Discharge Driven Nitrogen Dynamics in a Mesoscale River Basin As

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Discharge driven nitrogen dynamics in a mesoscale river basin as constrained by stable isotope patterns Christin Mueller, Matthias Zink, Luis Samaniego, Ronald Krieg, Ralf Merz, Michael Rode, and Kay Knoeller Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b01057 • Publication Date (Web): 22 Jul 2016 Downloaded from http://pubs.acs.org on July 25, 2016

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Environmental Science & Technology

1

Discharge driven nitrogen dynamics in a mesoscale

2

river basin as constrained by stable isotope patterns

3

Christin Mueller1*, Matthias Zink2, Luis Samaniego2, Ronald Krieg1, Ralf Merz1, Michael Rode3,

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Kay Knöller1

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[1] Helmholtz Center for Environmental Research – Department Catchment Hydrology - Stable

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Isotope Group, Theodor-Lieser-Straße 4, D-06120 Halle (Saale), Germany

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[2] Helmholtz Center for Environmental Research – Department Computational Hydrosystems -

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Permoserstraße 15, 04318 Leipzig, Germany

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[3] Helmholtz Center for Environmental Research – Department Aquatic Ecosystem Analysis -

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Brückstraße 3a, 39114 Magdeburg, Germany

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*Corresponding Author. Christin Mueller Address: Helmholtz Centre for Environmental Research, UFZ Theodor-Lieser-Str. 4 06120 Halle (Saale) – GERMANY Phone: +49 345 558 5490 E-Mail: [email protected]

18 19

KEYWORDS: stable isotope pattern, regional process dynamics, mesoscale hydrological model (mhM), TERENO

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ACS Paragon Plus Environment


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ABSTRACT: Nitrate loads and corresponding dual-isotope signatures were used to evaluate large

22

scale N dynamics and trends in a river catchment with a strong anthropogenic gradient (forest

23

conservation areas in mountain regions, and intensive agriculturally used lowlands). The Bode

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River catchment with an area of 3200 km2 in the Harz Mountains and central German lowlands

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was investigated by a two years monitoring program including 133 water sampling points each

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representing a sub-catchment. Based on discharge data either observed or simulated by the

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mesoscale Hydrological Model (mhM) a load based interpretation of hydrochemical and isotope

28

data was conducted. Nitrate isotopic signatures in the entire catchment are influenced by (I) the

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contribution of different nitrogen sources, (II) by variable environmental conditions during the

30

formation of nitrate, and (III) by a minor impact of denitrification. For major tributaries, a

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relationship between discharge and nitrate isotopic signatures is observed. This may in part be

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due to the fact, that during periods of higher hydrologic activity a higher wash out of isotopically

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lighter nitrate formed by bacterial nitrification processes of reduced or organic soil nitrogen

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occurs. Beyond that, in-stream denitrification seems to be more intense during periods of low

35

flow.

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INDRODUCTION

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Nitrogen cycling in river systems impacted by human activity. Nitrogen is an essential

38

nutrient for the synthesis of nucleic acids and proteins, for the growth of biomass and thus for

39

agriculture production. On the other hand, it is a major multiplier on eutrophication of rivers,

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lakes and coastal ecosystems. The emission of nitrate has increased in the last decade triggered

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by three main causes: (1) the application of organic or inorganic fertilizer; (2) the widespread

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cultivation of rice, legumes, and other crops that are capable of converting atmospheric N2 to

43

organic nitrogen and (3) the combustion of fossil fuels which transforms gaseous N2 and fossil N


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to reactive nitrogen oxides1-3. To control the impact of nitrate and its sustainable mitigation in

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aquatic systems, it is crucial to understand and quantify sources as well as biochemical processes

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which (permanently) remove nitrate4-8. Isotope signatures of nitrate can help identify and quantify

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potential nitrate sources and characterize biogeochemical N-transformation processes9-14.

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Detailed information about relevant biochemical processes of natural and anthropogenic nitrate

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sources in the nitrogen cycle can be found in the support information part.

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Microbiological isotope fractionation in N-compounds. The two major nitrate consuming

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bacterial processes in aquatic systems, NO3- denitrification and assimilation, result in significant

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isotopic fractionation rates (Table S1, supporting information). Nitrate which contains light

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isotopes (14N and

16

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isotopes (15N and

18

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processes, which is measured in the streams, is generally more enriched in heavy isotopes.

