Journal of Hydrology 570 (2019) 637–646

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Journal of Hydrology
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Research papers

Optimizing the riparian zone width near a river for controlling lateralmigration of irrigation water and solutes

T⁎

V. Phogata,b, , J.W. Coxa,c, Rai S. Kookanad, J. Šimůneke, T. Pitta, N. FlemingaaSouth Australian Research and Development Institute, GPO Box 397, Adelaide, SA 5001, Australia
CCS Haryana Agricultural University, Hisar 125 004, Indiac
The University of Adelaide, PMB1, Glen Osmond, SA 5064, Australiad
CSIRO Land and Water, PMB No. 2, Glen Osmond, SA 5064, AustraliaeDepartment of Environmental Sciences, University of California, Riverside, CA 92521, United Statesb

A R T I C LE I N FO

A B S T R A C T

This manuscript was handled by Peter K.
Kitanidis, Editor-in-Chief, with the assistance ofKarl Vanderlinden, Associate Editor

Riparian zones are essential to preserve water quality of rivers adjacent to large areas of irrigated agriculture. We
used HYDRUS (2D/3D) to quantify the long-term (8 years) inﬂuence of crops (almonds, wine grapes and potatocarrot) irrigated with recycled water on water and solute exchange at the Gawler River interface in relation tovegetation buﬀer widths from 10 to 110 m. We found that under almond and annual horticulture the likely
average annual water ﬂow from the irrigated area to the river was nearly twice as much (2.1 and 1.8, respectively) that under wine grapes. The hydraulic exchange at river interface for diﬀerent irrigated crops was foundto be sensitive to the buﬀer widths. For wine grapes, almonds and annual horticulture, the average annual
hydraulic balance reached an equilibrium at 20, 65 and 55 m buﬀer widths, respectively. Furthermore, for wine
grapes, with a 20 m buﬀer width, the average annual load of salts became negligible. This study shows thatbuﬀer widths of 20, 60, and 40 m for irrigated wine grapes, almond, and annual horticulture, respectively, areneeded to restrict the migration of salts to the river. Further reﬁnements are possible by incorporating theinﬂuence of preferential ﬂow paths, improved water stress response functions, and addressing the data limitations for calibration of the model for solute dynamics.

Keywords:
Buﬀer width
Irrigated crops
HYDRUS
Leaching
Solute dynamics
Water quality

1. Introduction
Crop production, especially in arid and semi-arid regions of theworld where rainfall is not able to meet the evapotranspiration needs ofthe crops, depends on supplemental irrigation. Irrigated agriculture
contributes 40% of the world food production from 20% of the cropped
area, thus makes a major contribution to the global food security (FAO,
2016). However, irrigated agriculture may become unsustainable due
to its contribution to soil degradation, salinization, waterlogging, andenvironmental pollution. Global water security warrants beneﬁcial
reuse of recycled water, such as irrigation, but with minimal potential
harmful impacts on ecosystems. Ecosystem impairment, particularly
reduced soil quality, biodiversity loss, and harm to amenity and cultural
heritage values, is a growing global problem (FAO, 2011). Therefore,
future irrigation schemes must address trade-oﬀs, particularly withrespect to inter-sectoral water allocations and environmental impacts.
Aquatic ecosystems adjacent to irrigated agriculture are most at risk
due to the transport of irrigation induced chemicals such as soluble⁎

salts, nitrates, and pesticides (Zhang et al., 2010). The fate of thesechemicals in the soils and their migration to receiving environments
depend on a number of factors including the vegetation, topography,
climate, soil, irrigation, groundwater level, and ﬂow conditions in thestream (Klatt et al., 2017; Schilling et al., 2018). Riparian vegetation
can moderate the movement of water and solutes to water bodies byinterception and attenuation of chemicals moving through the buﬀer
zone (King et al., 2016).
Several investigations have examined the functions of buﬀer zones
for stream ecosystems (e.g., Mayer et al., 2007; Hansen et al., 2010).
However, these have mostly dealt with the overland movement of solutes via surface runoﬀ and sediment transport. Only, a limited numberof modelling or case studies have evaluated the role played by buﬀer
zones in reducing the migration of irrigation induced soluble salts/
contaminants via subsurface ﬂow to streams (e.g., Naiman et al., 2005;
Allaire et al., 2015). Subsurface ﬂow paths can exhibit wide variations
depending on speciﬁc local conditions (Naiman et al., 2005) including
subsurface lithology and stratigraphy (DeVito et al., 2000; Hill et al.,Corresponding author at: South Australian Research and Development Institute, GPO Box 397, Adelaide, SA 5001, Australia.
E-mail address: vinod.phogat@sa.gov.au (V. Phogat).

https://doi.org/10.1016/j.jhydrol.2019.01.026
Received 24 July 2018; Received in revised form 13 January 2019; Accepted 21 January 2019
Available online 25 January 20190022-1694/ Crown Copyright © 2019 Published by Elsevier B.V. All rights reserved.

