Figure 2 . a – Spatial variation of the TT modes in the 166
catchments with an R² ≥ 0.6. b – Spatial variation of the overlapping
retention in all of the analyzed catchments (n = 238). c – Histogram of
the mode TTs. d – Histogram of the retention for the overlapping time
(beige curve) and the convolved retention (grey curve) with their
corresponding medians (dashed lines). e – Scatter plot of the
overlapping retention versus the mode TTs, with the corresponding
medians for both measures (dashed lines). Excluding one outlier with
negative retention.
3.3. Controls of catchment’s response and retention
The PLSR for predicting the mode TTs in the selected catchments with a
good fit (R2 ≥ 0.6) explained 49% of the total
variance. Variables that are connected to the catchment’s
hydroclimatological characteristics were found to be most important
(Supporting Information Figure S4.1.). Potential evapotranspiration
(PET) was analyzed as the most important variable (VIP 2.1) indicating
longer mode TTs with higher PET. The seasonality index of precipitation
(P_SI, see Supporting Information S1 for detailed description) was with
an almost same VIP value (2.10 vs. 2.07) the second most influential
predictor (VIP = 2.1). The higher the mean difference between monthly P
averages and the annual average, the shorter the mode TT. The other
three most important parameters indicate shorter TT related with 1)
higher coefficients of variation of discharge (VIP = 1.9), 2) higher
topographic wetness indices (TWI; VIP = 1.5) and 3) higher median winter
discharges (VIP = 1.3).
The N retention across the catchments was well predicted by the PLSR
(R2 = 0.72). Four of the five most important
parameters (Supporting Information Figure S4.2.) referred to subsurface
characteristics, while one predictor was a hydrological descriptor. High
specific discharge was connected to low N retention and was the most
important predictor (VIP = 2.3). Second most important factor for
predicting retention was the depth to bedrock (VIP = 1.8). The positive
coefficient indicated that a higher depth to bedrock is associated with
a higher retention. Consolidated (VIP = 1.5) and porous aquifer
materials (VIP = 1.4) were associated with low retention while vice
versa unconsolidated aquifers (VIP = 1.4) favored higher retention.
4. Discussion
4.1. Nitrogen transport times and its controlling parameters
The high number of catchments showing a good fit between N input and
riverine N export using a log-normal TTD indicate that the applied
methodology is appropriate for the analyzed Western European catchments.
This also shows that the temporal pattern of annual flow-weighted
NO3-N concentrations observed in the streams is mainly
controlled by the pattern of the diffuse N input.
The PLSR that explained 49% of the variability of mode TTs between the
catchments, reveals the importance of hydroclimatic variables (via PET,
precipitation and discharge variability, winter discharge) and
morphology (via TWI), which is partly in line with previous knowledge
that stated recharge rate (besides aquifer porosity and thickness) as a
major control for mean groundwater travel times (Haitjema, 1995). We
note the close connection between hydroclimatic descriptors (e.g.
between long-term mean precipitation, PET, discharge; Supporting
Information Figure S5.; as established through the Budyko (1974)
framework), but only discuss here the ones ranked as most important for
TTs according to the PLSR.
Especially regions with highest intra-annual precipitation seasonality
(Figure 1c) like in the Armorican Massif and the Alpine foothills showed
short TTs with modes shorter than 5 years. Precipitation seasonality,
entailing changing wetness conditions, can cause changing aquifer
connectivity (Blume & Van Meerveld, 2015; Roa-Garcia & Weiler, 2010),
which is known as a major control of NO3 export from
catchments (Molenat et al., 2008; Ocampo et al., 2006; Wriedt et al.,
2007). In terms of hydrological connectivity, Birkel et al. (2015) and
Yang et al. (2018) stated that the activation of shallow flow paths
during runoff events favors young water ages. Hence, we hypothesize that
these high-flow events efficiently export young NO3 from
the shallow subsurface to the stream and thus lowers N TT scales. High
median winter discharge as another VIP, common in the Alpine foothills
favoring short TTs, is in line with our hypothesis and the previous
findings by Wriedt et al. (2007). The correlation between high TWI
values and short TTs for N may be also attributed to a prevalence of N
exports by shallow subsurface flow paths: lowland catchments,
characterized by higher TWI’s, show strong seasonal changes of
discharging streams and the artificial drainage network (Van der Velde
et al., 2009). As these drains favor rapid, shallow subsurface flows,
their temporal connection during high-flow events favor short travel
times (Van der Velde et al., 2009). Long N TTs were found in the western
Massif Central and south of it where PET was highest among the study
catchments and recharge likely low, corroborating Haitjema’s (1995)
finding for groundwater travel times.
The clear link between TTs for N and hydroclimatic settings make
catchment N transport vulnerable to the changing future climate. Based
on past observations since the 1960s, the intensity of extreme weather
has been predicted to increase in most parts of Europe (EC, 2009).
Hydroclimatic projection studies in general suggest drier conditions in
Atlantic climatic zones in Europe in terms of longer drought durations
and lower low flows under warming climates (Marx et al., 2018; Samaniego
et al., 2018). Both extremes, heavy precipitation events and longer
droughts, are more likely. According to the discussed influence of
precipitation and discharge variability on N dynamics, TTs are supposed
to decrease in the future. The stronger ET with increasing temperature
(Donnelly et al., 2017) is counteracting this trend by favoring longer
TTs. Since the climate is expected to manifest differently within
Europe, reliable predictions on future N TTs on regional scales will
need further research.
