HYDROLOGICAL PROCESSES
Hydrol. Process. 28, 1820–1834 (2014)
Published online 27 February 2013 in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/hyp.9709

Nitrate dynamics in a rural headwater catchment:
measurements and modelling
Philip J. Smethurst,1* Kevin C. Petrone,2 Günter Langergraber,3 Craig C. Baillie,1Dale Worledge1 and David Nash4
1

3

CSIRO Ecosystem Sciences, CRC for Forestry, and Landscape Logic CERF, Private Bag 12, Hobart TAS 7001, Australia
2
CSIRO Land and Water, and Landscape Logic CERF, Private Bag 5, Wembley WA 6913, Australia
Institute of Sanitary Engineering and Water Pollution Control, University of Natural Resources and Life Sciences (BOKU), Muthgasse 18, A-1190
Vienna, Austria
4
Department of Primary Industries, 1301, Hazeldean Road, Ellinbank VIC 3821, Australia

Abstract:
This study was designed to improve our understanding of, and mechanistically simulate, nitrate (NO3) dynamics in a steep 9.8 harural headwater catchment, including its production in soil and delivery to a stream via surface and subsurface processes. A twodimensional modelling approach was evaluated for (1) integrating these processes at a hillslope scale annually and within storms,(2) estimating denitriﬁcation, and (3) running virtual experiments to generate insights and hypotheses about using trees instreamside management zones (SMZs) to mitigate NO3 delivery to streams. Total ﬂow was mathematically separated into quickand slow-ﬂow components; the latter was routed through the HYDRUS software with a nitrogen module designed forconstructed wetlands. Flow was monitored for two years. High surface-soil NO3 concentrations started to be delivered to thestream via preferential subsurface ﬂow within two days of the storm commencing. Groundwater NO3-N concentrations decreased
from 1.0 to less than 0.1 mg l 1 from up-slope to down-slope water tables, respectively, which was attributed to denitriﬁcation.
Measurements were consistent with the ﬂushing of NO3 mainly laterally from surface soil during and following each storm. The
model accurately accounted for NO3 turnover, leading to the hypotheses that denitriﬁcation was a minor ﬂux (<3 kg N ha 1)
compared to uptake (98 127 kg N ha 1), and that SMZ trees would reduce denitriﬁcation if they lowered the water table. This
research provides an example of the measurement and modelling of NO3 dynamics at a small-catchment scale with high spatial
and temporal resolution. Copyright © 2013 John Wiley & Sons, Ltd.
KEY WORDS

forest; hydrology; nitrogen cycling; pasture; stream ﬂow; water quality

Received 30 August 2012; Accepted 7 January 2013

INTRODUCTION
Nitrate (NO3) concentrations in stream water and annual
rates of export are of concern for down-stream uses
including potable water, livestock production, aquaculture,irrigation, and the provision of natural habitat in rivers andestuaries. Agricultural production in many regions of theworld occurs in conjunction with high rates of atmosphericor fertilizer nitrogen (N) inputs. This leads to NO3
concentrations in stream and ground waters exceeding
recommended or allowable levels. Hence, during the pastfew decades, there has been interest in understanding
landscape processes that deliver NO3 to streams (Rasiah
et al., 2010; Rozemeijer et al., 2009; Shabaga and Hill,
2010; Zhu et al., 2011).
When attempting to understand, simulate, and predict
temporal patterns of concentrations and annual export
rates of NO3 from a catchment, it is important toconceptualize spatially and temporally both the sources
and transport mechanisms that result in NO3 delivery to*Correspondence to: Philip J. Smethurst, CSIRO Ecosystem Sciences,
CRC for Forestry, and Landscape Logic CERF, Private Bag 12, Hobart
TAS 7001, Australia.
E-mail: Philip.Smethurst@csiro.au
Copyright © 2013 John Wiley & Sons, Ltd.

the stream. This conceptualization phase has beenreported already for many catchments and has shown,for instance, that the concentration–discharge patterns ofNO3 depend predominantly on the contributions ofoverland-, inter-, and baseﬂow to water ﬂow and NO3
concentrations. Because rainwater commonly has a low
concentration of NO3, major overland ﬂow events can be
expected to dilute higher-NO3-concentration water thatseeps from the subsoil and dominates base-ﬂow conditions(Rusjan et al., 2008; Holz, 2010). However, if rainwaterpercolates through the soil and interﬂow dominates, highconcentration water can be mobilized and rapidly delivered
to the stream during and shortly after a storm. This process
has been referred to as NO3 ﬂushing (Creed and Band,
1998). Over longer time periods (weeks to months), highNO3 surface water also percolates slowly down the soil
proﬁle in predominantly a vertical direction and recharges
groundwater, which in-turn leads to seasonal changes in
the concentration of NO3 in baseﬂow (Rusjan et al., 2008).
Flushing leads to a positive relationship between ﬂow andNO3 concentration seasonally (Ohrui and Mitchell, 1998;
Ohte et al., 2001; Chiwa et al., 2010) or within rainfall
events (Poor and McDonnell, 2007). Within-event patterns
can change from a concentration pattern to a dilution
pattern due to season (Petrone et al., 2007), contaminant

1821

NITRATE DYNAMICS IN A HEADWATER CATCHMENT(Holz, 2010), or land use (Poor and McDonnell, 2007).
NO3 concentrations in baseﬂow may be high or low
compared to storm ﬂow, depending on the relative sources
and mobilization of NO3 in soil versus ground water.
Patterns of concentration–discharge can be simulatedusing three-end-member mixing models if concentrationsin the end members are known and their relative
contributions to ﬂow can be deduced from tracers or byother means (Evans and Davies, 1998; Katsuyama et al.,
2001; Petrone et al., 2007). However, such models do notaccount for landscape processes that lead to the ﬂow orconcentration inputs required for the model. A need toquantitatively model landscape processes that produce
and deliver NO3 to streams has already led to moremechanistic models, e.g. Hill-vi (Weiler and McDonnell,2006), JAMS/J2000-S (Kralisch and Krause, 2006;
Krause et al., 2006), SINIC (Hong et al., 2006), andSWIM (Huang et al., 2009). Yet, these approaches focus
mainly on large catchment outcomes, and none haveattempted to simulate within-soil water and solute
behaviour using the Richard’s equation for saturated–unsaturated water ﬂow, and Fickian-based advection–
dispersion equations for solute transport. The combinationof these mechanisms currently provides the most mechanistic approach available for simulating water and solutetransport in soil proﬁles (Šimůnek et al., 2008).
The HYDRUS software incorporates these mechanistic
water ﬂow and solute transport equations and can be usedin one-, two-, or three-dimensional applications withpreferential ﬂow and other options (Šimůnek et al., 2008).
Its 2D application includes a model describing N
dynamics (i.e. the CW2D biokinetic model of the
HYDRUS wetland module) developed for simulatingNO3 removal from efﬂuent in constructed wetlands(Langergraber and Šimůnek, 2005, 2012), but this module
had not been evaluated for use on soils, hillslopes, or
small catchments that were not completely ﬂooded.
Although the HYDRUS wetland module was of interestfor this application, it was not designed to simulate thedown-slope temporal dynamics and solute composition of
overland ﬂow. This limitation could potentially be overcome by identifying a quick (overland)-ﬂow and slow-ﬂow
component of total ﬂow using an acceptable mathematicalprocedure of ﬂow analysis (e.g. Lyne and Hollick, 1979),
and routing the slow-ﬂow component through the
HYDRUS software to later be discharged to the stream
as the combination of base- and inter-ﬂow components.
In Australia and internationally, few studies havesought to understand and simulate NO3 and water
dynamics at the headwater catchment scale, yet it is in
headwater catchments that most NO3 is generally
delivered to streams and where water quality is poorest(Weaver et al., 2001). The use of streamside managementzones (SMZs, also called vegetated buffer strips andriparian buffers; Neary et al., 2010) that contain trees andshrubs is widely accepted as an option for mitigating
nonpoint source pollution on farms (Tilman et al., 2002;
Dosskey et al., 2010; Zhang et al., 2010). Some authors
have speculated that adding trees to SMZs will add
Copyright © 2013 John Wiley & Sons, Ltd.

