+h2⋅tanα.
Combining the Manning–Strickler equation
v=n-1⋅Rhy2/3⋅s1/2.
where n stands for Manning's n (T1/3 L-1), Rhy (L) for the hydraulic radius,
s stands for the hydraulic gradient (approximated with streambed slope –
L L-1) with the following assumption after Moore and Anderson (1990):
Rhy=ξ⋅A1/2,
where the constant ξ (–) depends on the side–slope ratio of the stream
bank and streambed width to depth ratio (Moore and Anderson, 1990)
Eqs. (10) to (3) can be transformed into
h=b+b2+4⋅tanαQ⋅ns1/2⋅ξ2/33/41/22⋅tanα.
Applying Eqs. (9), (10) and (14) with actual stream reach discharges
(Qi), τ can be determined individually for each stream reach and discharge.
Empirical nitrate removal rates ki for the five data sets observed at
Reach 1 and for the two data sets (campaign No. 1 and No. 10) observed
throughout the stream network can then be determined by rearranging Eq. (8) to
ki=-lnCiτiCi(0)τi.
In order to calculate ki for all the sampling campaigns, we try to
relate observed ki (for campaigns No. 1 and No. 10 and the five detailed
sampling campaigns in Reach 1) with systematically measured parameters. For
this, we developed the transfer coefficient TAWET
(∘C L-1; air–water–energy transfer)
TAWET,i=TairΔTiTair-Ti,
which is based on observed mean daytime air temperatures Tair
(∘C) on the day of each sampling campaign (8 a.m. to 8 p.m.), reach-scale stream water
heating ΔT (∘C L-1), and the temperature gradient
between Tair and stream water temperatures Ti (∘C). We
try to consider the spatial variability of energy inputs into the stream
system as a control of biological activity by accounting for the effect of
shading (which slows down the increase of ΔT) and the effect of
local groundwater contributions at the upstream end of a stream reach, which
cools down Ti and thus increases the gradient between air and water
temperatures.
Uncertainties for empirical in-stream nitrate removal rates ki and
removal rates estimated with the empirical relationship for TAWET are
done by propagating (Gaussian error propagation) measurement errors and
associated uncertainties of observed water and air temperatures and nitrate concentrations.
Standardized comparison of in-stream nitrate removal processes with
stream/catchment specific properties is commonly done, following the
recommendations of the Stream Solute Workshop (1990), by calculating (among
other factors) in-stream uptake rates kC, which equal ki
(introduced above), and areal nitrate uptake Ui (M L-2 T-1),
which is defined by
Ui=Ci(0)⋅hi⋅ki.
Implementation of the in-stream-mixing-and-removal model
Accounting for lateral drainpipe discharges (Sect. 3.2) and stream network
discharge patterns, lateral source/drainpipe nitrate concentrations
(Sect. 3.3) and in-stream nitrate removal processes (Sect. 3.4), we define a
conceptual data-driven in-stream-mixing-and-removal model by combining
previous equations as follows:
Ci+1=Ci(0)⋅exp-kiτi⋅Qi+Cdi+11⋅Qdi+11+Cdi+12⋅Qdi+12Qi+1.
Model application is done by using the measured/estimated C(0) of the
uppermost reach, the measured/estimated Cdi of the drainpipes, the
Qi/Qdi calculated from the endmember mixing and ki
estimated with TAWET as input variables for the successive
calculation of stream network nitrate concentrations from upstream to
downstream. All parameters, nitrate concentrations and discharges integrated
into Eq. (18) are estimated without any calibration. Taking into account that
modelling uncertainties will be influenced not only by the uncertainties of
Qi (successively estimated from downstream to upstream) and ki
estimated with TAWET but also by the uncertainties implied through
the assumptions which were made for the estimations of τi and
drainpipe nitrate concentrations, the uncertainties in our modelling results
will be larger than the differences within our simulations. Hence, we will
refrain from an uncertainty analysis of stream network modelling results.
However, observed versus predicted comparisons of various parameters
quantified the overall error.
