Increased anthropogenic inputs of nitrogen (N) to the
biosphere during the last few decades have resulted in increased groundwater and
surface water concentrations of N (primarily as nitrate), posing a global
problem. Although measures have been implemented to reduce N inputs, they
have not always led to decreasing riverine nitrate concentrations and loads.
This limited response to the measures can either be caused by the
accumulation of organic N in the soils (biogeochemical legacy) – or by long
travel times (TTs) of inorganic N to the streams (hydrological legacy).
Here, we compare atmospheric and agricultural N inputs with long-term
observations (1970–2016) of riverine nitrate concentrations and loads in a
central German mesoscale catchment with three nested subcatchments
of increasing agricultural land use. Based on a data-driven
approach, we assess jointly the N budget and the effective TTs of N through
the soil and groundwater compartments. In combination with long-term
trajectories of the C–Q relationships, we evaluate the potential for and
the characteristics of an N legacy.
We show that in the 40-year-long observation period, the catchment (270 km2) with 60 % agricultural area received an N input of
53 437 t, while it exported 6592 t, indicating an overall retention of
88 %. Removal of N by denitrification could not sufficiently explain this
imbalance. Log-normal travel time distributions (TTDs) that link the N input
history to the riverine export differed seasonally, with modes spanning
7–22 years and the mean TTs being systematically shorter during the high-flow season as compared to low-flow conditions. Systematic shifts in the
C–Q relationships were noticed over time that could be attributed to strong
changes in N inputs resulting from agricultural intensification before 1989,
the break-down of East German agriculture after 1989 and the
seasonal differences in TTs. A chemostatic export regime of nitrate was only
found after several years of stabilized N inputs. The changes in C–Q
relationships suggest a dominance of the hydrological N legacy over the
biogeochemical N fixation in the soils, as we expected to observe a stronger
and even increasing dampening of the riverine N concentrations after
sustained high N inputs. Our analyses reveal an imbalance between N input
and output, long time-lags and a lack of significant denitrification in the
catchment. All these suggest that catchment management needs to address
both a longer-term reduction of N inputs and shorter-term mitigation of
today's high N loads. The latter may be covered by interventions triggering
denitrification, such as hedgerows around agricultural fields, riparian
buffers zones or constructed wetlands. Further joint analyses of N budgets
and TTs covering a higher variety of catchments will provide a deeper insight into N trajectories and their controlling parameters.
Introduction
In terrestrial, freshwater and marine ecosystems nitrogen (N) species are
essential and often limiting nutrients (Webster et al., 2003; Elser et al.,
2007). Changes in strength of their different sources like atmospheric
deposition, wastewater inputs and agricultural activities caused major
changes in the N cycle (Webster et al., 2003). In particular, two major
innovations from the industrial age accelerated anthropogenic inputs of
reactive N species into the environment: artificial N fixation and the
internal combustion engine (Elser, 2011). Therefore the amount of reactive N
that enters into the element's biospheric cycle has been doubled in
comparison to the preindustrial era (Smil, 1999; Vitousek et al.,
1997). However, the different input sources of N show diverging rates of
change over time and space. While the atmospheric emissions of N oxides and
ammonia have strongly declined in Europe since the 1980s (EEA, 2014), the
agricultural N input through fertilizers declined but is still at a high
level (Federal Ministry for the Environment and Federal Ministry of Food,
2012). In the cultural landscape of western countries, most of the N
emissions in surface and groundwater bodies stem from diffuse agricultural
sources (Bouraoui and Grizzetti, 2011; Dupas et al., 2013).
The widespread consequences of these excessive N inputs are significantly
elevated concentrations of dissolved inorganic nitrogen (DIN) in groundwater
and connected surface waters (Altman and Parizek, 1995; Sebilo et al., 2013;
Wassenaar, 1995), leading to increased riverine DIN fluxes (Dupas et al.,
2016) and causing the ecological degradation of freshwater and marine
systems. This degradation is caused by the ability of N species to increase
primary production and to change food web structures (Howarth et al., 1996;
Turner and Rabalais, 1991). In particular, the coastal marine environments,
where nitrate (NO3) is typically the limiting nutrient, are affected by
these eutrophication problems (Decrem et al., 2007; Prasuhn and Sieber,
2005).
Several initiatives in the form of international, national and federal
regulations have been implemented, aiming at an overall reduction of N
inputs into the terrestrial system and its transfer to the aquatic system.
In the European Union, guidelines are provided to its member states for
national programs of measures and evaluation protocols through the Nitrate
Directive (CEC, 1991) and the Water Framework Directive (CEC, 2000).
The evaluation of interventions showed that policy-makers still struggle to
set appropriate goals for water quality improvement, particularly in heavily
human-impacted watersheds. Studies in Europe and the United States showed
that interventions like reduced N inputs mainly in agricultural land use do
not immediately result in declining riverine NO3–N concentrations
(Bouraoui and Grizzetti, 2011; Sprague et al., 2011; Howden et al., 2011)
and fluxes (Worrall et al., 2009), although fast responding headwaters have
been reported as well (Rozemeijer et al., 2014).
In Germany considerable progress has been achieved in the improvement
of water quality, but the diffuse water pollution from agricultural sources
continues to be of concern (Wendland et al., 2005). This limited response to
mitigation measures can partly be explained by nutrient legacy effects,
which stem from an accumulation of excessive fertilizer inputs over decades
creating a strongly dampened response between the implementation of measures
and water quality improvement (van Meter and Basu, 2015). Furthermore, the
multi-year travel times (TTs) of nitrate through the soil and groundwater
compartments cause large time lags (Howden et al., 2010; Melland et al.,
2012) that can substantially delay the riverine response to applied
management interventions. For a targeted and effective water quality
management, we therefore need a profound understanding of the processes and
controls of time lags of N from the source to groundwater and surface water
bodies. Bringing together N balancing and accumulation with estimations of N
TTs from application to riverine exports can contribute to this lack of
knowledge.
Estimation of the water or solute TTs is essential for predicting the
retention, mobility and fate of solutes, nutrients and contaminants at
catchment scale (Jasechko et al., 2016). Time series of solute
concentrations and loads that cover both input to the geosphere and the
subsequent riverine export can be used not only to determine TTs (van Meter
and Basu, 2017), but also to quantify mass losses in the export as well as
the behavior of the catchment's retention capacity (Dupas et al., 2015).
Knowledge on the TT of N would therefore allow understanding on the N
transport behavior, defining the fate of injected N mass into the system
and its contribution to riverine N response. The mass of N being transported
through the catchment storage can be referred to as hydrological legacy. Data-driven or simplified mechanistic approaches have often been used to derive
stationary and seasonally variable travel time distributions (TTDs) using
input and output signals of conservative tracers or isotopes (Jasechko et al.,
2016; Heidbüchel et al., 2012) or chloride concentrations (Kirchner et
al., 2000; Bennettin et al., 2015). Recently, van Meter and Basu (2017)
estimated the solute TTs for N transport at several stations across a
catchment located in Southern Ontario, Canada, showing decadal time-lags
between input and riverine exports. Moreover, systematic seasonal variations
in the NO3–N concentrations have been found, which were explained by
seasonal shifts in the N delivery pathways and connected time lags (van
Meter and Basu, 2017). Despite the determination of such seasonal
concentration changes and age dynamics, there are relatively few studies
focussing on their long-term trajectory under conditions of changing N
inputs (Dupas et al., 2018; Howden et al., 2010; Minaudo et al., 2015;
Abbott et al., 2018). Seasonally differing time shifts, resulting in
changing intra-annual concentration variations are of importance to aquatic
ecosystems' health and their functionality. Seasonal concentration changes
can also be directly connected to changing concentration–discharge (C–Q)
relationships – a tool for classifying observed solute responses to
changing discharge conditions and for characterizing and understanding
anthropogenic impacts on solute input, transport and fate (Jawitz and
Mitchell, 2011; Musolff et al., 2015). Investigations of temporal dynamics in
the C–Q relationship are a valuable addition to approaches based on N
balancing only (e.g., Abbott et al., 2018), when evaluating the effect of
management interventions.
The C–Q relationships can be on the one hand classified in terms of their
pattern, characterized by the slope b of the ln(C)–ln(Q) regression (Godsey
et al., 2009): with enrichment (b>0), dilution (b<0) or
constant (b≈0) patterns (Musolff et al., 2017). On the other hand,
C–Q relationships can be classified according to the ratio between the
coefficients of variation of concentration (CVC) and of discharge
(CVQ; Thompson et al., 2011). This export regime can be either
chemodynamic (CVC/ CVQ>0.5) or chemostatic, where
the variance of the solute load is more dominated by the variance in
discharge than the variance in concentration (Musolff et al., 2017). Both
patterns and regimes are dominantly shaped by the spatial distribution of
solute sources (Seibert et al., 2009; Basu et al., 2010; Thompson et al.,
2011; Musolff et al., 2017). High source heterogeneity and consequently high
concentration variability is thought to be characteristic for nutrients
under pristine conditions (Musolff et al., 2017; Basu et al., 2010). It was
shown in Germany and the United States that catchments under intensive
agricultural use evolve from chemodynamic to more chemostatic behavior
regarding nitrate export (Thompson et al., 2011; Dupas et al., 2016).