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Impact of environmental conditions on the N-cycle in a river system. Nitrate process

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dynamics in river catchments are influenced by climate related variables and their changes,

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especially increasing stream temperature and discharge variability due to changed precipitation

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patterns15. Similarly, denitrification rates increase with higher water temperatures2,7. Boyacioglu

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et al.15 found that especially in wet periods and with the canalization of streams, denitrification

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rates decrease. However, discharge variability has a higher impact on denitrification than

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seasonal temperature changes15. In contrast, Wexler et al.16 observed microbial denitrification in

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temperate forest catchments in groundwater only, whereby surface water showed no evidence for

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the occurrence of bacterial denitrification. They also found that during small rain events

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precipitation mobilizes nitrate that makes up about 34 % of NO3- in the stream water. This nitrate

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is obviously derived from the microbial nitrification of soil-nitrogen. Generally, N-inputs by

O) is generally more rapidly consumed by bacteria than nitrate with heavy O). Thereby, the residual nitrate pool during microbiological fractionation



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atmospheric deposition exceed the export from catchments. This implies an accumulation in the

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soil zone or a loss as gaseous N2 during denitrification17, 18.

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OBJECTIVES

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Past research related to the quantitative assessment of the nitrogen balance in catchments is

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mainly focused on small catchments6,16 or does not include the advantage of stable isotope

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observations19. In this study, we clarify potentially occurring nitrogen transformation processes

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on a meso- to large scale river catchment considering hydrochemical and stable isotope

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observations. The purpose of this study is to examine spatio and temporal variabilities of nitrate

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stream loading related to land use and human impact on the N-cycle. This includes the

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identification of different nitrogen sources and microbiological turnover processes that consume

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or produce nitrate (Table S1 in supporting information) using

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combined with water chemical parameters and hydrological modeling. Furthermore, we intended

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to recognize and evaluate predominate catchment transformation zones on a large scale and the

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impact of different hydrological conditions (base flow and high flow) on the catchment scale

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nitrogen dynamics. Also, we want to investigate to what catchment size (stream order)

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information on N-sources and N-turnover processes can be reliably differentiated by isotope

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methods. With our study, we want to contribute a tool set that helps predict the nitrate export

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from large river systems as a prerequisite for suggesting future safety precautions that guarantee a

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good ecological status of the water bodies from headwater to coast.

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STUDY AREA

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The Bode catchment is a hydrologically and hydrochemically intensively studied region in

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central Germany (Figure 1). It is part of the Terrestrial Environmental Observatories (TERENO)

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network which is hosted within the Helmholtz Association of German Research Centres20. The

15


N and

18

O nitrate isotope data

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Bode catchment is characterized by a spatial gradient of diverse nitrate sources. The southern

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natural protected headwaters in the Harz Mountains represent the low nitrate concentrated region.

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With increasing river length the impact of agricultural land use increases. Major tributaries can be

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differentiated into four main groups after Mueller et al.21:

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

Firstly, the mountainous river headwaters with a discharge of about 28 % of total flow

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discharging into the Rappbode drinking water reservoir including the Kalte Bode, Warme

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Bode, Rappbode and Luppbode stream.

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

Secondly, transfer streams (Hassel, Selke and Holtemme with 27 % of the total

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discharge) flow between protected areas dominated by forest and areas of agricultural

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land use. Within the group of transfer streams, 21 sub-catchments close to the headwater

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catchments are solely covered by forest. Further downstream, 9 sub-catchments are

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characterized by an increasing impact of agricultural land use and a mixed forest and

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agricultural zone (forest and agricultural land use covering each 50 % ± maximal 10 %).

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The tributaries are dominated mainly by agricultural land use (>60 % of each sub-

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catchment is covered by arable land) which is the case for 13 sub-catchments.

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

with 21 % of total discharge).

107 108 109

Thirdly, the northern agricultural lowlands are dominated by arable land (Großer Graben



Fourthly, other minor tributaries which are not considered in the sampling network or diffuse inflow (including groundwater) account for 22 % of the total discharge.

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Further characterization of major streams, climatic conditions as well as landscape information

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can be found in Mueller et al.21.

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The spring water from the Harz Mountains is used for drinking water and electrical power supply

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which is collected in the Rappbode reservoir. The LHW (Landesbetrieb für Hochwasserschutz ACS Paragon Plus Environment


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und Wasserwirtschaft) Saxony-Anhalt has 30 observation gauges which continuously register

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water-levels along the Bode River and major tributaries (Figure 1).

