Geochimica et Cosmochimica Acta, Vol. 67, No. 14, pp. 2529 –2542, 2003
Copyright © 2003 Elsevier Science Ltd
Printed in the USA. All rights reserved
0016-7037/03 $30.00 ⫹ .00
Pergamon
doi:10.1016/S0016-7037(03)00085-1
Modelling nitrogen and oxygen isotope fractionation during denitrification in a lacustrine
redox-transition zone
MORITZ F. LEHMANN,1,* PETER REICHERT,2 STEFANO M. BERNASCONI,1 ALBERTO BARBIERI,3 and JUDITH A. MCKENZIE1
2
1
Institute of Geology, ETH Zürich, Sonneggstrasse 5, CH-8092 Zürich, Switzerland
Swiss Federal Institute for Environmental Science and Technology (EAWAG), CH-8600 Dübendorf, Switzerland
3
Laboratorio Studi Ambientali, Riva Paradiso, CH-6900 Lugano, Switzerland
(Received February 28, 2002; accepted in revised form January 22, 2003)
Abstract—The stable isotope composition (␦15N and ␦18O) of nitrate was measured during Summer 1999 in
the anaerobic hypolimnion of eutrophic Lake Lugano (Switzerland). Denitrification was demonstrated by a
progressive nitrate depletion coupled to increasing ␦15N and ␦18O values for residual nitrate. Maximum ␦15N
and ␦18O values amounted to 27.2 and 15.7‰, respectively.15N and 18O enrichment factors for denitrification
(␧) were estimated using a closed-system model and a dynamic diffusion-reaction model. Using the Rayleigh
equation (closed-system approach), we obtained ␧ values of ⫺11.2 and ⫺6.6‰ for nitrogen and oxygen,
respectively. The average ␧ values derived using the diffusion-reaction model were determined to be ⫺20.7
⫾ 3.8 for nitrogen and ⫺11.0 ⫾ 1.7 for oxygen. Both N and O isotope fractionation appeared to be lower
when denitrification rates where high, possibly in association with high organic carbon availability. In
addition, variations in the isotope effects may be attributed to the variable importance of sedimentary
denitrification having only a small isotope effect on the water column. The combined measurement of N and
O isotope ratios in nitrate revealed that coupled nitrification-denitrification in the open-water was of minor
importance. This is the first study of nitrogen and oxygen isotope effects associated with microbial denitrification in a natural lake. Moreover, this study confirms the high potential of ␦18O of nitrate as a valuable
biogeochemical tracer in aquatic systems, complementing nitrate ␦15N. Copyright © 2003 Elsevier Science
Ltd
et al., 1995; Ganeshram et al., 1995, 2000; Emmer and Thunell,
2000).
To use natural abundance nitrogen and oxygen isotope ratios
as tracers for bacterially-mediated nitrate reduction and to
obtain quantitative estimates on its intensity, it is essential to
constrain the associated isotope fractionation. Several marine,
groundwater and culture studies addressed the nitrogen isotope
effect of denitrification. Measured nitrogen isotope enrichment
factors (␧) range from ⫺40 to ⫺5‰ (Table 1), reflecting a
variety of environmental and experimental conditions. Estimates of oxygen isotope fractionation during denitrification are
rare (Table 1) and neither nitrogen nor oxygen isotope enrichment factors for microbial nitrate reduction in lacustrine environments exist. In addition, the variability of isotope enrichment factors, as a function of the microbial nitrate-reduction
rate, has not been examined in a lake.
In the present study, we investigate the isotopic fractionation
of nitrogen and oxygen isotopes associated with denitrification
in the southern basin of Lake Lugano (Switzerland/Italy). We
present the evolution of nitrate concentration and ␦15N and
␦18O profiles during the seasonal thermal stratification of the
hypolimnion. Using these data, we derive the first nitrogen and
oxygen isotope enrichment factors of microbial denitrification
for lacustrine systems. We apply a closed-system (Rayleighdistillation) model and a dynamic vertical diffusion-reaction
model to simulate the distribution of ␦15N and ␦18O values for
nitrate in the water column and compare the results from the
two models. The dynamic character of the reaction-diffusion
model allows the assessment of variations in nitrate-reduction
rates and the examination of the effect of these variations on the
isotope enrichment. Moreover, in contrast to the closed-system
1. INTRODUCTION
Lakes represent an important continental sink for fixed nitrogen (Billen et al., 1991). Besides the burial of particulate
nitrogen and export through the outflows, fixed nitrogen is
eliminated from lakes by emission of N2 and N2O to the
atmosphere during dissimilative nitrate reduction or denitrification within suboxic and anoxic waters or sediments. The
understanding and quantification of nitrogen cycling processes
in eutrophic lakes is crucial for nitrogen budget modelling and
the application and evaluation of lake restoration measures.
Several approaches to assess freshwater NO3⫺ elimination
have been made (Seitzinger, 1988; Mengis et al., 1997, and
references therein). These include the measurement of natural
abundance stable isotope ratios in dissolved nitrate (e.g., Aravena et al., 1993; Mengis et al., 1999). Like other uni-directional biogeochemical reactions, microbial nitrate reduction,
including denitrification and dissimilatory nitrate reduction to
ammonium (DRNA), exhibits a significant nitrogen and oxygen isotope fractionation (Cline and Kaplan, 1975; Mariotti et
al., 1981; McCready et al., 1983; Amberger and Schmidt, 1987;
Böttcher et al., 1990; Horrigan et al., 1990; Aravena and
Robertson, 1998; Brandes et al., 1998; Cey et al., 1999; Mengis
et al., 1999), so that the ␦15N and ␦18O values of dissolved
nitrate are effective indicators of nitrate dissimilation in aquatic
systems, and the isotope signal of nitrate reduction may be
recorded by organic matter in the sedimentary archive (Altabet
* Author to whom correspondence should be addressed, at the Department of Geosciences, Princeton University, Princeton, NJ 08544, USA
([email protected]).
2529
2530
M. F. Lehmann et al.
Table 1. Estimates of nitrogen (␧N) and oxygen (␧O) isotope enrichment factors for denitrification in culture experiments and natural environments.
Setting
␧N (‰)
Culture
Culture
Culture
Culture
Culture
Culture
Culture
Culture
Groundwater
Groundwater
Groundwater
Groundwater
Groundwater
Groundwater
River (sediments in stagnant annex)
Coastal marine sediments
Continental margin sediments
Central Arabian Sea
Eastern tropical North Pacific
Eastern tropical North Pacific
Eastern tropical North Pacific
–28.6
–12.3 to –13.3
–13.4 to –20.8
–24.6 to –29.4
–20
–30
–2 to –12
–19 to –20
–22.9
–15.9
–4.7 to –5
–27.6
–13.9
–30 ⫾ 6
–1.5
⫾0
0 to –3
–22 to –25
–25 to –30
–30 to –40
–30 ⫾ 7.5
model, the reaction-diffusion model has potential to distinguish
between sedimentary and water-column denitrification.