Several decades of human N inputs seem to dampen the discharge-dependent
concentration variability, resulting in chemostatic behavior, where
concentrations are largely independent of discharge variations (Dupas et
al., 2016). Also Thompson et al. (2011) stated observational and model-based
evidence of an increasing chemostatic response of nitrate with increasing
agricultural intensity. This shift in the export regimes is caused by a
long-term homogenization of the nitrate sources in space and/or at depth
within soils and aquifers (Dupas et al., 2016; Musolff et al., 2017).
However, effective denitrification in the subsurface can create
concentration variability over depths and flow path age and thus has been shown
to result in chemodynamic exports even with intensive agriculture (van der
Velde et al., 2010; Musolff et al., 2017). Long-term N inputs lead to a
loading of all flow paths in the catchment with mobile fractions of N and by
that the formation of a hydrological N legacy (van Meter and Basu, 2015)
and chemostatic riverine N exports. On the other hand, excessive fertilizer
input is linked to the above-mentioned buildup of legacy N stores in the
catchment, changing the export regime from a supply- to a transport-limited
chemostatic one (Basu et al., 2010). This legacy is manifested as a
biogeochemical legacy in the form of increased, less mobile, organic N content
within the soil (Worral et al., 2015; van Meter and Basu, 2015; van Meter
et al., 2017a). This type of legacy buffers biogeochemical variations, so
that management measures can only show their effect if the buildup source
gets substantially depleted (Basu et al., 2010).
Depending on the catchment configuration, both forms of legacy –
hydrological and biogeochemical – can exist with different shares of the
total N stored in a catchment (van Meter et al., 2017a). However,
biogeochemical legacy is hard to distinguish from hydrological legacy when
looking at time lags between N input and output or at catchment-scale N
budgets only (van Meter and Basu, 2015). One way to better disentangle the
N legacy types is applying the framework of C–Q relationships as defined by
Jawitz and Mitchell (2011) and Musolff et al. (2015, 2017). In the case of a hydrological legacy, strong changes in fertilizer
inputs (such as increasing inputs in the initial phase of intensification
and decreasing inputs as a consequence of measures) will temporarily
increase spatial concentration heterogeneity (e.g., comparing young and old
water fractions in the catchment storage), and therefore also shift the
export regime to more chemodynamic conditions. On the other hand, a dominant
biogeochemical legacy will lead to sustained concentration homogeneity in
the N source zone in the soils and to an insensitivity of the riverine N
export regime to fast changes in inputs.
Common approaches to quantify catchment-scale N budgets and to characterize
legacy or to derive TTs are either based on data-driven (Worral et al.,
2015; Dupas et al., 2016) or on forward-modeling (van Meter and Basu, 2015;
van Meter et al., 2017a) approaches. So far, data-driven studies focused
either solely on N budgeting and legacy estimation or on TTs. Here, we
conducted a joint data-driven assessment of the catchment-scale N budget, the
potential and characteristics of an N legacy, and the estimation of TTs of
the riverine exported N. We utilized the trajectory of agricultural
catchments in terms of C–Q relationships, their changes over longer timescales and their potential evolution to a chemostatic export regime. The
novel combination of the long-term N budgeting, TT estimation and C–Q
trajectory will help understanding of the differentiation between
biogeochemical and hydrological legacy, both reasons for missed targets in
water quality management. This study will address the following research
questions:
How high is the retention potential for N of the studied mesoscale catchment
and what are the consequences in terms of a potential buildup of an N
legacy?
What are the characteristics of the TTD for N that links change in the
diffuse anthropogenic N inputs to the geosphere and their observable effect
in riverine NO3–N concentrations?
What are the characteristics of a long-term trajectory of C–Q
relationships? Is there an evolution to a chemostatic export regime that can
be linked to a biogeochemical or hydrological N legacy?
To answer these questions, we used time series of water quality data over
four decades, available from a mesoscale German catchment, as well as
estimated N input to the geosphere. We linked N input and output on annual
and intra-annual timescales through consideration of N budgeting and the
use of TTDs. This input–output assessment uses time series of the Holtemme
catchment (270 km2) with its three nested subcatchments
along a land use gradient from pristine mountainous headwaters to a lower
basin with intensive agriculture and associated increases in fertilizer
applications. This catchment, with its pronounced increase in anthropogenic
impacts from up- to downstream, is quite typical for many mesoscale
catchments in Germany and elsewhere. Moreover, this catchment offers a
unique possibility to analyze the system response to strong changes in
fertilizer usage in East Germany before and after reunification. Thereby, we
anticipate that our improved understanding gained through this study in
these catchment settings is transferable to similar regions. In comparison
to spatially and temporally integrated water quality signals stemming solely
from the catchment outlet, the higher spatial resolution with three stations
and the unique length of the monitoring period (1970–2016) allow for a more
detailed investigation about the fate of N, and consequently findings may
provide guidance for effective water quality management.
Data and methodsStudy area
The Holtemme catchment (270 km2) is a subcatchment of the
Bode River basin, which is part of the TERENO (TERrestrial ENvironmental Observatories) Harz/Central German Lowland
Observatory (Fig. 1). The catchment, as part of the TERENO
project, exhibits strong gradients in topography, climate, geology, soils, water
quality, land use and level of urbanization (Wollschläger et al., 2017).
Due to the low water availability and the risk of summer droughts that might
be further exacerbated by a decrease in summer precipitation and increased
evaporation with rising temperatures, the region is ranked as highly
vulnerable to climate change (Schröter et al., 2005; Samaniego et al.,
2018). With these conditions, the catchment is representative of other
German and central European regions showing similar vulnerability (Zacharias
et al., 2011). The observatory is one of the meteorologically and
hydrologically best-equipped catchments in Germany (Zacharias et al.,
2011; Wollschläger et al., 2017) and provides long-term data for many
environmental variables including water quantity (e.g., precipitation,
discharge) and water quality at various locations.
The Holtemme catchment has its spring at 862 m a.s.l. in the Harz Mountains
and extends to the northeast to the central German lowlands with an outlet
at 85 m a.s.l. The long-term annual mean precipitation (1951–2015) shows a
remarkable decrease from a colder and humid climate in the Harz Mountains
(1262 mm) down to the warmer and dryer climate of the central German
lowlands on the leeward side of the mountains (614 mm; Rauthe et al., 2013;
Frick et al., 2014). Discharge time series, provided by the State Office of
Flood Protection and Water Management (LHW) of Saxony-Anhalt show a mean annual
discharge at the outlet in Nienhagen of 1.5 m3 s-1 (1976–2016), corresponding to 172 mm a-1.
The geology of the catchment is dominated by late Paleozoic rocks in the
mountainous upstream part that are largely covered by Mesozoic rocks as well
as Tertiary and Quaternary sediments in the lowlands (Frühauf and
Schwab, 2008; Schuberth, 2008). Land use of the catchment changes from
forests in the pristine, mountainous headwaters to intensive agricultural
use in the downstream lowlands (EEA, 2012). According to Corine Land Cover
(CLC) from different years (1990, 2000, 2006, 2012), the land use change
over the investigated period is negligible. Overall 60 % of the catchment
is used for agriculture, with a crop rotation of wheat, barley, triticale,
rye and rapeseed (Yang et al., 2018b), while 30 % is covered by forest
(EEA, 2012). Urban land use occupies 8 % of the total catchment area
(EEA, 2012) with two major towns (Wernigerode, Halberstadt) and several
small villages. Two wastewater treatment plants (WWTPs) discharge into the
river. The town of Wernigerode had its WWTP within its city boundaries until
1995, when a new WWTP was put into operation about 9.1 km downstream in a
smaller village, called Silstedt, replacing the old WWTP. The WWTP in
Halberstadt was not relocated but renovated in 2000. Nowadays, the total
nitrogen load (TNb) in cleaned water is approximately 67.95 kg d-1
(WWTP Silstedt: NO3–N load 55 kg d-1) and 35.09 kg d-1
(WWTP Halberstadt: NO3–N load 6.7 kg d-1; mean daily loads
2014; Müller et al., 2018). Referring to the last 5 years of
observations, NO3–N load from wastewater made up 17 % of the total
observed NO3–N flux at the midstream station (see below) and 11 % at
the downstream station. Despite this point source N input, the major
nitrate contribution is due to inputs from agricultural land use
(Müller et al., 2018), which is predominant in the mid- and downstream
part of the catchment (Fig. 1).
Map of the Holtemme catchment with the selected sampling
locations. Map created from ATKIS data.
