Technical Report No. 28
Streamside management zones
for buffering streams on farms:
observations and nitrate modelling

March 2011

Published by Landscape Logic, Hobart Tasmania, March 2011.
This publication is available for download as a PDF from www.landscapelogicproducts.org.au
Cover photo: Two types of streamside management zones (SMZs) are shown, both of which included fences
to exclude livestock. In the foreground the SMZ was planted with Acacia melanoxylon (blackwoods) and notintended for commercial wood production. In the background is an SMZ containing commercial 20-year-old
Eucalyptus nitens that was harvested and reported in Neary et al. (2010).
Preferred citation: Smethurst PJ, Petrone KC, Baillie CC, Worledge D, Langergraber G (2010) Streamside
management zones for buffering streams on farms: Observations and nitrate modelling. Landscape Logic
Technical Report No. 28, Hobart.
Contact: Dr Philip Smethurst, CSIRO Ecosystem Sciences, Philip.Smethurst@csiro.au
Landscape Logic advises that the information contained in this publication comprises general statements basedon scientific research. The reader is advised that such information may be incomplete or unable to be used inany specific situation. No reliance or actions must therefore be made on that information without seeking priorexpert professional, scientific and technical advice. To the extent permitted by law, Landscape Logic (including its
employees and consultants) excludes all liability to any person for any consequences, including but not limited
to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly from using thispublication (in part or in whole) and any information or material contained in it.
ISBN 978-0-9870694-7-4

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Landscape Logic Technical Report No. 28

Streamside management zones for buffering
streams on farms: observations and nitratemodelling
Philip J. Smethurst1, Kevin C. Petrone2 , Craig C. Baillie1, Dale Worledge1 andGünter Langergraber3
1 CSIRO Ecosystem Sciences, Landscape Logic CERF Hub, and CRC for
Forestry, Private Bag 12, Hobart, Tasmania 7001, Australia;
Email: Philip.Smethurst@csiro.au, Tel: +61 3 6237 5653
2 CSIRO Land and Water and Landscape Logic CERF Hub, Private Bag 5,
Wembley, Western Australia 6009, Australia
3 Institute of Sanitary Engineering and Water Pollution Control – University of
Natural Resources and Life Sciences (BOKU), Muthgasse 18, A-1190 Vienna,
Austria

Summary
Natural resource managers need quantitative information on the effectiveness of streamside management zones (SMZ) in agricultural landscapes for protecting water quality. Analysis of buffer experimentsinternationally had previously suggested that a buffer width of 15 m would remove about 80% of nitrogen(N). Nitrate is the main form of N of interest, but until recently there were few Australian data or model
predictions available on buffer effectiveness. In 2007, a research project commenced in the Landscape
Logic CERF Hub that focused on buffering a headwater stream from N contamination, with the aim of (1)quantifying the N-buffering effect at a small catchment scale, and (2) developing a model that integrated
the salient processes and that potentially could be applied to other catchments. This research complimented a related project in the CRC for Forestry that included a much lower level of nitrogen monitoring.
This report summarises our progress on these two aims. Frequent measurements were made in apreviously-established, steep, paired-catchment experiment with adjacent buffered and unbuffered reference headwater streams in a low-intensity grazing system. Less frequent measurements were also
made in six other nearby unbuffered catchments to provide replication of the reference condition.
Modelling utilised the HYDRUS model, which has wide acceptance internationally for mechanisticallysimulating soil water and solute processes. We also used its N module (CW2D) that was developed for
simulating nitrate removal by constructed wetlands. The 10 ha buffered catchment had an area of grazed
pasture (62%) low in the landscape, and the rest was native forest. Approximately 10% of the pasture
was fenced around the stream to exclude stock and to allow the establishment of a forest plantation. The
adjacent 4 ha catchment in the same paddock was 99% grazed pasture and cattle had free access to its
stream when stock were in the paddock. Stream water from both catchments was monitored for various
forms of N. No fertiliser was applied to the pasture and only a small amount of hay was used as a feed
supplement. A small amount of diammonium phosphate fertilizer was buried beside each tree seedlingin the plantation soon after planting. Pools and fluxes of N measured in the buffered catchment were:
pasture N uptake, N mineralisation and nitrification, and N concentrations in rain water, soil water, soilleachate, and the watertable.
Between large rainfall events (storms), nitrate concentrations in stream water were low and similar to
those in the watertable of the hillslope. During monitored storms, which lasted several days, nitrate-richwater in surface soil that built up during drier periods began entering the buffered stream a day or two
after the storm commenced, and continued for a day or two after rain stopped, suggesting preferential
flow processes. This effect, commonly referred to as a flushing effect, was most pronounced in the buffered catchment, but it was probably not related to buffering. Annually, N export was 70–90% dissolved
organic N (DON), 11–18% particulate N (PN), and <5% nitrate. Total N measured in stream flow during
the drought year starting May 2008 was <1 kg/ha in both catchments, but during the subsequent wet
year 9 kg N/ha was measured in stream flow of the unbuffered catchment and 6 kg N/ha in the bufferedcatchment. Grab samples covering a 3-year period indicated that buffering substantially reduced E.
coli, phosphate and sediment (turbidity) concentrations. Lower concentrations of ammonium and nitratein the buffered catchment in 2009 could not be fully attributed to a buffering effect, because similar

Streamside management zones for buffering streams on farms: observations and nitrate modelling3differences in concentrations were present in 2007 before the SMZ was established.
A method was developed for pre- and post-processing of rainfall and flow data for HYDRUS that
accounted for overland flow and adequately simulated early nitrate dilution during a storm (0.08 to
0.01 mg/L) followed by an increase in nitrate concentration (0.01 to 2.4 mg/L). The HYDRUS-CW2D
model was used to simulate the buffered catchment for a dry year that included measured daily rainfall
and resultant flows and nitrate fluxes (including denitrification). Denitrification was predicted to occur
throughout the hillslope in the saturated zone, but overall there was a negligible predicted annual rateof denitrification (2.5 kg N/ha/year), which was not predicted to increase due to trees in the buffer. A
hypothetical higher-denitrification, higher-stream-flow scenario was developed where higher denitrification was simulated (12.6 kg N/ha/year) as well as a decrease in nitrate-N concentration as waterdrained through the riparian zone. In this scenario, buffer establishment (deep roots added in the SMZ)
led to no change in denitrification and increased N uptake by vegetation. In a third scenario, additionalorganic matter was added in the SMZ with the tree roots; under these conditions the change in N fluxes
was predicted to be +4% nitrification, +4% uptake, and –71% denitrification. While many uncertainties
remain about these scenarios, our modelling did not support the assertion that denitrification would
increase due to the establishment of trees in the SMZ, unless trees had a negligible effect on anaerobic
conditions (depth of water table) and they led to substantially increased soil organic matter concentrations, for which in-turn we found little support in the literature.
Hence, our measurements did not indicate reduced N delivery to the stream due to buffering.
Modelling suggested that if an effect was to develop it would not be via increased denitrification. Mostof the observed effect could be expected to be due to decreased particulate delivery that was not
simulated by the model, and that an additional contribution might be expected in the longer term via
increased uptake of N by vegetation. Further, the nutrient mitigation capacity of buffers might need tobe rejuvenated periodically by removing nutrients contained in plant materials by careful harvesting,grazing or mowing. To enhance future modelling using HYDUS-CW2D we provide several suggestions
for model development.

4

Landscape Logic Technical Report No. 28

Contents
Background

7

Objectives

8

Methods
Site, Treatments, and Measurements
Modelling

9910

Results
Observations
Simulations

14
14
20

Discussion
Buffering Effects
Modelling

22
22
23

Conclusions

25

References

26

Appendix 1. Guide to Using HYDRUS-CW2D for Simulating Catchment Nitrate Dynamics

28

Streamside management zones for buffering streams on farms: observations and nitrate modelling5

Acknowledgements
We thank Jirka Šimunek for HYDRUS training and advice, David Nash and the Department of Primary
Industries Victoria for advice and provision of the automatic water samplers, Daniel Neary for advice, Chris
White for access to his property, and for assistance and advice, Rob Smith and Private Forests Tasmania for
advice and support, and the Landscape Logic CERF Hub, CRC for Forestry and CSIRO for support and theopportunity to conduct this research. CSIRO support was provided by the Water for a Healthy Country andSustainable Agriculture National Research Flagships.