Brandes and Devol (1997) observed that sedimentary denitrification in the highly reactive sediments of Puget Sound did not
produce any apparent nitrogen isotope fractionation of downward diffusing NO3⫺ (␧Napp ⫽ 0). They hypothesized that the
lack of isotope effect is due to limitation of denitrification by
the diffusion of nitrate into reactive microsites, leading to
complete consumption of nitrate in the sediments. Similarly,
Sebilo et al. (2003) explained minimal N-isotopic fractionation
(␧ ⫽ ⫺1.5 ) associated with benthic denitrification in a stagnant annex of the Marne River. Other authors have recently
attributed very low effective fractionation factors in the Santa
Barbara Basin (Sigman et al., in press) and along the Washington and Mexican continental margins (Brandes and Devol,
2002) to the effect of sedimentary denitrification. Another
purpose of this study is to extend the limited dataset of open
water ␦18O values for nitrate and, therefore, to promote investigations into the potential of this parameter as a geochemical
tracer in aquatic systems, complementing nitrate ␦15N values.
As the nitrogen and oxygen isotopic composition of nitrate are
affected differently by N-cycling processes (e.g., nitrification),
the coupled analysis of nitrate ␦15N and ␦18O holds the potential to deconvolve the effects of coexistent biogeochemical
processes influencing aquatic nitrate pools and, therefore, to
elucidate nitrate elimination and production pathways.
2. MATERIAL AND METHODS
2.1. Water-Column Sampling and Hydrochemical Analysis
The southern basin of Lake Lugano is mono-holomictic, has a
maximum depth of 95 m, and, due to the high primary productivity
levels, is characterized by the progressive development of suboxic and
anoxic bottom waters between June and December. A detailed description of the lake is given by Barbieri and Polli (1992). Water-column
samples from discrete depths were collected in Summer 1999 from the
deepest part of the basin. The sampling campaign was part of an
ongoing lake monitoring and research program conducted by the Labo-
␧O (‰)
–15
–8
–18.3
Reference
Barford et al. (1999)
Blackmer and Bremner (1977)
Delwiche and Steyn (1970)
Mariotti et al. (1981)
Myake and Wada (1971)
Olleros (1983)
Wada (1980)
Wellman et al. (1968)
Aravena and Robertson (1998)
Böttcher et al. (1990)
Mariotti et al. (1988)
Mengis et al. (1999)
Smith et al. (1991)
Vogel et al. (1981)
Sebilo et al. (2003)
Brandes and Devol (1997)
Brandes and Devol (2002)
Brandes et al. (1998)
Brandes et al. (1998)
Cline and Kaplan (1975)
Voss et al. (2001)
ratorio Studi Ambientali in Lugano (L.S.A., 1980 –2000). Waters were
collected with 5-L Niskin bottles and immediately filtered (0.45 ␮m)
before analysis for oxygen and ion concentrations and further sample
processing for isotopic measurements. Nitrogen species concentrations
were measured colorimetrically with an automatic analyser (TRAACS
2000, Bran & Luebbe). The detection limit of the instrument is better
than 5 ␮gN/L. The standard deviation at the given concentrations was
⬍ 1%. O2 concentrations were determined iodometrically by Winkler
titration (American Public Health Association, 1989).
2.2. Preparation for Isotope Analysis
For ␦15N and ␦18O analysis, nitrate is concentrated from water
samples by anion exchange (Chang et al., 1999; Downs et al., 1999;
Silva et al., 2000). Sample solutions are passed through prefilled,
disposable anion exchange resin columns (Biorad AG1-X8 in chloride
form) and sample nitrate is desorped with 1 mol/L HCl. Following the
technique of Silva et al. (2000), the eluate is neutralized with Ag2O,
filtered to remove the AgCl precipitate and then freeze-dried to obtain
a salt mixture containing primarily AgNO3. Two liters of water sample
were enough to provide a sufficient amount of inorganic nitrogen for
multiple ␦15N analyses, even at the lowest nitrate concentration measured. For ␦18O analysis, an aliquot of ⬃6 mg AgNO3 extract is
redissolved in 20 mL of deionized water. Excess BaCl2 is added to the
heated (90°C) solution to precipitate sulphate and phosphate, which
would bias O isotopic analyses. The filtered solution is then passed
through a strong acid cation-exchange resin (Biorad AG-50W-X8 in
hydrogen form) to remove excess Ba2⫹ and Ag⫹, neutralized with
Ag2O and the resulting AgCl precipitate and excess Ag2O is filtered
out. To remove dissolved oxygen-containing organic compounds, 3 mg
of activated carbon is added and the suspension is shaken (180 rpm) for
20 min. The major part of dissolved organic matter has presumably
already been adsorbed on the cation exchange resin in the previous
sample-processing step. After removal of the activated carbon with a
0.2-␮m nylon filter, the resulting nitrate solution is freeze-dried. The
AgNO3 extract needs to be stored with minimal exposure to light to
prevent photodegradation (Silva et al., 2000).
2.3. On-Line Determination of Nitrogen Isotope Ratios
For the nitrogen isotopic analysis of extracted nitrate, 0.5 to 0.7 mg
AgNO3 are enclosed in silver capsules and measured on a Carlo Erba
elemental analyzer coupled to a Fisons Optima stable isotope ratio
mass spectrometer (EA-IRMS) with a standard set up for N2 gas.
N and O isotope fractionation during lacustrine denitrification
Nitrogen isotope ratios are reported in the conventional ␦-notation (‰)
with respect to atmospheric nitrogen (AIR):
␦ sample ⫽
冉
R sample
–1
R standard
冊
⫻ 1000
(1)
where R is the 15N/14N ratio. ␦-Values are calibrated using international
standards (IAEA-N1, IAEA-N3) and a AgNO3 working standard
(EIL62, ␦15N ⫽ 18.5‰) provided by the Environmental Isotope Laboratory of the University of Waterloo. Instrument precision based on
multiple measurements of standard AgNO3 is ⫾0.15‰ (n ⫽ 10). Silva
et al. (2000) suggested adding an excess of carbon (e.g., sucrose) to the
AgNO3 sample to guarantee the complete reduction to N2 and to
improve reproducibility. We found, however, that the addition of
sucrose did not have any effect. To test the precision and accuracy of
the ion-exchange method for waters within the range of typical nitrate
concentrations, natural freshwater and solutions prepared from NaNO3
reagent (␦15N ⫽ ⫺2.7‰) and distilled water were passed through the
columns and processed. The reproducibility for ␦15N was ⫾0.3‰. The
blank contribution and concentration effects were negligible.
2.4. On-Line Determination of Oxygen Isotope Ratios
Combining the methods of Werner et al. (1996) and Farquhar et al.
(1997), the oxygen isotopic ratios are determined for CO gas produced
via pyrolysis in an EA-IRMS system. Extracted AgNO3 (0.3– 0.5 mg)
and nickelized carbon (0.3– 0.5 mg), as an additional reactant and
catalyst to produce CO, are loaded into silver capsules and pyrolized
over glassy carbon at 1080°C in a continuous flow of ultra pure He gas.
Before the transfer to the mass spectrometer via an open-split system,
pyrolysis products, mainly N2 and CO, are passed through water and
CO2 absorption traps, and are subsequently separated (N2 produces an
isobaric interference on mass 28) in a 1-m 5-Å-molecular-sieve chromatographic column held at room temperature. There are no official
intercalibrated standards for ␦18O in nitrate. Oxygen isotope values
were standardized to V-SMOW using NBS18 (CaCO3, ␦18O⫽7.96‰),
EIL62 (AgNO3, ␦18O⫽20.5‰), and IAEA-N3 (KNO3), for which we
determined a ␦-value of 23.8‰, consistent with data reported by
Mengis et al. (1999). Analytical reproducibility, including sample
processing, was better than ⫾0.5‰. Blank corrections were applied to
account for a significant total procedural blank. The greater error for
oxygen isotope data can partially be attributed to the uncertainty of the
oxygen contribution to the blank.