Despite a high number of catchments with a good fit using our TT
estimations, we acknowledge the inherent uncertainties and limitations
of the database as well as of the method itself. With better knowledge
on the temporal evolution of waste water inputs and anthropogenic
modifications in the catchment hydrology, like damming, more reliable TT
estimations and a potentially better explainability among the catchments
may have been possible. Furthermore the method, assuming a constant
log-normal TTD, is only supposed to mirror the dominant long-term TT
behavior, disregarding known temporal variability of water travel times
in catchments (Benettin et al., 2013; Botter et al., 2011; Harman, 2015;
Van der Velde et al., 2010). Moreover, we estimated TTs from the small
fraction of total N inputs that left the catchment as
NO3-N (median 28%). Long-term tracer studies using
labeled 15N compounds (e.g. Sebilo et al., 2013) hold
promising avenues for a more detailed and hedged evaluation of the fate
of N.
4.2. Nitrogen retention
and controlling parameters
According to the PLSR, the variability in retention among the catchments
was mainly explained by subsurface properties that can be connected to
biogeochemical conditions and the specific discharge. This finding was
in line with Merz et al. (2009) and Nolan et al. (2002), who stated that
spatial differences in NO3 retention or contamination,
respectively, result from a combination of the geochemical environment
and the hydraulic conditions. We argue that the highly-ranked subsurface
predictors describe favorable biogeochemical conditions for either
permanent removal by denitrification or storage in the soils as
biogeochemical legacy.
Areas with a high depth to bedrock and an unconsolidated aquifer (Figure
1d), which showed retention above 75%, were particularly common in the
Northern German Lowlands and in the Alpine foothills. This is in line
with Ebeling et al. (2020), who attributed areas with large depth to
bedrock and unconsolidated (sedimentary) aquifers to natural attenuation
or retention processes based on riverine NO3-N
concentration-discharge relationships. Unconsolidated deposits in the
terrestrial subsurface, like in the Northern German Lowlands, are often
associated with iron sulphide minerals (pyrite; Bouwman et al., 2013).
The pyrite oxidation acts as electron donor for denitrification under
anaerobic conditions (Zhang et al., 2009). For the unconsolidated
aquifers in northern Germany, a recent study (Knoll et al., 2020)
connected the high denitrification potential to strongly anaerobic redox
conditions in the groundwater. Although denitrification permanently
removes N from the catchment, it can be a source for
N2O, an important greenhouse gas, being 300-fold more
effective in trapping heat than carbon dioxide (Griffis et al., 2017).
Lastly, long-term consumption of reactants via denitrification can alter
the reduction capacity of the aquifer (Merz et al., 2009), decreasing
the catchment’s N retention over time.
In contrast to northern Germany, for the unconsolidated sediments in the
Alpine foothills different studies (BMU, 2003; Knoll et al., 2020)
proposed aerobic subsurface conditions, hindering denitrification. Also
Ebeling et al. (2020) found in this area evidence for a lack of
denitrification. Excluding denitrification and long TTs (see Section
4.1.), we hypothesize biogeochemical legacy as a likely process of the
high retention in the Alpine foothills. In comparison to northern
Germany, soils here contain higher degrees of silt and clay. These grain
sizes are prone to microaggregate formation and anion sorption, both
sequestering organic N in the mineral subsoil for long periods of time
(Bingham & Cotrufo, 2016; Von Lützow et al., 2006). Also mineral N
fixed on clays can make a significant contribution to the soil N stock
(Allred et al., 2007; Stevenson, 1986).
In contrast, areas with a high share of consolidated subsurface
materials and a small depth to bedrock, like the Armorican Massif, parts
of the Massif Central or the Harz Mountains showed N retention below
75%. In general, denitrification and biogeochemical legacies can only
evolve if favorable biogeochemical conditions in soils and groundwater
are abundant in the catchment. An important part for denitrification is
the contact area and contact time with organic-rich soils (Bouwman et
al., 2013). Due to abundant crystalline rocks, water moves along
fissures in the weathered zone (Wyns et al., 2004), while it is
dependent on joints and fractures in deeper depth (Wendland et al.,
2007). Hence, there is only a limited reactive surface for
NO3 within the areas dominated by consolidated materials
(Wendland et al., 2007). Furthermore, Knoll et al. (2020) showed oxic
conditions in consolidated units for Germany that do not allow for
denitrification in groundwater.
The only hydrological predictor for N retention was the specific
discharge. High specific discharges were found in the Armorican Massif,
the western part of the Massif Central, in the Harz Mountains and the
southern Alpine foothills, were often spatially connected to areas with
consolidated subsurface materials and had N retention below 75%. High
discharge areas connect to short residence times in the catchment
compartments like root zone, aquifer or riparian zone and therefore
decreases denitrification efficiency through a reduced contact time
(e.g. Howarth et al., 2006; Kunkel & Wendland, 2006; Wendland et al.,
2007). This assumption is in line with a recent study by Dupas et al.
(2020), arguing that higher runoff lowers denitrification. Tesoriero et
al. (2017) and Knoll et al. (2020) stated high recharge rates as
important predictors for aerobic conditions. Furthermore, high discharge
may be driven by a high degree of shallow flow paths (Birkel et al.,
2015; Yang et al., 2018), favoring a fast wash-out of N or an export
before immobilization, thus decreasing retention as well.
With regard to climate change, the increase in European rainfall
erosivity is estimated in the range from 10 to 15% until 2050 (Panagos
et al., 2015). Especially in southern France and Germany, this may cause
soil loss in arable lands up to 10 t ha-1yr-1 (Panagos et al., 2015). We argue that such
mobilization of soils with high biogeochemical legacy (e.g. Alpine
foothills) can contribute to further deterioration of downstream river
water quality.