Results
Nitrate spatio-temporal patterns on the reach and stream network
Besides the main spring, we detected, in total, 11 active drainpipes (plus
one tributary, Fig. 1) of which 6 were intermittent. At three locations, two
pipes drain at one point into the stream. Stream network nitrate
concentrations sampled during campaign No. 1 and No. 10 upstream, downstream,
and inside all active drainpipes revealed spatial concentration patterns with
increasing concentrations from upstream to downstream (Fig. 2) and with
different concentration changes among the stream reaches. Nitrate
concentrations in the drainpipes differed clearly from in-stream
concentrations. In most of the stream reaches, nitrate concentrations
decreased, particularly within stream reach No. 1 (Fig. 2, inset), where
nitrate was additionally sampled during five snapshot campaigns with a higher
spatial resolution.
Observed spatio-temporal variations in in-stream and drainpipe
nitrate concentrations along the stream network for sampling campaigns No. 1
(27 June 2012) and No. 10 (9 August 2012) and during five sampling campaigns
at Reach 1 (inset).
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Stream network discharge patterns
We determined all drainpipe discharges for each sampling campaign applying
Eqs. (1) to (7) using the obtained EC data (and T, chloride or sulfate
data, where two drainpipes were located at one position) and the discharges
observed at the catchment outlet. Discharge varied among all drainpipes and
between all campaigns between 0.05 and 1.7 L s-1 with a mean error of
0.21 L s-1. While the main spring and drainpipes D1–D6 never
contributed more than 0.5 L s-1, drainpipes D7.1, D7.2 and D8
delivered most of the time either distinctly more than 0.5 L s-1 or
remained dry. Using the individual discharge contribution of all drainpipes
we determined distinct stream network discharge patterns for each campaign
(Fig. 3a and b) with a mean absolute discharge increase of
0.2 ± 0.06 L s-1/100 m and a mean relative discharge increase
of 8 ± 7 %/100 m. Comparing observed discharges with calculated
discharges, we find a good agreement with an R2 of 0.51
(p < 0.0001; n = 24) and a mean absolute error of
0.83 L s-1 (Fig. 3a, inset). The patterns of relative longitudinal
discharge evolution show a clear change between the different sampling
campaigns.
Overview on stream reach residence times τ and stream reach
specific parameters applied in Eqs. (9) to (12).
Reach
Reach
Stream
Mean
Max.
Min.
Mean
Min.
Max.
No.
length
bed slope
discharge
discharge
discharge
residence
residence
residence
time
time
time
[m]
[m m-1]
[L s-1]
[L s-1]
[L s-1]
[s]
[s]
[s]
1
100
0.075
0.2
0.5
0.02
642
441
1092
2
150
0.052
0.5
1.1
0.1
836
640
1184
3
195
0.039
0.8
1.5
0.2
1068
854
1517
4
185
0.022
1.1
1.9
0.2
1133
937
1583
5
140
0.019
1.5
2.4
0.4
820
704
1138
6
50
0.023
1.6
2.4
0.4
267
234
358
7
145
0.014
2.0
3.0
0.6
877
772
1178
8
235
0.019
2.4
5.2
1.1
1211
969
1428
9
35
0.021
3.1
5.2
1.7
163
140
188
(a) Simulated stream network discharge patterns Qi for
all days. Inset in (a): comparison of calculated (Qi) and
measured discharges (Qi,obs). (b) Calculated patterns of
relative discharges fnet,i for all days. Sampling campaigns
No. 1–No. 10 are colour-coded from blue to red. Dashed lines (a, b)
symbolize the positions of the drainpipes. Shaded bars (a) represent
the locations of salt dilution gauging.
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Based on a digital elevation model with a spatial resolution of 1 m2
and a vertical resolution of 0.1 m, we determined the mean slopes of the
streambed per reach. Mean channel roughness was estimated with a
Manning's n of 0.0585 (s1/3/m) for the total stream network, following
the procedure described in Schneider and Arcement (1989). Stream bank angles
were uniformly approximated with 30∘ and mean streambed width was set
to 0.38 m, based on the observed mean streambed width obtained during a
random sampling of stream morphology (the channel was restructured in the
1970s, and is very homogenously shaped). By applying Eqs. (9) to (14), the
residence times of each stream reach was derived, which varied between
234 and 1583 s. Variations of residence times between the reaches and the
different campaigns depend only on the differences of reach lengths,
streambed slopes and actual discharge (Table 2).