6

Landscape Logic Technical Report No. 28

Background
For the past two decades at least, researchers andpractitioners have been interested in quantifying
the nitrogen mitigation effects (buffering) of streamside management zones (SMZs) in the agriculturallandscape. Recent reviews of international experience suggest that the effects can be substantial.
For example, Mayer et al. (2007) summarised thenitrate removal effectiveness of 89 individual buffers
in 45 published studies. These studies included various types of SMZ vegetation, width, and landscapecharacteristics. Effectiveness varied widely (-258%
to 100% effective) and was most effective for wide
SMZs (> 25 m) and surface delivery, irrespectiveof vegetation type. Inconsistency in the results suggested important influences of soil type, subsurfacehydrology, and biogeochemistry.
Using 73 published studies, only two of whichwere the same as those used by Mayer et al. (2007)
for nitrate, Zhang et al (2010) found that SMZ width
explained 44% of the variation in nitrogen removal
efficiency that included three forms of nitrogen(total N, ammonium and nitrate), and that treed
systems were more effective than those containing grass only or grass and trees. A buffer widthof 15 m was predicted to remove 80% of N entering the up-slope side of the buffer. However, all ofthese studies of nitrate were done at a paddock- orplot-scale, and several only included overland flow.
Hence, SMZ effects on nitrate delivery to headwater
streams at a catchment scale were not quantified,and at this scale subsoil, channelized flow, and instream processes can be important. Our study
aimed to partially address this knowledge gap bymeasuring SMZ effects at a headwater catchment
scale, by integrating overland and subsoil flow processes, and by including all forms of nitrogen.
Because the SMZ effect varies widely between
situations, it would be useful to quantify the effectin a wide variety of situations and build up enoughexperience to reliably predict their effects at
proposed sites. However, quantification of SMZ
effectiveness is time consuming and expensive,
especially at a catchment scale, which precludes
numerous case-by-case measurements. We were
therefore interested in testing or developing amechanistic model that could be adapted to awide variety of situations and that took account of
the salient processes involved in SMZ mitigation of
nitrate delivery to streams.
Instead of nitrate being delivered to a stream
it can be intercepted by plant or microbial uptake
and denitrification. Hence, a suitable model neededto account for the hydrology of the system andthe production, transfers and transformations of
nitrate. This meant that such a model would need tomechanistically simulate within-soil water and nitrogen processes and that it needed to be spatially
explicit enough to represent the effects of various
width SMZs and their nitrate uptake ability. Hence,a two-dimensional (hillslope) or three-dimensional(catchment) model was needed that could be usedat the scale of a few hectares (i.e. headwater catchment scale).
We considered a range of potentially usefulmodels, and were most attracted to the HYDRUS
model (Šimunek et al. 2008) because it could simulate highly mechanistically the within-soil behaviourof water and solutes. In a simplistic manner it could
also simulate overland flow. For example, Guan etal. (2010) used HYDRUS to simulate water dynamics
of a hillslope with preferential flow, and Hilton et al.
(2008) used it to simulate runoff from a grass roof.
In its two-dimensional application HYDRUS also
includes a nitrogen module (CW2D) developed to
predict nitrate removal from constructed wetlands(Langergraber and Šimunek 2005), and it had auser-friendly interface and a high degree of spatial
and temporal flexibility.

Streamside management zones for buffering streams on farms: observations and nitrate modelling7

Objectives
Our objectives were to:(1) quantify at a headwater catchment scale thebuffering effects of an SMZ, particularly for
nitrogen, that combined cattle exclusion andplantation establishment,(2) adapt the HYDRUS-CW2D model to simulate
the salient processes governing water and nitrogen dynamics at a hillslope scale, and thereby
estimate the relative importance of denitrification and uptake for nitrate mitigation,8(3) use HYDRUS-CW2D to estimate the effect on
nitrate delivery to streams of reforestation of thestreamside management zone, and(4) provide practical guidance and development recommendations for setting up andinterpreting hillslope simulations using the
HYDRUS-CW2D model.

Landscape Logic Technical Report No. 28

Methods
Site, Treatments, and Measurements
In collaboration with the CRC for Forestry we established a paired-catchment experiment in a single
paddock with a northerly aspect in a mixed grazing
and native forest landscape in southern Tasmania(Photo 1, Figure 1). These catchments were part ofthe larger Forsters Rivulet catchment in southern
Tasmania, Australia. Fertilizers were not used in thecatchment for several years prior to or during thestudy, except during the plantation establishment
phase. Grazing was conducted at a moderate stocking density (c. 2-3 head per ha) for 2-6 weeks at
2-3 month intervals. Cattle have free access to all
unfenced streams. A plantation was established in2008 in the SMZ of one of the catchments (Photo 2).
The catchments are 3.5 ha (control) and 9.8
ha (SMZ catchment) in area. The area of the SMZ
was 0.6 ha which 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 amound on which a tree seedling was planted. Treeseedlings were planted in August 2008. 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 each mound top and there was no saturated
riparian zone. All eucalypt seedlings received 200 gof diammonium phosphate within 2 months of planting, 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 per ha.

A weather station was installed on the boundaryof the two catchments in 2007, which recorded rainfall and several other parameters. Farmer records 2
km south of the site indicate average annual rainfall
1991–2006 was 722 mm (range 501–975 mm). The
catchments are in steep terrain (average 17º slope).
Soils are c. 3 m deep and derived from interlaid
slope deposits of cretaceous syenite and permian
mudstone.
A 60º aluminium plate, V-notch weir was installedin each catchment in early 2008 and included
water level readings with a capacitance probeevery 5 minutes. Water level was converted to flowusing a standard equation. Water quality measurements commencing in 2007 provided pre-SMZ
data. Changes in water quality as a result of theSMZ (treatment catchment) were determined bycomparison with these pre-SMZ measurements,
and with the adjacent catchment that did not havean SMZ (reference catchment), and with six othernearby reference catchments.
Nitrogen and other parameters were monitored
3-weekly or less frequently using grab samples. Also
included in the grab sample program were 6 otherheadwater catchments within the Forsters Rivulet
catchment that did not have SMZs, and which provided replication of the control catchment. Water inboth weirs was automatically sampled every few hours
for several days during three storms (Table 1); thesesamples were measured for concentrations of various
forms of nitrogen (particulate total N – retained by a 45
mm filter, and dissolved – filtered – total N, ammonium,
nitrate and nitrite) and other water quality parameters.
At the two weirs, water level was measured at 5 minute intervals and temperature, electrical conductivity,
pH, dissolved oxygen, and turbidity were measured
at 15 minute intervals.
Figure 1.
The plantationbufferestablishment
experimentis locatednear Cygnet,
Tasmania,
Australia(indicated by
the arrow).

Photo 1. The paired-catchment SMZ experiment consists of catchments with (1) and without (2) an SMZ containing aplantation of eucalypts and acacias planted in August 2008. Additional SMZs shown adjacent (left) and below theseheadwater catchments are one year older. Water from these headwater streams converge to form Forsters Rivulet, whichflows out of the bottom left of the photo.