The Holtemme River has a length of 47 km. Along the river, the LHW of
Saxony-Anhalt maintains long-term monitoring stations, providing the daily
mean discharge and the biweekly to monthly water quality measurements
covering roughly the last four decades (1970–2016). Three of the water
quality stations along the river were selected to represent the
characteristic land use and topographic gradient in the catchment. From up-
to downstream, the stations are named Werbat, Derenburg and Nienhagen (Fig. 1) and in the following are referred to as upstream, midstream and downstream.
The pristine headwaters upstream represent the smallest (6 % of total
catchment area) and the steepest area among the three selected
subcatchments with a mean topographic slope about 3 times higher than
the downstream parts (DGM25; Table 1). According to the latest Corine Land
Cover dataset (CLC, 2012; EEA, 2012), the land use is characterized by
forest only. The larger midstream subcatchment that represents one-third of
the total area is still dominated by forests, but with growing anthropogenic
impact due to increasing agricultural land use and the town of Wernigerode.
More than half of the agricultural land in this subcatchment is
artificially drained with open ditches (midstream: 38 %, downstream:
82 %) and tube drains (midstream: 62 %, downstream: 18 %; LHW,
2011; Table 1; Fig. S1.1 in the Supplement). The largest subcatchment (61 %) constitutes the
downstream lowland areas which are predominantly covered by Chernozems
(Schuberth, 2008), representing one of the most fertile soils within Germany
(Schmidt, 1995). Hence, the agricultural land use in this subcatchment is
the highest (81 %) in comparison to the two upstream subcatchments (EEA,
2012).
General information on the study area, including input–output datasets;
n – number of observations, Q – discharge.
UpstreamMidstreamDownstreamnQ16 132–12 114n nitrate–N (NO3–N)646631770Period of NO3–N time series1972–20141970–20111976–2016Subcatchment area (km2)15.0688.50165.22Cumulative catchment area (km2)15.06103.60268.80Stream length (km)1.519.324.4Mean topographic slope (∘)9.827.522.55Mean topographic slope in nonforested area (∘)–3.21.9Land use (Corine Land Cover; EEA, 2012)Forest land use (%)1005611Urban land use (%)–178Agricultural land use (%)–2781Fraction of agricultural area artificially drained (%)–59.120.5Nitrogen input
The main N sources were quantified over time, assisting the data-based
input–output assessment to address the three research questions regarding
the N budgeting, effective TTs and C–Q relationships in the catchment.
A recent investigation in the study catchment by Müller et al. (2018)
showed that the major nitrate contribution stems from agricultural
land use and the associated application of fertilizers. The quantification
of this contribution is the N surplus (also referred to as agricultural
surplus) that reflects N input that is in excess of crop and forage needs.
For Germany there is no consistent dataset available for the N surplus that
covers all land use types and is sufficiently resolved in time and space.
Therefore, we combined the available agricultural N input (including
atmospheric deposition) dataset with another dataset of atmospheric N
deposition rates for the nonagricultural land.
The annual agricultural N input for the Holtemme catchment was calculated
using two different datasets of agricultural N surplus across Germany
provided by the University of Gießen (Bach and Frede, 1998; Bach et
al., 2011). Surplus data (kg N ha-1 a-1) were available at the
federal state level for 1950–2015 and at the county level for 1995–2015,
with an accuracy level of 5 % (see Bach and Frede, 1998, for more
details). We used the data from the overlapping time period (1995–2015) to
downscale the state-level data (state: Saxony-Anhalt) to the county level
(county: Harzkreis). Both (the state level and the aggregated county to
state level) datasets show high correspondence with a correlation (R2)
of 0.85, but they differ slightly in their absolute values (by 6 % of the
mean annual values). The mean offset of 3.85 kg N ha-1 a-1 was
subtracted from the federal state level data to yield the surplus in the
county before 1995.
Both of the above datasets account for the atmospheric deposition, but only
on agricultural areas. For other nonagricultural areas (forest and urban
landscapes), the N source stemming from atmospheric deposition was
quantified based on datasets from the Meteorological Synthesizing Centre –
West (MSC-W) of the European Monitoring and Evaluation Programme (EMEP). The
underlying dataset consists of gridded fields of EU-wide wet and dry
atmospheric N depositions from a chemical transport model that assimilates
different observational records on atmospheric chemicals (e.g., Bartnicky
and Benedictow, 2017; Bartnicky and Fagerli, 2006). This dataset is
available with annual time-steps since 1995, and with data every 5 years between 1980
and 1995. Data between the 5-year time steps were linearly interpolated to
obtain annual estimates of N deposition between 1980 and 1995. For years
prior to 1980, we made use of global gridded estimates of atmospheric N
deposition from the three-dimensional chemistry-transport model (TM3) for
the year 1860 (Dentener, 2006; Galloway et al., 2004). In absence of any
other information, we performed a linear interpolation of the N deposition
estimates between 1860 and 1980.
To quantify the net N fluxes to the soil nonagricultural land use types,
the terrestrial biological N fixation had to be added to the atmospheric
deposition. Based on a global inventory of terrestrial biological N fixation
in natural ecosystems, Cleveland et al. (1999) estimated the mean uptake for
temperate (mixed, coniferous or deciduous) forests and (tall/medium or
short) grassland as 16.04 kg N ha-1 a-1 and 2.7 kg N ha-1 a-1, respectively. The atmospheric deposition and biological fixation
for the different nonagricultural land uses were added to the agricultural
N surplus to achieve the total N input per area. In contrast to the widely
applied term net anthropogenic nitrogen input (NANI), we do not account for
wastewater fluxes in the N input but rather focus on the diffuse N input and
connected flow paths, where legacy accumulation and time lags between input and output potentially occur.
Nitrogen outputDischarge and water quality time series
Discharge and water quality observations were used to quantify the N load
and to characterize the trajectory of NO3–N concentrations and the C–Q
trajectories in the three subcatchments.
The data for water quality (biweekly to monthly) and discharge (daily) from
1970 to 2016 were provided by the LHW of Saxony-Anhalt. The biweekly to
monthly sampling was done at gauging stations defining the three
subcatchments. The datasets cover a wide range of instream
chemical constituents including major ions, alkalinity, nutrients and in
situ measured parameters (pH, O2, water temperature, electrical
conductivity). As this study only focuses on N species, we restricted the
selection of parameters to nitrate (NO3; Fig. 2), nitrite (NO2;
Fig. S1.2.2) and ammonium (NH4; Fig. S1.2.1).
NO3–N concentration and discharge (Q) time series: upstream (a), midstream (b) and downstream (c).
Discharge time series at daily timescales were measured at two of the water
quality stations (upstream, downstream; Fig. 2). Continuous daily discharge
series are required to calculate flow-normalized concentrations (see the
following Sect. 2.3.2 for more details). To derive the discharge data for
the midstream station and to fill measurement gaps at the other stations
(2 % upstream, 3 % downstream), we used simulations from a grid-based
distributed mesoscale hydrological model mHM (Samaniego et al., 2010; Kumar
et al., 2013). Daily mean discharge was simulated for the same time frame as
the available measured data. We used a model setup similar to Müller et al. (2016) with robust results capturing the observed variability of
discharge in the nearby studied catchments. We note that the discharge
time series were used as weighting factors in the later analysis of
flow-normalized concentrations. Consequently it is more important to capture
the temporal dynamics than the absolute values. Nonetheless, we performed a
simple bias correction method by applying the regression equation of
simulated and measured values to reduce the simulated bias of modeled
discharge. After this revision, the simulated discharges could be used to
fill the gaps of measured data. The midstream station (Derenburg) for the
water quality data is 5.6 km upstream of the next gauging station.
Therefore, the nearest station (Mahndorf) with simulated and measured
discharge data was used to derive the bias correction equation that was
subsequently applied to correct the simulated discharge data at the
midstream station, assuming the same bias between modeled and observed
discharges at the gauging station.
Weighted regression on time, discharge, and season (WRTDS) and wastewater correction
The software package “Exploration and Graphics for RivEr Trends” (EGRET)
in the R environment by Hirsch and DeCicco (2019) was used to estimate daily
concentrations of NO3–N utilizing “Weighted Regressions on Time,
Discharge, and Season” (WRTDS). The WRTDS method allows the interpolation
of an irregularly sampled concentration to a regular series at a daily
timescale using a flexible statistical representation for every day of the
discharge record and proved to provide robust estimates (Hirsch et al.,
2010; van Meter and Basu, 2017). In brief, a regression model based on the
predictors discharge and time (to represent long-term trend and seasonal
component) is fitted for each day of the flow record with a flexible
weighting of observations based on their time, seasonal and discharge
“distance” (Hirsch et al., 2010). Results are daily concentrations and
fluxes as well as daily flow-normalized concentrations and fluxes.