Nitrate dynamics along the stream network
Nitrate concentrations in the drainpipes ranged between 8.7 and
48 mg L-1 with a mean increase of 1.3 mg L-1/100 m from
upstream to downstream (R2 = 0.21; p < 0.05; n = 24).
Between campaign No. 1 and No. 10, eight drainpipes showed decreasing
concentrations with a mean decrease of 5.2 ± 2.7 mg L-1 and four
drainpipes showed increasing concentrations with a mean increase of
2.3 ± 0.9 mg L-1.
(a) Estimated (ki) and empirical (ki,obs)
in-stream nitrate removal rates. (b) Observed (Ci,obs
symbols) and calculated (Ci lines) in-stream nitrate concentration
patterns for all days. Sampling campaigns No. 1–No. 10 are colour-coded from
blue to red. Dashed lines symbolize the positions of the drainpipes.
(c) Comparison of modelled and observed in-stream nitrate
concentrations for campaigns No. 1 (blue circles) and No. 10 (red diamonds).
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By applying Eq. (15) to the observed in-stream nitrate concentration changes
within the reaches, the empirical in-stream nitrate removal rate ki was
calculated, and varies between 3.5 × 10-6 and
5 × 10-4 s-1. Relating the empirical nitrate removal
rate ki to the conceptual transfer coefficient TAWET shows a
significant linear correlation (R2 = 0.82; p < 0.0001;
n = 21). In order to avoid the prediction of negative removal rates,
the log-transform of ki is tested against TAWET. This yields a
linear correlation with lower statistical power (R2 = 0.63;
p = 0.0002; n = 16). Comparing the resulting regression model
with empirical in-stream nitrate removal rates, we find a good approximation
with a mean relative error of 40 %, which seems to be appropriate, though
deviations between empirical and estimated removal rates increase only when
the observed removal rates become very small (Fig. 4a).
(a) In-stream nitrate loads per source for all days (the black line
presents cumulative nitrate load emissions without in-stream removal).
(b) Maximum, median and minimum in-stream nitrate load removal per source
relative (%) to the total emitted nitrate load.
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Applying the in-stream-mixing-and-removal model (Eq. 18) to all stream
network data sets (spatially discretized drainpipe discharges and nitrate
loads) we find distinct patterns of nitrate concentrations along the stream
network (Fig. 4b). Stream nitrate concentration patterns show that the impact
of nitrate sources regarding the downstream changes of in-stream nitrate
concentrations is directly connected with interaction between local source
fluxes and in-stream nitrate and water fluxes. The temporal variability of
removal processes simulated for different stream reaches is clearly changing
the picture. Some of the nitrate sources and stream reaches show a distinctly
stronger impact on the temporal and spatial evolution of in-stream nitrate
concentrations than others. The simulation results were tested against
in-stream nitrate concentrations observed during sampling campaigns No. 1 and
No. 10 (Fig. 4b (blue and red lines/symbols) and Fig. 4c). With an R2
of 0.91 for sampling campaign No. 1 and an R2 of 0.97 for sampling
campaign No. 10 (Fig. 4c) the observations are reproduced quite well. This
includes the following temporal changes of in-stream nitrate concentrations:
at the beginning of the study (sampling campaign No. 1) in-stream nitrate
concentrations were generally less variable throughout the stream network
than at the end of the study (sampling campaign No. 10), when very low
concentrations occurred as well.
Hierarchy of nitrate sinks and sources
The effects of nitrate sinks and sources on in-stream nitrate dynamics are
visualized considering the spatial and temporal distribution of nitrate
loads throughout the stream network (Fig. 5a). For each sampling campaign
distinct nitrate load distributions and contributions were found. The
detailed spatial representation of nitrate sinks and sources in Fig. 5 shows
that absolute and relative impacts of distinct sinks and sources on total
nitrate load at the catchment outlet are more pronounced than the variations
of nitrate concentration (Fig. 4b) and discharge dynamics (Fig. 3a). Median
relative nitrate removal per source (i.e. the magnitude of in-stream removal
per source at the catchment outlet, Fig. 5b) clearly depends on the
position of a source in the stream network (R2 = 0.95; p < 0.0001;
n = 12). Nitrate loads emitted at the catchment spring
are removed between 20 and 50 %, while loads emitted in the lower sections
of the stream network show a much lower relative removal. In contrary, the
differences of relative nitrate load removal per source between adjacent
nitrate sources are not related to the specific reach lengths.