Streamside management zones for buffering streams on farms: observations and nitrate modelling9Table 1. Summary of rainfall and stream flow during thethree storms that were automatically sampled during
2009.
Storm 1

Storm 2

Storm 3RainfallStart date

13 May3 June26 November

Duration (d)

5

4

5

Total (mm)

33.4

38.4

30.4

Peak Intensity(mm/15 minutes)

1.2

1.4

2.2

Flow
Average (kL/d)

18.6

117.6

28.5

Peak (kL/d)

35.5

241.4

68.6

Day of peak

3

3

3

Peak:initial

17.6

37.8

279.1

Modelling
The HYDRUS model (Šimunek et al. 2008, version1.05) was used in a 2-dimensional, sloped, rectangular (trapezoidal) configuration. The model canbe accessed at: http://www.pc-progress.com/en/
Default.aspx?hydrus-3d. Units used were cm forlength and mg/L for concentration. An atmospheric(precipitation) boundary condition was usuallyspecified for the surface, with a vertical seepage
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 seepage facewith an atmospheric boundary condition (saturation
excess), and can include components of interflow
soon after rainfall, stored soil water, and groundwater entering the soil profile from an aquifer. We
use the term runoff to specifically mean overland
flow in excess of infiltration. Some authors use theterm deep seepage to imply movement of water

deep into a soil profile or into an unconfined aquifer.
Such a process was not needed in our simulations,but this could potentially be simulated in HYDRUS
as a drainage or constant pressure head boundary
condition. We used a no-flux lower boundary condition and therefore assumed no interaction with aregional aquifer as a source or sink.
Because we wanted to simulate hillslope processes in two dimensions, an average hillslope
length was calculated as catchment area divided
by stream length. At least 1,197 spatial nodes wereused (96 lateral by 21 vertical). The spacing of lateral and vertical nodes was closest at the lower
slope and surface soils zones. Time-steps started atvery low values and increased during stable periods to a maximum of 1 d. Simulations were built
up by specifying firstly water only, then by adding
transpiration (root water uptake), and followed bysolute transport. No evaporation rate was included.
Before rainfall events were simulated, setting up
of a simulation included pre-runs (up to 200 d) of
average rainfall and solute inputs that enabled aquasi steady-state to be achieved for seepage rateand concentration. Simulated seepage and runoff
fluxes were in two-dimensional units (cm2/d) andconverted to three-dimensional output by multiplying by the length of the third dimension (catchment
length = catchment area/length of hillslope = 2 x
stream length).
At an early stage, two methods of simulating runoff were tested as follows, i.e. rainfall that is
instantaneously in excess of infiltration (method A),
and the use in HYDRUS of a hypothetical layer at the
top of the soil profile with extremely high porosityand hydraulic conductivity (method B). However,neither of these methods adequately simulated theshort-term temporal dynamics of stream flow during
rainfall events (Smethurst et al. 2009). Instead, a third
method was developed whereby measured stream
flow was analyzed by the Lyne and Hollick (1979)
method to estimate the quick-flow and slow(base)flow components. The slow-flow component
was then routed through HYDRUS as a precipitation input. Resultant seepage estimatedby HYDRUS was combined with the quick-flow
component in a post-HYDRUS spreadsheet

Photo 2. A view of the farm where streamsideplantations are being established in a pairedcatchment experiment. Shown in the foreground is
the 2008-established buffer on a headwater streamof one of the paired catchments. The plantationbuffer consists of Acacia melanoxylon (blackwood)planted in the saturated riparian zone, surroundedby several rows of Eucalyptus globulus (blue gum) or
E. nitens (shining gum). In the middle ground is the
2007-established buffer.
10

Landscape Logic Technical Report No. 28

to estimate stream flow. Also in the spreadsheet,nitrate concentrations in seepage (as simulated by
HYDRUS) and runoff (as user-prescribed values)
were combined to provide an estimate of nitrate instream-flow. In this manner, flow and solute dynamics in the May storm event (storm 1; Table 1) were
simulated using inputs summarized in Table 2.
For an annual period that required nitrate transformations (i.e. nitrification and denitrification), andusing measured daily rainfall and evapotranspiration, the CW2D module was used with HYDRUS. The
CW2D module was designed primarily to simulate
nitrate removal from effluent waters draining throughflooded constructed wetlands (Langergraber andŠimunek 2005) by accounting for changes in microbial, organic matter, and some inorganic pools ofcarbon, nitrogen and phosphorus. The dynamics of
13 solutes (including 3 fractions of organic matter,oxygen and one inert tracer) are simulated using 9
processes and 4 types of microbes. For our application, this complexity was reduced by artificially
fixing the depth-dependent concentrations of oxygen and ammonium using hypothetically very highvalues of the respective solid-liquid phase partition
coefficients. The option of including temperaturedependency of reactions was not used, and all simulations were conducted using a measured averageannual soil temperature (12.5oC). Input setups for
specific simulations are summarized in Tables 3 and4. HYDRUS-CW2D outputs were post-processed ina spreadsheet to estimate annual fluxes and poolchanges for water and nitrate.

Table 2. Description of the simulated May storm: salient HYDRUS inputs.
Attribute

Value

General Description

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.

Slope length (m)515.2Catchment area (ha)11.15

Duration (d)7Water fluxes

Hourly rainfall and potential transpiration assuming no evaporation

Root water uptake

Feddes model parameters (no solute stress): P0 -10, POpt -25, P2H -300, P2L -1000, P3-1100, r2H 0.5, r2L 0.1

Spatial Nodes

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 thesoil profile)Time steps (d)10-7-10-3

HYDRUS units

cm length, mg/L liquid concentration, mg/kg solid concentration, g/cm3 soil bulk density

Slope (O)

17.4

Rainfall (mm)33.4

Horizon 1: 0.24, sandy loam, 3x105
Soil Horizons: thickness (m),Horizon 2: 0.23, sandy loam, 106.1
texture1, Ksat (cm/d)
Horizon 3: 2.55, silty clay, 0.48
Root depths native forest:
pasture: SMZ (m)3.0:0.5:0.5

Streamside management zones for buffering streams on farms: observations and nitrate modelling11

Table 3. Description of annual scenarios: salient HYDRUS inputs.
Attribute

Scenario 1

Scenario 2

Scenario 3

Scenario 4

As for scenario 1,
except deep roots(trees) added tobottom 25 m ofslope (SMZ).

Hypothetical low
slope, high rainfall
As for scenario 3,
and nitrate, andexcept deep roots
higher temperature(trees) added to(18o C). Vegetation
bottom 25 m ofas for scenario
slope (SMZ).
1, i.e. no trees in
SMZ.

GeneralDescription

Hillslope as
for catchment
1 in photo
1, with tuned
water balance,
nitrification andnitrate uptake.
Model estimates ofdenitrification andnitrate seepage.
Deep roots (native
forest) for top 38%
of slope. Shallow
roots (pasture)
for lower 62% of
slope.

Slope length (m)515.2Catchment area
(ha)

11.15

Duration (d)365

Water fluxes

Daily rainfall and potential transpiration assuming no evaporation

Root water uptake

Feddes model parameters (no solute stress): P0 -10, POpt -25, P2H -300, P2L -1000, P3 -1100, r2H
0.5, r2L 0.1

Spatial Nodes

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)Time steps (d)0.01-1HYDRUS units

cm length, mg/L liquid concentration, mg/kg solid concentration, g/cm3 soil bulk density

Slope (O)

17.4

2.0

Rainfall (mm)

572

1200

Horizon 1: 0.95, sandy loam, 106.1
Soil Horizons:
Horizon 2: 1.41, silty clay loam, 1.68
thickness (m),texture1, Ksat (cm/d) Horizon 3: 0.64, silty clay, 0.48

Horizon 1: 0.95, sandy loam, 106.1
Horizon 2: 1.41, loamy sand, 350.2
Horizon 3: 0.64, sand, 1000

Root depths native
forest: pasture:
SMZ (m)3.0:0.5:0.5

3.0:0.5:0.5

3.0:0.5:3.0

3.0:0.5:3.0

Scenario 5

As for scenario
4, plus enhanced
carbon supply, lessanoxic conditions,and double thewidth trees in theSMZ (50 m).

3.0:0.5:3.0

1. Texture as selected in HYDRUS from default options.

12

Landscape Logic Technical Report No. 28

Table 4. Description of annual scenarios: salient CW2D inputs.
Attribute

Scenario 1

Scenario 2

Scenario 3

Scenario 4

Scenario 5

Soil specificparameters (allhorizons)

Bulk density 1.5 g/cm3, Disp L 0.5, Disp T, 0.1, Fract 1, ThImob 0

Solute specificparameters (forsolutes 1-131)

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

Oxygenatmosphericboundary
condition (mg/L)

11

Kd for solutes 1-13 105, 106, 106, 106, 0, 0, 0, 106, 0, 0, 0, 107, 0
Maximum
concentrations for
uptake (mg/L)

Ammonium 560, nitrate 450, phosphate 1.4

Temperature

Set constant at 12.5ºC, no temperature dependence of reactions

1 Solutes 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 ammonia, 9 nitrite, 10 nitrate, 11 dinitrogen, 12, phosphate, 13 tracer.