4.3. Joint analysis of nitrogen transport times and retention
The joint analyses of N TT estimations and N retention (Figure 2e)
revealed a discrepancy between the two in the studied catchments. The
rather observed short TTs indicate that the largest part
(75th-percentile) of N input should have been exported after at least
20 years. In contrast, the observed retention indicates that 72% of
total N input was not exported. The retention was similarly high (70%)
when convolving N input taking into consideration estimated TTs. The
missing relation between TTs and retention as well as the different
predictors for both through the PLSR, indicate that hydrologic legacies
of N alone could not explain the failure of measures to improve water
quality in Western European catchments (e.g. Bouraoui & Grizzetti,
2011), despite decreasing N-inputs. We rather assume a dominance of
non-hydrologic retention, namely biogeochemical legacy and
denitrification.
After the implementation of regulations such as the EU Nitrate Directive
(CEC, 1991), the diffuse N input decreased between the 1980s and 2010s
by more than 20 kg N ha-1 yr-1(36%) in the studied Western European catchments. The responses of
riverine N loads to this decrease in input was limited (< 1.5
kg N ha-1 yr-1). Hence, the
retention decreased but catchments still received (in the 2010s) excess
N of almost 30 kg N ha-1 every year, which is
two-thirds of the diffuse input.
Besides failure to implement good agricultural practices, these results
imply either a hindered substantial exploitation of the (already
massive) biogeochemical legacy by mineralization and/or an ongoing
exhaustion of the catchment’s denitrification potential.
According to the discussed subsurface and hydrological catchment
characteristics favoring biogeochemical legacy, and due to the specific
conditions required for effective denitrification that are only
fulfilled in a few areas, we argue that biogeochemical legacy is the
dominant retention process in most of the study catchments. We explain
the missing catchment response for decreasing N inputs with the buffer
effect stemming from the accumulated biogeochemical legacy acting as a
secondary source and constituting a system inert to decreasing N inputs.
A biogeochemical dominance was also found in a recent study for
catchments in northwestern France (Dupas et al., 2020). They concluded
two-third of the retention being stored in the subsoil with the
potential to recycle this N in the agroecosystem. Also Ascott et al.
(2017) concluded that the vadose zone is globally a significant
NO3 store. If not being recycled and in light of limited
denitrification potential, the stored N would further leach to the
deeper subsurface (or groundwater), when being mineralized again (Van
Meter & Basu, 2015). The missing export of three-quarters of the past N
inputs in the study catchments therefore constitutes a huge challenge
for efforts to reach effective water quality improvements now and in the
future.
5. Conclusions and implications
In this study we used long-term time series of N input and riverine
NO3-N output from 238 Western European catchments to
estimate the N TTs, retention amount as well as the controlling
catchment characteristics for both.
The analysis of catchment responses revealed peak TTs around 5 years
with 70% of the catchments showing a peak export within the first 10
years after N enters the system. Hence, when assessing the
effectiveness of measures, catchment managers have to be aware of the
hydrological transport dependent decrease in N concentrations after
around 5 years that should not falsely be attributed to successfully
taken measures. Conversely, assessing the effect of regulations on the
N input before the arrival of needed peak TTs, is not recommended.
- Our analyses indicate a minor role of hydrologic legacy meaning that
storage of NO3 in groundwater is not the dominant
process explaining 72% of ingoing N being retained. We rather see
evidence for a widespread biogeochemical legacy of N, while
biogeochemical conditions for a permanent removal by denitrification
are only rarely achieved. Therefore, decreasing concentrations within
the first 10 years mean neither that most of the N was already
exported nor that restoration efforts can be reduced. Management in
such cases would need rather long-term strategies to reduce ongoing
leaching from soil N pools, for example by recycling the retained N
within the soil or by fostering denitrifying conditions.
- While TTs were mainly controlled by hydroclimatic parameters with low
PET and high precipitation seasonality favoring more rapid transport
of N to the streams, retention was mainly controlled by specific
discharge and subsurface parameters as low specific discharge and a
high share of thick, unconsolidated aquifers in the catchments favor
high retention. Thus, catchment managers can estimate from subsurface
and hydroclimatic data, the natural conditions for retention and the
dimension of TTs, which can be a helpful tool to explain the failure
of measures or to advise a realistic management plan.
- From a management perspective, a better spatial and temporal knowledge
of denitrification efficiency at larger scales should be aimed at.
Being associated with this, research on long-term changes of N storage
capacities in agricultural soils is required. These data-driven
analyses can be used to support or compliment modelling approaches
assisting different large scale water quality management activities.
Data
Please note that the used data base adheres to Enabling FAIR Data
Project requirements and is referenced in the manuscript linking to the
data bases and repositories.
Water quality data for France is publicly available athttp://naiades.eaufrance.fr/ . Water quantity data for France are
available at http://hydro.eaufrance.fr/ . Diffuse N input data for
France were derived from Poisvert et al. (2017).
Water quality and quantity data for Germany are available athttps://www.hydroshare.org/resource/a42addcbd59a466a9aa56472dfef8721/(Musolff, 2020).
Catchment characteristics for Germany and France are available at
https://www.hydroshare.org/resource/c7d4df3ba74647f0aa83ae92be2e294b/
(Ebeling & Dupas, 2020).
References
Ai, L., Shi, Z. H., Yin, W., & Huang, X. (2015). Spatial and seasonal
patterns in stream water contamination across mountainous watersheds:
Linkage with landscape characteristics. Journal of Hydrology, 523,
398-408. doi:10.1016/j.jhydrol.2015.01.082
Allain, M. (1951). FRANCE GEOLOGIQUE. Atlas universel Quillet physique,
économique, politique. Tome 1: France et union française (1951).