Flow normalization uses the probability distribution of discharge of the
specific day of the year from the entire discharge time series. More
specifically, the flow-normalized concentration is the average of the same
regression model for a specific day applied to all measured discharge values
of the corresponding day of the year. While the non-flow-normalized
concentrations are strongly dependent on the discharge, the flow-normalized
estimations provide a more unbiased, robust estimate of the concentrations
with a focus on changes in concentration and fluxes independent of
interannual discharge variability (Hirsch et al., 2010). To account for
uncertainty in the regression analysis of annual and seasonal
flow-normalized concentration and fluxes, we used the block bootstrap method
introduced by Hirsch et al. (2015). We derived the 5th and 95th
percentile of annual flow-normalized concentration and flux estimates with a
block length of 200 d and 10 replicates. The results are utilized to
communicate uncertainty in both the N budgeting and the resulting TT
estimation.
The study of Müller et al. (2018) indicated the dominance of N from
diffuse sources in the Holtemme catchment but also stressed the impact of
wastewater-borne nitrate during low-flow periods. Because our purpose was to
balance and compare N input and outputs from diffuse sources only, the
provided annual flux of total N from the two WWTPs was therefore used to
correct flow-normalized fluxes and concentrations derived from the WRTDS
assessment. We argue that the annual wastewater N flux is robust enough to correct
the flow-normalized concentrations, but it does not allow for the correction
of measured concentration data on a specific day. Both treatment plants
provided snapshot samples of both NO3–N and total N fluxes to derive
the fraction of N that is discharged as NO3–N into the stream. This
fraction is 19 % for the WWTP Halberstadt (384 measurements between
January 2014 to July 2016) and 81 % for Silstedt (eight measurements
from February 2007 to December 2017). We argue that the fraction of N
leaving as NH4, NO2 and Norg does not interfere with the
NO3–N flux in the river due to the limited stream length and therefore
nitrification potential of the Holtemme River impacted by wastewater (see
also Sect. S1.2.3). We related the wastewater-borne NO3–N flux to
the flow-normalized daily flux of NO3–N from the WRTDS method to get a
daily fraction of wastewater NO3–N in the river that we used to correct
the flow-normalized concentrations. Note that this correction was applied to
the midstream station from 1996 on, when the Silstedt treatment plant was
taken to operation. In the downstream station, we additionally applied the
correction from the Halberstadt treatment plant, renovated in the year 2000.
Before that, we assume that wastewater-borne N dominantly leaves the
treatment plants as NH4-N (see also Fig. S1.2.1).
Based on the daily resolved flow-normalized and wastewater-corrected
concentration and flux data, descriptive statistical metrics were calculated
on an annual timescale. Seasonal statistics of each year were also
calculated for winter (December, January, February), spring (March, April,
May), summer (June, July, August) and fall (September, October, November).
Note that statistics for the winter season incorporate December values from
the calendar year before.
Following Musolff et al. (2015, 2017), the ratio of CVC/ CVQ and the
slope (b) of the linear relationship between ln(C) and ln(Q) were used to
characterize the export pattern and the export regimes of NO3–N along
the three study catchments.
Input–output assessment: nitrogen budgeting and effective travel times
The input–output assessment is needed to estimate the retention potential
for N in the catchment as well as to link temporal changes in the diffuse
anthropogenic N inputs to the observed changes in the riverine NO3–N
concentrations. The stream concentration of a given solute, e.g., as shown by
Kirchner et al. (2000), is assumed at any time as the convolution of the TTD
and the rainfall concentration throughout the past. This study applies the
same principle for the N input as incoming time series that, when convolved
with the TTD, yields the stream concentration time series. We selected a
log-normal distribution function (with two parameters, μ and σ) as a convolution transfer function, based on a recent study by Musolff et al. (2017), who successfully applied this form of a transfer function to
represent TTs. The two free parameters were obtained through optimization
based on minimizing the sum of squared errors between observed and simulated
N exports. The form of selected transfer function is in line with Kirchner
et al. (2000) stating that exponential TTDs are unlikely at catchment scale,
but rather a skewed, long-tailed distribution would be likely. Note that we used the
log-normal distribution as a transfer function between the temporal patterns
of input (N load per area) and flow-normalized concentrations on an annual
timescale only and not as a flux-conservative transfer function. TTDs were
inferred based on median annual and median seasonal flow-normalized
concentrations and the corresponding N input estimates. To account for the
uncertainties in the flow-normalized concentration input, we additionally
derive TTDs for the confidence bands of the concentrations (5th and
95th percentile) estimated through the bootstrap method (see Sect. 2.3.2 for more details). Here, we assumed that the width of the confidence
bands provided for the annual concentrations also applies to the seasonal
concentrations of the same year.
ResultsInput assessment
In the period from 1950 to 2015, the Holtemme catchment received a
cumulative diffuse N input (excluding the wastewater point sources) of
80 055 t with the majority of this associated with agriculture-related N
application (74 %). Within the period when water quality data were
available, the total sum is 63 396 t (1970–2015), with 76 % agricultural
contribution. The N input showed a remarkable temporal variability (see Fig. 6; purple, dashed line). From 1950 to 1976, the input was characterized by a
strong increase (slope of linear increase = 2.4 kg N ha-1 a-1 per year) with a maximum annual, agricultural input of 132.05 kg N ha-1 a-1 (1976), which is 20 times the agricultural input in 1950. After
more than 10 years of high but more stable inputs, the N surplus dropped
dramatically with the peaceful reunification of Germany and the collapse of
the established agricultural structures in East Germany (1989–1990; Gross,
1996). In the time period afterwards (1990–1995), the N surplus was only
one-sixth (20 kg N ha-1 a-1) of the previous input. After another
8 years of increased agricultural inputs (1995–2003) of around 50 kg N ha-1 a-1, the input slowly decreased, with a mean slope of -0.8 kg N ha-1 a-1 per year, but showed distinctive changes in the input
between the years.
The median N input upstream (53 t a-1) is less than
7 % of the total catchment input (760 t a-1).
Hence, the input to the upstream area was only minor in comparison to the
ones further downstream that are dominated by agriculture.
As land use change over the investigated period is negligible, the N input
from biological fixation stayed constant.
Output assessmentDischarge time series and WRTDS results on decadal statistics
Discharge was characterized by a strong seasonality throughout the entire
data record, which divided the year into a high-flow season (HFS) during
winter and spring, accounting for two-thirds of the annual discharge and a
low-flow season (LFS) during summer and fall. Average discharge in the
subcatchments is mainly a reflection of the strong spatial precipitation
gradient across the study area being on the leeward side of the Harz
Mountains. The upstream subcatchment contributed 21 % of the median
discharge measured at the downstream station (Table 2). The midstream
station, representing the cumulated discharge signal from the up- and
midstream subcatchments, accounted for 82 % of the median annual
discharge at the outlet. Although the upstream subcatchment had the highest
specific discharge, the major fraction of total discharge (61 %) was
generated in the midstream subcatchment. The seasonality in discharge
was also dominated by this major midstream contribution, especially during high-flow conditions, and vice versa: especially during HFSs, the median downstream
contribution was less than 10 %, while during low-flow periods, the
downstream contribution accounted for up to 33 % (summer).
Descriptive statistics on discharge at the three observation
points; LFS – low-flow season (June–November), HFS – high-flow season
(December–May).
The flow-normalized NO3–N concentrations in each subcatchment showed
strong differences in their overall levels and temporal patterns over the
four decades (Fig. 3a; see also Figs. 2 and 6 for details). The lowest
decadal concentration changes and the earliest decrease in concentrations
were found in the pristine catchment. Median upstream concentrations were
highest in the 80s (1987), with a reduction of the concentrations to about
one-half in the latter decades. Over the entire period, the median upstream
concentrations were smaller than 1 mg L-1, so that the described
changes are small compared to the NO3–N dynamics of the more
downstream stations. High changes over time were observed in the two
downstream stations with a tripling of concentrations between the 1970s and
1990s, when maximum concentrations were reached. While median concentrations
downstream decreased slightly after this peak (1995/1996), the ones
at the midstream station (peak: 1998) stayed constantly high. At the end of the observation
period, at the outlet (downstream), the median annual concentrations did not
decrease below 3 mg L-1 of NO3–N, a level that was exceeded after the
1970s. The differences in NO3–N concentrations between the pristine
upstream and the downstream station evolved from an increase by a factor of
3 in the 1970s to a factor of 7 after the 1980s.
Flow-normalized median NO3–N concentrations (a) and NO3–N
loads (b) for each decade of the time series and the three stations.
Whiskers refer to the 5th and 95th percentiles of the WRTDS
estimations.
Calculated loads (Fig. 3b) also showed a drastic change between the
beginning and the end of the time series. The daily upstream load
contribution was below 10 % of the total annual export at the downstream
station in all decades and then the estimates decreased from 9 % (1970s)
to 4 % (2010s). The median daily load between the 1970s and 1990s tripled
at the midstream station (0.1 to 0.3 t d-1) and more than doubled
downstream (0.2 to 0.5 t d-1). In the 1990s, the
Holtemme River exported on average more than 0.5 t d-1 of NO3–N,
which, related to the agricultural area in the catchment, translates into
more than 3.1 kg N km-2 d-1 (maximum 13.4 kg N ha-1 a-1 in 1995).