Streamside management zones for buffering streams on farms: observations and nitrate modelling13

Results
Observations
Various Constituents in Grab Samples
We compared grab samples from the paired catchments and those from the control catchments
elsewhere in the Forsters Rivulet catchment for threeflow seasons (2007-2009, Fig. 2). Salinity (electricalconductivity, EC) was usually substantially higher inthe paired catchments compared to the other control catchments, and the difference was greatest
during the relatively dry year of 2008 when the buffered catchment also had consistently higher salinity
than its paired control catchment.
Concentrations of E. coli were highly variable,
and usually no catchment or buffering effect wasevident (Fig. 2). However, on two occasions in 2009,
which was after the SMZ was established (andhence cattle had been excluded from the streamof that catchment), E. coli concentrations were very
high in the stream of the control catchment (c. 5600
colony forming units (cfu) per 100 mL) and at thesame time concentrations were more than 90%
lower in the buffered stream (128-269 cfu/ 100 mL).
These samples were taken during the wet, highflow period of 2009, during which grazing cattle
severely disturbed the stream and riparian zone of
the control catchment. On other occasions during
2009, E. coli concentrations in the control catchment
were lower than or similar to those in the bufferedcatchment.
No patterns in unfiltered total N concentrations
were evident (Fig. 2), and although ammonium andnitrate concentrations during the second half of the2009 flow season were consistently lower in the buffered than in the control catchment, a similar effect
was already evident in 2007 prior to SMZ establishment. Hence, lower concentrations of ammonium
and nitrate measured in the buffered catchment
after SMZ establishment cannot be fully attributed toa buffering effect.
Phosphate concentrations in the buffered catchment were consistently higher than those in thepaired control catchment during the 2009 flow
season (Fig. 2), and no effect was evident in 2007
or 2008, i.e. prior to the combination of fencing,grazing and high stream flow. This result (and thatmirrored in 2009 storm samples – data not presented) strongly suggests that the buffering effectof the SMZ had reduced phosphate delivery to thestream. This effect was not evident in grab samples
for particulate or total P or other constituents (DON,
pH, dissolved oxygen, and turbidity – data notpresented).

14

Nitrate during storms
Nitrate analyses for storms sampled in May andJune 2009 in the buffered catchment indicated initial decreases (dilution) followed by increases (Fig.
3). In November, concentrations overall were much
lower than the previous two monitored storms.
A dilution phase was evident, but again concentrations increased during the storm. The control(unbuffered) catchment also indicated a concentration increase during the storm in June, but May
concentrations were very low (mostly at the detection limit) with a small increase in concentrations
evident in two samples during the storm. Nitrateconcentrations in the control catchment were highin November with only a minor dilution effectobserved and no increases in concentration during
the storm.

N Forms
The percentage of N in stream water present as
nitrate in both paired catchments ranged from aminimum of 0-2% to a maximum of 40% during theJune storm (Fig. 4). Nitrate was general present at
a concentration similar to or lower than particulate
N (PN), which in-turn was generally less than dissolved organic N (DON). Ammonium and nitrite
concentrations usually made up less than 5% of
total unfiltered N, but in the control catchment during the June storm, ammonium concentrations
almost reached those of PN in three samples. These
percentages of N forms cover the range that we
generally observed in other storm and grab samples (data not presented) and indicate that DON
and PN are the dominant N forms during base flow
conditions, and that nitrate (and to lesser extent
ammonium) reached similar concentrations to PN
during parts of some storms.
From grab samples, we estimated that annual N
export during May 2008 to April 2010 was 60-90%
dissolved organic N (DON), 12-31% particulate N(PN), and 0-9% nitrate. Total N export during thedry first year was <1kg N/ha in both catchments,
and 9 and 6 kg N/ha in the unbuffered and bufferedcatchments respectively.

High temporal resolution monitoring of
turbidity
High resolution temporal patterns of turbidity incontrol and buffered catchments between April
and August 2009 indicated benefits due to the SMZ
during both low and high flows (Fig. 5). During
the low-flow period 1/4/2009 to 4/6/2009, turbidity in the control catchment was usually more thanLandscape Logic Technical Report No. 28

EC (µµ S/cm )4
Other Controls
Control
Buffered
3.5

3

2.5

2

1/07/2007

30/12/2007

30/06/2008

30/12/2008

1/07/200931/12/2009

1/07/200931/12/2009

1/07/200931/12/2009

E. coli (cfu/100 mL)
5

4

3

2

1

0
1/07/2007

30/12/2007

30/06/2008

30/12/2008

Total N Unfiltered (mg/L)
1
0.8
0.6
0.4
0.2
0
-0.2
1/07/2007

30/12/2007

30/06/2008

30/12/2008

Figure 2. Patterns of various constituents measured during 2007-2009 in water from the paired catchments andheadwater control catchments elsewhere in Forsters Rivulet catchment (bars indicate 95% confidence interval of themean, n = 6). All Y-axis values are on a log10 scale.

Streamside management zones for buffering streams on farms: observations and nitrate modelling15

Ammonium (mg/L)
0

-1

-2

-3
1/07/2007

30/12/2007

30/06/2008

30/12/2008

1/07/200931/12/2009

1/07/200931/12/2009

1/07/200931/12/2009

Nitrate (mg/L)
0

-1

-2

-3
1/07/2007

30/12/2007

30/06/2008

30/12/2008

Phosphate (mg/L)
-1.7

-2.2

-2.7
1/07/2007

30/12/2007

30/06/2008

30/12/2008

Date
Figure 2 (continued). Patterns of various constituents measured during 2007-2009 in water from the paired catchments andheadwater control catchments elsewhere in Forsters Rivulet catchment (bars indicate 95% confidence interval of the mean,n = 6). All Y-axis values are on a log10 scale.

16

Landscape Logic Technical Report No. 28

Rain(mm/15 minutes)

0.0

0.5

1.0

1.5
0.6

4

2.0

3

1.5

2

1.0

1

0.5

0

0.0

Flow
(L/s)

0.4

0.2

10

Nitrate Concentration(mg/L)

Nitrate Concentration(mg/L)

0.0

BufferedControl

1
0.1
0.01

0.001

12
13
May

14

15

16

17

18

4
Jun

5

6

7

8

9

10

26
27
Nov

28

29

30

1
Dec

Date

Figure 3. Temporal patterns of rainfall, stream flow and nitrate concentrations during three storms in 2009. Nitrateconcentrations are shown for the two paired catchments. Stream flow is only shown for the buffered catchment, but thetemporal pattern of relative flow was similar in the control catchment.

100

PN
DON
Ammonia
NitratePercentage

80

Nitrite

60

40

20

0
4

6

8

10

12

4

6

8

10

12

Date

Figure 4. Patterns of percentage contribution of different N forms during the June 2009 storm in the buffered (left) andcontrol (right) catchments.

Streamside management zones for buffering streams on farms: observations and nitrate modelling

17

300

Control
Buffered

Turbidity (NTU)

200

100

0
1/4/2009

1/5/2009

1/6/2009

1/7/2009

twice that in the buffered catchment, except for theperiod 15-19/4/2009, which was during a storm thatsignalled the autumn break. We attributed higher
turbidity in the buffered catchment during thesefew days to sediment delivery from the cultivatedmounds, because there was visual evidence of sediment being washed off some mounds, and pits were
full of turbid water and had spilled, implying connectivity between mounds and the stream. Cattle
presence during this low-flow period in the control
catchment appeared to not increase turbidity at thattime, but it might have contributed to a cumulative
effect detected later.
Peak turbidity values coincided with peak flows(data not shown) and tunnel erosion in both catchments greatly contributed to high turbidity values
during the wet winter period. Tunnel erosion, which
is a feature of the dispersive soil in these two and1/8/2009

similar catchments in southern Tasmania, channelises highly turbid water and therefore bypasses
the buffering effect of the SMZ. During peak flows
without cattle, turbidity values were equally high inthe control and buffered catchments, but between
flow peaks turbidity was highest in the bufferedcatchment. A greater tunnel erosion effect in the
SMZ catchment could be due to several effects that
we were unable to separate. Firstly, the SMZ catchment size is larger than the control catchment, yet
the stream lengths are similar. Hence the water flux
per unit stream length, and hence erosive potential,
might be highest in the SMZ catchment. Secondly,
we suspect that the SMZ catchment is more affectedby soil development from syenite rock than occurs
in the control catchment, and the saline nature ofsyenite might be more conducive to dispersive
soils than the Permian sediments. Thirdly, some

Turbidity (Log10 NTU)

3

2

1

0

-1
1/07/2007

30/12/2007

30/06/2008

Figure 5. High temporal resolution
turbidity patterns in control and bufferedcatchments between April and August
2009. Cattle were in the paddock during
the periods indicated by horizontal bars.
During the low-flow period 1/4/2009
to 4/6/2009, turbidity in the control
catchment was usually more than twicethat in the buffered catchment, except
for the 15–19/4/2009, which was during
a storm that signalled the autumn break.
We attributed higher turbidity in thebuffered catchment during these few
days to sediment delivery from thecultivated mounds. On the 4/6/2009 tunnel
erosion commenced in the bufferedcatchment and similarly on the 7/6/2009
in the control catchment. Starting on the
3/8/2009 turbidity values in the control
catchment were usually much higher
than those in the buffered catchment, andoften off-scale (>250 NTU maximum forthese probes). This period coincided withsevere cattle disturbance in the control
stream and saturated soil conditions inmost of both catchments.