Retrieved from Librairie Aristide Quillet
https://www.mapmania.org/map/76435/geology_of_france__carte_geologique_de_
la_france__5038 _x_4687
Allred, B. J., Bigham, J. M., & Brown, G. O. (2007). The Impact of Clay
Mineralogy on Nitrate Mobility under Unsaturated Flow Conditions. Vadose
Zone Journal, 6(2), 221-232. doi:10.2136/vzj2006.0064
Ascott, M. J., Gooddy, D. C., Wang, L., Stuart, M. E., Lewis, M. A.,
Ward, R. S., & Binley, A. M. (2017). Global patterns of nitrate storage
in the vadose zone. Nature communications, 8(1), 1–7.
doi:10.1038/s41467-017-01321-w
Bach, M., & Frede, H. G. (1998). Agricultural nitrogen, phosphorus and
potassium balances in Germany-Methodology and trends 1970 to 1995. Z.
Pflanzenernähr. Bodenk. 161, 8.
Bain, D. J., Green, M. B., Campbell, J. L., Chamblee, J. F., Chaoka, S.,
Fraterrigo, J. M., . . . Leigh, D. S. (2012). Legacy Effects in Material
Flux: Structural Catchment Changes Predate Long-Term Studies.
BioScience, 62(6), 575-584. doi:10.1525/bio.2012.62.6.8
Ballabio, C., Lugato, E., Fernandez-Ugalde, O., Orgiazzi, A., Jones, A.,
Borrelli, P., . . . Panagos, P. (2019). Mapping LUCAS topsoil chemical
properties at European scale using Gaussian process regression.
Geoderma, 355, 113912. doi:10.1016/j.geoderma.2019.113912
Basu, N. B., Destouni, G., Jawitz, J. W., Thompson, S. E., Loukinova, N.
V., Darracq, A., . . . Rao, P. S. C. (2010). Nutrient loads exported
from managed catchments reveal emergent biogeochemical stationarity.
Geophysical Research Letters, 37(23). doi:10.1029/2010gl045168
Benettin, P., Rinaldo, A., & Botter, G. (2013). Kinematics of age
mixing in advection-dispersion models. Water Resources Research, 49(12),
8539-8551. doi:10.1002/2013wr014708
Bingham, A. H., & Cotrufo, M. F. (2016). Organic nitrogen storage in
mineral soil: Implications for policy and management. Sci Total Environ,
551-552, 116-126. doi:10.1016/j.scitotenv.2016.02.020
Birkel, C., Soulsby, C., & Tetzlaff, D. (2015). Conceptual modelling to
assess how the interplay of hydrological connectivity, catchment storage
and tracer dynamics controls nonstationary water age estimates.
Hydrological Processes, 29(13), 2956-2969. doi:10.1002/hyp.10414
Blume, T., & van Meerveld, H. J. (2015). From hillslope to stream:
methods to investigate subsurface connectivity. Wiley Interdisciplinary
Reviews-Water, 2(3), 177-198. doi:10.1002/wat2.1071
BMU, B. f. U., Naturschutz und Reaktorsicherheit. (2003). Hydrologischer
Atlas von Deutschland. Grundwasser. Retrieved from
http://www.hydrology.uni-freiburg.de/forsch/ had/ had_bezug.htm
Botter, G., Bertuzzo, E., & Rinaldo, A. (2011). Catchment residence and
travel time distributions: The master equation. Geophysical Research
Letters, 38(11). doi:10.1029/2011gl047666
Bouraoui, F., & Grizzetti, B. (2011). Long term change of nutrient
concentrations of rivers discharging in European seas. The Science of
the total environment, 409(23), 4899–4916.
doi:10.1016/j.scitotenv.2011.08.015
Bouwman, A. F., Beusen, A. H., Griffioen, J., Van Groenigen, J. W.,
Hefting, M. M., Oenema, O., . . . Stehfest, E. (2013). Global trends and
uncertainties in terrestrial denitrification and N(2)O emissions. Philos
Trans R Soc Lond B Biol Sci, 368(1621), 20130112.
doi:10.1098/rstb.2013.0112
CEC, C. o. t. E. U. (1991). Council Directive 91/676/EEC of 12 December
1991 concerning the protection of waters against pollution caused by
nitrates from agricultural sources. (No L 375 / 1). Official Journal of
the European Communities Retrieved from
https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX%3A31991L0676
CGMW, B. (2005). The 1:5 Million International Geological Map of Europe
and Adjacent Areas (IGME 5000). Retrieved from
https://geoviewer.bgr.de/mapapps4/resources/apps/
geoviewer/index.html?cover=geologie_igme5000_ags&tab=geologie&lang=de
CIESIN, C. f. I. E. S. I. N.-C. U. (2017). Gridded Population of the
World, Version 4 (GPWv4): Population Density, Revision 10. Retrieved
from https://doi.org/10.7927/H4DZ068D. https://doi.org/10.7927/H4DZ068D
CLC. (2000). CORINE Land Cover 2000. Retrieved from
https://land.copernicus.eu/pan-european/corine-land-cover.
CLC. (2016). CORINE Land Cover 2012 v18.5. . Retrieved from
https://land.copernicus.eu/pan-european/corine-land-cover.
Cleveland, C. C., Townsend, A. R., Schimel, D. S., Fisher, H., Howarth,
R. W., Hedin, L. O., . . . Wasson, M. F. (1999). Global patterns of
terrestrial biological nitrogen (N2) fixation in natural ecosystems.