Journal of Hydrology 570 (2019) 637–646

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Fig. 1.

2004). To our knowledge, no information is currently available in theliterature on the role played by buﬀer zones in dealing with the irrigation induced solute interception or inﬂuencing its migration to water
bodies.
Field experiments for assessing the role of a buﬀer zone on thesubsurface water and salts movement from irrigated cropping system toan adjoining river is both a complex and expensive exercise. Therefore,
numerical models are increasingly being used (e.g., Flipo et al., 2014;
Xian et al., 2017) for such assessment. Hydraulic exchange across thestream-aquifer has been modelled with buﬀer zones (e.g., Phogat et al.,2017a) or without (e.g., Baratelli et al., 2016). Similarly, Kidmose et al.
(2010) employed a conceptual groundwater ﬂow and reactive transfer
model to establish a relationship between ﬂow paths and the fate of apesticide in a riparian wetland. Alaghmand et al. (2013) used a numerical model (Hydrogeosphere) to evaluate the interaction between ariver and a saline ﬂoodplain in relation to groundwater ﬂuctuations,
incorporating evapotranspiration losses by riparian vegetation. Klatt
et al. (2017) explored the capability of a coupled hydro-biogeochemical
model to evaluate the eﬀectiveness of buﬀer strips to reduce nitrogenloads into aquatic systems. However, most of these modelling studies
have been either conceptual and/or only partially calibrated for sitespeciﬁc ﬂow and/or solute dynamics. The complicated nature of waterand solute transport processes (Sophocleous, 2010) and the inherent
uncertainty of input data are some of the challenges in simulating waterﬂow and solute transport with physically based models. Nevertheless,such models are valuable in understanding water ﬂow and solutetransport/reaction processes involved in complex bio-geological environments.
This study uses a two-dimensional ﬁnite element numerical model
HYDRUS (2D/3D (referred to below as HYDRUS; Šimůnek et al., 2016)
to quantify the extent of water and solute exchange across a streambuﬀer interface. The study involves complex heterogeneous geologicalformations involving real-time climatic, vegetative (crop and buﬀer),
and stream ﬂow conditions. The key objectives of this investigation
were: i) to calibrate and validate a numerical model (HYDRUS) forwater table dynamics in an area adjacent to a seasonal river (Gawler
River) by incorporating daily water level ﬂuctuations in the river,groundwater dynamics, crop evapotranspiration, riparian zone vegetation evapotranspiration, and soil heterogeneities; ii) to estimate theimpact of diﬀerent buﬀer zone widths on the ﬂux exchange at the riverbuﬀer interface under diﬀerent cropping systems, iii) to optimize theriparian width to control the irrigation-induced solute movement to theriver for diﬀerent irrigated crops; and iv) to estimate the residence timeof the solute tracer migrating to the adjoining water body through thesubsurface under shallow water table conditions.

2.2. Soil characteristics
The soils of the NAP are highly heterogeneous with depth. There iscommonly a shallow clay layer at a variable depth, which determines
the root growth and crops to be grown. Broader soil groups and geologyof the site were obtained from the stratigraphic information of the site
and well logs within the vicinity of the site. There are in general 6 major
geological layers, which include red friable sandy loam soil, light brownsilty topsoil, sandy clay, sandy non-calcareous clay, non-calcareous ﬁne
sandy clay, and sand. The soil particle size distributions and bulk
densities of these soil groups were obtained from the previous soil
analysis reported in ASRIS (ASRIS, 2011) and the APSIM (Holzworth
et al., 2014) data base. The particle size and bulk density data wereused to estimate soil hydraulic parameters using the ROSETTA module
embedded in the HYDRUS software environment. The saturated hydraulic conductivity (Ks), and the α and n parameters were further
adjusted during the calibration process and their ﬁnal optimized values
are presented in Table 1.
2.3. Buﬀer and crops parameters
The vegetation buﬀer at the study site is dominated by river red
gums (Eucalyptus spp.) but its width is highly variable along the longitudinal distance of the river, ranging from a couple to hundreds ofmetres. The area adjoining the riparian buﬀer is used for intensive
cropping such as almonds, wine grapes, potato, carrot, and onion allalong the river. On the southern side of the river where the modellingdomain was established, the land has been used for the wine grape
cultivation.
Daily crop evapotranspiration (ETC) for river red gum (Eucalyptusspp.) in the buﬀer zone and irrigated crops (wine grape, almond, carrot,
potato) grown in the adjacent river corridor were estimated from dailyreference crop evapotranspiration (ET0) data and local crop coeﬃcients(Kc). The ET0 (Allen et al., 1998) data was obtained from the nearby
weather station (Edinburg Raaf). The daily ETC values were divided into
evaporation (Es) and transpiration (Tp) components based on the leaf
area index (LAI) as follows (Ritchie, 1972):