30/12/2008

Figure 6. Patternsof turbidity
measured
during 20072009 in waterfrom the paired
catchments andheadwater control
catchments
elsewhere in
Forsters Rivulet
catchment (barsindicate 95%
Other Controls
confidence
interval of theControlmean, n = 6).
BufferedY-axis values are
1/07/200931/12/2009 on a log10 scale.

Date

18

Landscape Logic Technical Report No. 28

Observed Flow (kL/d)50
base-flow40

base-flow + quick-flow

30
20
10
0

13/05/2009 0:00 14/05/2009 0:00 15/05/2009 0:00 16/05/2009 0:00 17/05/2009 0:00 18/05/2009 0:00 19/05/2009 0:00

Date Time

Simulated Flow (kL/d)

50

Figigure 7. Observed flow(top) in the Willow Bend
catchment 13-19 May 2009 andits simulation (bottom) using
quick-flow analysis and the
HYDRUS model.

40

30

20

c

10

0
0

1

2

3

4

5

6

Day of Simulation

Nitrate Concentration (mg/L)

10

1

0.1

0.01
13/05/09

14/05/09

15/05/09

16/05/09

17/05/09

18/05/09

19/05/09

4

5

6

Figire 8. Measured (top)and simulated (bottom,
solid line) concentrationsof nitrate-N in stream
water at the Willow
Bend site. Assumed
concentrations of nitrate-N
in overland flow (quickflow) are indicated by thebroken line in the bottom
graph.

Nitrate Concentration (mg/L)

Date

10

1

0.1

0.01
0

1

2

3

Day of Simulation

Streamside management zones for buffering streams on farms: observations and nitrate modelling19

cultivation pits of the SMZ intersected pre-existingtunnels in the soil of the SMZ catchment, whichmight have facilitated or exacerbated sedimentdelivery in this catchment.
Buffering resulted in reduced turbidity whencattle were present and soils were very wet (Fig.
5). Unfortunately, instrument failure in the control
catchment on 16/8/2009 precluded high temporal
resolution comparisons after that date. Two grab
sampling dates also indicated higher turbidity inthe control than in the buffered catchment, and very
high variability amongst other control catchments(Fig. 6).

Simulations

Annual Scenarios
By tuning parameters that controlled soil water
status, nitrification and uptake, observations ofthese components of the buffered catchment were

May Storm
Routing slow flow through HYDRUS as precipitation and seepage, then recombining seepage
with quick flow, reproduced closely the patternsof total observed flow and its estimated slow flow

Table 5. Simulated water and nitrate dynamics at theWillow Bend Farm site for the period May 2008 to
April 2009. Setting up of the model required various
parameters to be tuned to achieve the required waterbalance and rates of nitrification and nitrate uptake inthe pasture base case (scenario 1). To simulate tree
vegetation in the SMZ (scenario 2,) roots were extended
to the full depth of the soil profile in the SMZ (lowest 25 mof slope). Tuned values are shaded grey.
SMZ Vegetation
Pool or Flux

component for the May 2009 storm (Fig. 7). By
including high surface soil nitrate and high Ksat values (as a substitute for preferential flow), the patternof nitrate simulated in combined overland flow andsubsurface flow (seepage) closely matched thetemporal pattern and absolute values of those measured (Fig. 8). However, one source of error is theunknown temporal pattern of nitrate concentrations
in overland flow. The potential contribution of nitratein overland flow can be seen in Fig. 8, where low
nitrate (0.01 mg/L) was assumed during the first twodays, and peaked at 3.8 mg/L at 3.7 d.

Pasture(scenario 1)

Table 6. Simulated water and nitrate dynamics at a
hypothetical site with lower slope, higher flow and higher
nitrate concentrations than at the Willow Bend site.
Setting up of the model required various parameters
to be tuned to achieve the required water balance andrates of nitrification and nitrate uptake in the pasture basecase (scenario 3). To simulate tree vegetation in the SMZ(scenario 4,) roots were extended to the full depth of thesoil profile in the SMZ (lowest 25 m of slope). A further
scenario (scenario 5) included extended the width of the
SMZ to 50 m and reduced the level of anoxia in the watertable from 1 to 5 mg O/L. Tuned values are shaded grey.
SMZ Vegetation
Pool or Flux

Trees(scenario 2)

Water Balance (mm)Pasture(scenario 3)

25 m tree
buffer(scenario 4)

50 m tree
buffer plus
less anoxic(scenario 5)

Water Balance (mm)Precipitation

572

572

Precipitation

1200

1200

1200

Evaporation

00

Evaporation

0

0

0

Transpiration

616

625

Transpiration

811

806

843

Overland flow

15

15

Overland flow

170

189

162

Seepage

30

Seepage

228

200

173

Soil Water

-63

-69

Soil Water

8

41

31

Runoff Coefficient (%)

3

3

Runoff Coeff. (%)

32

31

28

Nitrogen Balance (kg ha-1 year-1)

Nitrogen Balance (kg ha-1 year-1)

Nitrification

154.1

152.6

Nitrification

701.7

702.6

729.7

Uptake

127.3

121.5

Uptake

255.9

247.4

265.9

Denitrification

2.5

2.5

Denitrification

12.6

12.8

3.6

Seepage0.0

0.0

Seepage

0.0

0.0

0.3

Soil nitrate

24.3

28.6

Soil nitrate

433

441

460

Stream nitrate (mg/L)

0.017

0.000

Stream nitrate (mg/L)

0.008

0.008

0.081

20

Landscape Logic Technical Report No. 28

adequately simulated (Table 5, scenario 1). By
adding deep roots to a 25 m SMZ (scenario 2),
predicted transpiration was increase by 1% andseepage halted. The runoff coefficient in bothcases was 3%, which is indicative of the dry yearthat was simulated. Uptake was 83% of nitrification,and denitrification was predicted to be only 1.6% of
nitrification. There was negligible export of nitrate
in stream flow (seepage), and adding deep roots to
the SMZ had little affect on nitrate fluxes.
Because rates of denitrification in scenarios 1
and 2 were very low, a hypothetical scenario wasdeveloped that was wetter, warmer, of lower slope
and of higher nitrate, and that thereby increased
rates of denitrification from 2.5 to 12.6 kg/ha/year(Table 6, scenario 3). Adding deep roots to a 25 m
SMZ (scenario 4) surprisingly decreased transpiration by 0.6% and seepage by 14%, as overland flow
increased by 11% and the overall runoff coefficientdecreased from 32% to 31%. There were predictions of only minor changes to nitrate fluxes.
In a further case (scenario 5), transpiration
was increased by 5% by doubling the width of
the SMZ with deep roots. In addition, we assumed
tree roots would lower the water table and lead tomore aeration of subsoils, and also add carbon.
Nitrification was increased by reducing anoxia from
1 to 5 mg/L dissolved oxygen in the subsoil, andincreasing the maximum potential rate of nitrification. Concentrations of readily and slowly available
organic matter were doubled and higher potential
rates of denitrification used (Figs. 9-10). The balance of these effects was to increase nitrificationand nitrate uptake by 4% and decrease denitrification by 71%.
The average nitrate concentration in stream
water was calculated assuming overland flow contained no nitrate. Average concentrations therefore
depended mostly on the volume of seepage and its
nitrate concentration. Results from these scenarios(Table 6) suggest that, if trees reduced anaerobic
conditions in the riparian zone and thereby denitrification, average nitrate concentrations in stream
water could increase despite reduced seepage andincreased water and nitrate uptake by buffer vegetation. This result was not observed at our site (Figs
2 and 3). These results highlight the complexity ofpredicting the integrated effects of buffering on
stream nitrate concentrations.