carbon to the system and promote denitriﬁcation (Rassam
et al., 2008). However, trees might also increasetranspiration rates in this zone, and thereby lower watertables and reduce denitriﬁcation. Under such conditions,
N uptake by trees might be of greater importance thandenitriﬁcation for enhancing NO3 mitigation. To better
integrate our understanding of NO3 uptake by plants andmicrobes with catchment scale hydrological processes,
we need mechanistic models that operate in at least two
dimensions (hillslope transect), and with enough ﬂexibility to conduct virtual experiments in various types ofcatchments and climatic scenarios (Stutter et al., 2012).
We saw the HYDRUS wetland module as possibly
providing such a capability and therefore explored its use.
The objectives were to combine ﬁeld measurements
and modelling of N cycling processes to: 1) quantify the
major pools and ﬂuxes of water and NO3 in a small rural
headwater catchment, 2) evaluate a method of modellingthese processes during storm events and annually using
ﬂow analysis in combination with the CW2D biokinetic
model of the HYDRUS wetland module (i.e. HYDRUSCW2D), and, if this evaluation was favourable, 3) use thismodel to estimate ecosystem denitriﬁcation, simulate
virtual experiments, and thereby develop a working
hypothesis on the potential effects of SMZs containing
deep-rooted trees.

MATERIALS AND METHODSSite

The study area was a north-facing headwater catchment(9.8 ha) of Forsters Rivulet, which ﬂows into the Huon
River in southern Tasmania, Australia. Land use is
grazing (68%) in the lower part, and native forest (32%)
in the upper part (Figure 1). Farmer records indicate
average annual rainfall (1991–2006) was 722 mm (range
501–975 mm; ca. 5th percentile mm rainfall 551, 10th 560,
50th 709, 90th 913, 95th 990). The catchment is in steep
terrain (average 17o slope). Soils are 2–3 m deep, derived
from interlaid and mixed slope deposits of Cretaceous
syenite and Permian mudstone. The 0–0.2 m soil horizon
is a dark brown silty loam and moderately well structured.
Clay and grit contents increase with depth to a gritty
medium or heavy clay texture at 2–3 m depth. Syeniterock fragments are present in proﬁles throughout thecatchment, and the presence of mudstone fragments
increases from none at the top of the catchment (exposed
syenite rock) to being the dominant rock fragment on thewestern boundary of the catchment. Surface-soil (0–10 cm
depth; < 2 mm) properties were loam or clay-loam textureswith 0.7–1.0 g cm 3 bulk density, 0.081 dS m 1 electricalconductivity (1:5 soil:water), 5.5 pHwater, 0.46 mg kg 1
total N, 16 carbon (C):N, 40.8 mg kg 1 availablephosphorus (P) (Colwell), 17.5 meq 100 g 1 effective cationexchange capacity, and 94% base saturation (Rayment and
Higginson, 1992). The soil fraction < 2 mm was 51–99%
in surface-soil samples, but only 1% in some samples at thebottom of the proﬁle (3 m).
Hydrol. Process. 28, 1820–1834 (2014)

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P. J. SMETHURST ET AL.

In the lower, northern half of the SMZ, Eucalyptus nitens(shining gum) was planted on each mound, and Acaciamelanoxylon (blackwood) was planted in the saturated
riparian zone. In the top, southern half of the SMZ,
Eucalyptus globulus (blue gum) was planted on eachmound top, and there was no saturated riparian zone.
Fertilizers were not used in the catchment for several years
prior to or during the study, except during plantation
establishment in the SMZ. The pasture consisted of grasses
and non-leguminous dicotyledons. Within 2 months ofplanting, all eucalypt seedlings received weed control and
200 g of diammonium phosphate (17.5% N 20.0% P;
equivalent of 5.1 kg N and 5.8 kg P ha 1 for the total
catchment), which was split between two spade slits in thesoil about 15 cm on opposite sides of the planting position.
The overall stocking of the plantation was 1419 trees ha 1.

Measurements

Figure 1. Map of Australia showing the location of the study site (indicatedby arrow in the inset map), and map of the study site showing the locationsof catchment boundary (broken line), stream (bold solid line), contours(solid lines with elevations indicated), vegetation types (shaded is forest,
blank is pasture, irregular block around the stream marked by a solid line is
the fenced SMZ plantation), weather station (shaded rectangle), weir(solid trapezoid), TDR instrument (triangle), and piezometers (circles)

For the year preceding the study (2007), the catchment
was irregularly grazed within a general regime of moderate
stocking density (c. 2–3 head per ha) for 2–6 week periods
at 2–3 month intervals. Cattle had free access to all
unfenced streams. A mixed-species plantation was established in 2008 in the 5–30 m variable-width fenced SMZ.
The area of the SMZ (0.6 ha) was 6% of the catchment and
10% of the pasture area in the catchment. Within the SMZ,
soil outside the saturated riparian zone was cultivated by a‘scoop-and-pile’ method using a mini-excavator, which
created a pit adjacent to a mound on which a tree seedlingwas planted. Tree seedlings were planted in August 2008.

An automatic weather station was installed on theboundary of the catchment in 2007 that recorded rainfall.
Annual rainfall at the site was 603 mm for 2007, 501 mm
for 2008, and 879 mm for 2009. On two occasions,rainwater was sampled over a period of 3 days into a clean
plastic funnel and bottle, and analysed for NO3 as
described below for other water samples.
A 60O, aluminium plate, V-notch weir was installed at
the outlet of the catchment in early 2008 and included
water level readings with a capacitance probe recording
every 5 min. Water level was converted to ﬂow using astandard rating curve equation for 60 V-notch weirs.
NO3 and other parameters were monitored every three to
six weeks using one grab sample taken from each weir.
An automatic sampler was used to sample stream water
every few hours for several days during three storms(Table I). Samples were analysed for NO3 using thecolorimetric cadmium reduction method on a ﬂow-injection
analyser (Lachat Method 12-107-04-1-F).
Volumetric soil water content in the SMZ wasmeasured hourly by time domain reﬂectometry at several
locations in a 200 m2 area of the SMZ. Probes (n = 3)
were placed at 0–15 cm, 30–45 cm, and 60–75 cm. Due toequipment failure, these measurements were made forseveral 3-week periods during the 2008 and 2009, rather

Table I. Summary of rainfall and stream ﬂow during the three storms that were automatically sampled during 2009

RainfallStart date
Duration (d)Total (mm)Peak Intensity (mm 15 min 1)
Flow
Average (kl d 1)
Peak (kl d 1)
Day of peak
Peak:initial

Copyright © 2013 John Wiley & Sons, Ltd.