(a) Hierarchy and range of nitrate loads per source ranked
by their median nitrate load emission. (b) Hierarchy and range of
in-stream nitrate removal rates ki per reach sorted from upstream to
downstream. (c) Range of areal uptake rates Ui per reach sorted
from upstream to downstream. Boxplots present the 0.01, 0.25, 0.5, 0.75 and
0.99 quantiles of each measure.
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Among the different nitrate sources, we have found a distinct hierarchy
(Fig. 6a), which is more controlled by drainpipe discharge (median nitrate
load vs. drainpipe discharge: R2 = 0.85; p < 0.0001;
n = 120) than by nitrate concentrations (no significant correlation
between median nitrate loads and drainpipe nitrate concentrations). During
most of the days, some sources contribute the major part of total nitrate
loads (D8, D6, D4.1) while other sources vary between major nitrate load
contributions and no contributions at all (i.e. intermittent drainpipes,
e.g. D7.1, D7.2). Positioning along the stream shows no correlation with the
rank of the source contribution.
When comparing the rankings of median in-stream nitrate removal ki
(Fig. 6b) and median areal nitrate uptake rates Ui (Fig. 6c), we find a
different order of stream reaches: while in-stream nitrate removal rates
decrease from upstream to downstream (R2 = 0.74; p = 0.0029;
n = 9), the areal nitrate uptake rates Ui do not show such a clear
pattern. In the downstream reaches (Reach 7, 8, and 9) areal uptake rates are
the highest but there is no significant relation within the ranking of areal
nitrate uptake Ui and the spatial location along the stream network.
(a) Comparison of estimated in-stream nitrate removal rates
ki (s - 1) and areal nitrate uptake rates Ui (mg m2 s)
per stream reach. (b) Comparison of observed relative changes in
nitrate concentrations with observed relative changes in the ratio of
nitrate/chloride per stream reach observed during the sampling campaigns
No. 1 and No. 10 and during the additional sampling campaigns at Reach 1.
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Discussion
We have quantified nitrate sinks and sources, which contribute to the spatial
patterns of in-stream nitrate concentrations along a first-order stream
network and their evolution in time. We are able to show how distinct nitrate
sinks and sources persistently dominate these patterns over time. These
findings are supported by several recent studies which show the uniqueness of
spatial water quality composition for larger scales based on stream sampling
campaigns (e.g. Lam et al., 2012; Vogt et al., 2015) or based on modelling
approaches describing the spatial distribution of nitrate export in stream
networks (e.g. Isaak et al., 2014). Both approaches show the importance of
spatial “hot spots” regarding nitrate sources. The originality of our work,
in comparison to these studies, is that we have studied the temporal
variations of nitrate contributions, with an emphasis on local flux
contributions, based on a data-driven modelling approach.
Nitrate sources
The unique setting in our study area (known locations of groundwater inflow
and negligible stream water losses) allowed us to infer water and nitrate
fluxes and flux changes along the stream without neglecting important
contributions. Looking at the longitudinal stream profiles of absolute and
relative discharges (Fig. 3a and b) we find a high temporal variability
within the spatial patterns of the catchment drainage system. This can be
explained by specific discharge recessions for different landscape
elements/hydrogeological storages during baseflow periods (Payn et al.,
2012). The different subcatchments (or rather the areas connected to the
drainpipes) show differences regarding their spatial extent, elevations and
land use combinations. This high variability was not expected before, though
Mallard et al. (2014) show that characteristic longitudinal stream discharge
profiles can be found for specific catchments (e.g. with a certain shape and
channel network). For the observed time period, our data show that these
patterns are rather unstable. Consequently, the impact of certain
subcatchments on total nitrate export changes over time and the spatial
changes can be more or less dominant.