Streamside management zones for buffering streams on farms: observations and nitrate modelling

21

Discussion
Buffering Effects
The potential buffering effects of SMZs are wellestablished. For example, Zhang et al. (2010)
summarised data from 73 published studies andconcluded that they can be very effective at removing sediment, N, P and pesticides. From incomingwater, 97% of the sediment, 93% of pesticides, 92%
of N, 90% of P were removed on average with buffersof c. 20 m wide buffers. Because removal efficiency
as a function of buffer width was asymptotic, evennarrower buffers removed substantial amounts ofcontaminants on average. A limitation of the dataset
was that it was largely limited to plot- or paddockscale studies, and it was dominated by overland
flow measurements that are important for colloidal or sediment-associated contaminants, but lessimportant for some dissolved contaminants. Many

Figure 9. Input parameters
for CW2D microbial growth.
The second value for someparameters was that used
for scenarios 3-5, which
increased rates of nitrificationand denitrification.

Figure 10. Input parameters
for CW2D stoichiometries
and reaction rates. The
second value for one
parameter was that used
for scenarios 3-5, which
increased the rate ofnitrification.
22

Landscape Logic Technical Report No. 28

of these studies would not have captured processes
that are important at larger scales, e.g. flow concentration effects (Fox et al. 2010). More information is
also needed on subsurface removal efficiencies fornitrate and phosphate at catchment scales.
In general agreement with Zhang et al. (2010),
we found that phosphate concentrations and somelow- and high-flow turbidity values were substantial
reduced by the SMZ, and that this occurred within ayear of its establishment. We also observed positiveeffects on E. coli counts on two occasions, but we
did not target sampling for E. coli to coincide withstorm events that would have yielded the highest
counts (McKergow et al. 2010).
The buffering effects of SMZs often exhibit a lag
in response of water quality or populations of desirable organism in the order of years or decades,
even when such practices are well-designed andimplemented (Meals et al. 2010). This lag can be
due to delays in the effect being delivered to thewater resource, the time required for the water
body to respond, and the effectiveness of the monitoring program to detect the response. For example,
in one SMZ plantation study it took 8-12 years toachieve substantial reafforestation (Newbold et al.
2010).
Although the evidence of Mayer et al. (2007),
Zhang et al. (2010) and the studies cited therein isvery strong that buffers can have a strong mitigation
effect on water quality, we also need to recognisethat in some circumstances a measureable effecton water quality has not eventuated. For example,
in the Choptank River Catchment, USA, there was
no improvement in stream water N and P concentrations during the period 2003-2006 despite
11% restoration of streamside vegetation during
1998-2005 (Sutton et al. 2010). Possible reasonsfor this lack of effect were insignificant area (widthby stream length), connectivity and maturationof the buffers, and increased agricultural inputs.
Agricultural drainage networks can also allow
contaminated water to bypass vegetative buffer systems. For example, an SMZ containing commercial
plantations species in the Bear Creek Catchment,
Iowa, USA, was established in a region with large
networks of subsoil field drainage systems thatprovide the majority of base flow to some streams(Shultz et al. 2009). Nitrate and phosphate are transported from below the crop root zone directly to thestream bypassing the SMZ.
A concern about using commercial forest plantations in SMZs is that inappropriate harvesting mightlead to increased sediment delivery to the streams
they were designed to protect. This effect might
result from disturbed soil and a reduced sediment
filtering effectiveness. This concern was largely

allayed by Neary et al. (2010) who reported thatharvesting SMZs using best management practices
largely avoids sediment production. Further, thenutrient sink strength and mitigation capacity of buffers might need to be rejuvenated periodically by
removing nutrients contained in plant materials bycareful harvesting, grazing or mowing (Dosskey etal. 2010).

Modelling
During the past couple of decades, interest has
increased in developing models that include
dynamic, within-soil processes that govern thetransport and composition of water delivered to
steams (e.g. Creed and Band 1998). Much effort
has focussed only on water, and one cannot hope tosuccessfully simulate solutes if the pools and fluxesof water are not first understood and represented.
These modelling efforts have developed at various temporal and spatial scales and using different
modelling methods. For example, Chen et al. (2010)
used the TOPMODEL, the spatial variability of soil
properties, and the temporal variability of precipitation and evapo-transpiration to simulate over several
years overland- and base-flows in catchments ofabout 40 km2. At much smaller spatial and temporal scales, Hilton et al. (2008), Guan et al. (2010)and Lorentz et al. (2008) used the HYDRUS model
to simulate runoff from green (grassed) rooves andshort sections of hillslopes during storms.
Solutes have also been incorporated in thesetypes of models. Neumann et al. (2010) used theThales model to examine the effect of spatial variability in soil properties on annual delivery of salt,
sediment and phosphorus to the catchment outlet.
Rassam et al. (2008) used the HYDRUS model tosuggest where in a catchment the greatest potential
rates of denitrification occurred. Krause et al. (2009)
tested the JAMS/J2000-S model for simulating water
quality in a 540 km2 catchment.
Despite these advances, much complexity isavoided in many models by capturing complex
processes in one or a few empirically calibrated
factors. The complex interactions of overland
flow and seepage processes are manifest in concentration-discharge (C-Q) relationships in thereceiving water during storms. Evans and Davies(1998) categorized various C-Q patterns in relation to the dominance of rain, soil or ground water,
but until recently these had not been simulated
mechanistically.
Haygarth et al. (2004) and Holz (2010) also
identified several types of concentration-discharge
relationships for solutes that depend on chemical
form, source and transport mechanism. Haygarth
et al. (2004) identified that a future challenge wasStreamside management zones for buffering streams on farms: observations and nitrate modelling23

to develop quantitative models that simulated thesedifferent situations. Vidon et al. (2010) identified
a similar need for simultaneously modelling bothtransport-driven and process-driven phenomena incatchments. Weiler and McDonnell (2006) adapted
the Hill-Vi model to demonstrate how complex
hillslope water dynamics could be coupled withdepth-dependent solute concentrations to conduct
virtual experiments for producing C-Q relationships
and typical flushing patterns of nitrate, dissolved
organic carbon, and dissolved organic carbon. In
their simulations, use of the depth-dependent specification of solute concentrations avoided the needto mechanistically simulate these concentrations,which is a much harder challenge.
To provide a more mechanistic method of simulating solute processes within the soil profile in acatchment context, Smethurst et al. (2009) demonstrated how C-Q relationships could be generatedusing the HYDRUS model, but problems remainedwith simulation of the overland flow component. For
this current report we used an alternative methodof including overland flow and thereby reproduced

24

the nitrate flushing phenomenon observed in ourcatchment (Figs. 7-8), and by invoking its nitrogen
module (CW2D) we adequately simulated annual
water and nitrogen balances (Tables 5-6). This
result represents an important development in theapplication of HYDRUS to hillslope and small catchment situations, because it provides a means ofempirically including overland flow and mechanistically simulating within-soil processes.
Whilst applying the HYDRUS-CW2D model
to our hillslope situation, we identified various
aspects of the model that should be considered
for further development (see Conclusions andRecommendations, and Appendix 1). Key amongst
these in HYDRUS is the inclusion of overland flow.
Overland flow is a focus of model development in
its own right because of the complexity and importance of the process for predicting stream flows
and erosion. An example is provided by Bhardwaj
and Kaushal (2009), who used similar mathematicalmethods to those in HYDRUS, i.e. Richards equation
for water transport and use of a finite element solution method.