Es = ETc . e - Kgr x LAI(1)

Tp = ETc − Es
Here, Kgr is the light extinction coeﬃcient for global solar radiation andits value was set to 0.5 (Aubin et al., 2000; Phogat et al., 2017a) for all
vegetations. The LAI data for wine grapes and almonds was obtained
from other studies (Phogat et al., 2017b, 2018b) and for the annual
horticulture (carrot and potato) crops from the literature (Reid andEnglish, 2000; Deshi et al., 2015). Canopy interception by the buﬀer
vegetation was assumed to be 15% of precipitation (Xiao et al., 2000).
Estimated daily Es and Tp values and daily rainfall were used as input in
HYDRUS simulations.
The roots of the buﬀer strip vegetation were assumed to be distributed linearly from the soil surface to a depth of 200 cm. Althoughroots of Eucalyptus can grow to a depth of 6–7 m (Phogat et al., 2017a),
however, due to shallow water table conditions at the site, roots generally did not grow far below a water table due to the lack of the oxygen
supply (Baker et al., 2001). Similarly, the rooting depths of 100, 200,
and 60 cm for wine grape, almond, and annual horticulture (carrot andpotato), respectively, were used in the modelling study based on relevant studies from the region (Phogat et al., 2017b, 2018b). The root
water uptake parameters for almond and wine grape were also takenfrom these studies and the HYDRUS database (potato and carrot). Since
HYDRUS does not allow using diﬀerent parameters for stress response
functions for the crop and buﬀer zones, hence the same Feddes’ parameters (Feddes et al., 1978) were used for both parts of the domain. It

2. Materials and methods
2.1. Experimental site
The study was carried out at the Virginia Park (34°38′2.6″S and
138°32′27.6″E) gauging station at Gawler River which is situated at
12 m above the Australian Height Datum (AHD). The Gawler River only
ﬂows during the rainy season (July to October). However, stagnant
water (about 30–100 cm)/base ﬂow conditions prevail at other times at
the gauging station. The adjacent area, being a part of the vast Northern
Adelaide Plains (NAP), has a relatively ﬂat topography with a gentle
slope to the west. All relevant features of the study site are shown in
Fig. 1. The NAP experiences a Mediterranean climate, which is characterised by hot, dry summers and cool to cold winters. Long-term(1900–2016) average rainfall in the region amounts to 475 mm(Department of Environment, Water and Natural Resources, 2016) andannual evapotranspiration amounts to 1308 mm, resulting in the irrigation demand for crop production. Water table ﬂuctuations in the areaadjacent to the river were monitored in the shallow wells (PTA100,
PTA101, PTG078, and PTG080). Location of these wells is shown in638

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Gawler River
PTG087N

PTG080Virginia Park
PTA100

PTA101

Fig. 1. Map of the study site showing the Gawler River, the gauging station, shallow wells (yellow circles) and adjacent cropped area. (For interpretation of thereferences to colour in this ﬁgure legend, the reader is referred to the web version of this article.)
Table 1
Optimized soil hydraulic parameters used in numerical simulations.
Sr No

Textural class

Depth
(m)

θr
(cm3cm−3)θs(cm3cm−3)α(cm−1)nKs(cm d−1)

l

1
2
3
4
5
6

Sandy loam
Silty loam
Clay loam
Silty clay loam
Sandy clay
Sand

0–1
1–2
2–5.5
3.5–6
6–8
8–12

0.07
0.06
0.07
0.08
0.09
0.05

0.44
0.45
0.43
0.44
0.44
0.41

0.024
0.03
0.02270.023
0.0234
0.124

1.45
1.5
1.414
1.303
1.268
2.28

56.1
69.8
39.9
25.0
18.8
350.