Storm 1

Storm 2

Storm 313th May
5
33.4
1.2

3rd June
4
38.4
1.4

26th November
5
30.4
2.2

18.6
35.5
3
17.6

117.6
241.4
3
37.8

28.5
68.6
3
279.1

Hydrol. Process. 28, 1820–1834 (2014)

NITRATE DYNAMICS IN A HEADWATER CATCHMENT

than the entire period. Soil in this location was
representative of the SMZ.
NO3 in soil water was sampled using two methods. Soilwater held at low tension (leachate) was sampled usinginert porous suction samplers (Prenart©). Nine were
installed in three zones across the hillslope, i.e. upper
mid-slope pasture, lower mid-slope pasture, and in theSMZ (buffer), for in situ sampling of leachate. Samplers
were placed 45o from vertical and at 75 cm depth (i.e. below
the root zone of pasture). Water samples were collected overperiods of 1–3 d and refrigerated in the laboratory at 4  C for
up to 14 d prior to NO3 analysis. Leachate samples could becollected only when the soil was quite wet, and not all
samplers yielded soil water at each attempt. At similar
locations to the leachate samplers, piezometers were
installed to measure water table depths and NO 3
concentrations (method as for stream water samples).
Water removal prior to sampling was found to beunnecessary. Dissolved oxygen was measured at various
depths in the water table at four locations on one date (April2009) using a galvanic probe.
Soil water was also sampled using a paste method(Smethurst et al., 1997), from which concentrations of
NO3 in soil solution were estimated (analysis as for
stream water samples). Surface-soil samples (0–10 cm
depth) were collected on three occasions (September
2009 to March 2010) from three zones across thecatchment (up-slope forest, mid-slope pasture, and lowslope pasture-plantation SMZ). At each sample location,
20 soil cores were bulked per sample analysed.
Annual rates of net N mineralization and nitriﬁcation
for the 0–10 cm depth were estimated using aerobic
incubations and the SNAP model that was developed fortemperature Australian conditions (Paul et al., 2002). The
measurements were based on three incubated cores per
location and 15 sampling locations covering the samezones as those used for soil solution sampling. Cores were
sampled when relatively moist (September 2009) andincubated at 27.5o C for 30 d. Nitriﬁcation in the 0–10 cm
depth was assumed to be half of that occurring for the
total soil proﬁle (Moroni et al., 2004).
Pasture vegetation was sampled periodically from0.25 m2 quadrates (n = 3) within animal-excluded area
near the weir and the weather station. Samples were ovendried (70o C), weighed, ground, and analysed for total N(Lowther, 1980).
Modelling

The HYDRUS software (Šimůnek et al., 2008; version1.05) was used in a two-dimensional, sloped, rectangular(trapezoidal) conﬁguration. The model can be accessed at:
http://www.pc-progress.com/en/Default.aspx?hydrus-3d.
Units used were cm for length, d for time, and mg l 1 forconcentration. An atmospheric (precipitation) boundary
condition was speciﬁed for the surface, with a verticalseepage face at the bottom of the slope, and no-transfer
boundaries for other faces. Seepage refers to water
movement out of a soil proﬁle at a face with an
Copyright © 2013 John Wiley & Sons, Ltd.

1823

atmospheric boundary condition (saturation excess) and
can include components of interﬂow soon after rainfall,
stored soil water, and ground water entering the soil
proﬁle from an aquifer. Runoff was deﬁned as overland
ﬂow in excess of inﬁltration, seepage as the combinationof baseﬂow and interﬂow, and stream ﬂow as seepage
plus runoff. No seepage into basement rock was
assumed.
In order to simulate hillslope processes in two
dimensions, an average hillslope length was calculated
as catchment area divided by twice the stream length. A
total of 1197 spatial nodes were used (96 lateral by 21
vertical). The spacing of lateral and vertical nodes was
closest at the lower slope and surface-soil zones. Timesteps started at very low values (0.0001 d) and increased
during stable periods to a maximum of 1 d. Evaporation
was included in the potential evapotranspiration ﬂux
based on that estimated by the weather station. Before
rainfall events were simulated, a pre-simulation included
runs (up to 200 d) of average rainfall and solute
inputs that enabled an effective steady state to beachieved for seepage rate and concentration. Simulated
seepage and runoff ﬂuxes were in two-dimensional units(cm2 d 1) and converted to three-dimensional output by
multiplying by the length of the third dimension(catchment length = catchment area/length of hillslope = 2
 measured stream length).
Previously, two methods of simulating runoff were
tested, i.e. rainfall that is instantaneously in excess of
inﬁltration (method A), and the use in HYDRUS of ahypothetical layer at the top of the soil proﬁle withextremely high porosity and hydraulic conductivity(method B). However, neither of these overland ﬂow
methods was able to adequately simulate the short-term
temporal dynamics of stream ﬂow during rainfall events(Smethurst et al., 2009). Instead, a third method wasdeveloped in the current study whereby measured stream
ﬂow was analysed mathematically by the Lyne andHollick (1979) method (i.e. ﬂow analysis) to estimate thequick-ﬂow and slow-ﬂow components, which approximate to overland ﬂow and inter- plus baseﬂow,
respectively. As the slow-ﬂow component had tooriginate from rainfall, it was routed through HYDRUS
as precipitation. Resultant seepage estimated by HYDRUS
was combined with the quick-ﬂow component in a postHYDRUS spreadsheet to estimate stream ﬂow. Also, in the
spreadsheet, NO3 in seepage (as simulated by HYDRUS)
and NO3 in runoff (as user-prescribed values) werecombined to provide an estimate of NO3 in stream ﬂow.
In this manner, ﬂow and solute dynamics in the May storm
event (storm 1; Table I) were simulated using inputs
summarized in Table II.
For an annual period of NO3 transformations (i.e.
nitriﬁcation and denitriﬁcation), the CW2D biokinetic
model was used with HYDRUS, along with measured
daily rainfall and potential evapotranspiration. The
CW2D model was designed primarily to simulate NO3
removal from efﬂuent waters draining through variably
ﬂooded constructed wetlands (Langergraber and Šimůnek,Hydrol. Process. 28, 1820–1834 (2014)