Global Biogeochemical Cycles, 13(2), 623-645. doi:10.1029/1999gb900014
Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. M., &
Jones, P. D. (2018). An Ensemble Version of the E-OBS Temperature and
Precipitation Data Sets. Journal of Geophysical Research: Atmospheres,
123(17), 9391-9409. doi:10.1029/2017jd028200
de Vries, W., Leip, A., Reinds, G. J., Kros, J., Lesschen, J. P.,
Bouwman, A. F., . . . Winiwarter, W. (2011). Geographical variation in
terrestrial nitrogen budgets across Europe. . In M. A. Sutton, C. M.
Howard, J. W. Erisman, G. Billen, A. Bleeker, P. Grennfelt, H. van
Grinsven, & B. Grizzetti (Eds.), The European Nitrogen Assessment.
Sources, effects and policy perspectives (pp. 317-344). Cambridge:
Cambridge University Press.
Donnelly, C., Greuell, W., Andersson, J., Gerten, D., Pisacane, G.,
Roudier, P., & Ludwig, F. (2017). Impacts of climate change on European
hydrology at 1.5, 2 and 3 degrees mean global warming above
preindustrial level. Climatic Change, 143(1-2), 13-26.
doi:10.1007/s10584-017-1971-7
Dupas, R., Curie, F., Gascuel-Odoux, C., Moatar, F., Delmas, M.,
Parnaudeau, V., & Durand, P. (2013). Assessing N emissions in surface
water at the national level: comparison of country-wide vs. regionalized
models. Sci Total Environ, 443, 152-162.
doi:10.1016/j.scitotenv.2012.10.011
Dupas, R., Delmas, M., Dorioz, J. M., Garnier, J., Moatar, F., &
Gascuel-Odoux, C. (2015). Assessing the impact of agricultural pressures
on N and P loads and eutrophication risk. Ecological Indicators, 48,
396-407. doi:10.1016/j.ecolind.2014.08.007
Dupas, R., Ehrhardt, S., Musolff, A., Fovet, O., & Durand, P. (2020).
Long-term nitrogen retention and transit time distribution in
agricultural catchments in western France. Environmental Research
Letters, 15(11). doi:10.1088/1748-9326/abbe47
Dupas, R., Jomaa, S., Musolff, A., Borchardt, D., & Rode, M. (2016).
Disentangling the influence of hydroclimatic patterns and agricultural
management on river nitrate dynamics from sub-hourly to decadal time
scales. The Science of the total environment, 571, 791–800.
doi:10.1016/j.scitotenv.2016.07.053
Ebeling, P., & Dupas, R. (2020). CCDB - catchment characteristics data
base France and Germany. Retrieved from
https://www.hydroshare.org/resource/c7d4df3ba74647f0aa83ae92be2e294b/
Ebeling, P., Kumar, R., Weber, M., Knoll, L., Fleckenstein, J. H., &
Musolff, A. (2020). Archetypes and Controls of Riverine Nutrient Export
Across German Catchments (Publication no. 10.1002/essoar.10503375.1 ).
https://www.essoar.org/doi/10.1002/ essoar.10503375.1
EC, E. C. (2009). REGIONS 2020. THE CLIMATE CHANGE CHALLENGE FOR
EUROPEAN REGIONS. Retrieved from
http://ec.europa.eu/regional_policy/sources/docoffic/working/regions2020/pdf/regions2020_climat.pdf
Ehrhardt, S., Kumar, R., Fleckenstein, J. H., Attinger, S., & Musolff,
A. (2019). Trajectories of nitrate input and output in three nested
catchments along a land use gradient. Hydrology and Earth System
Sciences, 23(9), 3503-3524. doi:10.5194/hess-23-3503-2019
EPA, U. S. E. P. A. (1972). Federal Water Pollution Control Amendments
of 1972. Clean Water Act. 33 U.S.C. § 1251 et seq. Retrieved from
https://www3.epa.gov/npdes/pubs/ cwatxt.txt
European Environment Agency, E. (2013). DEM over Europe from the GMES
RDA project (EU-DEM, resolution 25m) - version 1, Oct. 2013. . Retrieved
from
https://sdi.eea.europa.eu/catalogue/srv9008075/api/records/66fa7dca-8772-4a5d-9d56-2caba4ecd36a
European Environment Agency, E. (2016). CORINE Land Cover 2012 v18.5. .
Retrieved from
https://land.copernicus.eu/pan-european/corine-land-cover.
European Environment Agency, E. (2017). Waterbase - UWWTD: Urban Waste
Water Treatment Directive – reported data. . Retrieved from
https://www.eea.europa.eu/data-and-maps/data/waterbase-uwwtd-urban-waste-water-treatment-directive-5#tab-european-data
European Environment Agency, E. (2018). European waters. Assessment of
status and pressure (TH-AL-18-005-EN-N). Retrieved from Copenhagen:
https://www.eea.europa.eu/publications/state-of-water
FAO/IIASA/ISRIC/ISSCAS/JRC. (2012). Harmonized World Soil Database
(version 1.2). Retrieved from
https://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/
Fiori, A., Russo, D., & Di Lazzaro, M. (2009). Stochastic analysis of
transport in hillslopes: Travel time distribution and source zone
dispersion. Water Resources Research, 45(8). doi:10.1029/2008wr007668
Godsey, S. E., Aas, W., Clair, T. A., Wit, H. A. d., Fernandez, I. J.,
Kahl, J. S., . . . Kirchner, J. W. (2010). Generality of fractal 1/f
scaling in catchment tracer time series, and its implications for
catchment travel time distributions. Hydrological Processes, 24(12),
1660–1671. doi:10.1002/hyp.7677
Griffis, T. J., Chen, Z., Baker, J. M., Wood, J. D., Millet, D. B., Lee,
X., . . . Turner, P. A. (2017). Nitrous oxide emissions are enhanced in
a warmer and wetter world. Proc Natl Acad Sci U S A, 114(45),
12081-12085. doi:10.1073/pnas.1704552114
Haitjema, H. M. (1995). On the residence time distribution in idealized
groundwatersheds. Journal of Hydrology, 172, 20.