3. DATA
3.1. Distribution of Dissolved Oxygen and Inorganic
Nitrogen
With the development of thermal stratification in the water
column and with the intense fluxes of biomass from the photic
zone during spring, the oxygen concentration in near-bottom
waters dropped from ⬃7.7 mg O2/L (240 ␮mol/L) after complete mixing of the water column in February to ⬍0.2 mg O2/L
(6 ␮mol/L) in June (Fig. 1). Between June and October, the
redoxcline is rising by ⬎10 m from the sediment-water interface into the hypolimnion. The expansion of the zone of complete anoxia is associated with the development of NO3⫺
concentration gradients from the oxic-anoxic interface (⬎1 mg
N/L) to the sediment (⬍0.2 mg N/L). With the onset of anoxic
conditions, considerable accumulation of NH4⫹ (and other
reduced compounds, e.g., CH4) is observed in the water column. Under the given redox conditions, NH4⫹ can be a product
of DRNA. Estimates of the relative importance of this nitratereduction process vary strongly. In coastal marine sediments,
rates of DRNA have been reported to be qualitatively as important as denitrification rates (Rysgaard et al., 1996; Bonin et
al., 1998). In general, however, denitrifiers are assumed to be
2531
more effective than NO3⫺ ammonifiers (Seitzinger, 1988), and
N-tracer experiments in two other eutrophic Swiss lakes have
shown that DRNA contributes little to the total NO3⫺ reduction
rate (Höhener and Gächter, 1994; Mengis et al., 1997). Therefore,
we assume that microbial remineralization of organic matter in the
sediment and in the water column rather than DRNA is responsible for the observed increase of [NH4⫹] in the water column.
15
3.2. Nitrogen and Oxygen Stable Isotopes in Dissolved
Nitrate
The vertical distributions of ␦15N and ␦18O display the same
pattern (Fig. 2). Above the redoxcline, hypolimnetic ␦15N and
␦18O values are relatively constant throughout the hypolimnion
(␦15N ⫽ 7.5 ⫾ 0.5‰, ␦18O ⫽ 4.5 ⫾ 0.4‰), whereas below the
aerobic-anaerobic interface, they increase as the NO3⫺ concentration decreases. Between June and August 1999, dissolved
nitrate in the deep hypolimnion becomes increasingly enriched
in the heavier isotopes, suggesting that microbial NO3⫺ reduction is the mechanism of nitrate removal. The increase in ␦15N
is more pronounced than for ␦18O. Maximum ␦15N and ␦18O
values (⫹27.2 and ⫹15.7‰, respectively) were measured in
August. A plot of ␦15N vs. ␦18O values for samples from below
85 m water depth (Fig. 3) shows a very good linear relationship
(r2 ⫽ 0.97; n ⫽ 10), where the enrichment ratio ⌬␦18O/⌬␦15N
is 0.57 ⫾ 0.03. This is in good agreement with values previously reported to be characteristic for the process of microbial
denitrification (Böttcher et al., 1990; Aravena and Robertson,
1998; Cey et al., 1999; Mengis et al., 1999).
The ␦-values of nitrate in surface waters, in particular the
␦18O value, are more positive than the ones for waters from the
upper and intermediate hypolimnion (Fig. 2). It can be assumed, however, that during the summer, when there is a
well-defined thermocline, processes taking place in the epilimnion (e.g., nitrate assimilation) and variations in the external
NO3⫺ loading do not significantly impact the nitrate inventory
of deeper waters. Thus, during the months of July and August,
the hypolimnion can be considered a closed system for which
we can determine a bulk initial NO3⫺ concentration, as well as
its N and O isotopic compositions, before alteration due to
nitrate reduction.
4. MODELLING TECHNIQUES
4.1. Closed-System Model
As a first approximation, the observed enrichments of 15N
and 18O in nitrate from the suboxic and anoxic hypolimnion
([O2]⬍10 ␮mol/L) can be described with a Rayleigh-distillation model. This process is expressed by
共 ␣ – 1兲ln f ⫽ ln
R
Ri
(2)
or with approximations made by Mariotti et al. (1981):
␦ ⫽ ␦ i ⫹ ␧ln f (Rayleigh Equation)
(3)
where ␣ is the fractionation factor associated with microbial
nitrate reduction, f is the unreacted portion of substrate NO3⫺
([NO3⫺]/[NO3⫺initial]), R and Ri the stable nitrogen or oxygen
2532
M. F. Lehmann et al.
Fig. 1. Concentrations of dissolved oxygen, nitrate, and ammonium vs. water depth throughout an annual cycle in the
southern basin of Lake Lugano.
isotope ratios of the residual and initial NO3⫺, respectively,
and ␦ and ␦i the nitrogen or oxygen isotopic compositions (‰)
of the reactant nitrate at a given time and before alteration due
to nitrate reduction, respectively. ␧ (‰) is the isotopic enrichment factor, which is related to ␣ by the expression
␧ ⫽ 共 ␣ – 1兲 ⫻ 1000.
(4)
The ␦15N and ␦18O values of the NO3⫺ increase proportionally
to the logarithm of the residual substrate fraction. This type of
isotopic evolution of the remaining NO3⫺ pool due to the
Fig. 2. Depth distribution of the ␦15N and ␦18O values of dissolved nitrate in the southern basin of Lake Lugano.
N and O isotope fractionation during lacustrine denitrification
⭸C 15N16O ,lake
3
⭸t
⫽
1 ⭸
A ⭸z
–
⭸C 14N18O16O ,lake
2
⭸t
䡠
–
⭸C 14N16O ,sed
3
⫺
Fig. 3. ␦ O vs. ␦ N for NO3 collected from hypolimnetic waters
below 85 m water depth (July and August 1999 data). The good linear
relationship and the ratio of isotope enrichment ⌬␦18O/⌬␦15N ⫽ 0.57
suggest that denitrification occurs in the redox-transition zone of the
southern basin of Lake Lugano. The potential effect of water column
nitrification is depicted schematically in the graph.
15
冊
⭸C 15N16O ,lake
3
AK z
⭸z
冉
冊
– k den,lakeC part,lake
C 15N16O ,lake
K O2
␧N
1⫹
K O2 ⫹ C O2,lake
1000 K NO3 ⫹ C 15N16O ,lake
䡠
18
冉
2533
⭸t
3
1 dA D 63␪
共C 15N16O ,lake – C 15N16O ,sed 兲
A dz h sed/ 2
3
1 ⭸
A ⭸z
⫽
3
冉
(6)
3
冊
⭸C 14N18O16O ,lake
AK z
– k den,lakeC part,lake
⭸z
2
冉
冊
C 14N18O16O ,lake
K O2
␧O
1⫹
K O2 ⫹ C O2,lake
1000 K NO3 ⫹ C 14N18O16O ,lake
2
2
1 dA D 64␪
共C 14N18O16O ,lake – C 14N18O16O .sed 兲
A dz h sed/ 2
2
⫽ – k den,sed
2
(7)
C 14N16O ,sed
K O2
K O2 ⫹ C o2,sed K NO3 ⫹ C 14N16O ,sed
3
3
⫹
1 D 62 ␪
共C 14N16O ,lake – C 14N16O ,sed 兲
␪ h sed h sed/ 2
3
3
(8)
⭸C 15N16O ,sed
K O2
⫽ – k den,sed
⭸t
K O2 ⫹ C O2,sed
3
activity of denitrifying bacteria has been previously reported
from marine, culture and groundwater studies (Delwiche and
Steyn, 1970; Mariotti et al., 1981; Böttcher et al., 1990;
Brandes et al., 1998; Mengis et al., 1999; Voss et al., 2001).