Nitrate sinks
In this study, stream network nitrate sinks are defined as the sum of all
in-stream nitrate removal processes on each reach. We do not use the
presented approach to distinguish between different biogeochemical processes
but to empirically simulate the net effect of biogeochemical processes on
downstream nitrate concentrations. For other catchments, additional nitrate
mass losses along the stream channel (i.e. indirect groundwater recharge)
have to be considered. Mallard et al. (2014) showed that cumulative gross
channel discharge losses could retain large parts of the discharges generated
in the headwaters (and thus large parts of the nitrate loads emitted from the
headwaters). Depending on the spatial differences in groundwater nitrate
concentrations, the hydrological turnover could then partly overlay the
processes described in this study. However, the hydrological turnover will
similarly influence downstream groundwater nitrate concentrations and thus
the magnitude of downstream nitrate sources.
We estimated in-stream nitrate removal rates ki using the empirical
transfer coefficient TAWET, which describes the energy limitation
of a specific stream reach. Comparing the ranking of in-stream nitrate
removal rates ki and areal uptake rates Ui (Fig. 7a), we find an
increasing uptake efficiency (i.e. lower removal rates causing equal areal
uptake) from upstream to downstream. Considering that for a given reach,
Ui and ki are linked by stream reach water levels and nitrate
concentrations (Eq. 17), we can conclude that the increase in uptake
efficiency can be likewise caused by increasing water levels or nitrate
concentrations. Nonetheless, observable changes in in-stream nitrate
concentrations are larger in upstream reaches than in the downstream reaches.
However, on smaller scales (such as the study area) the temporal variability
of in-stream nitrate concentrations cannot be explained by land use alone
(e.g. Mulholland et al., 2008; Ruiz et al., 2002). A higher spatial
resolution of geomorphic or physicochemical information is needed. Although
we know that gross primary production and in-stream nitrate turnover in
stream ecosystems is directly linked to water temperatures and incoming
radiation (e.g. Fellows et al., 2006; Hall and Tank, 2003; Lomas and Glibert,
1999), the high spatial resolution of our study did not allow a direct
comparison of observed in-stream nitrate removal to atmospheric conditions.
We found a significant correlation for Ti and empirical removal rates
ki on the reach scale (Reach 1), which was not valid on the network
scale. This can be explained by the spatial variability of inflowing
groundwater/nitrate sources, channel geomorphology or vegetation density.
Hence, we explicitly consider the impacts of local shading, upstream stream
water temperatures (which are a measure of surface travel time) and local
cooling effects of inflowing groundwater for the derivation of
TAWET. A more physically based interpretation of the involved
processes would have required deeper knowledge on the spatial distribution of
streambed geomorphology and vegetation. In many other studies (e.g. Alexander
et al., 2009; Basu et al., 2011; Hensley et al., 2015) water levels alone
were used for the estimation of in-stream removal processes. Though existing
hydraulic information is commonly used to estimate both, stream reach
residence times (Stream Solute Workshop, 1990) and areal nitrate uptake rates
Ui (Eq. 14), we think that the independent estimation of ki, by
using additional measurements of stream water temperatures, groundwater
temperatures and air temperatures, improves the reliability of the presented
non-calibrated and data-driven modelling approach. Nonetheless, one must
consider that hyporheic exchange processes (and thus denitrification by
heterotrophic organisms) contribute to nitrate removal processes as well
(Harvey et al., 2013; Kiel and Cardenas, 2014; Zarnetske et al., 2011).
Hence, the interdependency of hydraulic conditions and energy availability at
the reach scale cannot be easily resolved. For the present study, we could
show that the change in nitrate concentrations per reach relates almost
1 : 1 to the change in nitrate-N/chloride ratios per reach for all our
observations (Fig. 7b). This is also true for the three observations where an
increase in nitrate concentrations occurred from upstream to downstream.
Nitrate-N to chloride mass ratios has been previously used to indicate that
other processes, such as dilution (Schilling et al., 2006) or rather
denitrification (Tesoriero et al., 2013), are responsible for the change in
nitrate concentrations. Hence, we conclude that both controls are relevant
for a specific stream network and thus the decision to use one measurement or
the other should be made with great care.