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METHODS

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Monitoring approach. In 2012, we started our two-year monitoring campaign in the Bode River

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catchment. It includes 133 sampling sites (Figure 1) which represent nearly undisturbed

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mountainous regions, forests, settlement areas as well as anthropogenic impacted areas by waste

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water and agricultural land use. Further information about regional, climatic and hydrological

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aspects are given in Zacharias et al.20 and Mueller et al.21. Spatial high resolution sampling

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campaigns were performed three times per year. Low resolution sampling campaigns including

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25 representative test sites were sampled monthly (starting in 2013), between the high resolution

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campaigns. All main tributaries flowing into the Bode River were included into the monitoring

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network. Precipitation was collected monthly at the 25 representative test sites. During high and

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low resolution sampling campaigns, water samples were collected at the outlets of the major sub-

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catchments. Water samples were filtered in the field (0.45 µm cellulose acetate filter) and stored

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in high density polyethylene (HDPE)-bottles in the refrigerator. Additionally, all water samples

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were measured for pH-value, temperature and electrical conductivity in the field. The density of

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sampling spots is much higher in the southern mountainous area due to the high number of small

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creeks compared to the northern lowland area.

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Laboratory analysis: nitrate and isotopic signatures. Laboratory analyses on all water samples

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were conducted for stable isotope signatures of nitrate (δ15N and δ18O) and corresponding NO3

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contents within one month after sampling. Concentration of nitrate in water was analyzed by ion

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chromatography using a Dionex ICS-2000 combined with AS50. Nitrate isotopic signatures were

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measured with the denitrifier method by using bacteria strains of Pseudomonas chlororaphis


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(ATCC #13985)23,24. Isotopic ratios are expressed in delta notation (equation 1). δ15NNO3 is

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reported relative to atmospheric nitrogen (AIR), whereby δ18ONO3 is expressed relative to Vienna

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Standard Mean Ocean Water (VSMOW), calculated as:

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𝑅𝑠𝑎𝑚𝑝𝑙𝑒

𝛿𝑠𝑎𝑚𝑝𝑙𝑒 [‰] = (𝑅

𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑

− 1) × 1000

[1]

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Ratio R describes the relation between heavier and lighter isotopes. For the analytical

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measurement a mass DELTA V Plus spectrometer was used in combination with a GasBench II

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from Thermo Scientific. The standard deviation of the described analytical measurement

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including sampling and preparation uncertainties is ±1.6 ‰ for δ18O and ±0.4 ‰ for δ15N.

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Isotope measurements are conducted in duplicates and are repeated when the standard deviation

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exceeds the quality control threshold stated above. For calibration of nitrogen and oxygen isotope

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values, the reference nitrates IAEA-N3 (δ15N: +4.7 ‰ AIR; δ18O: +25.6 ‰ VSMOW), USGS-32

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(δ15N: +180 ‰ AIR; δ18O: +25.7 ‰ VSMOW), USGS-34 (δ15N: -1.8 ‰ AIR; δ18O: -27.9 ‰

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VSMOW), and USGS-35 (δ15N: +2.7 ‰ AIR; δ18O: +57.5 ‰ VSMOW) were used.

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Regionalization of runoff: Hydrological modeling and nitrogen loads. The purpose of

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simulating the discharge for all investigated sub-catchments of the Bode River basin is to provide

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information on nitrate export from the respective sub-catchments and to understand the process

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dynamics across scales. Large stream basins are the sum of its associated sub-catchments and can

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be regionalized with different rainfall-runoff models, as explained and compared in e.g. Oudin et

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al.25. The mesoscale Hydrologic Model mHM used in this study is based on accepted

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hydrological conceptualizations and employs a multi-scale parameter regionalization (available at

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www.ufz.de/mHM). It is predicated on numerical approximations of commanding hydrological

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processes. Detailed information can be found in Samaniego et al.26. The software mHM is a



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distributed hydrological model which treats grid cells as hydrological units. It is a conceptual

160

hydrological model similar to HBV27 or VIC28, which considers interception, snow accumulation

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and melting, infiltration, soil water retention, recharge, groundwater storage and runoff

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generation as main hydrological processes. The generated discharge consists of direct runoff,

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slow and fast interflow and baseflow. It is routed through the study domain using the Muskingum

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method after Tewolde et al.29.

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A key feature of mHM is the estimation of effective parameters called Multiscale Parameter

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Regionalization technique (MPR26). Effective model parameters are estimated using a two-step

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approach. In a first step the parameters (e.g. porosity) are estimated on the resolution of the

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morphological input data (i.e. 100 x 100 m). With the help of transfer functions (e.g. pedotransfer

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function) and a couple of transfer parameters (e.g. coefficients of the pedotransfer function),

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which are associated with terrain characteristics (e.g. sand and clay content), spatially distributed

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parameter fields are estimated. In a second step, this parameter fields are upscaled to the

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resolution of the hydrological simulation (i.e. 1 x 1 km2). Thus, mHM explicitly accounts for sub

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grid variabilities and depends only on a couple of transfer or global parameters, which are space

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and time invariant. They are estimated during an automated calibration. MPR enables mHM to

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perform satisfying across different locations and scales26.