Input–output balance: N budget
We jointly evaluated the estimated N inputs and the exported NO3–N
loads to enable an input–output balance. This comparison on the one hand
allowed for an estimation of the catchment's retention potential and on the
other hand enabled us to estimate future exportable loads.
Nitrogen retention potentials derived for the midstream and
downstream subcatchment based on flow-normalized fluxes. Numbers in
brackets refer to the 5th and 95th percentiles of the WRTDS flux
estimation.
MidstreamDownstreamRetention cumulative (%)75 (71–78) (Up- + midstream)88 (86–89) (Up- + mid- + downstream)Retention subcatchment (%)73 (68–76)94 (94–95)Retention per year (N kg a-1)251 589 (235 778–263 833)917 823 (968 085–979 679)Retention per area (N kg a-1 ha-1)28.43 (26.64–29.81)58.82 (58.60–59.30)
The load stemming from the most upstream, pristine catchment accounted for
less than 10 % of the exported riverine load at the outlet. To focus on
the anthropogenic impacts, the data from the upstream station are not
discussed on its own in the following. At the midstream station, a total sum
of input of 16 441 t compared to 4109 t of exported NO3–N for the
overlapping time period of input and output was analyzed (1970–2011). The
midstream subcatchment received 73 % (Table 3) more N mass than it
exported at the same time. Note that the exported N is not necessarily the N
applied in the same period due to the temporal offset, as is discussed later in
detail. With the assumption that 43 % (agricultural N input of
subcatchment N input) of the diffuse input resulted from agriculture, the
subcatchment exported 616 kg N ha-1 (537–719 kg N ha-1) from
agricultural areas. The cumulated N input from the entire catchment
(measured downstream) from 1976 to 2015 (overlapping time of input and output)
was 53 437 t, while the riverine export in the same time frame was only 12 % (6 kg N ha-1 a-1; 11 %–14 %), implying an agricultural export of
370 kg N ha-1 (325–415 kg N ha-1; Fig. 4). This mass discrepancy
between input and output translates into a retention rate in the entire
Holtemme catchment of 88 % (86 %–89 %). In relation to the entire
subcatchment area (not only agricultural land use), the annual retention
rate of NO3–N was around 28 kg N ha-1 a-1 (27–30 kg N ha-1 a-1) in the midstream subcatchment and 59 kg N ha-1 a-1 (59–59 kg N ha-1 a-1) in the flatter and more
intensively cultivated downstream subcatchment.
Cumulative annual diffuse N inputs to the catchment and measured
cumulative NO3–N exported load over time for midstream (a) and
downstream (b) stations. Shaded grey confidence bands refer to the 5th and
95th percentile of the WRTDS flux estimation.
Effective TTs of N
We approximated the effective TTs for all seasonal NO3–N concentration
trajectories at the midstream and downstream stations by fitting the
log-normal TTDs (Fig. 5; Table 4). Note that the upstream station was not
used here due to the lack of temporally resolved input data on the
atmospheric N deposition (estimated linear input increase between 1950 and
1979). In general, the optimized distributions were able to sufficiently
capture the time lag and smoothing between the input and output
concentrations (R2≥0.72; see also Figs. S2.1 and S2.2).
Systematic differences between stations and seasons can be observed, best
represented by the mode of the distributions (peak TTs). The average
deviation between the best- and worst-case estimation of the fitted TTDs from
their respective average value was only 4 % with respect to the mode of
the distributions (Table 4).
Best-fit parameters of the log-normal TTDs for the N input and
output responses. Parameters in brackets are derived by using the 5th
and 95th percentiles of the bootstrapped flow-normalized concentration
estimates.
Seasonal variations in the fitted log-normal distributions of
effective travel times between nitrogen input and output responses for
midstream (a) and downstream (b) stations.
The TTDs for all seasons taken together showed longer TTs for the midstream in
comparison to the downstream station. The comparison of the TTD modes for
the different seasons at the midstream station showed distinctly differing peak TTs between
11 years (spring) and 22 years (fall), which represented a doubling of the
peak TT. The fastest times appeared in the HFSs while modes of the TTDs appeared
longer in the LFSs. Note that the shape factor σ of the effective
TTs also changed systematically: the HFS spring exhibited a higher shape
factor than those of the other seasons. This refers to a change in the
coefficient of variation of the distributions at the midstream station from 0.6 in spring
to 0.2 in fall.
The modes of the fitted distributions for the downstream station for each
season were shorter than the ones at the midstream station. The mode of the
TTs ranged between 7 years (spring) and 15 years (winter, fall). The shape
factors of the fitted TTDs ranged between 0.8 (spring) and 0.3 (summer) for
the downstream station. In summary, HFS spring in both subcatchments had
shorter TTDs than the other seasons and the midstream subcatchment showed longer TTDs than
downstream.
Seasonal NO3–N concentrations and C–Q
relationships over time
As described above, the Holtemme catchment showed a pronounced seasonality
in discharge conditions, producing the HFS in December–May (winter + spring) and the LFS in June–November (summer + fall). Therefore, changes
in the seasonal concentrations of NO3–N also reflect in the annual C–Q
relationship. Analyzing the changing seasonal dynamics therefore provide a
deeper insight into N trajectories in the Holtemme catchment.
In the pristine upstream catchment, no temporal changes in the seasonal
differences of riverine NO3–N concentrations could be found (Fig. 6a).
Also the C–Q relationship (Fig. 6d) showed a steady pattern (moderate
accretion), with the highest concentrations in the HFSs, i.e., winter and spring.
The ratio of CVC/ CVQ indicates a chemostatic export regime and
changed only marginally (amplitude of 0.2) over time.
At the midstream station (Fig. 6b), the early 1970s showed an export pattern
with highest concentration during HFSs similar to the upstream catchment,
but with a general increase in concentrations from 1970 to 1995. During the
1980s, the increase in concentrations in the HFS was faster than in the LFS,
which changed the C–Q pattern to a strongly positive one (bmax=0.42,
1987; red to orange symbols in Fig. 6e). This development was characterized
by a tripling of intra-annual amplitudes (Cspring–Cfall) of up
to 2.4 mg L-1 (1987). With a lag of around 10 years, in the 1990s
the LFSs also exhibit a strong increase in concentrations (Cmax=3.1 mg L-1, 1998, Fig. 6b). The midstream concentration time series shows
bimodality. The C–Q relationships (Fig. 6e) evolved from an intensifying
accretion pattern in the 1970s and 1980s (red to orange symbols in Fig. 6e)
to a constant pattern between C and Q in the 1990s and afterwards (yellow
symbols). The CVC/ CVQ increased during the 1970s and strongly decreased
afterwards by 0.4 between 1984 and 1995, showing a trajectory
starting from a more chemostatic to a chemodynamic and then back to a
chemostatic export regime.
At the downstream station (Fig. 6c) the concentrations in the HFSs were
found to be comparable to the ones observed at the midstream station. As
seen at the midstream station, the N concentrations during the LFSs peaked with a delay
compared to those of the HFSs. The resulting intra-annual amplitude showed a
maximum of 2.4 mg L-1 in the 1980s (1983–1984), with strongly positive
C–Q patterns (bmax=0.4, 1985; red symbols in Fig. 6f). In contrast
to the bimodal concentration trends in the mid- and downstream HFSs, the
LFSs downstream showed an unimodal pattern peaking around 1995–1996 with
concentrations above 6 mg L-1NO3–N (Cmax=6.9 mg L-1).
In the 1990s, the concentrations in the LFSs were higher than those noticed
in the HFSs, causing a switch to a dilution C–Q pattern (orange symbols in
Fig. 6f). Due to the strong decline of LFS concentrations after 1995 (Fig. 6c), the dilution pattern evolved to a constant C–Q pattern (yellow symbols
in Fig. 6f) from the 2000s onward. After an initial phase with chemostatic
conditions (1970s), the CVC/ CVQ strongly increased to a
chemodynamic export regime in the 1980s (max. CVC/ CVQ=0.8,
1984). Later on CVC/ CVQ declined by 0.8 between 1984 and 2001
(min. CVC/ CVQ=0.03), which indicates the C–Q trajectory is
coming back to a chemostatic export nitrate regime.
DiscussionCatchment-scale N budgeting
Based on the calculated budgets of N inputs and riverine N outputs for the
three subcatchments within the Holtemme catchment, we discuss here
differences between the subcatchments and potential main reasons for the
missing part in the N budget: (1) permanent N removal by denitrification or (2) the buildup of N legacies.
The N load stemming from the most upstream, pristine catchment accounted for
less than 10 % of the exported annual load over the entire study period.