1824

P. J. SMETHURST ET AL.

Table II. Description of the simulated May storm: salient HYDRUS inputs
Attribute
General Description
Slope length (m)Duration (d)Water ﬂuxes
Root water uptake
Spatial nodes

Time steps (d)HYDRUS units
Slope (O)Rainfall (mm)
Soil horizons: thickness (m), texture1, Ksat (cm d 1)
Root depths (m)Initial NO3-N concentrations

Value
Hillslope as for catchment 1 in photo 1, with tuned water balance.
Deep roots (native forest) for top 38% of slope. Shallow roots(pasture) for lower 62% of slope.
515.2
7
Hourly rainfall and potential transpiration assuming no evaporation
Feddes model parameters (no solute stress): P0 -10, POpt -25,
P2H -300, P2L -1000, P3 -1100, r2H 0.5, r2L 0.1
Horizontal: 96 (2.5 m apart at the bottom of slope to 5.7 m apartat the top of slope)
Vertical: 21 (0.027 m apart at the top of the soil proﬁle to 0.27 m
apart at the bottom of the soil proﬁle)
10 7–10 3
cm length, mg l 1 liquid concentration, mg kg 1 solid concentration,g cm 3 soil bulk density
17.4
33.4
Horizon 1: 0.24, sandy loam, 3105; Horizon 2: 0.23, sandy loam,
106.1; Horizon 3: 2.55, silty clay, 0.48
3.0:0.5:0.5 for native forest: pasture: SMZ
Surface soil (0–0.5 m depth) had 0.082 mg l 1 at 0–0.1 m depth and0.0082 mg l 1 at 0.1–0.4 m depth, except for a the bottom 0.5–10.0 mof the hillslope, which contained a NO3 hot spot (7 mg l 1 at surface
decreasing linearly to 0.0082 mg l 1 at 0.4 m depth). Subsoil(0.5 to 3.0 m depth) contained 0.082 mg l 1 NO3.

2005) by accounting for changes in microbial biomass,organic matter, and some inorganic pools of N and P. The
dynamics of 13 solutes (oxygen, three fractions of organic
matter, four microbe species, ammonium (NH4), nitrite(NO2), NO3, dinitrogen (N2), phosphate (PO4), and an inert
tracer) are simulated using nine processes. For thisapplication of HYDRUS, complexity was reduced byartiﬁcially ﬁxing the depth-dependent concentrations of O2
and NH4 using high values of their respective solid–liquid
phase partition coefﬁcients. The option of including
temperature dependency of reactions was not used, andsimulations were conducted using a measured averageannual soil temperature (12.5  C). Inputs for simulating
this catchment are summarized as scenario 1 in Tables III
and IV. HYDRUS-CW2D outputs were post-processed ina spreadsheet to estimate annual ﬂuxes and pool changes
for water and NO3. Speciﬁcation of the grazing orplantation management regime in the SMZ was limited
to root depth and therefore evapotranspiration. While
management regime can affect water use, such details werebeyond the scope of this study.
Scenario 1 and four additional scenarios were used tosimulate potential SMZ plantation effects (Tables III
and IV):• Scenario 1: SMZ pasture (not plantation) with roots to
0.5 m depth. Native forest in the top half of thecatchment was assumed to have roots of the same water
uptake properties, but extending to 3.0 m depth.
• Scenario 2: an SMZ plantation replaced the pasture
within 25 m of the stream. The plantation was
simulated by extending root depth from 0.5 m to 3.0 m.
Copyright © 2013 John Wiley & Sons, Ltd.

• Scenario 3: an SMZ plantation was not included, butconditions were more conducive to denitriﬁcation thanscenario 1.
• Scenario 4: an SMZ plantation was added to scenario 3.
• Scenario 5: as for scenario 4, but with double the widthof plantation, more carbon input and soil drying from it.

RESULTS
Rainfall and stream ﬂow

The ﬂow season was deﬁned as starting at the
beginning of May each year, as this approximated theend of the dry period with low ﬂows. During the two-year
period May 2008 to April 2010, 572 mm of rain fellduring the ﬁrst ﬂow season and 814 mm during thesecond. It rained every month, with a minimum of
16.6 mm in May 2008 and maximum of 133.4 mm in
September 2009. June–October 2009 was the wettest
four-month period, but otherwise there was no apparent
trend in monthly rainfall (data not presented). Hence, theﬁrst year of the study was relatively dry and the second
relative wet, which resulted in relatively low and higher
stream ﬂow, respectively (Figure 2). Daily rainfall peakedat 39.4 mm on 27th September 2009, and maximum daily
rainfall during sampled storms was 11.8–17.4 mm. Twostorms were sampled May–June 2009, which were theﬁrst major storms after the dry period, and which marked
the start of the second ﬂow season. A third storm was
sampled in November 2009, i.e. near the end of major
ﬂow of the second season. Of the storms sampled, total
storm rainfall ranged from 30.4 to 38.4 mm (Table I).
Hydrol. Process. 28, 1820–1834 (2014)

Copyright © 2013 John Wiley & Sons, Ltd.

a

Scenario 2

Scenario 3

Scenario 4

Scenario 5

Hillslope as for study catchment
As for scenario 1, except Hypothetical low slope, highAs for scenario 3, except As for scenario 4, plus enhanced(Figure 1), with tuned water balance,
deep roots (trees) added
deep roots (trees) added
rainfall and NO3, and higher
carbon supply, less anoxic
nitriﬁcation and NO3 uptake. Modeltemperature (18 oC). Vegetation to bottom 25 m
to bottom 25 m
conditions, and double theestimates of denitriﬁcation and NO3
as for scenario 1, i.e. no treesof slope (SMZ).
of slope (SMZ).
width trees in the SMZ
seepage. Deep roots (native forest) forin SMZ.
(50 m).
top 38% of slope. Shallow roots(pasture) for lower 62% of slope.
515.2
365
Daily rainfall and potential transpiration assuming no evaporation
Feddes model parameters (no solute stress): P0 -10, POpt -25, P2H -300, P2L -1000, P3 -1100, r2H 0.5, r2L 0.1
Horizontal: 96 (2.5 m apart at the bottom of slope to 5.7 m apart at the top of slope)
Vertical: 21 (0.027 m apart at the top of the soil proﬁle to 0.27 m apart at the bottom of the soil proﬁle)0.01–1
cm length, mg l 1 liquid concentration, mg kg 1 solid concentration, g cm 3 soil bulk density
17.4
2.0
572
1200
Horizon 1: 0.95, sandy loam, 106.1
Horizon 1: 0.95, sandy loam, 106.1
Horizon 2: 1.41, silty clay loam, 1.68
Horizon 2: 1.41, loamy sand, 350.2
Horizon 3: 0.64, silty clay, 0.48
Horizon 3: 0.64, sand, 1000
3.0:0.5:0.5
3.0:0.5:3.0
3.0:0.5:0.5
3.0:0.5:3.0
3.0:0.5:3.0

Scenario 1

Texture as selected in HYDRUS from default options.