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The objective of the calibration is to minimize the Nash Sutcliffe Efficiency (NSE)30 of discharge

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and the logarithm of discharge using the Dynamical Dimensioned Search Algorithm31. The NSE

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(equation 2) is calculated as

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𝐸 = 1.0 −

∑𝑁 𝑖=1(𝑄𝑜𝑏𝑠 (𝑖)− 𝑄𝑠𝑖𝑚 (𝑖))² ∑𝑁 𝑄𝑜𝑏𝑠 (𝑖)− 𝑄̅𝑜𝑏𝑠 )² 𝑖=1


[2]

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̅ obs the mean where Qobs denotes the observed discharge, Qsim the simulated discharge and Q

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observed discharge for all observations N. The calibration is done on the spatial resolution of

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2 km in the time period 2000-2009, whereas discharge simulations for analyzing nitrate loads are

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conducted at 1 km spatial resolution. The calibration of mHM at a coarser resolution is reasoned

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in the savings in computational time. Kumar et al.32 showed that this is a reasonable choice.

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Since the Bode catchment is highly influenced by human interaction, the model has been fed by

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measured discharge time series downstream the area of the biggest reservoir, the Rappbode dam.

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Thus, the simulated upstream discharge time series has been neglected at this point. The feeding

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or inflow gauge is located at gauge B1 shown in Figure 1. For calibration the gauges B3 and S4

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are used (Figure 1). B3 is the gauge which is representing the headwaters of the Bode River and

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advantageously, is still not influenced by the hydraulic connection to the neighboring catchment.

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S4 is the outlet of the Selke River, which is the most natural behaving sub-catchment in the Bode

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basin. Furthermore, it is used for detailed nitrate dynamics analysis within this study. The

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estimated discharge at 1 x 1 km2 is evaluated at 20 out of 30 gauges, which were not or only

194

insignificantly influenced by human activity. All simulations are conducted with a spin up period

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of 10 years to ensure that the initial conditions are reasonable.

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RESULTS AND DISCUSSION

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Discharge simulation. The discharge simulations conducted with the hydrological model mHM

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could not be evaluated at all gauges available in the Bode catchment, because gauges downstream

199

of the Rappbode dam are influenced by upstream water regulations or uptake. The results of the

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majority of the remaining gauges indicate a sufficient estimation of discharge with an average

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NSE of 0.65 for all gauges in the validation period 1980-1999 (Table 1) according to Moriasi et

202

al.33. The gauge GG1 reveals low performances because a hydraulic connection from the Großer



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Graben to the neighboring catchment does exist. This connection leads to the exchange of water

204

between both catchments. At the river Holtemme and its tributaries, many water regulations are

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located in the upstream areas. In consequence, the downstream gauges are impacted by these

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regulations. This is the reason for lower NSEs at the gauges HO1, HO3 and HO10. One of the

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largest summer floods during decades took place in June 2013 in the Mulde, Saale and Elbe

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catchment, of which the Bode is a tributary34. This extreme event is captured by the model

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simulation, but the peak discharge is usually underestimated. One of the reasons is the magnitude

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of the event, which has rarely been observed before. This induces errors in the observed

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discharge time series, due to considerable uncertainties in the rating curve. Another reason could

212

be erroneous precipitation data, caused by the high wind speeds during the storm event occurring

213

in connection with the flood event and insufficient spatial density of rainfall gauges for that

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event. The underestimation of the flood event in May and June of 2013 can be observed in

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Figure 2 for the Selke tributary and the entire Bode catchment. The underestimation of discharge

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at the Bode outlet (Figure 2) during this period is additionally influenced by supplementary water

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entering the Bode catchment from the neighboring catchment via the hydraulic connection at the

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tributary Großer Graben. Finally, the model simulations are sufficient for relating loads observed

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at the sampling points with simulated discharge.