Landscape Logic Technical Report No. 28

Conclusions
1. Monitoring of stream water in the paired catchment experiment during the first full flow season
after cattle exclusion and plantation establishment provided strong evidence that the SMZ
treatment consistently reduced concentrations ofphosphate by up to 70% (0.020 mg/L without the
SMZ, 0.006 with the SMZ).
2. On two occasions under these very wet conditions, we observed that spikes in E. coli
concentrations of c. 5600 cfu/100 mL without
the SMZ were mitigated by the SMZ (128-269
cfu/100 mL).
3. Turbidity was also reduced by 30-80% in dry
weather conditions (e.g. 20-40 NTU without the
SMZ, < 10 with the SMZ) and when cattle were
present in very wet conditions (>250 NTU without the SMZ, 150-240 NTU with the SMZ). SMZ
establishment led to a small transient increase inturbidity (c. 15 NTU above that in the non-SMZ
catchment) during the first major storm of theseason, and cultivation might have exacerbatedtunnel erosion that is common in similar catchments in southern Tasmania.
4. Patterns of particulate N, dissolved N, total N,
ammonium, nitrate, particulate P did not seemto change in response to SMZ establishment,
but international experience suggests that morepositive effects can be expected in the future asthese trees age.
5. The HYDRUS-CW2D model was adapted tosimulate the salient processes governing waterand nitrate dynamics at a hillslope scale. This
involved flow analysis to identify the quick- andslow-flow components of stream flow, and routingof slow-flow through HYDRUS as precipitation.
6. Water and nitrate dynamics could be simulated
during storms or over annual periods, if overland flow contributions were already known.

Uptake of nitrate appeared to be the dominant
nitrate mitigation processes over denitrification.
Simulations supported concerns that establishing trees in SMZs could potentially reduce
denitrification if it leads to greater aeration of
the riparian zone or does not add substantial
amounts of carbon in the root zone.
7. Simulations demonstrated the potential usefulness of including mechanistic soil processes inthe simulation of catchment hydrogeochemistry. However, much more data on these types offluxes at a hillslope or small catchment scale areneeded to support further model development
and validation.
8. For hillslope or headwater catchment simulations, a priority for HYDRUS development is toinclude overland flow processes and diffusive
nutrient supply to uptake surfaces. For CW2D,
the priority is to simplify the representation of
nitrification and denitrification dynamics, which
would also require a more empirical approachand reduce the run-time considerably. An example is provided by the denitrification module of
the APSIM suite of models (Thorburn et al. 2010).
This module also splits nitrogen emissions into
N2 and N2O forms, which has important greenhouse gas implications.
9. Guidelines are provided for setting up andinterpreting hillslope simulations using the
HYDRUS-CW2D model. Without CW2D, thismethod should be suitable for solutes where
there is a need to mechanistically simulate theeffects of with-in soil processes on concentrations
in stream water. The CW2D module provides
an example of how modules can be developedfor HYDRUS to account for solute dynamic processes that are otherwise not already provided
for in HYDRUS.

Streamside management zones for buffering streams on farms: observations and nitrate modelling25

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Streamside management zones for buffering streams on farms: observations and nitrate modelling27

Appendix 1. Guide to Using HYDRUS-CW2D for
Simulating Catchment Nitrate Dynamics
This guide assumes a working knowledge of
HYDRUS version 1.05 and its CW2D module, forwhich manuals, training, and other forms of support are available online (www.pc-progress.com/
en/Default.aspx?hydrus-3d) and periodically incourses and workshops. This guide builds on theseresources with the aim of assisting the developmentof hillslope scenarios for use in a catchment context.
Only salient details are provided. Additional details
are available in an example provided separately.

Pre-HYDRUS Data Processing
Spreadsheet 1 – Flow Analysis.xls
1. Conduct a flow analysis using, for example, theLyne and Hollick (1979) method, as available in
Spreadsheet 1 – Flow Analysis. The key outputof this analysis is a time series of slow-flow andquick-flow.

HYDRUS Set-up
Domain Geometry
2. Geometry Information: Within HYDRUS, set up
the geometry as a 2D sloped rectangle (rhomboid) to represent your hillslope. Calculate theaverage slope length as catchment area divided
by twice the length of stream(s). Depth shouldbe that of the average soil profile in your catchment. Slope should be the average for yourcatchment. More complicated geometries areprobably possible, e.g. uneven top and bottom
surfaces and therefore depths, but we did nottest this possibility. At Type of Geometry, choose
2D – Vertical Plane XZ. At Domain Definitionchoose Rectangular. At Units we used cm.
3. Choose the arrangement of spatial nodes. We
spaced nodes c. 2.5–10 m horizontally and
2.75–25 cm vertically, with closer nodes at thebottom of the slope and at the top of the profile. It
is important to define early a workable geometry and set of spatial nodes, because some later
inputs need to be re-entered when geometry or
nodes are changed.

Flow and Transport Parameters
4. Main Processes: When using CW2D, select all
options except Inverse Solution, i.e. Water Flow,
Solute Transport, Heat Transport, and Root Water
Uptake. If you do not wish to use CW2D, e.g. in
pre-runs to tune the water balance, select only
Water Flow and Root Water Uptake.
28

5. Time Information: We chose Days as the time
Unit, and Time-Variable Boundary Conditions.
6. Water Flow – Soil Hydraulic Model: Preferential
flow options are not available when using
CW2D. So, matrix flow parameters will need tobe adjusted to approximate water dynamics asrequired. We used the van Genuchten – Mualem
model with no hysteresis.
7. Solute Transport: We set mass unit as mg, as concentration is then reported as g/cm3, whichequals mg/L. At Reaction Parameters, set any
constant boundary condition concentrations
required, e.g. dissolved oxygen at cAtm of
11mg/L, and cRoot values (maximum concentration of root uptake) for N and P uptake bymassflow. Kd values of each material (soil horizon) can also be set as desired. We used very
high values (105–7) for oxygen, organic matter,ammonium, and phosphate which effectively
maintained during simulations the concentrations of the initial conditions anywhere in thesoil profile. Constructed wetland parameters areshown in Figs 9–10. Although the Temperature
Dependence box had been checked under
Solute Information (as instructed for CW2D use),
we worked with constant temperature by specifying zero Temperature Dependence of Solute
Transport Reaction Parameters, a TBound1
condition of the desired temperature for precipitation inputs, and Temperature Amplitude ofzero.
8. Root Water Uptake: We chose to use theFeddes model with No Solute Stress, and grassparameters.
9. Time Variable Boundary Conditions: As precipitation input, copy-and-paste the slow-flow output
from the flow analysis (Spreadsheet 1) with thecorrect units and time steps. Also include Evap.
and Transp. as potential rates; we used zero
Evap., and Transp. as the potential ET reported
by our weather station. Set the temperature ofand concentrations in precipitation at TValue2,
cVal2–1 (oxygen), cVal2–13. Surface area associated with transpiration: this variable can betuned to help provide the target water balance.
10. Default Domain Properties: For the top node,
set Code = –4 (atmospheric boundary condition). Set the material and root codes for each
depth node, as well as the default temperatureand liquid and solid phase solute concentrations.
When initially setting up a simulation, we usually
set pressure head (h) to be as required at the
top and bottom of the soil profile, and clicked theLandscape Logic Technical Report No. 28

‘Linear Interpolation of Pressure Head between
the first and final layer’. After long-term simulations with constant average precipitation, astable distribution of soil water content could
be obtained on the hillslope. We found it helpful
for model stability to do this prior to introducing variable precipitation. Because CW2D only
recognises microbes on the solid phase, default
initial values need to be specified here, i.e. for
S5–7.

FE-Mesh11. Rectangular Domain Discretization: Specify thevertical (x) and horizontal (z) node positions. We
found it useful to prepare this set of values in an
Excel spreadsheet, the copy and paste them tothese columns. We set RS1 = 1, and RS2 = 10.
Click ‘Generate’ to generate the mesh. Everytime you generate a new mesh, many previously
entered input values need to be re-entered.

without a variable boundary condition, there will
be difficulty reaching a mathematical solution
within HYDRUS, performance will greatly slow,
and even if the simulation proceeds, substantial inaccuracies in the solutions can eventuate.
If a variable boundary conditions is needed, set
this within ‘BDRC Options’ as the option ‘Applyatmospheric boundary conditions to nonactive
seepage face’.
15. Solute transport: set flux nodes as ‘Third-type’,
and vector 2 at atmospheric nodes (to correspond with that vector in the Time Variable
Boundary Conditions).

Run-Time
We found that execution of hillslope simulations withCW2D and daily precipitation could be very slow,
depending on the occurrence of near-saturation
conditions near surface nodes. A typical simulation
took 6–7 hours.

Domain Properties

Results in HYDRUS

12. Default domain properties were set earlier. Herespecific nodes can be changed for Material Typeand Root Water Uptake. We did not work with
Nodal Recharge, Scaling Factors, Anisotropy or
Subregions. Observations nodes can be added
here for anywhere in the domain.