This minor contribution can be attributed to the lack of agricultural and
urban land use as dominant sources for N. Consequently, the N export from
the upstream subcatchment was dominantly controlled by N inputs from
atmospheric deposition and biological fixation.
Annual N input (referring to the whole catchment, second y axis) to
the catchment and measured median NO3–N concentrations in the stream
(first y axis) over time at three different locations: upstream (a, d),
midstream (b, e), downstream (c, f). Lower panels show plots of slope b vs.
CVC/ CVQ for NO3–N for the three subcatchments following the
classification scheme provided in Musolff et al. (2015). The x axis gives the
coefficient of variation of concentrations (C) relative to the coefficient
of variation of discharge (Q). The y axis gives the slope b of the linear
ln(C)–ln(Q) relationship. Colors indicate the temporal evolution from
1970 to 2015 along a gradient from red to yellow.
The total input over the whole catchment area was quantified as more than
53 000 t N (1976–2015) and, compared to the respective output over the same
time period, yielded export rates of 25 % (22 %–29 %) at the midstream
and 12 % (11 %–14 %) at the downstream station (Table 3), respectively.
There can be several reasons for the difference in export rates between the
two subcatchments. The most likely ones are due to differences in
discharge, topography and denitrification capacity among the subcatchments,
which are discussed in the following.
Load export of N from agricultural catchments is assumed to be mainly
discharge-controlled (Basu et al., 2010). Many solutes show a lower variance
in concentrations compared to the variance in streamflow, which makes the
flow variability a strong surrogate for load variability (Jawitz and
Mitchell, 2011). This can also be seen in the Holtemme catchment, which
evolved over time to a more chemostatic export regime with high N loads
(Fig. 6b). The highest N export and lowest retention were observed in the
midstream subcatchment, where the overall highest discharge contribution
can be found.
Besides discharge quantity, we argue that the midstream subcatchment favors
a more effective export of NO3–N. The higher percentage of artificial
drainage by tiles and ditches (59 % vs. 21 %; Sect. S1.1) as
well as the steeper terrain slopes (3.2∘ vs. 1.9∘) in
the nonforested area of the midstream catchment promotes rapid, shallow
subsurface flows. These flow paths can more directly connect agricultural N
sources with the stream and in turn cause elevated instream NO3–N
concentrations (Yang et al., 2018a). In addition, the steeper surface
topography suggests a deeper vertical infiltration (Jasechko et al., 2016)
and therefore a wider range of flow paths of different ages than those
observed in the flatter terrain areas, and vice versa: fewer drainage
installations, a flatter terrain and thus in general shallower flow paths
may decrease the N export efficiency (increase the retention) potential
downstream.
The only process able to permanently remove N input from the catchment is
denitrification in soils, aquifers (Seitzinger et al., 2006; Hofstra and
Bouwman, 2005), and at the stream-aquifer interface such as in the riparian
(Vidon and Hill, 2004; Trauth et al., 2018) and hyporheic zones (Vieweg et
al., 2016). As the riverine exports are signals of the catchment or
subcatchment processes, integrated in time and space, separating a buildup
of an N legacy from a permanent removal via denitrification is difficult. A
clear separation of these two key processes, however, would be important for
decision makers as both have different implications for management
strategies and different future impacts on water quality. Even if
groundwater quality measurements that indicate denitrification were
available, using this type of local information for an effective catchment-scale estimation of N removal via denitrification would be challenging
(Green et al., 2016; Otero et al., 2009; Refsgaard et al., 2014). Therefore,
we discuss the denitrification potential in the soils and aquifers of the
Holtemme catchment based on a local isotope study and a literature review of
studies in similar settings. A strong argument against a dominant role of
denitrification is provided by Müller et al. (2018) for the study area.
On the basis of a monitoring of nitrate isotopic compositions in the
Holtemme River and in tributaries, Müller et al. (2018) stated that
denitrification played no or only a minor role in the catchment. However, we
still see the need to carefully check the potential of denitrification to
explain the input–output imbalance considering other studies.
If 88 % of the N input (53 437 t, dominantly agricultural input) to the
catchment between 1976 and 2015 (39 years) were denitrified in the soils of
the agricultural area (161 km2), it would need a rate of
74.9 kg N ha-1 a-1. Considering the derived TTs, denitrification
of the convolved input would need a slightly lower rate (66.7 kg N ha-1 a-1, 1976–2015). Denitrification rates in soils for Germany (NLfB,
2005) have been reported to range between 13.5 and 250 kg N ha-1 a-1,
with rates larger than 50 kg N ha-1 a-1 may be found in carbon-rich and waterlogged soils in the riparian zones near rivers and in areas
with fens and bogs (Kunkel et al., 2008). As water bodies and wetlands make
up only 1 % of the catchment's land use (Fig. 1; EEA, 2012), and
consequently the extent of waterlogged soils is negligible, denitrification
rates larger than 50 kg N ha-1 a-1 are highly unlikely. In a
global study, Seitzinger et al. (2006) assumed a rate of 14 kg N ha-1 a-1 as denitrification for agricultural soils. With this rate only
19 % of the retained (88 %) study catchment's N input can be
denitrified. On the basis of a simulation with the modeling framework
GROWA-WEKU-MEPhos, Kuhr et al. (2014) estimate very low to low
denitrification rates, of 9–13 kg N ha-1 a-1, for the soils of
the Holtemme catchment. Based on the above discussion we find for our study
catchment, the denitrification in the soils, including the riparian zone,
may partly explain the retention of NO3–N, but there is unlikely to be a
single explanation for the observed imbalance between input and output.
Regarding the potential for denitrification in groundwater, the literature
provides denitrification rate constants of a first-order decay process
between 0.01 and 0.56 a-1 (van Meter et al., 2017b; van der Velde et
al., 2010; Wendland et al., 2005). We derived the denitrification constant
by distributing the input according to the fitted log-normal distribution of
TTs, assuming a first-order decay along the flow paths (Kuhr et al., 2014;
Rode et al., 2009; van der Velde, 2010). The denitrification of the 88 %
of input mass would require a rate constant of 0.14 a-1. This
constant is in the range of values reported by the abovementioned modeling studies.
However, in a regional evaluation of groundwater quality, Hannappel et al. (2018) provide strong evidence that denitrification in the groundwater of
the Holtemme catchment is not a dominant retention process. More
specifically, Hannappel et al. (2018) assess denitrification in over 500
wells in the federal state of Saxony-Anhalt for nitrate, oxygen, iron
concentrations and redox potential and connect the results to the
hydrogeological units. Within the hard rock aquifers that are present in our
study area, only 0 %–16 % of the wells showed signs of denitrification.
Taking together the local evidence from the nitrate isotopic composition
(Müller et al., 2018), the regional evidence from groundwater quality
(Hannappel et al., 2018), and the rates provided in literature for soils and
groundwater, we argue that the role of denitrification in groundwater is
unlikely to explain the observed imbalance between N input and output.
Lastly, assimilatory NO3 uptake in the stream may be a potential
contributor to the difference between input and output. But even with maximal
NO3 uptake rates as reported by Mulholland et al. (2004; 0.14 g N m-2 d-1) or Rode et al. (2016; max. 0.27 g N m-2 d-1, estimated for a catchment adjacent to the Holtemme), the annual
assimilatory uptake in the river would be a minor removal process, estimated
to contribute only 3 % of the 88 % discrepancy between input and output.
According to the rates reported by Mulholland et al. (2008; max. 0.24 g N m-2 d-1), the Holtemme River would need an area 45 times larger to
be able to denitrify the retained N. Therefore denitrification in the stream
can be excluded as a dominant removal process.
In summary, the precise differentiation between the accumulation of an N
legacy and removal by denitrification cannot be fully resolved on the basis
of the available data. Also a mix of both may account for the missing
88 % (86 %–89 %, downstream) or 75 % (71 %–78 %, midstream) in the
N output. Input–output assessments with time series from different
catchments, as presented in van Meter and Basu (2017), covering a larger
variety of catchment characteristics, hold promise for an improved
understanding of the controlling parameters and dominant retention
processes.
The fact that current NO3 concentration levels in the Holtemme River
still show no clear sign of a significant decrease calls for a continuation
of the NO3 concentration monitoring, best extended by additional
monitoring in soils and groundwater. Despite strong reductions in
agricultural N input since the 1990s, the annual N surplus (e.g., 818 t a-1, 2015) is still much higher than the highest measured export
(loadmax=216 t a-1, 1995) from the catchment. Hence, the
difference between input and output is still high with a mean factor of 6
during the past 10 years (mean factor of 7 with the shifted input according
to 12 years of TT). Consequently, either the legacy of N in the catchment
keeps growing instead of getting depleted or the system relies on a
potentially limited denitrification capacity. Denitrification may
irreversibly consume electron donors like pyrite for autolithotrophic
denitrification or organic carbon for heterotrophic denitrification (Rivett
et al., 2008).