0.5
0.5
0.5
0.5
0.5
0.5

resulted in 10,000 2D elements in a standard rectangular 2D domain.
On the upper left side of the domain (Fig. 2), the atmosphericboundary was considered through which the inﬁltrative inﬂux or theevapotranspirative eﬄux occurs. A time-variable ﬂux boundary condition (treated similarly as an atmospheric boundary condition) was
imposed on the upper right side of the domain to represent the buﬀer
zone, which had diﬀerent ﬂuxes than the irrigated surface. The ﬂux atthis boundary was given by the diﬀerence between daily rainfall anddaily potential evaporation (Es). A special HYDRUS boundary condition(BC) was speciﬁed in the river. This special BC assigns the hydrostaticpressure head on the boundary below the water level in the river and aseepage face BC on the boundary above the water level. The speciﬁed
water levels in the river are linearly interpolated in time in order tosmooth the impact of daily ﬂuctuations of water levels in the river(Phogat et al., 2017a). Measured values of water table depths in thewell near the left boundary of the domain (PTA100) were used to deﬁne
initial and time-variable pressure head boundary conditions. No ﬂow
was assumed as the boundary. The initial pressure head condition in thedomain was speciﬁed by interpolating measured mean water table
depths in the shallow wells (Fig. 1) in the adjacent area while considering hydrostatic equilibrium conditions in the vertical direction.
Daily rainfall in excess of the soil inﬁltration capacity is accounted for
as run oﬀ by HYDRUS. The longitudinal dispersivity was assumed as
one tenth of the modeling domain (with the transverse dispersivity

must be noted that root water uptake in HYDRUS depends on theavailability of water in the soil, the root spatial distribution, and differential transpiration ﬂuxes in the crop and buﬀer zones. The root
water uptake was assumed to be linearly distributed with depth, withthe maximum at the soil surface and zero at the bottom of the rootingzone.
The “trigger irrigation” option was used to generate irrigation
schedules for all crops (wine grape, almond, annual horticulture). The
trigger pressure used for wine grapes, almond, and carrot-potato, respectively, were −60, −25, and −15 kPa at a depth of 30 cm. Similar
trigger pressures have been used for these crops in previous studies(Green, 2010; Phogat et al., 2018a,b).
2.4. Transport domain, initial and boundary conditions
The transport domain represents a 400 m cross section from the
middle of the river (Fig. 2). The vertical dimension represents the distance from the Australian Height Datum (AHD) to the soil surface(12 m) at the experimental site. The top width of the river was 10 m, thebottom width 4 m, and the depth 4 m at the study site. The width of thebuﬀer zone is 30 m from the river bank. Therefore, the lateral width of
the riparian zone at the Virginia Park gauging station from the middleof the river is approximately 35 m, which also includes an unsealed
road which runs along the river. The ﬁnite element discretization

Fig. 2. Schematic representation of the ﬂow domain showing the material distribution, the river, the buﬀer strip, irrigated crops, and imposed boundary conditions.
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between the measured and simulated water table depths were estimated as:

being one tenth of the longitudinal dispersivity) (Cote et al. 2003;
Phogat et al., 2014) and the molecular diﬀusion coeﬃcient in water
equal to 1.66 cm2/day (Phogat et al., 2018b).

N

ME =2.5. Calibration and validation of the model1∑ (Mi − Si)
N i=1

(2)

N

Measured water table depths (average of four quarterly measurements in a year) in the shallow wells (PTA101, PTG080 and PTG087,
Fig. 1) near the study site were used for the calibration and validationof the model. Simulations were carried out for 1461 days (1st July 2009
to 30th June 2013) to calibrate the model for water table depths at the
middle of the domain (X = 200 m). For most sensitive model parameters including the saturated hydraulic conductivity (Ks), and thecoeﬃcients α and n of diﬀerent soil layers were varied manually and noautomated parameter optimization procedure was used to calibrate themodel. In addition to a visual comparison of observed and simulated
water table depths, a quantitative evaluation of the model performance
was undertaken using goodness-of-ﬁt measures (see Section 2.7) similar
to other studies (e.g., Alaghmand et al., 2013, 2014). The calibrated
model was validated for 1461 days (1st July 2013 to 30th June 2017)by comparing the measured and simulated water table depths. The
calibrated and validated model was then used to assess the impact ofother irrigated crops (almond and annual horticulture crops such as
carrot and potato) and the buﬀer zone widths on the migration of waterand solutes to the river. More details on diﬀerent scenarios are given
below in the Scenario Analysis section.
To understand the movement of irrigation-induced solutes/agrochemical tracers to the river water, we considered Total Dissolved
Solids (TDS) as representative of all soluble solutes which is consistent
with numerous studies (e.g., Ramos et al., 2011; Phogat et al., 2014,2018b). The initial soil conditions in the domain were assumed to besolute free. The average quantity of TDS (1200 mg/L) of Class-A treated(recycled) water from the Bolivar treatment plant (Stevens et al., 2003)
was applied during all triggered irrigations at the atmosphericboundary where crop is being grown. However, the model calibration
for solute dynamics could not be conducted due to the non-availabilityof site-speciﬁc data for solute transport processes.