When modelling our data using Eqn. 3, we conceptually
divide the suboxic water column into distinct, effectively
closed, horizontal zones, where nitrate reduction is active for
different periods of time. With the rise of the redoxcline into
the hypolimnion, increments of the water column subsequently undergo oxygen depletion followed by nitrate consumption and, therefore, a time series of continuous nitrate
reduction is well represented by a vertical sample profile.
冉
䡠 1⫹
⭸C 14N16O ,lake
3
⭸t
⫽
1 ⭸
A ⭸z
冉
⭸C 14N16O ,lake
3
AK z
⭸z
冊
C 14N16O ,lake
K O2
K O2 ⫹ C O2,lake K NO3 ⫹ C 14N16O ,lake
3
–k den,lakeC part,lake
3
–
1 dA D 62 ␪
共C 14N16O ,lake – C 14N16O ,sed 兲
A dz h sed/ 2
3
3
(5)
3
3
1 D 63 ␪
⫹
共C 15N16O ,lake – C 15N16O ,sed 兲
␪ h sed h sed/ 2
3
3
(9)
⭸C 14N18O16O ,sed
K O2
⫽ – k den,sed
⭸t
K O2 ⫹ C O2,sed
2
冉
䡠 1⫹
4.2. Diffusion-Reaction Model
In the hypolimnion of a lake, due to turbulent diffusion and
exchange of nitrate between the water column and the sediment
pore water, the closed system model described in section 4.1 is
only a rough approximation. We developed a more realistic
model that accounts for nitrate reduction in the water column,
as well as in the sediment, and considers turbulent diffusion in the
water column and diffusive exchange with the sediment. This
model distinguishes the concentrations of 14N16O3, 15N16O3 and
14 18 16
N O O2 in the water column and in the sediment pore water.
Resolving the vertical dimension only, this leads to six coupled
partial differential equations as follows:
冊
C 15N16O ,sed
␧N
1000 K NO3 ⫹ C 15N16O ,sed
⫹
␧O
1000
冊
C 14N18O16O ,sed
2
K NO3 ⫹ C 14N18O16O ,sed
2
1 D 64 ␪
共C 14N18O16O ,lake – C 14N18O16O ,sed 兲.
␪ h sed h sed/ 2
2
2
(10)
In these equations, t is time, z is water depth in the lake, C is
the concentration of the compound given in the first index in
the compartment given in the second index, A is the crosssectional area of the lake (as a function of water depth), K z is
the coefficient of vertical turbulent diffusivity in the deep
hypolimnion, k den,lake is the maximum nitrate reduction rate per
volume and per unit of particle concentration (C part,lake) in the
water column, k den,sed is the maximum nitrate reduction rate
per volume in the sediment, K O2 is the inhibition constant for
nitrate reduction due to oxygen, K NO3 is the half-saturation
concentration for nitrate reduction, ␧N is the isotopic enrichment factor for 15N, ␧O is the isotopic enrichment factor for
18
O, D 62 is the molecular diffusivity of nitrate, D 63 and D 64 are
molecular diffusivities of 15N16O3 and 14N18O16O2 estimated
from D 62 by application of a mass correction for an aqueous
⫹ 18)
medium (e.g., D 63 ⫽ D 62 公62(63
63(62 ⫹ 18) , Clark and Fritz, 1997).
Similar process formulations with multiplicative limitation and
inhibition terms (considering additional substrates) and propor-
2534
M. F. Lehmann et al.
dissolved oxygen. Oxygen concentrations in the sediment layer
are assumed to be zero.
A major uncertainty in modelling nitrate reduction with the
equations given above is the estimation of turbulent diffusivity,
Kz. As described in Appendix, turbulent diffusivity in the deep
hypoliminon is estimated from wind forcing, taking into account wind measurements by the Swiss Meteorological Institute at two measuring sites in the vicinity of the lake, measured
data of stratification, and estimates of wind forcing power
dissipation by bottom friction. Figure 4 shows profiles of calculated water column stability and Kz values.
4.3. Parameter Estimation and Uncertainty Analysis
Fig. 4. (a) Brunt-Vaisälä frequency N2, a measure of water-column
stability calculated from the exponential fits to measured density ␳ with
g ⭸␳
N2 ⫽
. (b) Estimates of turbulent diffusivity resulting from the
␳ ⭸z
turbulence model applied (see Appendix).
tionality to heterotrophic biomass are used in activated sludge
models of the waste water treatment process (Henze et al.,
1986, 1995, 1999; Gujer et al., 1999) and in river water quality
models (Reichert et al., 2001). Multiplicative limitation and
inhibition terms are also used in biogeochemical lake models
(Omlin et al., 2001) in which bacterial biomass is often not
considered explicitly, but absorbed into a maximum conversion
rate, because the diffusion process from the water column into
the sediment is often the rate-limiting process. The sediment is
approximated by a mixed sediment layer with a thickness of
h sed and a porosity ␪ of 0.9. Due to the simplified approximation of the sediment as a mixed layer with high denitrification
capacity, h sed rather than k den,sed becomes the model parameter
describing the diffusion-limited denitrification effect of the
sediment. The inhibiting influence of dissolved oxygen on
nitrate reduction considered in the equations given above can
be implemented using measured oxygen concentrations. This
allows us to omit additional partial differential equations for
The model described in section 4.2 contains more parameters
than can be estimated from the small number of data points
available during the stratification period. For this reason only
the most uncertain parameters kden,lake,July, kden,lake,August (different values for July and August) and the fractionation parameters, ␧N and ␧O (constant values or different values for July
and August), being the main objective of this study, were
estimated by a weighted least squares procedure. The uncertainty in the other model parameters was described by probability distributions and accounted for by a combination of
Monte Carlo simulation with parameter estimation. For each
point of a latin hypercube sample (McKay et al., 1979) of the
probability distribution of these parameters, an estimation procedure for the parameters kden,lake,July, kden,lake,August, ␧N and ␧O
(or alternatively kden,lake,July, kden,lake,August, ␧N,July, ␧N,August,
␧O,July and ␧O,August) was carried out. This led to estimates of
these parameters (the mean of all Monte Carlo estimates), to a
standard deviation of the Monte Carlo sample, summarizing the
effect of the uncertainty of the other parameters, and to a
standard deviation calculated by the fit algorithm, describing
the uncertainty of the measurement process (and also some
model structure deficiencies). These two sources of uncertainty
were assumed to be independent, therefore, the final standard
deviation of the estimate was calculated as the square root of
the sum of the squares of these two standard deviations. Table
2 summarizes how the uncertainty of the different parameters
was considered.
Table 2. Consideration of uncertainty of the parameters of the diffusion-reaction model. Lognormal distributions were used for describing the
uncertainty of KO2, KNO3, hsed, and Kz.