Time steps (d)HYDRUS units
Slope (O)Rainfall (mm)
Soil horizons:
thickness (m),texturea , Ksat (cm d 1)
Root depths native
forest: pasture: SMZ (m)Slope length (m)Duration (d)Water ﬂuxes
Root water uptake
Spatial nodes

General Description

Attribute

Table III. Description of annual scenarios: salient HYDRUS inputs

NITRATE DYNAMICS IN A HEADWATER CATCHMENT

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Hydrol. Process. 28, 1820–1834 (2014)

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P. J. SMETHURST ET AL.

Table IV. Description of annual scenarios: salient CW2D inputs
Attribute
Soil speciﬁc parameters (all horizons)
Solute speciﬁc parameters (for solutes 1–13a )Oxygen atmospheric boundary condition (mg l 1)
Kd for solutes 1–13
Maximum concentrations for uptake (mg l 1
Temperature
Initial NO3-N concentration

Values for all scenarios
Bulk density 1.5 g cm 3, Disp L 0.5, Disp T, 0.1,
Fract 1, ThImob 0
Difus W: 0.072, 0.0456, 0.0456, 0.0456, 0, 0, 0, 0.0801,
0.0801, 0.0801, 0.000801, 0.0000801, 0.05
Difus G: 769, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
11105, 106, 106, 106, 0, 0, 0, 106, 0, 0, 0, 107, 0
NH4-N 560, NO3-N 450, PO4-P 1.4
Set constant at 12.5  C, no temperature dependence of reactions
1 mg l 1 throughout the hillslopeaSolutes 1–13 in CW2D are 1 dissolved oxygen, 2 readily biodegradable organic matter, 3 slowly biodegradable organic matter, 4 inert organic matter,5 heterotrophic organisms, 6 autotrophic Nitrosomonas, 7 autotrophic Nitrobacter, 8 NH4, 9 NO2, 10 NO3, 11 N2, 12, PO4, 13 tracer.

Figure 2. Rainfall and stream ﬂow in the study catchment May 2008 to
April 2010. Arrows indicate storms that were automatically sampled

Rain water and soil solution NO3

Concentrations of NO3-N in rain water were 0.029 mg l 1
in September 2009 and 0.059 mg l 1 in October 2009.
Concentrations in soil solution were generally higher andranged from the detection limit of 0.002 to 107 mg l 1(Table V). These concentrations were consistent withsimilar measurements by others in Tasmania (Adams andAttiwill, 1991; Smethurst et al., 2001). The highest
concentrations of NO3-N in soil solution were measured
in the mid-slope pasture area in March 2010. Very low
concentrations were more common in the up-slope forest
area and the low-slope pasture-plantation SMZ area.
Leachate and water table NO3 and oxygen

Leachate samples could only be obtained when water
was freely draining below the root zone and while
equipment was functioning. Water tables could be morereliably sampled, but in upper mid-slope pasture positions, asubstantial water table was present only during the wettest
period of 2009. Maximum concentrations of NO3-N in
Copyright © 2013 John Wiley & Sons, Ltd.

leachate below the pasture root zone (0.5 m) were lower thanthose measured in soil solution in the root zone or the watertable (Figure 3). Upper mid-slope pasture positions had thehighest concentrations in leachate and the water table whensampled during May–Sept 2009. Concentrations of NO3-Nin the water table generally ranked upper mid-slope
pasture > lower mid-slope pasture > low-slope buffer,suggesting that NO3 was either removed as water drained
down the slope, or that low-NO3 water was added
preferentially down-slope.
Concentrations of NO3-N in stream water collected at
3- to 6-weekly intervals during each ﬂow season were in
the range <0.002–1.00 mg l 1, with a peak in the middle(2008) or early (2009) part of that period, and thereafterconcentrations decreased (data not presented). From
storm 1 to storm 2, there was an increase in initial NO3
concentration and a small decrease in peak NO 3
concentration, and storm 3 had substantially lower
concentrations throughout the event than was observedin the two earlier storms. During storms 1 and 2, anincrease in NO3 concentration was measured that waspreceded by a dilution early in the event. Also, duringstorm 3, an increase in concentration was observed, but
preceding dilution was not observed as early concentrations
were close to the detection limit anyway (0.002 mg l 1 NO3-N;
storm 3) (Figure 4). Peak concentrations followed peak ﬂow
by 1, 4, and 0 d after these three storms, respectively. Plotsof concentration as a function of discharge showed that NO3
concentrations increased with ﬂow, with a clear anticlockwise hysteresis pattern for storm 1, a predominantly
anti-clockwise pattern for storm 2, and a mixed clockwise
and anti-clockwise pattern for storm 3 (Figure 5).
The concentration of dissolved O2 decreased fromapproximately 5 mg l 1 at the top of the water table, above
which no denitriﬁcation would be expected, to levels near
2 mg l 1 at the bottom of the water table (Figure 6)
Mineralization, nitriﬁcation, and uptake

The rate of net N mineralization (NH4 production) inlaboratory incubations (Figure 7) weighted by area (forest
38.0%, mid-slope pasture 56.4%, low-slope pasture5.6%) was 1.68 mg g 1 d 1, of which 61.1% was nitriﬁed
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NITRATE DYNAMICS IN A HEADWATER CATCHMENT

Table V. Summary of NO3 concentrations in soil solution (0–10 cm depth) on three occasions
Nitrate concentration (mg l 1)
Slope position andconcentration attribute

10th September 2009

23rd November 200924th March 2010

0.12
0.03
0.17
3

<DLa
<DL
1.35
3

<DL
< DL
<DL
3

0.63
<DL
5.59
9

1.39
0.50
80.84
9

21.14
<DL
107.49
9

0.092
0.044
0.112
3

0.974
0.342
7.488
3

10.88
1.71
24.30
3

Up-slope forest
MedianMinimum
MaximumN
Mid-slope pastureMedianMinimum
MaximumN
Low-slope pasture plantation (SMZ)MedianMinimum
MaximumNa<DL = less than detection limit of 0.002 mg l 1

year 1 uptake of NO3-N above- and below-ground for thewhole of catchment.
Preferential ﬂow

Figure 3. Nitrate concentrations in leachate (a) and the water table (b) inrelation to slope position(NO3 production). High rates of net N mineralization in
the forest area (Figure 7) were probably related to higherorganic matter concentrations measured. The SNAP
model estimated an annual rate of net N mineralizationof 126 kg ha 1 year 1 in the top 10 cm of soil (Table VI),
with a minimum in July–August and maximum in
January–February (data not shown). Applying thelaboratory proportion of nitriﬁcation, and assuming halfof all N mineralized and nitriﬁed occurs in the top 10 cm
of soil, NO3 production was estimated to be 154 kg N
ha 1 year 1 (Table VI).
The measured rate of net N accumulation (assumed
uptake) in above-ground pasture biomass was 231 kg N
ha 1 year 1 mid-slope and 328 low slope (Table VI), withan average N concentration of 17 mg g 1. Assuming only
75 kg N ha 1 year 1 was taken up by the forest, 177 kg N
ha 1 year 1 was taken up above-ground on average
across the catchment, which equates to 155 kg N ha 1
Copyright © 2013 John Wiley & Sons, Ltd.