Based on the analyses and literature research, there is evidence but no
proof of the fate of missing N, although a directed water quality management
would need a clearer differentiation between N mass that is stored or
denitrified. However, neither tolerating the growing buildup of legacies nor
relying on finite denitrification represents sustainable and adapted
agricultural management practices. Hence, future years will also face
increased NO3–N concentrations and loads exported from the Holtemme
catchment.
Linking effective TTs, concentrations and C–Q trajectories with N
legacies
Based on our data-driven analyses, we propose the following conceptual model
(Fig. 7) for N export from the Holtemme catchment, which is able to
plausibly connect and synthesize the available data and findings on TTs,
concentration trajectories and C–Q relationships and allows for a
discussion on the type of N legacy.
Conceptual model of nitrogen legacy and exports from the midstream
and the downstream catchments. The four stacked boxes refer to the dominant
source layer of nitrate that is activated with changing water level and
catchment wetness during low-flow seasons fall (red) and autumn (orange) as
well as high-flow seasons winter (blue) and spring (green). Numbers in the
boxes refer to peak travel times of each season. The percentages refer to
the N imbalance between input and output explainable by travel times
(hydrological legacy). Background map created from ATKIS data.
Over the course of a year, different subsurface flow paths are active, which
connect different subsurface N source zones with different source strength
(in terms of concentration and flux) to streams. These flow paths transfer
water and NO3–N to streams, predominantly from shallower parts
of the aquifer when water tables are high during HFSs and exclusively from
deeper groundwater during low flows in LFSs (Rozemeijer and Broers, 2007;
Dupas et al., 2016; Musolff et al., 2016). This conceptual model allows us
to explain the observed intra-annual concentration patterns and the distinct
clustering of TTs into low-flow and high-flow conditions. Furthermore, it
can explain the mobilization of nutrients from spatially distributed
NO3–N sources by temporally varying flow-generating zones (Basu et al.,
2010). Spatial heterogeneity of solute source zones can be a result of
downward migration of the dominant NO3–N storage zone in the
vertical soil–groundwater profile (Dupas et al., 2016). Moreover, a
systematic increase in the water age with depths would, if denitrification
in groundwater takes place uniformly, lead to a vertical concentration
decrease. Based on the stable hydroclimatic conditions without changes in
land use, topography or the river network during the observation period,
long-term changes in flow paths in the catchment are unlikely. Assuming that
flow contributions from the same depths do not change between the years, the
observed decadal changes in the seasonal concentrations cannot be explained
by a stronger imprint of denitrification with increasing water age. Under
such conditions one would expect a more steady seasonality in concentrations
and C–Q patterns over time with NO3–N concentrations that are always
similarly high in HFSs and similarly low in LFSs, which we do not see in the
data. Additionally, previous findings have indicated no or only a minor role
of denitrification in the catchment (Hannappel et al., 2018; Kunkel et al.,
2008; Müller et al., 2018). In line with Dupas et al. (2016) we instead
argue that the vertical migration of a temporally changing NO3–N input
is one of the most likely plausible explanations for our observations with
regard to N budgets, concentrations and C–Q trajectories.
The faster TTs observed at the midstream station during HFSs are assumed to
be dominated by discharge from shallow (near-surface) source zones. This
zone is responsible for the fast response of instream NO3–N
concentrations to the increasing N inputs (1970s to mid-1980s). This faster
lateral transfer, especially in spring (shortest TT), may be also enhanced by
the presence of artificial drainage structures such as tiles and ditches. In
line with the longer TTs during the LFSs, low-flow NO3–N concentrations
were less impacted in the 1970s to mid-1980s as deeper parts of the aquifer
were still less affected by anthropogenic inputs. With time and a
downward migration of the high NO3–N inputs before 1990, those
deeper layers and thus longer flow paths also delivered increased concentrations
to the stream (1990s). In parallel with the increasing low-flow
concentrations (in the 1990s), the spring concentrations of NO3
decreased, caused by a depletion of the shallower NO3–N stocks
(see also Dupas et al., 2016; Thomas and Abbott, 2018). This depletion of
the stocks was a consequence of drastically reduced N input after the German
reunification in 1989. This conceptual model of N trajectories is supported
by the changing C–Q relationship over time. The seasonal cycle started with
increasing NO3–N maxima during high flows and minima during low flows,
since shallow source zones were getting loaded with NO3 first.
Consequently, the accretion pattern was intensified in the first decades,
accompanied by an increase in CVC/ CVQ. The resulting positive C–Q
relationship on a seasonal basis was found in many agricultural catchments
worldwide (e.g., Aubert et al., 2013; Martin et al., 2004; Mellander et al.,
2014; Rodríguez-Blanco et al., 2015; Musolff et al., 2015). However, after
several years of deeper migration of the N input, the catchment started to
exhibit a chemostatic NO3 export regime (after the 1990s), which was
manifested in the decreasing CVC/ CVQ ratio. This stationarity
could have been caused by a vertical equilibration of NO3–N
concentrations in all seasonally activated depth zones of the soils and
aquifers after a more stable long-term N input after 1995. According to the
50th percentile of the derived TT, after 20 years only 50 % of the
input had been released at the midstream station. Therefore without any strong changes in
input, the chemostatic conditions caused by the uniform, vertical NO3–N
contamination will remain. At the same time, this chemostatic export regime
supports the hypothesis of an accumulated N legacy rather than
denitrification as the dominant reason for the imbalance between input and output.
At the downstream station, the riverine NO3 concentrations during high
flows were dominated by inputs from the midstream subcatchment, which
explains the similarity with the midstream bimodality in concentrations as
well as the comparable TTs. The reason for these dominating midstream flows
is the strong precipitation gradient resulting in a runoff gradient on the
leeward side of the mountains. During low flows, the downstream
subcatchment can contribute much more to discharge and therefore to the
overall N export. During the LFSs, we observed higher NO3–N
concentrations with a unimodal trajectory and shorter TTs compared to the
midstream subcatchment. We argue that the lowland subcatchment supports
higher water levels and thus faster TTs during the low flows. Greater
prevalence of young streamflow in flatter lowland terrain was also
described by Jasechko et al. (2016). But besides the earlier peak time
during low flows, the concentration was found to be much higher than
at the midstream station. To cause such high intra-annual concentration changes, the
downstream NO3–N load contribution, e.g., during the concentration peak of
1995–1996, had to be high: the summer season export was 46 t, which is more
than twice the median contribution during summer (22 t). A more effective
export from the downstream catchment happened mainly during LFSs, which is
also supported by the narrower TTD (small shape factor σ) in the
summer and fall (Fig. 5b). The difference between the 75th and
25th percentiles (5 years) was also the smallest of all seasons in the
summer at the downstream station. This could be one reason for the high
concentrations in comparison to the midstream catchment and during the HFSs.
In contrast to the midstream catchment, the C–Q trajectory in the
downstream catchment temporarily switched from an enrichment pattern,
dominated by the high concentration during high flows from the midstream catchment to a
dilution pattern and a chemodynamic regime, when the high concentrations in
the LFS from the downstream subcatchment dominated. Although the low-flow
concentrations were slowly decreasing in the 2000s and 2010s, the
downstream catchment also finally evolved to a chemostatic NO3 export
regime, as was noticed at the midstream station (Fig. 6f).
Our findings support the evolution from chemodynamic to chemostatic
behavior in managed catchments, but also emphasize that changing inputs of
N into the catchment can lead to fast-changing export regimes even in
relatively slowly reacting systems. Our findings expand on previous
knowledge (Basu et al., 2010; Dupas et al., 2016) as we could show
systematic interannual C–Q changes that are in line with a changing input
and a systematic seasonal differentiation of TTs. Although our study showed
chemostatic behavior towards the end of the observation period (mid- and
downstream; Fig. 6e–f), this export regime is not necessarily stable as it
depends on a continuous replenishment of the legacy store. Changes in the N
input translate to an increase in spatial heterogeneity in NO3–N
concentrations in soil water and groundwater with contrasting water ages. The
seasonally changing contribution of different water ages thus results in more
chemodynamic NO3 export regimes. As described in Musolff et al. (2017), both export regimes and patterns are therefore controlled by the
interrelation of TT and source concentrations. We argue that a hydrological
legacy of NO3 in the catchment has been established that resulted in
the pseudo-chemostatic export behavior we observe nowadays. This supports the notion that a biogeochemical legacy corresponding to the buildup of
organic N in the root zones of the soil (van Meter et al., 2016) is less
probable. If we assume that all of the 88 % of the N input is
accumulating in the soils, we cannot explain the observed shorter-term
interannual concentration changes and trajectory in the C–Q relationships.