Parameter
KO2
KNO3
hsed
Kz
kden,lake
␧N
␧O
␪
kden,sed
Consideration of uncertainty
Monte Carlo analysis with lognormal distribution with mean 0.1 mg O/L and standard deviation 0.05 mg O/L
Monte Carlo analysis with lognormal distribution with mean 0.1 mg N/L and standard deviation 0.05 mg N/L
Monte Carlo analysis with lognormal distribution with mean 0.008 m and standard deviation 0.004 m
Monte Carlo analysis with lognormal distribution with mean given by the estimation procedure as described
above and standard deviation equal to 50% of the mean
Values fitted for July and August; uncertainty estimate from parameter estimation and Monte Carlo simulation
Values fitted (one average or separate values for July and August); uncertainty estimate from parameter
estimation and Monte Carlo simulation
Values fitted (one average or separate values for July and August); uncertainty estimate from parameter
estimation and Monte Carlo simulation
Value of 0.9 used; uncertainty included in uncertainty of kden,sed
Value of 100 mg N/L/d used; the result is insensitive to this value because it is sufficiently large to lead to a
negligible concentration of nitrate in the sediment layer at the location hsed/2
N and O isotope fractionation during lacustrine denitrification
2535
Fig. 5. Correlation of (a) nitrogen and (b) oxygen isotope enrichment (⌬␦⫽␦meas⫺␦init) with the natural logarithm of f, the
fraction of residual NO3⫺. f for 88 and 92 m water depth (only July and August 1999) is derived from interpolated nitrate
concentrations. The apparent isotope effects associated with microbial denitrification are estimated from the slope of the
linear regression lines (Rayleigh model). N-isotope data in parentheses are excluded in the regression. Dashed lines are
linear regression lines, where only data from above 92 m water depth (filled symbols) are considered. See text for
discussion.
5. MODEL RESULTS
5.1. Closed-System Model
In Figure 5 we plotted the ⌬␦15N (n⫽13) and ⌬␦18O (n⫽13)
vs. ln f. There is a good linear relationship for both data sets (r2
⬎ 0.92). For the initial substrate concentration Ri and isotopic
compositions ␦i, we used the values measured above the redoxcline ([NO3] ⫽ 1.2 mg N/L, ␦15N ⫽ 7.5‰, ␦18O ⫽ 4.5‰).
From the slope of the regression lines, we inferred the apparent
isotope enrichment factors ␧Napp⫽⫺11.2‰ for 15N and
␧Oapp⫽⫺6.6‰ for 18O. Nitrogen isotope measurements made in
September and October 1999 were not considered in the regression because, after September, the nitrate concentration
increased and the N stable isotope ratios decreased. This suggests that, from September onward, nitrate reduction is slower
than nitrate supply by diffusion and advection, and the hypolimnion can no longer be regarded as a closed system.
The nitrogen and oxygen enrichment factors determined with
the Rayleigh model fall into the lower range of isotope effects
reported from laboratory experiments and marine, soil and
groundwater studies (␧N⫽0 to ⫺40‰ and ␧O⫽⫺8 to ⫺18.3‰,
see Table 1). To our knowledge, comparable data for nitrogen
and oxygen isotope fractionation during nitrate reduction in
freshwater lakes are not available.
5.2. Diffusion-Reaction Model
Figure 6 shows simulated depth profiles for nitrate concentrations, nitrate ␦15N, and nitrate ␦18O values. Figure 6a shows
results from simulations where water-column nitrate reduction
rates were set to zero and hsed was fitted to allow sediment
denitrification to be strong enough to reproduce nitrate depletion profiles in the water column. This model is clearly not
capable of describing the observed variations of nitrate ␦15N
and ␦18O with depth in the deep hypolimnion of Lake Lugano.
In agreement with the model proposed by Brandes and Devol
(1997), the model-derived data indicate that sedimentary nitrate
reduction results, in fact, in an apparent isotope effect close to
0‰. Slightly increased ␦15N and ␦18O values for near-bottom
nitrate can be attributed to the N and O isotope fractionation
associated with molecular diffusion of nitrate into the sediment,
which is, as one would expect, more pronounced for oxygen.
In a second simulation where nitrate reduction was only
active in the water column and not in the sediments, modelderived data could describe the measured data satisfyingly well
(data not shown). In fact, Lazzaretti and Hanselmann (1992)
were not able to observe a clear nitrate concentration gradient
across the sediment-water interface during an earlier anoxic
period at the same sampling location in Lake Lugano. This
suggests that, below the redoxcline, nitrate reduction in the
sediments may be limited by the low diffusive flux from the
overlying water. Nevertheless, assuming that sedimentary nitrate reduction is negligible does not seem adequate. Before the
development of anaerobisis and subsequent nitrate deficiency
in near-bottom waters, nitrate fluxes into the sediment are
presumably much higher and sedimentary nitrate reduction
plays a significant role. This assumption is supported by measured water-column nitrate concentration and isotope data for
June, indicating that the initial nitrate depletion in near-bottom
water does not correspond to an observable isotope effect.
Hence, over the course of the whole stagnation period, we
suggest that microbial nitrate reduction takes place in both the
anaerobic water column and in the anoxic sediments.
Table 3 shows the results of the Monte Carlo parameter
estimation procedure described in section 4.3. Figure 6b shows
simulated depth profiles for [NO3⫺] as well as ␦15N and ␦18O
values for the mean parameter estimates (Table 3 “Variable
fractionation estimate”). Water-column nitrate reduction rates
displayed significant variation with time, being ⬃5 times
higher in July than in August. This is easily comprehensible
2536
M. F. Lehmann et al.
Fig. 6. Measured (symbols) and modelled (lines) depth distribution of [NO3⫺], and the ␦15N and ␦18O for dissolved
nitrate. (a) No water-column denitrification during simulation. The simulated profiles clearly do not describe measured data.
The model-derived nitrate ␦15N and ␦18O values indicate that the N and O isotope fractionation associated with sedimentary
denitrification is minimal. (b) Both water-column and sedimentary denitrification active during simulation. Model calculations are based on mean parameters for variable N and O isotope fractionation (see Table 3). For simulations with average
parameters (data not shown) there is a slightly bigger difference between the nitrate profiles in July and August than
measured, especially at the deepest measurement site. Note: different model-parameter values used in (a) and (b).
when considering the nitrate profiles (Fig. 1). Between June
and July, the NO3⫺ concentration decreased strongly, whereas,
between July and August, the established concentration gradient was maintained but did not increase. In the water column,
the availability of substrate organic matter (rather than the
nitrate concentration) is an important rate-determining factor
(Brettar and Rheinheimer, 1992). Accordingly, short-term
changes in the water-column nitrate-reduction rates are likely
to occur, as the primary productivity in the photic zone and,
hence, the hypolimnetic organic matter flux, is highly variable
during summer months (L.S.A., 1980 –2000).
Figure 7 depicts the model-based evolution of the ratio of
nitrate reduction rates in the sediment (rden,sed,eff), normalized
to the effect upon any given volume of water, and in the water
column (rden,lake) close to the lake bottom. Between June and
July, when the largest amount of nitrate is reduced, sedimentary
denitrification becomes less and less important with time. For
example, model-derived data indicate that between 93 and 94 m
water depth, sedimentary nitrate reduction accounted for ⬃30%
of the observed nitrate depletion at the end of June, whereas 3
weeks later nitrate consumption was almost entirely due to
water-column nitrate reduction. Also, and for obvious reasons,
the effect of the sediment on the overlying water was strongest
in proximity to the sediment-water interface. When looking at
the nitrate concentration data in Figure 6b, we find a significant
mismatch between modelled and measured data between 85
and 90 m water depth. This can be explained by the fact that the
model is not able to account for potential nitrate production
from ammonium oxidation in the uppermost sediments above
the redoxcline. Based on measured nitrate concentrations at the
sediment-water interface (Lazzaretti and Hanselmann, 1992),
we can assume that the net nitrate flux in/out of the sediments
between 70 and 85 m water depth is close to zero, whereas the
model assumes a net flux into the sediments. This shortcoming
of the model does not affect much the calculated ␦15N and ␦18O
profiles, since the isotope effect of sedimentary nitrate reduction upon overlying water is negligible.