All normal HYDRUS output information is available
with CW2D. Output animations can be visualisedin HYDRUS for the time layers specified. Default
graphical information is also available.

Initial Conditions
13. Values here for pressure head, temperature andsolute concentrations in liquid (L1–4, L8–13) andsolid phases (S5–8, S12–13) are initially as set by
the default values input earlier. Here values atindividual nodes can be changed, e.g. to specify
higher organic matter and lower oxygen in theriparian zone.

Boundary Conditions
14. Water flow: use –4 (atmospheric) for the soil
surface that will receive precipitation, –2 for the
seepage face. The depth of seepage face on thelower vertical side of the sloped rectangle will
determine the depth of the maximum saturated
zone in the lower slope zone and the profile ofsoil water content up-slope. We generally kept
the lower 50–70% of the vertical face as ‘no flux’
nodes. A variable seepage-atmospheric boundary condition can be chosen to allow calculationof saturation excess overland flow, which will benecessary if at any time during a simulation if soilat the surface becomes saturated. At those times,
precipitation inputs will cease at changed atmospheric nodes and thereby alter water balancecalculations. If saturation occurs at the surface

Post-HYDRUS Data Processing
Two spreadsheets to assist with post-HYDRUS dataprocessing are available. Spreadsheet 2 – Storm
Graphs.xls enables users to collate flow and solutedata and to graph their temporal patterns. We used
this spreadsheet to graph temporal patterns associated with storm events. Spreadsheet 3 – Annual
Table.xls enables users to calculate the net fluxesof water and nitrogen that occur during a simulation. We used this spreadsheet to tabulate annual
fluxes. Both these spreadsheets draw on HYDRUS
output files that are in text format. The files used are
Cum_Q.out and solute*.out (where * is a value 1–4,
or 8–13). In these files are tabulated the cumulativevalues for each time step, from which we calculate
rates at each time step (e.g. during a storm) andtotal fluxes at the final time step (e.g. for an annual
period).

Spreadsheet 2 – Storm Graphs.xls
16. Copy all data from file Cum_Q.out created by
HYDRUS, and paste it into the ‘Cum_Q’ worksheet such that the top left data values alignand therefore that all other data are positioned
correctly and provide for accurate calculations.
These data will be pasted as text; they then need
to be converted to data using the ‘Data/Text toColumns’ option of Excel.

Streamside management zones for buffering streams on farms: observations and nitrate modelling29

17. For the solute of interest (e.g. nitrate is solute10), copy all data from file solute*.out created by
HYDRUS and paste it into the ‘Solute’ worksheetsuch that the top left data values align and therefore that all other data are positioned correctly
and provide for accurate calculations. These data
will be pasted as text; they then need to be converted to data using the ‘Data/Text to Columns’
option of Excel.
18. Check in the StormCalc worksheet that all values from the other two worksheets have beencorrectly ‘looked up’. Some filling down of formulas affected by new values might be needed.
Copy quickflow values from Spreadsheet 1 –Flow Analysis (correcting for units) across tothis StormCalc worksheet into the QFRain column. If there is quickflow, concentrations therein
will need to be prescribed in the QFConc column. The maximum number of rows used ineach spreadsheet and graph might need to be
adjusted.

Spreadsheet 3 – Annual Table.xls
19. Copy the last line of data from the Cum_Q.out
and solute*.out files to the relevant positions in
Spreadsheet 3 – Annual Table.xls spread, Annual
Fluxes worksheet column B rows 91–96. These
data will be pasted as text; they then need tobe converted to data using the ‘Data/Text toColumns’ option of Excel.
20. At column T rows 7, 8 and 11, enter manually
total precipitation, runoff coefficient and baseflow proportion for your scenario. The respective
simulated values are tabulated in column V
beside these. Annualised fluxes of water are
tabulated in the green shaded area of columns
A–K rows 7–28. Average annual flow-weighted
concentrations of nitrate are tabulate with yellow
shading at Column M rows 26–27 for total flow
and slowflow.

Limitations
Despite demonstrating considerable progress,
modelling challenges became evident during thisresearch, some of which were specific to HYDRUS–
CW2D and others were more generic.

HYDRUS
 HYDRUS had very limited capability to simulate
runoff. Only instantaneous, infiltration-excess
runoff is reported as a sum for all atmosphericnodes. Overland flow dynamics, include downslope infiltration, surface roughness and slope
effects are not modelled. Our flow analysis estimate of quickflow and its bypass of the HYDRUS

30

simulation largely overcame this problem, albeit
in a highly empirical manner. Note that an overland flow module for HYDRUS is now available(http://www.pc-progress.com/en/Default.
aspx?h3d-overland); it simulates water but not
yet solutes.
 Precipitation input to individual nodes and thesurface as a whole does not account for slope
and thereby assumes that the slope horizontal.
As such, the precipitation input simulated for highslopes could be seriously overestimated, e.g. a
60o slope would be simulated to have received
twice the rainfall as that provided as input, unless
this input had previously been cosine-adjusted
for slope. This limitation was removed in a later
version of HYDRUS (Šimunek pers. comm.).
 Runoff as saturation excess is possible as a variable seepage zone, but rainfall is not continued
on that portion of the slope. Hence, the water balance is altered. Without a variable seepage zone,
conditions that approach saturation near the surface dramatically slow or stop the model due tomathematical difficulties.
 Only one root type is specified, wherever they
are placed in the simulation domain. However, a
two-root-type version of HYDRUS is being tested(Šimunek pers. comm.).
 Nutrient uptake is simulated only as the mass flow
component. This might not be a serious issue fornitrate, for which uptake in many systems is predominantly via massflow. However, for nutrientsthat are taken up mainly via diffusive processes,
e.g. ammonium, phosphorus and potassium,
simulated uptake is likely to be greatly underestimated using HYDRUS. A version of HYDRUS
is now available that included active and passivecompensated uptake (Šimunek and Hopmans2009).
 For hillslope applications of HYDRUS, with orwithout CW2D, it would be very useful to include
the pre- and post-processing outlined in the
spreadsheets, which would allow easy generationof, for example, graphs like those in Figs 7–8 andtables like those in Tables 5–6.

CW2D
 Over-parameterized for the application reported
here, i.e. organic and microbial pools would not
be required for simpler, useful soil N dynamics
models; This contributed to slow performance ofHYDRUS-CW2D in our application (7 hours foran annual simulation using daily rainfall), but we
recognise that our set-up might not have been
optimal for model performance.
Landscape Logic Technical Report No. 28 Net changes to the microbial N pool were
not included in mineralisation calculations in
Spreadsheet 3, because we were only concerned with nitrification. These changes should
be included for net N mineralisation calculations.
 Denitrification doesn’t split N gasses into N2 andN2O.
 We were unable to parameterise O diffusion into
the soil in a way that maintained a realistic O
profile.
 The preferential flow options of HYDRUS are notavailable with the CW2D module.
 Nitrification sensitivity to soil water content needs
further testing.
 Graphically, nitrate concentrations seemed to beabnormally high in parts of the soil, especiallywhere there was very low soil water content.
However, post-HYDRUS calculation of the concentration in seepage water and other aspects ofpools and fluxes (Table 5) were realistic and themass balance check of HYRDUS was acceptable.
Hence, calculation of concentrations only for thegraphical presentation of HYDRUS-CW2D outputs needs to be checked.
 Also in some graphical outputs some solute
fluxes as seepage are incorrectly reported asdrainage.

Apart from the above modelling challenges,
these sorts of simulations are also data-limited.
 Data were not available for a full definition of thespatial and temporal heterogeneity of the initial and boundary conditions for soil water andnitrate, including preferential flow, saturated andunsaturated hydraulic conductivity, nitrogen turnover and root distribution and activity.
 Water flux associated with overland flow is ahighly complex process that itself is difficult tosimulate.
 The solute dynamics of overland flow are also
highly complex, because they depend on poorly
understood and poorly predictable interactions
between overland flow and surface soil.
 In situ rates of denitrification are extremely difficult to measure in a way that captures spatial andtemporal variability.
The implication of data and modelling limitations
outlined above is that these types of simulations canonly be done as a gross simplification of reality, andthat the simplification of using a 2D hillslope model
as shown in this research should be satisfactory formany types of applications.

Streamside management zones for buffering streams on farms: observations and nitrate modelling31