MAE =

1
∑ |Mi − Si|
N i=1

(3)

N

1
∑ (Mi − Si )2
N i=1

RMSE =(4)

Many studies (e.g., Coﬀey et al., 2004; Alaghmand et al., 2013) haveused similar goodness of ﬁt measures (correlation coeﬃcients and
RMSE) as above.
We also evaluated the test of signiﬁcance between the measured andsimulated values of water table depths using the paired t-test (tcal) as
below:

tcal =

−

d
SDm

1
n

−

d = M − S and SDm =ns12 + ns22
2n − 2(5)Here, n is the number of comparable paired points, s1 and s2 are thestandard deviations of measured and simulated data, respectively, d is−−the diﬀerent between measured (M ) and simulated (S ) means values,
SDm is the standard deviation of the mean, and tcal is the calculated
paired t-test value. The null hypothesis tests that there is no signiﬁcantdiﬀerence between the mean values of measured and simulated watertable depths.
Nash and Sutcliﬀe (1970) model eﬃciency (E) is a normalized statistic that expresses the relative magnitude of the residual variancecompared to the variance of the measured data during the period underinvestigation, as given below:
N

E=1−∑i = 1 (Mi − Si )2N¯ )2∑i = 1 (Mi − M(6)

The range of E lies between −∞ and 1.0 (a perfect ﬁt). An eﬃciency
value between 0 and 1 is generally viewed as an acceptable level of
performance. Eﬃciency < 0 indicates that the mean value of the observed time series would be a better predictor than the model and denotes unacceptable performance (Moriasi et al., 2007; Legates andMcCabe, 1999).

2.6. Scenario analysis
The calibrated and validated model was then used to simulate thedynamics of the hydrological ﬂuxes and solute movement for diﬀerent
buﬀer widths and for various irrigated crops (wine grape, almond, andcarrot-potato rotation). The simulations were executed for 8 years (1stJuly 2009 to 30th June 2017) plus further 8 years if needed (if solute
did not reach the river) for all 3 irrigated crops for varying buﬀer zone
widths (10–110 m) from the centre of the river. These simulations were
established to evaluate the appropriate width of the riparian zone tocontrol the lateral movement of solutes to the river. In the simulations
extended in future climate (8 years), median climate change data for
the Edinburg RAAF station were used (Charles and Fu, 2015). Initialconditions and daily water level ﬂuctuations for such future simulations
were imported from the previous simulations (8 years). To estimate theextent of leaching, the annual water balance for diﬀerent crops wascomputed using inputs such as rainfall, irrigation, and model-simulated
evaporation and transpiration and assuming similar initial pumping
well and river exchange conditions in the domain.

3. Results and discussion
3.1. Calibration and validation of HYDRUS-2D
The data in Fig. 3 demonstrates a consistent performance of themodel (i.e. R2 of 0.66 and 0.64, and E = 0.34 and 0.34, respectively)
during calibration (2009–2013) and validation (2013–2017) period.
These values fell within the R2 values (0.35–0.84) reported in othermodelling studies (e.g., Coﬀey et al., 2004; Phogat et al., 2016). Other
statistical estimates (ME, MAE, RMSE and SD; see Fig. 3) during thecalibration and validation period were similar but slightly higher thanpreviously observed values (e.g. Alaghmand et al., 2013). This is because of wide ﬂuctuation within the input data. The paired t-test indicated that there was no signiﬁcant diﬀerence (p = 0.05) between themeasured and simulated mean depths of water table. Overall, all thesestatistics conﬁrm an adequate representation of groundwater ﬂuctuations by the model.
Calibration of the model for solute dynamics was not possible due tothe lack of site speciﬁc data. However, numerous studies have shownthat suﬃciently calibrated and validated model for complex hydraulicﬂuxes in a heterogeneous domain can oﬀer valuable practical understanding of bio-geochemical processes in the soil. For example,

2.7. Model evaluation
The model’s performance was evaluated by comparing measured(M) and HYDRUS simulated (S) water table depths. Correlation coeﬃcients were estimated to understand the relationship between measured
and simulated values of water table depths during the calibration andvalidation periods. The statistical error estimates [mean error (ME),mean absolute error (MAE), and root mean square error (RMSE)]
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Fig. 3. Relationship between measured and simulated water table depths, statistical error estimates (ME, MAE and RMSE), standard deviation (SD) and modeleﬃciency (E) values during the calibration (2009–13) and validation (2013–17) periods.