The average isotope fractionation estimates for ␧N and ␧O
were ⫺20.7⫾3.8 and ⫺11.0⫾1.7, respectively. Yet, model
results for variable isotope enrichment factors indicate that the
␧N and ␧O values were significantly different in July and
August, with August values being much higher (Table 3). The
N and O isotope fractionation during lacustrine denitrification
2537
Table 3. Results of Monte Carlo parameter estimation (as standard deviations from the fit, the estimated standard errors for the mean values of the
other parameters were used).
Standard deviation
Parameter
Average fractionation estimateb
kden,lake,July
kden,lake,August
␧N
␧O
Variable fractionation estimatec
kden,lake,July
kden,lake,August
␧N,July
␧N,August
␧O,July
␧O,August
Unit
Value
Monte Carlo
Fit
Overalla
mg N/mg/d
mg N/mg/d
‰
‰
0.077
0.018
–20.7
–11.0
0.027
0.009
2.9
1.1
0.007
0.002
2.4
1.3
0.028
0.009
3.8
1.7
mg N/mg/d
mg N/mg/d
‰
‰
‰
‰
0.09
0.013
–14.7
–38.0
–8.7
–18.2
0.03
0.008
1.7
16.1
0.7
6.0
0.010
0.003
2.3
7.0
1.4
3.0
0.03
0.009
2.9
17.0
1.6
7.0
a
Calculated as the square root of the sum of the squares of the standard deviation of the Monte Carlo sample and the standard deviation calculated
by the fit algorithm.
b
Assuming only different rates k for July and for August, respectively.
c
Assuming both different rates k and different ␧ values for July and for August, respectively.
very high uncertainty associated with the August isotope enrichment factors is a result of the very low nitrate turnover
during that time.
6. DISCUSSION
Both the closed-system and the diffusion-reaction models do
not distinguish between denitrification and DRNA. We are not
able to quantify the percentage of nitrate reduced to ammonium
in Lake Lugano sediments and bottom waters. However, as
mentioned above, DRNA generally does not play an important
Fig. 7. Diffusion-reaction-model-based evolution of the ratio of
sedimentary to water column denitrification during July. rden,sed,eff is
the denitrification rate in sediment normalized to its effect on the water
column, calculated from the actual denitrification rate in the sediment,
the thickness of the sediment layer, and the cross-sectional area of the
lake as a function of depth.
role in most freshwater systems, especially not in eutrophic
lakes, (e.g., Tiedje et al., 1982; Höhener and Gächter, 1994;
Mengis et al., 1997). Hence, although our ␧ values combine the
isotopic fractionation of both nitrate dissimilation processes,
we consider them good approximations of the isotope effect
resulting from pure denitrification. In the course of this discussion, we use the term denitrification and thereby include the
(probably minor) contribution from DRNA.
Nitrate deficiency in the water column can result from denitrification in the water column itself and in the sediment.
Therefore, the isotope fractionation factor derived from watercolumn isotope measurements with the Rayleigh equation represents a “community fractionation factor” (␣app), as defined by
Bender (1990), that integrates the isotope effects associated
with denitrification in the two compartments. The comparatively low nitrogen and oxygen enrichment factors for Lake
Lugano derived with the Rayleigh equation may partially be
due to sedimentary denitrification, occurring with a reduced
isotope effect of 0 to 3‰ (Brandes and Devol, 1997, 2002).
Given the morphology of Lake Lugano, the ratio of the sediment surface to the corresponding water volume is highest
close to the lake bottom. If nitrate is dissimilated in the sediment or at the sediment water interface, its relative importance
as a mechanism of nitrate loss should be greatest in the lowest
part of the water column. Also, and in agreement with modelderived normalized rates (Fig. 7), we would expect it to be
more important at the beginning of the stagnation period, when
water-column denitrification is still inhibited by ambient dissolved oxygen, and the nitrate flux into the sediment is high.
Consequently, considering the results of Brandes and Devol
(1997), the partition of the heavy and light nitrate nitrogen and
oxygen isotopes should be less marked in the near-bottom
waters. Indeed, if the ␦15N and ␦18O values determined at 92 m
water depth and below are excluded from consideration, the
“community” isotope enrichment factors derived with the Rayleigh equation are significantly higher (Fig. 5) with ␧N ⫽ ⫺
17.3‰ (r 2 ⫽ 0.84; n ⫽ 8) and ␧O ⫽ ⫺8.8‰ (r 2 ⫽ 0.89;
n ⫽ 8). Therefore, our nitrate isotope data seem to add con-
2538
M. F. Lehmann et al.
straints on the hypothesis of sedimentary denitrification producing no or only a minor isotope effect measurable in the open
water.
Calculated nitrogen and oxygen enrichment factors for denitrification are larger when derived with the diffusion-reaction
model instead of a closed-system model. The Rayleigh-distillation model does neither account for sedimentary denitrification nor for vertical diffusive mixing, and, therefore, tends to
underestimate the isotope effect for denitrification. The reaction-diffusion model-derived isotope enrichment factors, which
include realistic physical and biogeochemical parameters,
should better approximate the intrinsic N and O isotope fractionation during denitrification. The August isotope enrichment
factors are afflicted with large uncertainty, and at least the mean
␧ value for N isotope enrichment (⫺38‰) appears to be unrealistically high. But notwithstanding the large uncertainty,
model results indicate that N and O isotope fractionation associated with denitrification in Lake Lugano is variable with time.
From other biologically-mediated reactions, e.g., the assimilation of inorganic nitrogenous compounds (Wada and Hattori,
1978) or the dissimilative reduction of sulphate (Kaplan and
Rittenberg, 1964), it is known that isotope fractionation increases with decreasing reaction rate. By analogy, we anticipate
that denitrification shows a similar inverse relationship between
metabolic rate and isotope fractionation. Mariotti et al. (1982)
and Mariotti (1982) showed that isotopic enrichment factors
can vary markedly during denitrification of nitrates and nitrites
in various soil types and are correlated with the denitrification
rate (compilation in Mariotti et al., 1988). The significantly
higher isotope fractionation factors determined for August may
be directly related to the lower denitrification rate probably
resulting from reduced electron donor concentration in the
water column. There is some experimental evidence supporting
this reasoning. Chien et al. (1977) observed that the N-isotope
fractionation associated with soil denitrification was lowered
when glucose was added. Bryan et al. (1983) also found a
negative correlation between the nitrogen isotope enrichment in
substrate nitrite and the denitrification rate, when the rate in
experiments with cultures of Pseudomonas stutzeri was modulated by the reductant availability. Moreover, the comparatively high nitrogen fractionation factors for the eastern tropical
North Pacific (Table 1) may be explained by very low organic
carbon sedimentation rates, limiting the overall denitrification
rate (Voss et al., 2001). Continual hypolimnetic Corg-flux measurements are imperative to obtain reliable, time-integrative
estimates on the reductant availability in the reactive water
column especially during periods of short-term organic-matterflux changes due to periodic plankton blooms. Insufficient data
are available for this study. However, as both the primary
productivity and the total mass of phytoplankton in the euphotic zone were much higher during July than during August
(Fig. 8), it seems reasonable to assume that the organic matter
fluxes were indeed reduced in August, supporting the hypothesis of isotope fractionation, organic carbon limitation and
denitrification rates being directly linked. The Rayleigh distillation model does not consider variable isotope fractionation. It
results in averaged enrichment factors, which are dominated by
the isotope effect during the highest nitrate turnover. Therefore,
it is rather less sensitive towards a potentially augmented
microbial isotope effect associated with low denitrification
Fig. 8. Phytoplankton biomass and primary productivity during
summer 1999. Primary productivity data are given as monthly average.