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Table 1. Observed and simulated discharge at gauge stations (o - outlet, MQ - mean discharge,

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NSE - Nash-Sutcliffe efficiency)

Landscape type

Intensive agriculture

Intensive agriculture and forest

Headwater

ID

222 223

*2

NSE

Time period

MQ [m³ s-1]

2000-2009

1980-1999

2012-2013

5.48

0.90

0.91

0.73

B2

Bode

1961-2013

B3

Bode

1893-2013

7.80

0.89

0.85

0.63

B4

Bode

1930-2013

12.97

0.81

0.79

0.68

B5 GG1

Bode (o) Großer Graben (o)

1988-2013

12.64

0.71

0.75

0.67

1986-2013

2.41

0.44*1

0.51*1

0.57*1

HO1

Holtemme

1961-2013

0.28

0.38

0.40

0.38

HO2

Holtemme

1970-2013

0.34

0.67

0.60

0.58

HO3

Holtemme

1983-2013

0.13

0.59

0.40

0.45

Holtemme

1971-2013

1.34

0.65

0.72

0.59

HO8

0.47

*2

0.49

*2

0.56*2

HO10

Holtemme (o)

1982-2013

1.55

S1

Selke

2006-2010

0.33

0.72

-

-

S2

Selke

1948-2013

1.13

0.64

0.68

0.56

S3

Selke

1920-2013

1.46

0.70

0.69

0.45

S4

Selke (o)

1980-2013

1.73

0.79

0.74

0.54

KB1

Kalte Bode (o)

1950-2013

0.72

0.57

0.52

0.42

WB1

Warme Bode

1960-2013

2.06

0.81

0.69

0.75

WB2

Warme Bode (o)

1950-2013

2.42

0.64

0.67

0.75

HA1

Hassel (o)

1968-2013

0.54

0.80

0.52

0.69

RB1

Rappbode (o)

1950-2013

0.75

0.73

0.71

0.76

Luppbode (o)

1996-2013

0.35

0.60

0.80

0.47

LB1 *1

River

hydraulic connection to the neighboring catchment impact of regulated tributary (Zillierbach dam)

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Distribution of discharge and nitrate load on catchment scale. The average annual discharge

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intensity on the Bode River outlet (B5) is about 8.3 m3s-1 in 2012 and 14.9 m3s-1 in 2013 with a

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mean nitrate load of about 6.5 t per year. The distribution of mean discharge of major tributaries

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of the Bode catchment over the years 2012 and 2013 is shown in Figure 3A. Discharge at the

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Bode outlet is composed of four components of comparable size: (I) headwater tributaries, (II)

229

tributaries in mixed land use catchment with intensive agriculture and forest (transfer streams),

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(III) tributaries in catchments with agricultural land use only and (IV) minor tributaries directly to

231

the Bode River or other unspecified inflows such as groundwater.



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The corresponding calculated nitrate loads are distributed differently as shown in Figure 3B. The

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lowland agricultural tributaries (Großer Graben) and the transfer streams (Holtemme and Selke

234

River) feature the major nitrate impacts with 73 % of the total nitrate load. Tributaries upstream

235

the Rappbode dam in the high mountains transport low amounts of nitrate (6 % of the total nitrate

236

load). Most of the samples were taken in the agricultural regions with 56 samples representing

237

the major land cover type covering 75 % of the entire Bode catchment area. The forest region and

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headwaters are characterized by samples from 37 sub-catchments and 24 sub-catchments,

239

respectively. The remaining 16 sub-catchments show a mixed land cover type of intensive

240

agriculture and forest (each 50 % ± maximal 10 %).

242

243

For further temporal and spatial process dynamics analysis on catchment scale, the discharge and

244

corresponding nitrate loads are weighted to the respective sub-catchment size. The area-weighted

245

or so-called specific discharge (Qspecific) and nitrate loads ((NO3-)specific, are calculated using

246

equation (3) and (4)), respectively.

247

248

𝑄𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐 = 𝐴

𝑄𝑠𝑖𝑚

𝑠𝑢𝑏 𝑏𝑎𝑠𝑖𝑛

(𝑁𝑂3− )𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐 =

[mm a-1]

𝑁𝑂3− 𝑐𝑜𝑛𝑐. ∗ 𝑄𝑠𝑖𝑚 𝐴𝑠𝑢𝑏 𝑏𝑎𝑠𝑖𝑛

[3]

[g m-2 a-1]

[4]

249

Temporal and spatial dependency of specific nitrate loads and isotope signatures on specific

250

discharge. To detect possible nitrate sources and sinks, the comparison of specific discharge and

251

nitrate loads between major land cover types along the river system can provide useful

252

information21. In general, the specific nitrate load increases with increasing specific discharge.