Simultaneous measurements of soil water content at
60–75 cm depth and rainfall provided many examples of
rapid soil wetting at this depth in response to rainfall. In
the example provided (Figure 8), one of four locations in
a 30 m2 area at this depth twice responded rapidly torainfall, increasing soil water content on the second
occasion from 0.26 to 0.30 cm3 cm 3 over a period of
1.3 h, which suggests an effective vertical wetting front
speed of at least 11 m d 1 in some areas (probably
through macropores), or a higher rate if it had a lateralﬂow component. Measured preferential ﬂow is consistent
with observed cracking of these surface soils during dry
conditions.
Simulation of storm

Observed rainfall during 13–19 May had two storm
peaks with a minor peak at 1 d into the event and theother at 3 d, with almost no rain for a half-day period in
between (Figure 4). Flow analysis indicated, as expected,that baseﬂow was almost 100% of total ﬂow at the startand end of the storm, and also about 2 d into the storm
during the low-rain period (Figure 9 top). Baseﬂow
gradually increased during the storm to a peak at 3.4 d,after which no rain fell. Baseﬂow simulated as seepage
using the HYDRUS software was similar in trend andabsolute value to that observed or predicted using ﬂow
analysis (R2 = 0.86), except the initial rise in baseﬂow
was simulated much earlier than that observed (Figure 9
bottom).
Measured concentrations of NO3-N decreased from
0.10 to 0.01 mg l 1 for the ﬁrst day of storm 1, thenincreased and peaked at about 2.5 mg l 1 on day 3
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P. J. SMETHURST ET AL.

Figure 4. Patterns of rainfall, stream ﬂow, and NO3 concentration during the three automatically sampled storms

Figure 5. Concentration-ﬂow (hysteresis) patterns of NO3 for the three storms (circles indicate starting points, arrows indicate directions of main loops)(Figure 10 top). If overland ﬂow (i.e. quick ﬂow) was
assumed to contain no NO3, the dilution effects during thesecond part of the storm were unrealistic, and observations were that concentrations in stream water continued
to increase. A theoretical pattern of concentration in quick
ﬂow was found that, when combined with baseﬂow (i.e.
slow ﬂow), resulted in a similar simulated concentration
pattern to that measured in stream ﬂow (Figure 10
bottom). These results suggest that simulated concentrations in overland ﬂow dominated the concentration
pattern when signiﬁcant overland ﬂow was predicted,
and at other times the concentration pattern was
dominated by seepage concentrations.
Annual simulations and SMZ plantation effectFigure 6. Proﬁles of dissolved O2 in the water table on 16th April 2009.
Numbers indicate the depth of the surface of the water table at the time ofsampling. Codes in the legend refer to location of piezometers: elevation(m) and the side of the catchment (E east, W west)
Copyright © 2013 John Wiley & Sons, Ltd.

The runoff coefﬁcient (discharge as a percent of
rainfall) during the dry year was simulated to be 3%,compared to 4% measured. Whereas in the wet year both
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NITRATE DYNAMICS IN A HEADWATER CATCHMENT

1829

Figure 7. Net N mineralization and percentage nitriﬁcation measured on samples from three parts of the catchment (0–10 cm depth; n = 3 for forest andbuffer samples, n = 9 for pasture samples). Bars are 95% conﬁdence intervals of the mean

Table VI. Summary of annual rates of mineralization, nitriﬁcation, and uptake measured or estimated in the study catchment
Process

Value

Mineralization–Nitriﬁcation
Laboratory (measured):Net N mineralization (mg g 1 d 1)
Nitriﬁcation (%)
Field (SNAP model estimate):Net N mineralization (kg N ha 1 year 1, 0–10 cm depth)
Nitriﬁcation (kg N ha 1 year 1, 0–10 cm depth)
Nitriﬁcation (kg N ha 1 year 1, total soil proﬁle)UptakeForest above-ground N accumulation (kg N ha 1 year 1, assumed)
Pasture above-ground N accumulation (kg N ha 1 year 1, measured)
Buffer pasture above-ground N accumulation (kg N ha 1 year 1, measured)
Catchment above-ground total N uptake (kg N ha 1 year 1)
Catchment above- and below-ground NO3-N uptake (kg N ha 1 year 1)

1.68
61.1
126.3
77.2
154
75
231
328
177
155

Major assumptions and sources of error are (1) that it is valid to apply the SNAP model with its input errors and inherent errors of estimates, (2) the
proportion of nitriﬁcation measured in the laboratory applies to the ﬁeld, (3) the proportion of total-proﬁle net N mineralization is double that in the top
10 cm of the soil proﬁle, (4) net N accumulated in plant biomass approximates uptake, (5) above-ground N uptake is 70% of total-plant N uptake, (6)NH4 and NO3 are taken up in the same proportions as present in soil.

observed and simulated runoff coefﬁcients were 24%.
Hence, the simulated hydrological cycle was similar tothat observed in both years.
Model simulations were used to estimate rates ofdenitriﬁcation and seepage, and the change in the soil
NO3 pool (Table VII). By varying some parameters andinitial conditions in the model, water ﬂuxes, nitriﬁcation,and uptake were tuned to approximate observed values.
Predicted denitriﬁcation was only 2% of the nitriﬁcation
ﬂux for both years (Table VII), and it was predicted tooccur across the hillslope where a water table existed(data not presented). Predicted and observed seepage ofNO3 to the stream was < 0.05 kg N ha 1 year 1 for thedry year. In the wet year, predicted seepage of NO3 was
1.6 kg N ha 1 year 1 compared to an observed value of
0.3 kg N ha 1 year 1. Simulated ﬂow velocity (magnitude
and direction) depended on time and position (data notpresented), but, during low and moderate rainfall events,
Copyright © 2013 John Wiley & Sons, Ltd.

ﬂow directions were generally vertical down in theunsaturated zone and laterally down-slope in the saturated
zone. High rainfall events were associated with anincrease in lateral, down-slope ﬂow in surface soil.
Scenario 2 suggested that establishment of an SMZ
plantation would have little effect on denitriﬁcation, but itwould increase N uptake and thereby reduce N concentrations in stream water (Table 7I, Figure 11). Scenario 3 wastuned to be more conducive to denitriﬁcation, but, while
denitriﬁcation increased, it remained a minor source of N
loss from this system. Scenario 4, which also was tuned tobe more conducive to denitriﬁcation, and unlike Scenario 3
included an SMZ plantation, yielded similar results. In
Scenario 5, when the simulation was tuned to be more
oxygenated, rates of denitriﬁcation decreased substantially(72% decrease) compared to Scenarios 3 and 4, suggesting
what might occur if an SMZ plantation increased transpiration, reduced water table depths, and increased aeration.
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P. J. SMETHURST ET AL.