Total phytoplankton biomass data represent mean values (0 –20 m
water depth) for June 23, July 7, and August 8. Data from the L.S.A.
Lugano.
rates in August. Yet, as stated above, the ␦15N and ␦18O values
derived with the Rayleigh equation are markedly higher when
data points from 92 m water depth and below were excluded
from consideration (Fig. 5). This is mainly due to the diminished influence of sedimentary denitrification upon the upper
water column, but the elevated enrichment factors may also be
attributed to the effect of time-dependent isotopic fractionation.
Water samples above 92 m represent increments of the water
column that underwent denitrification for a shorter time than
the water column below 92 m. Therefore, the apparent isotope
enrichment factors derived with the Rayleigh equation are
likely to better approximate the increased microbial fractionation factors in August when the deepest water samples are
omitted.
Both models neglect a possible additional input of nitrate
from nitrification in the redox-transition zone, where oxygen
and ammonium concentrations are still sufficiently high. This
could affect the estimates of the isotope effect associated with
denitrification. In several benthic studies, coupled nitrificationdenitrification has been reported to be more important than the
influx of nitrate from overlying waters (Seitzinger, 1988, and
references therein), but the evidence is controversial (Christensen et al., 1990; Rysgaard et al., 1993; Mengis et al., 1997).
The importance of nitrification strongly depends on the spatial
co-existence of O2 and NH4⫹ and the concentration of NO3⫺ in
the water column (Christensen et al., 1990). The [NO3⫺] profiles in Figure 1 indicate that NO3⫺ consumption exceeds
NO3⫺ production in near-bottom waters, but they do not clearly
indicate that benthic nitrification is insignificant. Culture experiments revealed that nitrate from microbial nitrification contains two oxygen atoms derived from ambient H2O and one
derived from dissolved O2 (Andersson and Hooper, 1983;
Kumar et al., 1983; Hollocher, 1984; Yoshinari and Wahlen,
1985). Assuming no fractionation resulting from the incorporation of oxygen from O2 and H2O occurs (Kendall, 1998), the
nitrification process will result in ␦18O values for the product
nitrate of ⬃⫹3‰, following the equation
1
2
␦18ONO3– ⫽ ␦18OO2 ⫹ ␦18OH2O
3
3
(11)
assuming ␦18OO2 ⫽ ⫹23‰ for atmospheric O2 (Kroopnick and
N and O isotope fractionation during lacustrine denitrification
Craig, 1972) and ␦18OH2O ⫽ ⫺7‰ for water from Lake Lugano (H. Paul, unpublished data). This value is a low estimate
for the ␦18O of microbial nitrate because dissolved O2 in
oxygen-depleted water is likely to be enriched in 18O due to
isotope fractionation associated with respiration (Bender, 1990;
Kiddon et al., 1993; Brandes and Devol, 1997). Moreover,
Mayer et al. (2001) suggested that, depending on the nitrification pathway (chemolithotrophic vs. heterotrophic), only one
oxygen in the nitrate molecule is derived from ambient H2O, to
explain heavier than expected ␦18O values for microbial nitrate
(Mayer et al., 2001). In conclusion, nitrification should produce
␦18O values for dissolved NO3⫺ similar to, or higher than, the
␦18O values that have been determined for nitrate close to the
redoxcline (4 – 6‰). Lehmann at al. (2001) determined ␦15N
values for bottom-water ammonium collected from the southern basin of Lake Lugano to be 12.4‰ in July and 12.7‰ in
October. The ␦15N values for sedimentary and suspended particulate organic matter collected from the water-sediment interface in the southern basin range between 0 and 9‰ (Lehmann et al., 2002). In general, mineralization causes only a
small fractionation and, therefore, the ␦15N of released ammonium is usually in the range of the ␦15N of substrate organic
nitrogen (Kendall, 1998). The higher ␦15N values for ammonium may be indicative of some contribution from nitrification
leading to a 15N-enrichment in the substrate NH4⫹. Yet, based
on laboratory culture experiments, nitrification is generally
accompanied by marked nitrogen isotope fractionation effects
of ⬎⫺20‰ (e.g., Delwiche and Steyn, 1970; Mariotti et al.,
1981). Hence, nitrate produced by nitrifying bacteria in the
redox transition zone should be characterized by negative ␦15N
values ⬍⬃⫺5‰, much lower than the ␦15NNO values determined in this study. In turn, nitrification does not impact the
␦18O and ␦15N values of dissolved nitrate in the same way, and
should be detectable close to the redoxcline as an isotope
anomaly or as a deviation from the normal ratio of enrichment,
⌬␦18O/⌬␦15N, in nitrates (Fig. 3). Therefore, we conclude that,
at least in the water column, coupled nitrification-denitrification
is of minor importance and does not significantly contribute to
the overall denitrification. The observed NH4⫹ gradients above
the sediment-water interface (Fig. 1) may well be due to diffusive mixing processes, the anaerobic oxidation of ammonium
to N2 (annamox), and/or NH4⫹ incorporation during biosynthesis, rather than to water-column nitrification. Lehmann
(2002) have shown that up to 80% of the suspended biomass in
the same near-bottom water samples are synthesized in situ,
and ammonium is likely to serve as nitrogen source. Yet,
nitrification is likely to occur in the uppermost sediments at and
above the water-column redoxcline. In this case, NO3⫺ from
nitrifying activity must be removed to completion by nitratereducing organisms and, hence, nitrification does not affect the
water-column isotope signal.
Dissimilative reduction of nitrate is inhibited by O2 concentrations ⬎ 0.2 mg/L (⬃6 ␮mol/L) (Seitzinger, 1988). The
measured nitrate and oxygen concentration profiles do not
indicate any measurable nitrate deficiency in the hypolimnion
where the oxygen concentrations are ⬎1 mg/L (32 ␮mol/L)
and water-column denitrification can be excluded. Consequently, the net nitrate consumption in the anoxic sediments
above the water-column redoxcline is negligible with respect to
⫺
3
2539
the pool of hypolimnetic NO3⫺. Mass balance calculations for
the northern basin of Lake Lugano revealed that a significant
amount of the total nitrogen elimination might be attributed to
sedimentary denitrification above the permanent redoxcline
(Meile, 1996). If sedimentary denitrification is an important
process also in the oxic hypolimnion of the southern basin it
must be mainly fueled by NO3⫺ from the nitrification of NH4⫹.