253

For agricultural catchments specific nitrate load is much higher than for non-agricultural


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catchments for the whole range of specific discharges (Figure 4A). The specific nitrate loads in

255

agricultural areas are significantly higher compared to non-farming regions with a maximum

256

specific load of 70.3 g m-2 a-1 and a corresponding specific runoff of 2.2 mm a-1. The specific

257

nitrate loads for headwater, forest and mixed regions do not differ significantly between each

258

other. The strongest correlation between specific discharge and specific nitrate load is obvious for

259

agricultural catchments. For all other land use types, correlations are much weaker. This implies

260

that the nature of the nitrate source is different between agricultural and all other land use types.

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The temporal variations of nitrate concentrations in (mainly lowland) tributaries are higher than

262

in the main stream itself. The highest coefficients of variation cv related to temporal nitrate load

263

variability of up to 40 % are obvious for agricultural tributaries in the northern sub-basins

264

(Figure 4B). A significant role plays the temporal differentiated release of fertilizer in the

265

agricultural areas as well as fluctuating contribution of urban waste water.

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The relationship between mean isotopic signatures of nitrate (δ15N/δ18O) for each sub-catchment

267

and the proportion of agricultural land use is shown in Figure 5A and B. The nitrogen isotopic

268

signature generally increases with increasing agricultural portions (Figure 5A). The outlet of the

269

main river indicates a δ15NNO3 value of +12 ‰ ± 1 ‰. Headwaters and catchments with forest

270

land use show a variation range of δ15NNO3 from 0 ‰ to about 8 ‰ with low mean nitrate

271

concentrations of 4.4 mg L-1. These values are slightly higher than in other studies which report

272

δ15NNO3 in forested watersheds between -1 ‰ and +5 ‰35. For forested catchments with more

273

than 20 % of agricultural land use, a positive shift of the N isotope signatures up to +11 ‰

274

indicating the impact of N sources related to agricultural practice including waste water.

275

Generally, nitrate sources from agricultural land use are characterized by relatively elevated

276

nitrogen isotopic signatures up to +25 ‰13 which are obvious for agricultural dominated

277

tributaries (Figure 5C and E). Synthetic fertilizers show only insignificant impact on the isotopic ACS Paragon Plus Environment


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δ15N signature of the stream network, because they could only lower nitrogen isotopic values

279

towards typical signatures between -3 ‰ and +3 ‰ and corresponding oxygen isotopic signature

280

between +23 ‰ and +24 ‰13,37. Increasing inflow of urban or agricultural waste water into the

281

stream network (Figure 5E) is assumed to result in a higher pollution especially in the lower

282

Bode region36. On the contrary, the amount of waste water for the major Selke stream just

283

accounts 2.9 % of the entire nitrate loads36.

284

The oxygen isotopic signature of nitrate at the sub-catchment outlets varies between -0.3 and

285

+9.8 ‰. The main river is a mixture of these signatures and varies between +2.9 and +6.7 ‰.

286

Large δ18ONO3-variations in small catchments can be explained either by reactive isotope

287

fractionation processes during nitrate formation and their dependency on temperature and

288

hydrological conditions or by a minor yet variable direct input of atmospheric nitrate (δ18O-NO3

289

+75 ‰ for the Bode region after Mueller et al.21) into the stream system. Generally, the oxygen

290

isotopic signatures of nitrate do not show a significant correlation with major land use types

291

(Figure 5B). However, it can be noted that, especially in catchments with agricultural land use,

292

elevated N isotope signatures are accompanied by slightly elevated O isotope signatures (dual

293

nitrate plot in Figure 5E). This may indicate a certain impact of bacterial denitrification as a

294

nitrate consuming process occurring in groundwater feeding the springs or in the stream itself38.

295

Correlations between isotope signatures and nitrate concentration data do not provide additional

296

evidence for the occurrence of denitrification. This might be due to the fact that the variation of

297

the initial nitrate concentrations exceeds the concentration impact of denitrification.

298

Nitrate dynamics related to different hydrological conditions. Slightly elevated oxygen

299

isotope signatures suggest that only a small share of stream water nitrate in headwater catchments

300

directly derives from atmospheric deposition. Groundwater feeding the springs during base flow ACS Paragon Plus Environment

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conditions indicate recycling (immobilization with subsequent mineralization) of atmospheric

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nitrate within the soil zone. During high flow events (Figure 6A), oxygen isotopic signatures of

303

nitrate follow a similar pattern as during base flow conditions (Figure 6B). Elevated nitrate

304

concentrations during high flow, however, indicate a more intense mobilization of nitrified soil

305

nitrogen that originates either from the atmosphere or from the decay of organic material.