Figure 8. Volumetric soil water content (60–75 cm depth) at four
locations in relation to rainfall on the 2nd September 2008. Note that watercontent at one location responded almost instantaneously to peaks inrainfall, indicating preferential ﬂow from rainfall falling at the surface, but
no response was seen at other locations

DISCUSSION

Figure 10. Measured (top) and simulated (bottom, solid line) concentrations of NO3-N in stream water at the Willow Bend site. Assumed
concentrations of NO3-N in overland ﬂow (quick ﬂow) are indicated by
the broken line in the bottom graphNO3 concentrations in most soils vary seasonally due tothe interactive effects of microbial activity (as affected byrainfall and temperature), plant uptake, leaching, anddenitriﬁcation. Pasture and forest soils in Tasmania are no
exception (Moroni et al., 2002). Management practices
are also important, particularly those that affect organic
matter quality and quantity, e.g. stocking rates of cattle

and sheep, and inputs from mineral fertilizers andmanures (Holz, 2010). Delivery of NO3 to streams islargely a function of topography, soil type, land management, and the amount and pattern of rainfall. Base-ﬂow
concentrations of NO3 in streams with excessive N inputs

Figure 9. Observed ﬂow (top) in the Willow Bend catchment 13–19 May 2009 and its simulation (bottom) using quick-ﬂow analysis and the
HYDRUS software
Copyright © 2013 John Wiley & Sons, Ltd.

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NITRATE DYNAMICS IN A HEADWATER CATCHMENT

1831

Table VII. Simulated water and NO3 dynamics at the Willow Bendsite for the one-year periods starting May 2008 and May 2009.
Model inputs were as described in Tables III and IV, scenario 1.
Various parameters were tuned to achieve the required water balance
and rates of nitriﬁcation and NO3 uptake for the dry year. These
tuned values are shaded grey. For the dry year, rates of denitriﬁcationand seepage, and the soil NO3 pool are model predictions. For the
wet year, only precipitation was changed
Pool or Flux

May 2008 to
April 2009

Water Balance (mm)Precipitation
572
Evapotranspiration
616
Overland ﬂow
15
Seepage to stream
3
Runoff coefﬁcient (%)
3
Soil water change
63
Nitrogen balance (kg N ha 1 year 1)
Nitriﬁcation
154.1
Uptake127.3
Denitriﬁcation
2.5
0.0
Stream-ﬂow NO3
0.02
Stream NO3-N concentration(mg l 1; ﬂow weighted)

May 2009 to
April 2010

814
644
151
19
24
77
130.7
98.1
2.5
1.6
0.81

can be as high as 30 mg l 1 (Hefting et al., 2006;
Schilling et al., 2006; Huang et al., 2009; Kato et al.,2009), but less-intensively managed agricultural systems
and forested catchments commonly have much lower
concentrations (e.g. Verburg et al., 2012; Vink et al.,2007). Where ﬂushing patterns are observed, NO3concentrations during events can increase several fold
and up to 20 mg l 1 above base-ﬂow concentrations(Ohrui et al., 1998; Ocampo et al., 2006; Poor and
McDonnell, 2007; Rusjan et al., 2008). In an Australian

Figure 11. Simulated denitriﬁcation ﬂux, change in the soil NO3-N pool,and average concentration of NO3-N in stream water for scenarios 1–5.
Scenarios 1 and 2 are for the study site without and with an SMZ
plantation, respectively. Scenarios 3–5 are for hypothetical scenarios more
conducive to denitriﬁcation, i.e. higher rainfall, lower slope, and more soilorganic matter (see Table II for detail description)

study, NO3 ﬂushing was also observed, with peakconcentrations early in the ﬂow season reaching 5 mg l 1
with low intensity grazing and 24 mg l 1with a forest
plantation (Vink et al., 2007). In that study, and in Holz(2010), peak- and base-ﬂow concentrations progressively
decreased during most ﬂow seasons. Our observed
patterns of NO3 concentration, were generally low andpeaked early in the ﬂow season. These patterns wereTable VIII. Simulated water and NO3 dynamics at the study site (scenario 1) and with trees hypothetically in the SMZ (scenario 2). Also
shown are other hypothetical scenarios with (compared to the study site) lower slope, wetter soil, and higher NO3 concentrations, i.e. more
conducive to denitriﬁcation. The model required various parameters to be tuned to achieve the required water balance and rates of nitriﬁcation
and NO3 uptake in the pasture base cases (scenarios 1 and 3). To simulate tree vegetation in the SMZ (scenarios 2 and 4,) roots were extended
to the full depth of the soil proﬁle in the SMZ (lowest 25 m of slope). A further scenario (scenario 5) included extending the width of the SMZ
to 50 m and reduced the level of anoxia in the water table from 1 to 5 mg l 1 O2. Tuned values are shaded grey. Denitriﬁcation, soil NO3change, and stream water NO3 are shown in Figure 11.

Copyright © 2013 John Wiley & Sons, Ltd.

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P. J. SMETHURST ET AL.

consistent with mixed land-use catchments where lowintensity management includes low rates of N
fertilization. More intense land use would probably have
led to higher NO3 concentrations throughout the ﬂow
season (Holz, 2010; Ignatius et al., 2012)
In ﬂushing systems, initial dilution of NO3 concentrations during an event has been observed in water ofheadwater streams (Poor and McDonnell, 2007; Katoet al., 2009), which can be attributed to direct stream ornear-stream interception of low-NO3 rain water. Regardless if this dilution effect occurs, increasing concentration
during and shortly after the event indicates NO3-enrichedwater reaching the stream in interﬂow (Rusjan et al.,2008; Inamdar et al., 2009) or overland ﬂow (McColl andGibson, 1979; Cooke and Cooper, 1988; Robertson andNash, 2008) draining high-NO3 surface soils (Robertson
and Nash, 2008). On a seasonal or annual time scale,repeated ﬂushing tends to deplete the soil proﬁle of NO3
and concentrations in stream water trend downwards(Poor and McDonnell, 2007). These patterns were also
evident in our data, as we observed (1) a hysteresis
response during storms whereby low NO3 at the onset ofstorm ﬂow shifted to high NO3 at maximum ﬂow or
during ﬂow recession (Figures 4–5) and (2) generally
decreasing concentrations during the season. These
patterns are typical of NO3 ﬂushing systems.
The ﬂushing effect appears to have been facilitated by
preferential ﬂow within surface soil, because elevatedﬂows and NO3 concentrations persisted beyond theoccurrence of rainfall (Figure 4) and very high wetting
front velocities in some locations resulted in rapid
delivery of rainfall to the base of the soil proﬁle (Figure 8).
The potential for preferential ﬂow in surface soil tofacilitate the quick delivery of PO4 to streams has beenrecognized for many years (Stamm et al., 1998; Stevens
et al., 1999), and recently measured on texture-contrast
soils in southern Tasmania (Hardie et al., 2011). Our
results demonstrate and quantify this process for NO3.
Although the CW2D conﬁguration of HYDRUS precludes
the use of preferential ﬂow, graphical output shows thatwater velocities predicted across the hillslope were
maximal after high rainfall and in the bottom part of thesurface-soil horizon where the highest saturated hydraulicconductivity was speciﬁed in an attempt to mimic
preferential ﬂow. These ﬂow patterns were also consistent
with a surface-soil-NO3-ﬂushing hypothesis.
Several authors have classiﬁed within-event concentration-ﬂow patterns (hysteresis), as a diagnostic tool forinterpreting catchment processes (Evans and Davies,
1998; Holz, 2010). Results here demonstrate that thepattern of hysteresis varied considerably between storm
events, as also reported by numerous other studies(Figure 5; Petrone et al., 2007; Rusjan et al., 2008).
Concentration-ﬂow hysteresis alone therefore cannot be
used to infer mechanisms within a catchment, but such
patterns remain a useful way to illustrate and compare theoutcomes of catchment processes.
Dissolved oxygen concentrations decreased with depth
in the water table to values low enough to be conducive to
Copyright © 2013 John Wiley & Sons, Ltd.