Harman, C. J. (2015). Time-variable transit time distributions and
transport: Theory and application to storage-dependent transport of
chloride in a watershed. Water Resources Research, 51(1), 1–30.
doi:10.1002/2014wr015707
Häußermann, U., Bach, M., Klement, L., & Breuer, L. (2019).
Stickstoff-Flächenbilanzen für Deutschland mit Regionalgliederung
Bundesländer und Kreise – Jahre 1995 bis 2017- Methodik, Ergebnisse und
Minderungsmaßnahmen. Retrieved from Dessau-Roßlau:
https://www.umweltbundesamt.de/sites/default/files/medien/1410/publikationen/2019-10-28_texte_131-2019_stickstoffflaechenbilanz.pdf
Heidbüchel, I., Yang, J., Musolff, A., Troch, P., Ferré, T., &
Fleckenstein, J. H. (2020). On the shape of forward transit time
distributions in low-order catchments. Hydrology and Earth System
Sciences, 24(6), 2895-2920. doi:10.5194/hess-24-2895-2020
Hirsch, R. M., & de Cicco, L. (2019). EGRET: Exploration and Graphics
for RivEr Trends, R packafe version 3.0.2.
Hirsch, R. M., Moyer, D. L., & Archfield, S. A. (2010). Weighted
Regressions on Time, Discharge, and Season (WRTDS), with an Application
to Chesapeake Bay River Inputs. Journal of the American Water Resources
Association, 46(5), 857–880. doi:10.1111/j.1752-1688.2010.00482.x
Howarth, R. W., Swaney, D. P., Boyer, E. W., Marino, R., Jaworski, N.,
& Goodale, C. (2006). The influence of climate on average nitrogen
export from large watersheds in the Northeastern United States.
Biogeochemistry, 79(1-2), 163-186. doi:10.1007/s10533-006-9010-1
Howden, N. J. K., Burt, T. P., Worrall, F., Mathias, S., & Whelan, M.
J. (2011). Nitrate pollution in intensively farmed regions: What are the
prospects for sustaining high-quality groundwater? Water Resources
Research, 47(6). doi:10.1029/2011wr010843
Howden, N. J. K., Burt, T. P., Worrall, F., Whelan, M. J., & Bieroza,
M. (2010). Nitrate concentrations and fluxes in the River Thames over
140 years (1868-2008): Are increases irreversible? Hydrological
Processes, 24(18), 2657–2662. doi:10.1002/hyp.7835
HYDRO MEDDE, M. d. l. E., du Développement durable et de l’Energie.
(2019). Banque Hydro. Retrieved from http://hydro.eaufrance.fr/
Jasechko, S., Kirchner, J. W., Welker, J. M., & McDonnell, J. J.
(2016). Substantial proportion of global streamflow less than three
months old. Nature Geoscience, 9(2), 126–129. doi:10.1038/ngeo2636
Kirchner, Feng, & Neal. (2000). Fractal stream chemistry and its
implications for contaminant transport in catchments. Nature, 403(6769),
524–527. doi:10.1038/35000537
Knoll, L., Breuer, L., & Bach, M. (2020). Nation-wide estimation of
groundwater redox conditions and nitrate concentrations through machine
learning. Environmental Research Letters, 15(6).
doi:10.1088/1748-9326/ab7d5c
Kumar, R., Samaniego, L., & Attinger, S. (2013). Implications of
distributed hydrologic model parameterization on water fluxes at
multiple scales and locations. Water Resources Research, 49(1),
360–379. doi:10.1029/2012wr012195
Kunkel, R., & Wendland, F. (2006). Diffuse Nitrateinträge in die Grund-
und Oberflächengewässer von Rhein und Ems- Ist-Zustands- und
Maßnahmenanalysen. In F. J. GmbH (Ed.), Schriften des Forschungszentrums
Jülich, Reihe Umwelt/ Environment (Vol. 62, pp. 143). Jülich.
Lutzow, M. v., Kogel-Knabner, I., Ekschmitt, K., Matzner, E.,
Guggenberger, G., Marschner, B., & Flessa, H. (2006). Stabilization of
organic matter in temperate soils: mechanisms and their relevance under
different soil conditions - a review. European Journal of Soil Science,
57(4), 426-445. doi:10.1111/j.1365-2389.2006.00809.x
Marx, A., Kumar, R., Thober, S., Rakovec, O., Wanders, N., Zink, M., . .
. Samaniego, L. (2018). Climate change alters low flows in Europe under
global warming of 1.5, 2, and 3 °C. Hydrology and Earth System
Sciences, 22(2), 1017-1032. doi:10.5194/hess-22-1017-2018
McMahon, P. B., Dennehy, K. F., Bruce, B. W., Böhlke, J. K., Michel, R.