306

Catchment scale assessment of predominant nitrogen transformation zones. To assess nitrate

307

dynamics in a nearly natural environment we selected the Selke River, a tributary of the Bode, as

308

a special study site. Within this catchment we try to identify the influence of changing land use

309

along the river course on nitrate isotopic signatures (Figure 1). According to Jiang et al.37 and

310

Musolff et al.19, the mesoscale Selke River shows increasing inorganic nitrogen (IN as

311

ammonium, nitrate or nitrite) concentrations with rising runoff. Furthermore, the Selke region is

312

affected by high temporal and spatial variations of IN leaching, influenced by hydrological

313

transport processes37. The mean concentration of inorganic nitrogen along the Selke River

314

increases downstream at the discharge stations (S2 to S3 to S4) from 1.44 to 1.75 to 3.91 mg L-1

315

37

. Corresponding nitrate isotopic signatures show variations from +4 to +13 ‰ for δ15NNO3

316

(Figure 7A) and 0 to +5 ‰ for δ18ONO3 (Figure 7B), respectively. Generally, the nitrate isotopic

317

signature increases downstream the Selke River from gauges S2 to S3 to S4. This is due to the

318

increasing impact of agricultural land use. Coefficients of determination (R2) for the correlation

319

between measured discharge and N-isotope signatures of nitrate range between 0.30 and 0.47 for

320

Selke River stations. Lower R2 values for Bode River stations (0.16 to 0.20) indicate an even

321

weaker correlation. Even though weak, the observed correlation between discharge and N-isotope

322

signatures may in part be related to an advanced high flow mobilization of N from the

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nitrification of reduced or organic soil nitrogen (as indicated by a positive correlation between



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discharge and N-concentrations, see: Jiang et al.37 and Musolff et al.19) that relatively less

325

actively emits nitrate during periods of low flow.

326

Even though the coefficients of determination (R2) for the correlation between measured

327

discharge and O-isotope signatures of nitrate are very low (0.12 to 0.49 for Selke River stations),

328

a relationship seems obvious in a way that higher discharge is accompanied by decreased δ18ONO3

329

values. Such trend might in part be due to the enhanced mobilization of isotopically lighter nitrate

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formed by bacterial nitrification processes of reduced or organic soil nitrogen during periods of

331

higher hydrologic activity and the resulting higher wash out. During nitrification, the oxygen

332

isotope signature of nitrate is controlled by the incorporation of O-H2O and O-O2 (+23.5 ‰) into

333

the nitrate molecule. Normally, two O atoms are derived from ambient water while one O atom

334

comes from dissolved oxygen. However, due to variable kinetic and equilibrium fractionation

335

during O incorporation an additional uncertainty of up to 5 ‰ is introduced to the 2:1 ratio39.

336

Such different discharge scenarios and the related differences of the residence time in the reactive

337

zone, where nitrification is taking place, may have significant impact on the isotope fractionation

338

during O-incorporation. Consequently, oxygen isotope signatures of nitrate depend in part on the

339

discharge intensity (Figure 7B). The isotopic signature of NO3 in precipitation does not affect

340

significantly the isotopic signature of the Selke tributary because it would increase δ18ONO3

341

values21.

342

Beyond a different mobilization scheme during periods of higher flow, the observed correlation

343

pattern between isotope signatures, nitrate concentrations and discharge may in part be due to the

344

in-stream turnover of nitrate either by denitrification or by uptake. During low flow conditions, a

345

more intensive denitrification following oxygen depletion of stream water nitrate with biofilms

346

on the stream bed substrate would increase the impact of the nitrate turnover and result in


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347

elevated isotope signatures of both nitrogen and oxygen in nitrate (Fig. 7 A, B) and lower nitrate

348

concentrations37. The slope of the regression line between oxygen and nitrogen isotope signatures

349

measured at three gauging stations of the Selke River is close to 0.5, a value that is considered to

350

be characteristic for denitrification in terrestrial aquatic systems13. For the two Bode River

351

gauging stations this correlation becomes very weak as mixing processes of nitrate from all

352

tributaries mask the potential isotopic effect of in-stream nitrate turnover.

353

As a consequence of the general dependency of the variability of isotope signatures on discharge

354

intensity, a reliable resolution of source and process dynamics is only possible for smaller sub-

355

catchments. While an evaluation of the isotope data set with respect to source delineation and

356

certain target processes such as denitrification is still possible for the Selke River catchment, an

357

increasing impact of mixing processes on the isotope signal in the main river does not allow for a

358

differentiated analysis of the N dynamics anymore. After all, our study reveals that the nitrate

359

degradation potential in groundwater or stream water is insufficient to control the nitrate export

360

from the Bode River watershed. Therefore, nitrate loads in the river system can only be regulated

361

by preventive measures especially by an optimized land use management.