large amounts of recharge as indicated by large peaks in Fig. 5. During
summer, a large amount of irrigation is applied to crops in the adjoining
area, which reverses the ﬂow gradient across the buﬀer-river boundary.
Under such dynamic conditions, aquifer recharge or discharge may
occur depending on the river level (Ghazavi et al., 2012), leading toeither a gaining or losing river (Rassam, 2011; Phogat et al., 2017a).
However, irrigation-induced ﬂow to the river depends on the water
uptake/evapotranspiration pattern of buﬀer zone vegetation (Phogat
et al., 2017a) and on the extent of return ﬂow from the irrigated areas.
As shown earlier, the extent of irrigation-induced ﬂow (recharge) ishigher for almond and annual horticulture as compared to wine grapes(Fig. 4). As a result, the average annual amount of ﬂux exchange at theriver interface was nearly twice (1.8–2.1 times) as much under almondand annual horticulture than under wine grapes (Fig. 5). Additionally,
the high evaporation demand of the buﬀer zone vegetation may further
limit the net discharges to the river. This was probably not met by the
low drainage ﬂux under wine grapes. Overall, irrigation induced drainage, groundwater discharge to the stream, evapotranspiration by the
buﬀer zone vegetation and irrigated crops play a key role in deﬁning
the extent of exchange between the buﬀer zone and the river.
The impact of diﬀerent buﬀer widths on the average annual waterexchange at the river interface for diﬀerent irrigated crops is shown in
Fig. 6. For wine grapes, the average annual hydraulic balance was negative for the 10–20 m buﬀers during the simulation period (8 years,
2009–2017), indicating the dominance of ﬂow from the irrigated area
to the river system. However, the reverse was observed for buﬀer
widths > 20 m as the evapotranspiration demand of the buﬀer vegetation governed the water exchange at the river-buﬀer interface. In thecase of almond, however, the overall water balance remained negative(discharge to the river) for a buﬀer zone up to 65 m due to its 3 times
higher irrigation than for wine grapes. Similarly, under annual

Alaghmand et al. (2013, 2014) indicated that the complexity associatedwith the quantiﬁcation of the solute transport parameters restricted
them from validating the model on the observed concentration pattern.
However, their study unravelled the impacts of groundwater pumping
on salinization risks of a ﬂood plain. Similarly, Carr et al. (2018) calibrated and validated a 2D model on gauged groundwater elevations
and hypothesized that the accurate representation of ﬂow dynamics caninform environmental management involving transport of sediments,
nutrients, and heavy metals. Other studies also have used similar approaches (e.g., Rousseau et al., 2012).
3.2. Irrigation and annual water balance for crops
The extent of average annual irrigation among all crops during
2009–2017 (Fig. 4) was the lowest in wine grape (242 and 320 mm),followed by almond (760 and 920 mm) and the highest for annual
horticulture (951 and 1226 mm) reﬂecting their speciﬁc evapotranspiration requirements (Phogat et al., 2018b). Correspondingly, theleaching fraction/recharge ﬂux under almond (87–298 mm) and annual
horticulture (100–252 mm) was 3–3.8 times higher than under wine
grapes. Besides for wine grapes, a negative annual ﬂux balance wasrecorded in some years but, the overall average balance was positive
over 8 years. These observations are consistent with other studies(Green, 2010; Reynolds, 2010; Phogat et al., 2018b). It is well understood that the contribution of leaching fraction/irrigation return ﬂow
from irrigated crops can be a critical driver for the river-buﬀer hydraulic exchange (e.g., Berens et al., 2009).
3.3. Hydraulic exchange at the river-buﬀer interface
Numerous high rainfall/ﬂood events during 2009–2017 resulted inFig. 4. Annual irrigation (mm) and recharge/discharge (mm) in the domain under a) wine grape, b) almond, and 3) annual horticulture (carrot-potato) crops.
Positive ﬂuxes are recharge and negative ﬂuxes are discharge from the domain.
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Fig. 5. Daily ﬂux exchange at the river-buﬀer interface for almond, wine grape, and annual horticulture (carrot-potato) grown near the Gawler River during
2009–2017.

with 30 m buﬀer widths for wine grapes, almonds, and annual horticulture is shown in Fig. 7. Irrigation induced salts gradually moved
downwards as well as laterally, but, the vertical movement was faster.
Initially, salts continued to build up in the soil and then migrated to theshallow groundwater (water table at 4 m). It was noted that the irrigation induced salts entered the shallow groundwater within 2 years of
irrigation of almond and annual horticulture (Fig. 7), but it took longer
for wine grapes. It is noteworthy that after 8 years of simulation, thesalts were distributed in variable concentrations throughout the entire

horticulture (carrot and potato) crops, the overall hydraulic balance
was similar to almonds and the threshold buﬀer zone width for equilibrium ﬂow conditions reached at 55 m (Fig. 6). Based on irrigation
regime for irrigated crops, diﬀerent buﬀer zone widths are required forequilibrium ﬂow conditions at the river-buﬀer interface.
3.4. Impact of a buﬀer width on solute dynamics
Temporal dynamics of irrigation induced salts in the soil proﬁlesFig. 6. An average balance of water exchange across the stream-aquifer interface for diﬀerent buﬀer widths under a) wine grapes, b) almond, and c) annual
horticultural crops.
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Fig. 7. Temporal dynamics of irrigation induced salts in the soil proﬁle with a 30-m buﬀer width under a) wine grapes, b) almonds, and c) annual horticulture(carrot-potato) at the indicated times during 2009–2017. The river is situated at the top right corner of the domain.