L., Gurdak, J. J., & Hurlbut, D. B. (2006). Storage and transit time of
chemicals in thick unsaturated zones under rangeland and irrigated
cropland, High Plains, United States. Water Resources Research, 42(3).
doi:10.1029/2005wr004417
Meals, D. W., Dressing, S. A., & Davenport, T. E. (2010). Lag time in
water quality response to best management practices: a review. J Environ
Qual, 39(1), 85-96. doi:10.2134/jeq2009.0108
Merz, C., Steidl, J., & Dannowski, R. (2009). Parameterization and
regionalization of redox based denitrification for GIS-embedded nitrate
transport modeling in Pleistocene aquifer systems. Environmental
Geology, 58(7). doi:10.1007/s00254-008-1665-6
Molenat, J., Gascuel-Odoux, C., Ruiz, L., & Gruau, G. (2008). Role of
water table dynamics on stream nitrate export and concentration in
agricultural headwater catchment (France). Journal of Hydrology,
348(3-4), 363-378. doi:10.1016/j.jhydrol.2007.10.005
Musolff, A. (2020). WQQDB - water quality and quantity data base
Germany: metadata, HydroShare. Retrieved from https://doi.org/10.4211/
hs.a42addcbd59a466a9aa56472 dfef8721
Musolff, A., Fleckenstein, J. H., Rao, P. S. C., & Jawitz, J. W.
(2017). Emergent archetype patterns of coupled hydrologic and
biogeochemical responses in catchments. Geophysical Research Letters,
44(9), 4143–4151. doi:10.1002/2017gl072630
Musolff, A., Grau, T., Weber, M., Ebeling, P., Samaniego-Eguiguren, L.,
& Kumar, R. (2020). WQQDB: water quality and quantity data base
Germany. Retrieved from http://www.ufz.de/record/dmp/archive/7754
Nolan, B. T., Hitt, K. J., & Ruddy, B. C. (2002). Probability of
nitrate contamination of recently recharged groundwaters in the
conterminous United States. Environ Sci Technol, 36(10), 2138-2145.
doi:10.1021/es0113854
Ocampo, C. J., Sivapalan, M., & Oldham, C. (2006). Hydrological
connectivity of upland-riparian zones in agricultural catchments:
Implications for runoff generation and nitrate transport. Journal of
Hydrology, 331(3-4), 643-658. doi:10.1016/j.jhydrol.2006.06.010
Panagos, P., Borrelli, P., Poesen, J., Ballabio, C., Lugato, E.,
Meusburger, K., . . . Alewell, C. (2015). The new assessment of soil
loss by water erosion in Europe. Environmental Science & Policy, 54,
438-447. doi:10.1016/j.envsci.2015.08.012
Poisvert, C., Curie, F., & Moatar, F. (2017). Annual agricultural N
surplus in France over a 70-year period. Nutrient Cycling in
Agroecosystems, 107(1), 63-78. doi:10.1007/s10705-016-9814-x
Roa-García, M. C., & Weiler, M. (2010). Integrated response and transit
time distributions of watersheds by combining hydrograph separation and
long-term transit time modeling. Hydrology and Earth System Sciences,
14(8), 1537-1549. doi:10.5194/hess-14-1537-2010
Samaniego, L., Kumar, R., & Attinger, S. (2010). Multiscale parameter
regionalization of a grid-based hydrologic model at the mesoscale. Water
Resources Research, 46(5). doi:10.1029/2008wr007327
Samaniego, L., Thober, S., Kumar, R., Wanders, N., Rakovec, O., Pan, M.,
. . . Marx, A. (2018). Anthropogenic warming exacerbates European soil
moisture droughts. Nature Climate Change, 8(5), 421-426.
doi:10.1038/s41558-018-0138-5
Sebilo, M., Mayer, B., Nicolardot, B., Pinay, G., & Mariotti, A.
(2013). Long-term fate of nitrate fertilizer in agricultural soils.
Proceedings of the National Academy of Sciences of the United States of
America, 110(45), 18185–18189. doi:10.1073/pnas.1305372110
Shangguan, W., Hengl, T., Mendes de Jesus, J., Yuan, H., & Dai, Y.
(2017). Mapping the global depth to bedrock for land surface modeling.
Journal of Advances in Modeling Earth Systems, 9(1), 65-88.
doi:10.1002/2016ms000686
Shi, Z. H., Ai, L., Li, X., Huang, X. D., Wu, G. L., & Liao, W. (2013).
Partial least-squares regression for linking land-cover patterns to soil
erosion and sediment yield in watersheds. Journal of Hydrology, 498,
165-176. doi:10.1016/j.jhydrol.2013.06.031
Stevenson, F. J. (1986). Cycles of soil: Carbon, Nitrogen, Phosphorus,
Sulfur, Micronutrients. New York: Wiley.
Tesoriero, A. J., Gronberg, J. A., Juckem, P. F., Miller, M. P., &
Austin, B. P. (2017). Predicting redox-sensitive contaminant
concentrations in groundwater using random forest classification. Water
Resources Research, 53(8), 7316-7331. doi:10.1002/2016wr020197
Thomas, Z., & Abbott, B. W. (2018). Hedgerows reduce nitrate flux at
hillslope and catchment scales via root uptake and secondary effects. J
Contam Hydrol, 215, 51-61. doi:10.1016/j.jconhyd.2018.07.002
UNESCO, B. (2014). International Hydrogeological Map of Europe 1:
1,500,000 (IHME1500). Digital map data v1.1. . Retrieved from
http://www.bgr.bund.de/ihme1500/. http://www.bgr.bund.de/ihme1500/
Van der Velde, Y., de Rooij, G. H., & Torfs, P. J. J. F. (2009).