362

AUTHOR CONTRIBUTIONS

363

The manuscript was drafted by Christin Mueller, who performed the sampling, partly laboratory

364

analysis and interpretation of the data under the direct supervision of Kay Knöller and Ralf Merz.

365

Michael Rode gave useful information for interpreting the isotope results and a model approach.

366

Ronald Krieg was continuously involved in the sample collection and characterization of related

367

parameters. Luis Samaniego and Matthias Zink developed and performed the mesoscale

368

Hydrological Model.



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369

ACKNOWLEDGMENT

370

For the assistance in the field and laboratory we would like to thank Julia Jäger, Benedikt Ahner

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and Stefanie Kolbe. Furthermore, we give our sincere thanks to Martina Neuber, Silke Köhler,

372

Petra Blümel and Wolfgang Städter for running numerous chemical and isotope analyses. We

373

kindly acknowledge our data providers, the German Weather Service (DWD), the Joint Research

374

Center of the European Commission, the European Environmental Agency, the Federal Institute

375

for Geosciences and Natural Resources (BGR), the European Water Archive and the Global

376

Runoff data Centre. The project is supported by TERENO (Terrestrial Environmental

377

Observatories of the Helmholtz Association). The River Network is supported by ATKIS R DLM

378

1000 © Bundesamt für Kartographie und Geodäsie, 2003. Topographical information is based on

379

Geobasisinformation of the Vermessungs- und Katasterverwaltung by permission of Landesamt

380

für Landesvermessung und Datenverarbeitung Sachsen-Anhalt Gen.-Nr. I, Verm D/P/086/95.

381

SUPPORTING INFORMATION

382

Information about biochemical processes of natural and anthropogenic nitrate sources in the N-

383

cycle can be found in Supporting information (Text S1). Table S1 shows selected nitrogen

384

enrichment factors (ε) with relation to associated nitrate-oxygen ratios.

385

REFERENCES

386 387 388 389 390 391 392 393

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TOC TOC 142x80mm (96 x 96 DPI)


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Figure 1. Location of the Bode catchment with major sub-catchments and spatial distribution of stream discharge gauges. The Selke tributary is heightened with runoff stations and land use information22. Figure 1 570x413mm (72 x 72 DPI)



Figure 2. Simulated versus observed discharge at the Selke tributary outlet (S4) and the Bode River outlet (B5). Seasonal (black arrow) and monthly (grey arrow) sampling campaigns are indicated on the temporal scale. Figure 2 371x292mm (72 x 72 DPI)


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Figure 3 A. Annual mean discharge distribution of major Bode River tributaries in 2012 and 2013; B. Distribution of calculated nitrate loads based on measured discharge and nitrate concentration Figure 3 709x384mm (72 x 72 DPI)



Figure 4 A. Trend between specific discharge and specific nitrate load the stream differentiated between major land use types. B Relationship between calculated portion of agricultural land use for each subcatchment and the coefficient of variation cv in the nitrate concentration. Additionally, the spatial distribution of major land cover types and corresponding cv is shown for the entire Bode River catchment. Figure 4 340x511mm (72 x 72 DPI)


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Figure 5. Relationship between mean δ15N (A) and δ18O (B) for nitrate and portion of agricultural land use; Relationship between specific nitrate loads and nitrate isotopic signatures δ15N (C) and δ18O (D) as well as mean dual nitrate isotope plot (E) with typical NO3- source signatures Figure 5 493x508mm (72 x 72 DPI)



Figure 6. Contrasting hydrological conditions (high flow (A) and base flow (B)) with corresponding nitrate concentration and δ18O-NO3- of stream water and precipitation water related to major land-cover type and sub-catchment size. Figure 6 610x649mm (72 x 72 DPI)


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Figure 7. Relationship between measured nitrate isotopic signature (δ15N (A) and δ18O (B)) (S2-S3-S4, monthly measurements in 2013) and continuously measured discharge at stations along the tributary Selke River. For comparison, nitrate isotopic signatures and discharge measurements are shown for stations of the Bode River right downstream of the inflow of the Selke River into the Bode River (B4) and on the outlet of the Bode River (B5). Figure 7 415x533mm (72 x 72 DPI)


Discharge Driven Nitrogen Dynamics in a Mesoscale River Basin As

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