decreased (especially in almonds and annual horticulture), but, thetiming of occurrences of salts/tracer pulses in the river are similar irrespective of irrigated crops. Notably, a large solute peak occurred in
the river water after approximately 7 years of simulations (August to
December 2016), which transported diﬀerent amounts of salt to theriver at diﬀerent buﬀer widths. This time corresponds to the aftermathof a ﬂood event when the water level in the Gawler River reached thesoil surface and completely saturated the adjacent riparian zone. Subsequent receding water levels in the river created a steep gradient fromthe buﬀer zone to the river, which conveyed a large amount of salts
from the saturated zone to the stream. Such observations were also
reported in other studies (Bryan et al., 1998; McKergow et al., 2003).
Hence, the salts transported to the river may not be associated onlywith irrigation, but also with the generation of hydraulic gradients,
which push the salts laterally into the river. However, the impact ofhydraulic gradients gradually dissipates as the buﬀer width increases,
since the buﬀer zone acts as a barrier in transmitting the hydraulicresponse between the river and the irrigated area. Overall, it appears
that all components of soil, water, crop and climate, play a crucial roleand have a diﬀerent inﬂuence on the salt transport to the river and its
water quality.
Salts load transported to the river and the residence time of solutes
in the soil for diﬀerent buﬀer widths and crops are shown in Fig. 9. The
amount of salts for the 10 m buﬀer was very similar for almond andannual horticulture and about 40 times higher than for wine grapes.
Meanwhile, the salts transported to the river for the 20 m buﬀer werehigher for annual horticulture than for almond. When the buﬀer width
was increased to 60 m, only a small additional reduction in the salt load
was observed (99.9%). Similarly, for annual horticulture, the averageannual load of salts was reduced by 92.2% (to 1566 mg) for the 20 m
buﬀer width as compared to the 10 m buﬀer width. For a 99.9% reduction in the salt load, a 40 m buﬀer width is needed. Therefore, it isestablished that maintaining a 20, 60, and 40 m buﬀer widths for wine
grapes, almonds, and annual horticulture can eﬀectively reduce irrigation induced salts/tracers transport to the river by 99.9%.
The residence time was twice as long for wine grapes as comparedto almonds and annual horticulture for buﬀers < 40 m wide. For example, the residence time for a 10 m buﬀer was 2.5 months for almonds
and annual horticulture, and 5 months for wine grapes (Fig. 9). The
residence time for the solute movement to the stream starts increasing
exponentially for larger buﬀer widths (> 40 m) and wine grapes. It
took about 7 years for irrigation induced solutes, though concentrations
were very small, to appear in the stream when the buﬀer width was
60 m. The residence time for almonds and annual horticulture was very

domain, including along the river boundary, especially for almond andannual horticulture. The presence of salts along the river boundary
indicates that they may have already entered into the river system in
low concentrations (< 0.0011 mg/cm3) against the concentration gradient under these crops. In contrast, for wine grapes, salts travelled a far
shorter distance than for the other two crops and the salt plume remained far from the river front boundary (Fig. 7). Nonetheless, theextent and timing of salts migration into the river system was notclearly visible.
Excessive concentrations of soluble salts as shown on the right handcorner of Fig. 7 for almond and annual horticulture can have an adverse
impact on plant growth and transpiration losses. However, this situation was obtained for a 30 m buﬀer width (Fig. 8), which is an insuﬃcient width to control salts mobilization to the river for these crops.
This shows that in the absence of an adequate buﬀer width, there is achance of rapid secondary salinization close to the river. In such situations, adopting native vegetation such as river red gums (with a salt
tolerance of 30 dS m−1) or black box trees (salt tolerance of 55 dS m−1)
as buﬀer (Overton and Jolly, 2004), could help maintain appropriate
transpiration services in such situations (Alaghmand et al., 2013,2014).
The frequency, timing, and load of salt pulses entering into the riverduring 8 years (2009–2017) of simulation under diﬀerent crops as inﬂuenced by buﬀer zone widths is shown in Fig. 8. It is apparent that forwine grapes, the salts/tracer pulse appeared in the river only for buﬀer
widths < 20 m. Further widening the buﬀer (30–60 m) and extending
the simulations to 16 years (2009–2025; data not shown) did not produce any appreciable amount of salts in the river. In contrast, a signiﬁcant salts/tracer pulse continued to appear in the river for 50 and
60 m buﬀer for annual horticulture and almond irrigated crops, respectively, during 8 years (2009–2017) of simulation. Further extending the buﬀer (up to 100 and 110 m) and simulation time(2009–2025) resulted in a very small amount of tracer salt to appear in
the river (10−22 to 10−6 mg) for annual horticulture and almond.
Therefore, it is concluded that a buﬀer width of 20, 50 and 60 m forwine grapes, annual horticulture (carrot-potato) and almonds, respectively, is needed to restrict the subsurface migration of irrigation induced salts/tracer chemicals to the river.
In terms of salt load the same buﬀer width for diﬀerent crops released varying pulses in the river (Fig. 8). For example, for a buﬀer
width of 10 m for almond during the 8 years of simulation was approximately twice the salts load than that for annual horticulture,
which in turn had 10 times higher loads than wine grapes. However,with increasing buﬀer width (e.g. 20 m), salts loads were drastically643