Hierarchy of nitrate sinks and sources
Considering the relationship of in-stream water fluxes and nitrate
concentrations with water and nitrate flux contributions from landscape units
along the stream network, in-stream nitrate concentrations can change clearly
from upstream to downstream through enrichment and dilution processes. The
effect of the spatial arrangement of nitrate source areas and stream reaches
along the stream network with high or low retention potential is manifested
in the longitudinal nitrate concentration patterns observable along a stream
or river (e.g. Figs. 2 and 4a). It becomes clear that there is a direct
impact of the location of a tributary, or a groundwater source of nitrate and
stream reaches with high nitrate turnover rates, on downstream nitrate
concentrations. Nitrate loads emitted by specific upstream sources can be
removed to a large extent on their way through a stream network (Fig. 5).
The seasonal variations of in-stream nitrate concentrations could be larger
than the variations of nitrate concentrations presented within this study.
Nevertheless, these variations occur during relatively short time periods
(summer low flows) when ecological in-stream conditions are crucial for
in-stream habitat conditions, e.g. a nutrient surplus in combination with
warm temperatures and high solar radiation input can cause eutrophic
conditions in the stream ecosystem. Hence, a better understanding of the
evolution of apparent in-stream nitrate concentrations is relevant e.g. for
water quality threshold exceedances. Due to the stationary or slowly changing
conditions during low flow periods, spatial water quality patterns are little
affected by hydrodynamic and geomorphic dispersion of point
source/subcatchment nitrate emissions (Botter and Rinaldo, 2003). Hence,
observed step changes of in-stream concentrations can be expected as a
frequently occurring phenomenon. In many studies published on nitrate export,
the focus is on nitrate concentrations observed at a single location in the
stream (i.e. catchment outlet). Our results (specifically Figs. 2b and 4b)
illustrate that there is a clear need to better understand the
spatio-temporal hydrological connectivity (and thus water and matter fluxes)
of landscapes to the fluvial systems. For the in-stream-mixing-and-removal
model, applied to the Löchernbach catchment, distinct boundary conditions
could be defined. In other systems, where export processes to the stream
occur more diffusely and where non-negligible stream water losses occur
(i.e. groundwater–surface water interaction), an improved understanding of
nitrate sinks and sources is even more important. For these systems, we have
to additionally consider the variable interplay of local gradients between
groundwater and surface water (Krause et al., 2012) and their influence on
water and matter turnover processes in the stream network and the reverse
effect of in-stream-mixing-and-removal processes on local groundwater quality
dynamics. The study of Mallard et al. (2014) provided a first step into a
longitudinally more dynamic system understanding of water flux dynamics (and
thus water quality dynamics) in stream and river networks. We could show that
for biogeochemically active substances, such as nutrients, their approach
should be supplemented by the consideration of in-stream cycling and
retention processes and their masking effects from upstream to downstream.
Our results apply mostly to first-order stream networks. However, due to the
large effects on first-order catchment nitrate export and the dominance of
first-order catchments in the regional river network (Poff et al., 2006) our
results are relevant even on larger scales: our findings imply that a more complex
understanding of the hydro-ecological functioning of a specific stream or
river system, regarding the origin of water and of matter fluxes, has to be
applied for the planning of ecological measures or sustainable water resource
management. This concerns the distribution of different types of land use
within the catchment (e.g. intensive agriculture) as well as their
hydrological connectivity to the stream network. For example, when planning
river restorations, we have to recognize that e.g. the combination of high
soil nitrate concentrations and a shallow tile drain system may lead to
increased export rates for a specific subcatchment. For such a case the
downstream implementation of a restored river corridor could then have an
enhanced impact as a nitrate sink (compare e.g. Bukaveckas, 2007). In
contrast, in densely populated countries, as in the midwestern part of
Europe, the implementation of e.g. river restoration measures is usually done
in places where property rights (and legal terms) allow the implementation of
these measures. Furthermore, the integral impact of local ecological
in-stream measures on downstream nitrate concentration patterns, which are
more relevant for water quality threshold compliances than nitrate loads,
should be considered as well. This might even be economically useful in river
systems with downstream drinking water production plants and occurring stream
bank filtration processes. Moreover, the planning and operation of water
quality monitoring networks could be improved by regarding the spatial and
temporal covering of important nutrient sinks and sources.