We would rather expect a stronger and even growing dampening of the N input
to the subsurface with the buildup of a biogeochemical legacy in the form of
organic N. However, we cannot fully exclude the accumulation of a protected
pool of soil organic matter with very slow mineralization rates as described
in van Meter et al. (2017). Our conceptual model assigns the missing N to
the long TTs of NO3–N in soil water and groundwater and in turn to a
pronounced hydrological legacy. In the midstream subcatchment, the
estimated TTD explains 40 % of the retained NO3–N, comparing the
convolution of TTD with the N input time series to the actual riverine
export. The remaining 60 % cannot be fully explained at the moment and
may be assigned to a permanent removal by denitrification (see discussion
above), to a fixation due to the biogeochemical legacy or to more complex (e.g., longer tailed) TTDs, which are not well represented by our assumed log-normal
distribution. In the downstream subcatchment, our approach explains 29 %
of the observed export. This could in principle be caused by the same
processes as described for the midstream subcatchment. A hydrological
legacy store in deeper zones without significant discharge contribution is
also possible (Fig. 7). That mass of N is either bypassing the downstream
monitoring station (note that the downstream station is still 3 km upstream
of the Holtemme catchment outlet) or is affected by a strong time delay and
dampening not captured by our approach. Consequently, future changes in N
inputs will also change the future export patterns and regimes, since this
would shift the homogeneous NO3–N distributions in vertical soil and
groundwater profiles back to more heterogeneous ones.
Conclusion
In the present study we used a unique time series of riverine N
concentrations over the last four decades from a mesoscale German catchment
as well as estimated N input to discuss the linkage between the two on
annual and intra-annual timescales. From the input–output assessment, the
buildup of a potential N legacy was quantified, effective TTs of nitrate
were estimated and the temporal evolution to chemostatic NO3–N export
was investigated. This study provides four major findings that can be
generalized and transferred to other catchments of similar hydroclimatic and
landscape settings as well.
First, the retention capacity of the catchment for N is 88 % of the N
input (input and output referring to 1976 to 2015), which either can be
stored as a legacy or denitrified in the terrestrial or aquatic system.
Although we could not fully quantify denitrification, we argue that this
process is not the dominant one in the catchment to explain input–output
differences. The observed N retention can be more plausibly explained by
legacy than by denitrification. As a consequence, the hydrological N legacy,
i.e., the load of nitrate still on the way to the stream, may have strong
effects on future water quality and long-term implications for river water
quality management. With a median export rate of 162 t N a-1
(1976–2016, downstream station, 6 kg N ha-1 a-1), a depletion of
this legacy (<46 000 t N) via baseflow would maintain elevated
riverine concentrations for the next few decades. Although N surplus strongly
decreased after the 1980s, during the past 10 years there was still an
imbalance between agricultural input and riverine export by a mean factor of
5 (assuming the temporal offset of peak TTs between input and output of 12 years). This is a nonsustainable condition, regardless of whether the
retained nitrate is stored or denitrified. Export rates as well as retention
capacity derived for this catchment were found to be comparable to findings
of other studies in Europe (Worrall et al., 2015; Dupas et al., 2015) and
North America (van Meter et al., 2016).
Second, we derived peak time lags between N input and riverine export
between 7 and 22 years with systematic differences among the different seasons.
Catchment managers should be aware of these long time frames when
implementing measures and when evaluating them. This study explains the
seasonally differing lag times and temporal concentration evolutions with
the vertical migration of the nitrate and their changing contribution to
discharge by seasonally changing aquifer connection. Hence, interannual
concentration changes are not dominantly controlled by interannually
changing discharge conditions, but rather by the seasonally changing
activation of subsurface flows with differing ages and thus differing N
loads. As a consequence of this activation-dependent load contribution, an
effective, adapted monitoring needs to cover, different discharge conditions
when measures shall be assessed for their effectiveness. In the light of
comparable findings of long time lags (van Meter and Basu, 2017; Howden,
2011), there is a general need for sufficient monitoring length and
appropriate methods for data evaluation like the seasonal statistics of time
series.
Third, in contrast to a more monotonic change from a chemodynamic to a
chemostatic nitrate export regime that was observed previously (Dupas et
al., 2016; Basu et al., 2010), this study found a systematic change in the
nitrate export regime from accretion over dilution to chemostatic behavior.
Here, we can make use of the unique situation in East German catchments
where the collapse of agriculture in the early 1990s provided a large-scale
“experiment” with abruptly reduced N inputs. While previous studies could
not distinguish between biogeochemical and hydrological legacy to cause
chemostatic export behavior, our findings provide support for a hydrological legacy
in the study catchment. The systematic interannual changes in C–Q
relationships of NO3–N were explained by the changes in the N input in
combination with the seasonally changing effective TTs of N. The observed
export regime and pattern of NO3–N suggest a dominance of a
hydrological N legacy over the biogeochemical N legacy in the upper soils.
In turn, observed trajectories in export regimes of other catchments may be
an indicator of their state of homogenization and can be helpful to classify
results and predict future concentrations.
Fourth, although we observed long TTs, significant input changes also
created strong interannual changes in the export regime. The chemostatic
behavior is therefore not necessarily a persistent endpoint of intense
agricultural land use, but depends on steady replenishment of the N store.
Therefore, the export behavior can also be termed pseudo-chemostatic and may
further evolve in the future (Musolff et al., 2015) under the assumptions of
a changing N input. Depending on the legacy size, a significant reduction or
increase in N input can cause an evolution back to more chemodynamic regimes
with dilution or enrichment patterns. Simultaneously, input changes affect
the homogenized vertical nitrate profile, resulting in larger intra-annual
concentration differences and consequently chemodynamic behavior. Hence,
chemostatic behavior and homogenization may be characteristics of managed
catchments, but only under constant N input.
Recommendations for a sustainable management of N pollution in the studied
Holtemme catchment, also transferable to comparable catchments, focus on the
two aspects: depleting past inputs and reducing future ones.
Our findings could not prove a significant loss of NO3–N by
denitrification. To deal with the past inputs and to focus on the depletion
of the N legacy, end-of-pipe measures such as hedgerows around agricultural
fields (Thomas and Abbott, 2018), riparian buffers or constructed wetlands
may initiate N removal by denitrification (Messer et al., 2012).
We could show that there is still an imbalance of agricultural N input and
riverine export by a mean factor of 5. A reduced N input due to better
management of fertilizer and the prevention of N losses from the root zone
at the present time is indispensable to enable depletion instead of a further
buildup or stabilization of the legacy.
The combination of N budgeting, effective TTs with long-term changes in C–Q
characteristics proved to be a helpful tool to discuss the buildup and type
of N legacy at catchment scale. This study strongly benefits from the
availability of long time series in nested catchments with a hydroclimatic
and land-use gradient. This wealth of data may not be available everywhere.
However, we see the potential to transfer this approach to a much wider
range of catchments with long-term observations for understanding the
spatial and temporal variation and type of legacy buildup, denitrification
and TTs as well as their controlling factors. Data-driven analyses of
differing catchments covering a higher variety of characteristics may
provide a more comprehensive picture of N trajectories and their controlling
parameters. In addition to data-driven approaches emphasis should also be
put on robust estimations of water TT in catchments to constrain reaction
rates. Recent studies present promising approaches to derive TTs in
groundwater (Marcais et al., 2018; Kolbe et al., 2019) and at catchment
scale (Jasechko et al., 2016; Yang et al., 2018a)
Data availability
Discharge data (for all dates) and water quality
data (from 1993) can be accessed on the websites of the State Office of Flood Protection and Water Management (LHW)
of Saxony-Anhalt (http://gldweb.dhi-wasy.com/gld-portal/, LHW, 2017). Water quality data for nitrate (including those prior to 1993) are available at 10.4211/hs.9c57af9b5c1343bb840ba198a49ace1c (Ehrhardt, 2019).
Atmospheric deposition data between 1995 and 2015 can be accessed on the
website of the Meteorological Synthesizing Centre – West (MSC-W) of the
European Monitoring and Evaluation Programme (EMEP)
(http://www.emep.int/mscw/index_mscw.html, Norwegian Meteorological Institute, 2017), which is assigned
to the Meteorological institute of Norway (MET Norway).
The supplement related to this article is available online at: https://doi.org/10.5194/hess-23-3503-2019-supplement.
Author contributions
SE carried out the analysis, interpreted the
data and wrote the paper. AM designed the study and co-wrote the
paper. RK contributed discharge modeling results and atmospheric
deposition and co-wrote the paper. All authors contributed
to the study design and helped finalize the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We would like to thank the State Office of Flood Protection and Water
Management (LHW) of Saxony-Anhalt for supplying data for discharge and water
quality. Additionally, we thank the MET Norway, as a source of data to model
the atmospheric deposition. We gratefully acknowledge the provision of N
input from agriculture by Martin Bach, University of Gießen, Germany.
Christin Müller kindly provided wastewater data from the catchment.
Thanks to Dietrich Borchardt, Suresh Rao, Kim van Meter, Frank Blumensaat
and James Jawitz for helpful discussions. We acknowledge the valuable
comments and inputs from two anonymous reviewers.
Financial support
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 Nandita Basu and reviewed by two anonymous referees.
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