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Fig. 8. The eﬀect of buﬀer zone widths (10, 20, 30, 40, 50 and 60 m) on the lateral migration of irrigation induced salts into the river under almond (a to f), annual
horticulture (g to k), and wine grapes (l to m) during 2009–2017. The solute ﬂux scale at the vertical axes is diﬀerent in diﬀerent ﬁgures.

suﬃcient hydraulic gradient to push the salts into the river. Therefore,
the extent of irrigation return ﬂow is crucial for the longevity of solutes
in the soil system. The knowledge of the time required for the migrationof salts to the river helps in devising guidelines for maintaining the riversmall (< 100 days) for buﬀer widths < 30 m, then gradually increased
to 450 days for a 70 m, and to much longer values for larger buﬀer
widths. A longer residence time for wine grapes is associated with arelatively low recharge volume, which was unable to generate a

Fig. 9. The amount of salts (line graph) and their residence times (bars) for diﬀerent irrigated crops as a function of a buﬀer width during 2009–2017.
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water quality.

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4. Conclusions
This study was carried out to understand the extent, frequency andnature of hydraulic and solute dynamics relationship between river andirrigated crops interspersed with a buﬀer. The calibrated and validated
model (HYDRUS-2D) was used to evaluate long-term (8 years) scenarios
involving diﬀerent irrigated crops (wine grapes, almond, and annual
horticulture) and varied buﬀer widths (10–110 cm) aimed at quantifying the hydraulic connection between the river and the crop grownand optimizing buﬀer width for controlling solute movement to theriver.
Statistical evaluation (correlation coeﬃcient, ME, MAE, RMSE, t-test
and model eﬃciency) of the model for water movement showed a fairly
good matching between the measured and the simulated water table
depths. The results obtained from 8-years of simulations showed thatthree irrigated crops (wine grapes, almond, and annual horticulture)requiring diﬀerent annual irrigation applications have markedly different inﬂuence on the overall water and solute movement within thecrop-buﬀer-river ecosystem. Consequently, the average annual ﬂow
from the soil to the river was 2.1 and 1.8 times higher under almondand annual horticulture, respectively, compared to wine grapes. For
average annual water balance to reach equilibrium, buﬀer (Eucalyptusspp.) widths of 20, 65, and 55 m were needed for wine grapes, almond,and annual horticultural crops (carrot-potato), respectively.
From the standpoint of migration of irrigation induced soluble salts
into the river, the buﬀer widths of 20, 60, and 40 m were able to reduce
the salt load by > 99% under wine grapes, almond, and annual horticulture crops, respectively. Notably, existing guidelines do not diﬀerentiate between crop types when specifying optimum buﬀer widths.
Further work is required to assess how the seasonal variability in irrigation water quality inﬂuences these results, particularly in areas whererecycled water is used for irrigation. The modelling challenges and data
limitations included the absence of preferential ﬂow, the assumption ofsimilar water stress response functions for the buﬀer vegetation andirrigated crops, and lack of data for calibration of the model against
solute dynamics in the river. Further reﬁnements in the above ﬁndings
may be achieved by addressing these gaps.
Declaration of interests
None declared.
Acknowledgments
Authors highly acknowledge the ﬁnancial support by the Goyder
Institute for Water Research for this work under the project“Sustainable Expansion of irrigated Agriculture and Horticulture in
Northern Adelaide Corridor (Project Number ED-17-01).”
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