From several freshwater studies it has been reported that, even
at elevated nitrate concentration in the overlying water (⬎70
␮mol/L), nitrification appears to be the major source of nitrate
for denitrification in the sediments (compilation in Seitzinger,
1988). Again, based upon considerations by Brandes and Devol
(1997), coupled nitrification-denitrification in the sediment
does not necessarily impact the nitrogen and oxygen isotope
ratios of water-column nitrate. One year sampling of the main
external sources of nitrate of Lake Lugano revealed that the
weighted average ␦15NNO of the inputs is ⬃⫹7.2‰ (Lehmann
et al., in review) and, thus, not significantly different from the
␦15NNO value for the mixed water column. This indicates that
the N inputs from external sources control the isotopic composition of the whole-basin nitrate pool and denitrification has a
minor effect on the lake’s nitrogen isotope inventory. We
suggest that, if microbial denitrification is a quantitatively
important component in the N cycle of the south basin of Lake
Lugano, most of the nitrate is dissimilated with negligible
isotope fractionation in the anoxic sediments above the watercolumn redoxcline. Also, the deep hypolimnion may be regarded as a partly closed system where nitrate is almost completely consumed. Consequentially, when mixed to the rest of
the water volume, deep-hypolimnetic water masses that underwent water-column denitrification barely affect the whole-lake
␦15NNO and 18ONO value.
⫺
3
⫺
3
⫺
3
⫺
3
7. SUMMARY AND CONCLUSIONS
In this study, we demonstrate that isotope fractionation associated with microbial denitrification had a strong effect on
the ␦15N and ␦18O value of dissolved nitrate in anaerobic
bottom waters in the southern basin of Lake Lugano. The
models used to estimate nitrogen and oxygen enrichment factors simplify the complex processes in a lacustrine redoxtransition zone. But notwithstanding the relatively large confidence intervals determined for the N and O isotope enrichment,
our data provide valuable constraints from a natural environment, indicating that the nitrogen and oxygen isotope fractionation factors for water-column denitrification vary as a function
of the denitrification rate, with enhanced isotopic enrichment
associated with low denitrification rates. Sedimentary denitrification occurring with a reduced isotope effect tends to lower
the effective isotope enrichment perceived in the hypolimnion.
Yet, the combined model results do not permit the quantitative
disentanglement of the influence of sedimentary denitrification
and variable isotope fractionation on the effective isotope effect. The coupled measurement of nitrate ␦15N and ␦18O values
allow us to assess that the effect of nitrification on the water
column nitrate isotope signal in the southern basin is negligible.
Our findings have several important implications. According
to our results, reduced denitrification rates and low nitrate
depletion produce similar nitrate isotope signals in the water
column as high denitrification rates in association with high
2540
M. F. Lehmann et al.
nitrate depletion. If the degree of nitrate deficiency is known,
the isotopic signature in the residual nitrate may provide information on water column denitrification rates. A constant fractionation factor for denitrifcation in suboxic waters, may not be
assumed unless boundary conditions (NO3⫺ concentrations,
organic matter rain) are relatively stable. Accordingly, caution
is advised when using a community fractionation factor to
assess the partitioning of sedimentary vs. water-column denitrification under the assumption of a generally valid N and O
isotope fractionation factor associated with water column denitrification. For a better understanding of the coupling between
isotope fractionation and denitrification rates, studies in other
lakes or aquatic environments are necessary and should include
independent determinations of denitrification rates. Finally, this
study shows that the combined measurement of nitrate ␦15N
and ␦18O values is a valuable analytical tool to trace and
deconvolve co-existing biogeochemical processes in aquatic
environments.
Acknowledgments—We thank A. Wüest for his advice for the estimation of the deep hypolimnion turbulent diffusivities and the Swiss
Meteorological Institute for providing wind data at sites close to Lake
Lugano. We also thank M. Mengis for providing silver nitrate standards
for isotopic measurements. Constructive and helpful comments by D.
Sigman, J. Brandes, and two anonymous reviewers improved the manuscript significantly. This study was supported by Swiss National Science Foundation grants 21-52332.97 and 20-61626.00.
Associate editor: N. E. Ostrom
APPENDIX
A.1. Estimation of Turbulent Diffusivity
Turbulent diffusivity Kz in the deep hypoliminon is estimated from
wind forcing as follows (Wüest and Gloor, 1998; Wüest et al., 2000).
The power of wind forcing is estimated by
P wind ⫽ ␳ airC 10,airw 3
(A1)
where ␳air is the density of air, C10,air is the wind drag coefficient of the
atmospheric boundary layer, w is the wind 10 m above the lake
approximated by the average of hourly wind measurements by the
Swiss Meteorological Institute at two measuring sites in Lugano and
Stabio (in the vicinity of the lake). Hourly values of wind power are
averaged with an exponential weighting factor:
P៮ wind共t兲 ⫽
冕
⬁
P wind共t – t⬘兲k wexp共–k wt⬘兲dt⬘
(A2)
0
where kw is the inverse averaging time period. The power dissipated at
the bottom of the lake is estimated by a fraction of averaged wind
power:
P bottom ⫽ ␣ P៮ wind.
(A3)
An alternative formulation of dissipation by bottom friction is given by
P bottom ⫽ ␳ C 1,bottomv 3 ⫽ v · ␳ u *2
(A4)
where ␳ is the density of water and C1,bottom is the friction coefficient
for the bottom boundary layer. This led to estimates of the advective
velocity 1 m above the lake sediment:
v⫽
and of shear velocity
冉
P bottom
␳ C 1,bottom
冊
u* ⫽
冑C 1,bottom
·V
(A6)
With measured data of the Brunt-Vaisälä frequency
N2 ⫽
g ⭸␳
␳ ⭸z
(A7)
and estimates of dissipation
␧ diss ⫽
u *3
(A8)
␬ 共 z – z b兲
we finally end at the estimate for turbulent diffusivity in the deep
hypolimnion given by
冉
0.15␧ diss
N2
冢
N ␬共 z – z b 兲
K z ⫽ min ␬uⴱ共 z – z b兲,
⫽ min ␬ uⴱ共 z – z b兲,
冊
0.15u *3
2
冣
.
(A9)
In addition to these expressions, K z is bounded by minimal and maximal values, K z,min and K z,max, respectively. Based on Wüest and Gloor
(1998), Wüest et al. (2000) and personal recommendations by A.
Wüest, the parameter values used for the turbulence model are as
follows:
Parameter
Value
C10,air
C1,bottom
␣
␬
kw
Kz,min
Kz,max
0.0012
0.002
0.005
0.41
0.2 d–1
0.1 m2/d
5 m2/d
A.2. Model Implementation
The model equations were implemented in version 2.1d of the
computer program AQUASIM (Reichert, 1994; Reichert, 1998; http://
www.aquasim.eawag.ch, designed for simulation and data analysis of
aquatic systems). This program first discretizes the spatial derivatives
of the partial differential equations and then integrates the resulting
system of ordinary differential equations in time with the implementation DASSL (Petzold, 1983) of the implicit (backward differencing)
variable-step, variable-order GEAR integration technique (Gear, 1971).
The spatial resolution of the numerical discretization scheme was ⬃20
cm, the temporal resolution of the variable order, variable step size
Gear integration algorithm (as implemented in DASSL) guarantees a
very high integration accuracy of the discretized partial differential
equations. This guarantees independence of the solution from parameters of the numerical algorithms.
The objective function was the weighted sum of the squares of the
deviations of simulation results from measurements of NO3⫺, ␦15N and
␦18O for the months of July and August. The weights were chosen
empirically to make the contributions of the three different variables to
the total sum of weighted squares of a similar order (to ensure that all
goal variables are considered with similar weight for the fit).
The latin hypercube sample of size 1000 used for Monte Carlo
parameter estimation as described in section 4.3 was created by the
UNCSIM package (Reichert, in preparation; http://www.uncsim.
eawag.ch) and then used in the extended batch version of AQUASIM
to produce parameter estimates of the other parameters for each Monte
Carlo sample point.
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