Catchment-scale non-linear groundwater-surface water interactions in
densely drained lowland catchments. Hydrology and Earth System Sciences,
13(10), 1867-1885. doi:10.5194/hess-13-1867-2009
Van der Velde, Y., Rooij, G. H. d., Rozemeijer, J. C., van Geer, F. C.,
& Broers, H. P. (2010). Nitrate response of a lowland catchment: On the
relation between stream concentration and travel time distribution
dynamics. Water Resources Research, 46(11), 1–17.
doi:10.1029/2010wr009105
Van Meter, K. J., & Basu, N. B. (2015). Catchment legacies and time
lags: A parsimonious watershed model to predict the effects of legacy
storage on nitrogen export. PloS one, 10(5), e0125971.
doi:10.1371/journal.pone.0125971
Van Meter, K. J., & Basu, N. B. (2017). Time lags in watershed-scale
nutrient transport: An exploration of dominant controls. Environmental
Research Letters, 12(8), 084017. doi:10.1088/1748-9326/aa7bf4
Van Meter, K. J., Basu, N. B., & van Cappellen, P. (2017). Two
centuries of nitrogen dynamics: Legacy sources and sinks in the
Mississippi and Susquehanna River Basins. Global Biogeochemical Cycles,
31(1), 2–23. doi:10.1002/2016gb005498
Van Meter, K. J., Van Cappellen, P., & Basu, N. B. (2018). Legacy
nitrogen may prevent achievement of water quality goals in the Gulf of
Mexico. Science, 360(6387), 427-430. doi:10.1126/science.aar4462
Vero, S. E., Basu, N. B., Van Meter, K., Richards, K. G., Mellander,
P.-E., Healy, M. G., & Fenton, O. (2017). Review: the environmental
status and implications of the nitrate time lag in Europe and North
America. Hydrogeology Journal, 26(1), 7-22.
doi:10.1007/s10040-017-1650-9
Vigiak, O., Grizzetti, B., Zanni, M., Aloe, A., Dorati, C., Bouraoui,
F., & Pistocchi, A. (2019). Domestic waste emissions to European
freshwaters in the 2010s (v. 1.0). Retrieved from
https://data.jrc.ec.europa.eu/dataset/0ae64ac2-64da-4c5e-8bab-ce928897c1fb
Vigiak, O., Grizzetti, B., Zanni, M., Aloe, A., Dorati, C., Bouraoui,
F., & Pistocchi, A. (2020). Domestic waste emissions to European waters
in the 2010s. Sci Data, 7(1), 33. doi:10.1038/s41597-020-0367-0
Wang, L., & Burke, S. P. (2017). A catchment-scale method to simulating
the impact of historical nitrate loading from agricultural land on the
nitrate-concentration trends in the sandstone aquifers in the Eden
Valley, UK. The Science of the total environment, 579, 133–148.
doi:10.1016/j.scitotenv.2016.10.235
Wassenaar, L. I. (1995). Evaluation of the origin and fate of nitrate in
the Abbotsford Aquifer using the isotopes. Applied Geochemistry, 10, 15.
doi:10.1016/0883-2927(95)00013-A
Webster, J. R., Mulholland, P. J., Tank, J. L., Valett, H. M., Dodds, W.
K., Peterson, B. J., . . . Wollheim, W. M. (2003). Factors affecting
ammonium uptake in streams - an inter-biome perspective. Freshwater
Biology, 48(8), 1329–1352. doi:10.1046/j.1365-2427.2003.01094.x
Wendland, F., Blum, A., Coetsiers, M., Gorova, R., Griffioen, J., Grima,
J., . . . Walraevens, K. (2007). European aquifer typology: a practical
framework for an overview of major groundwater composition at European
scale. Environmental Geology, 55(1), 77-85.
doi:10.1007/s00254-007-0966-5
West, P. C., Gerber, J. S., Engstrom, P. M., Mueller, N. D., Brauman, K.
A., Carlson, K. M., . . . Siebert, S. (2014). Leverage points for
improving global food security and the environment. Science, 345(6194),
325-328. doi:10.1126/science.1246067
Wold, S., Sjostrom, M., & Eriksson, L. (2001). PLS-regression: a basic
tool of chemometrics. Chemometrics and Intelligent Laboratory Systems,
58(2), 109-130. doi:Doi 10.1016/S0169-7439(01)00155-1
Worldometers.info. (2020). Europe-population. Retrieved from
https://www.worldometers.info/ world-population/europe-population/
Wriedt, G., Spindler, J., Neef, T., Meißner, R., & Rode, M. (2007).
Groundwater dynamics and channel activity as major controls of in-stream
nitrate concentrations in a lowland catchment system? Journal of
Hydrology, 343(3-4), 154-168. doi:10.1016/j.jhydrol.2007.06.010
Wyns, R., Mathieu, F., Vairon, J., Legchenko, A., Lachassagne, P., &
Baltassat, J.-M. (2004). Application of proton magnetic resonance
soundings to groundwater reserve mapping in weathered basement rocks
(Brittany, France). Bulletin de la Société Géologique de France, 175(1),
21-34. doi:10.2113/175.1.21
Yang, J., Heidbüchel, I., Musolff, A., Reinstorf, F., & Fleckenstein,
J. H. (2018). Exploring the Dynamics of Transit Times and Subsurface
Mixing in a Small Agricultural Catchment. Water Resources Research,
54(3), 2317-2335. doi:10.1002/2017wr021896
Zambrano-Bigiarini, M., & Rojas, R. (2013). A model-independent
Particle Swarm Optimisation software for model calibration.
Environmental Modelling & Software, 43, 5-25.
doi:10.1016/j.envsoft.2013.01.004
Zhang, Y.-C., Slomp, C. P., Broers, H. P., Passier, H. F., & Cappellen,
P. V. (2009). Denitrification coupled to pyrite oxidation and changes in
groundwater quality in a shallow sandy aquifer. Geochimica et
Cosmochimica Acta, 73(22), 6716-6726. doi:10.1016/j.gca.2009.08.026