denitriﬁcation (2 mg l 1; Figure 6; Körner and Zumft,1989). NO3 concentrations in the saturated zone were also
noted to be highest up-slope, which is consistent with
denitriﬁcation being most advanced in deeper water
down-slope. Hence, it is postulated that denitriﬁcation
rates increased with water table depth and lower-slope
positions.
Modelling of catchment processes that deliver NO3 to a
stream has increased in sophistication during recent years
by the inclusion of spatial and temporal detail of waterﬂows and soil NO3 concentrations (Ignatius et al., 2012;
Weiler and McDonnell, 2006), or with detailed N
turnover (Hong et al., 2006; Huang et al., 2009). In thismanuscript, we demonstrate how the major processes
controlling NO3 concentrations in soil can be combinedwith detailed hillslope hydrologic modelling to simulate
NO3 delivery to a stream across short and long timescales. This development offers many opportunities forvirtual experiments of land-use change such as encouraged
by Weiler and McDonnell (2006) and simulation ofcatchment observations (Figure 9, Table VII).
Our virtual experiment included a forest plantation witha 50 m wide SMZ that increased transpiration (4%),
which in-turn decreased stream ﬂows (24%), andincreased NO3 uptake (4%). However, as nitriﬁcation
increased (4%) and denitriﬁcation decreased (75%), NO3concentrations in stream water increased (800%), whichin this scenario still remained low (0.08 mg NO3-N l 1)
(Table 7I, Figure 11). This scenario demonstrates thecomplexity of interactions that need to be consideredwhen speculating about the mitigation effect of SMZ
plantations. These scenarios were not fully validated, but
a paired catchment experiment has been established todetermine the water quality and stream ﬂow effects of thispractice (Smethurst et al., 2009), and most of the waterand NO3 ﬂuxes in these scenarios were similar to thosemeasured in the catchment, i.e. rainfall, streamﬂow,
nitriﬁcation, uptake, and seepage to the stream.
Results presented challenge the assertion of Rassam
et al. (2008) that tree establishment in an SMZ (riparianzone) will promote denitriﬁcation and thereby reduceNO3 delivery to the stream. This effect could only occur if
trees did not alter the soil water and NO3 status and if they
increased soil C concentrations, which would in turnpromote denitriﬁcation. Conversely, Fellows et al.
(2007), in a paired-site study at various locations inAustralia, found that trees in SMZs did not increase soil
carbon concentrations or rates of denitriﬁcation.
It is possible that trees will increase transpiration (Vink
et al., 2007) and NO3 uptake rates (Zhang et al., 2010),
which would lead to reduced rates of denitriﬁcation.
Observations and modelling by Speiran (2010) also
indicate that NO3 uptake by vegetation in riparian zones
and buffers should be given more emphasis than it hasreceived so far. This author also pointed out that mostincreased carbon availability brought about by forests isin surface soils that are aerated and unlikely to promote
denitriﬁcation, except during seasonal or temporary
ﬂooding. However, our results suggest that increased
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NITRATE DYNAMICS IN A HEADWATER CATCHMENT

nitriﬁcation, soil NO3 concentrations, and leaching as aresult of trees might negate the NO3-mitigating effects ofuptake. Our current working hypothesis therefore is that,
if trees established in SMZs mitigate NO3 delivery to
streams, if at all, it will be via increased NO3 uptake and not
via enhanced denitriﬁcation, but increased nitriﬁcation is arisk to these mitigating processes. This hypothesis was
generated by virtual experiments (Table 7I, Figure 11). The
N retention capacity of the SMZ might need to be renewed
periodically, however, by removing N in plant material
through tree harvesting. ‘Crash’ grazing of remnant pasture(short duration and intense) during dry periods might also
be an option when there is a low risk of stream
contamination, but manure inputs could negate forage
outputs. Managed burning could also be considered, but
could present a risk of P and sediment contamination of
stream water and damage to trees.
In this study, the observed patterns of NO3 concentration and ﬂow were consistent with a NO3 ﬂushing
hypothesis, i.e. storms delivered high NO3 in surface
water within 1–2 days from the onset of the storm.
Maximum NO3 concentrations in stream water during
early season storms were generally high considering thelack of use of fertilizers and other intensive inputs, anddespite the relatively high-ﬂow nature of the season inwhich the storms were automatically sampled. Overall,observed patterns of ﬂow and concentration during astorm could be simulated using ﬂow analysis and the
HYDRUS software. In its ﬁrst application to hillslope
NO3 dynamics, the HYDRUS-CW2D model was used tosimulate catchment pools and ﬂuxes of NO3. Thismodelling predicted that denitriﬁcation occurred throughout the catchment where a water table occurred. However,denitriﬁcation accounted for only a small component of
NO3 production. This prediction was consistent withother ﬂux measurements and observed concentrations of
NO3 in the water table that ranged from 2 mg l 1 up-slope
to 0.001 mg l 1 down-slope. The hypothetical addition of
an SMZ containing a forest plantation with deep tree roots
increased water and NO3 uptake but also resulted inincreased nitriﬁcation and a minor decrease in denitriﬁcation. Major sources of error in this analysis include
measurements of catchment-scale NO3 pools and ﬂuxes,
and several assumptions that were necessary to simplify
simulations. Nevertheless, the combined analysis was
useful for improving our understanding of complex waterand NO3 dynamics in this headwater catchment, and forindicating directions and challenges for future measurements and modelling of hillslope NO3. A 2D hillslopemodel as shown in this research might be satisfactory formany types of catchment applications and therefore
warrants further testing.

ACKNOWLEDGEMENTS

We thank variously for support, assistance, and advice:
Chris White, Rob Smith, Private Forests Tasmania,
Daniel Neary, Landscape Logic CERF Hub, CRC for
Copyright © 2013 John Wiley & Sons, Ltd.

Forestry, CSIRO Sustainable Agriculture Flagship,
CSIRO Water for a Healthy Country Flagship, and theVictorian Department of Primary Industries. We also
thank Marcus Hardie, Sébastien Lamontagne, David
Drew, and two anonymous reviewers for comments on
earlier versions of this manuscript.

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