RESEARCH ARTICLE
10.1029/2020WR027781
Key Points:• Soil hydraulic properties,
inﬁltration, transpiration, and plant
growth are affected by irrigation
with treated wastewater• Mixing treated wastewater withlocal freshwater can be one
approach to mitigate adverse effects
of efﬂuent irrigation on soil
properties• The second approach can be
adjusting irrigation managementand design by reducing irrigation
rate and increasing the wetted area
per treeCorrespondence to:
S. Assouline,
vwshmuel@agri.gov.il

Citation:
Assouline, S., Kamai, T., Šimůnek, J.,
Narkis, K., & Silber, A. (2020).
Mitigating the impact of irrigation with
efﬂuent water: Mixing with freshwater
and/or adjusting irrigation
management and design. Water
Resources Research, 56,
e2020WR027781. https://doi.org/
10.1029/2020WR027781
Received 22 APR 2020
Accepted 10 AUG 2020
Accepted article online 14 AUG 2020

©2020. American Geophysical Union.
All Rights Reserved.

ASSOULINE ET AL.

Mitigating the Impact of Irrigation With Efﬂuent Water:
Mixing With Freshwater and/or Adjusting
Irrigation Management and Design
Shmuel Assouline1, Tamir Kamai1, Jiri Šimůnek2, Kﬁr Narkis1, and Avner Silber3

1
Department of Environmental Physics and Irrigation, Institute of Soil, Water and Environmental Sciences, A.R.O. ‐Volcani Center, Rishon LeZion, Israel, 2Department of Environmental Sciences, University of California, Riverside, CA,
USA, 3Northern R&D, Kiryat Shemona, Israel

Abstract The use of treated wastewater (WW) for irrigation is steadily increasing worldwide. However,
irrigation with WW requires special attention as it may alter soil hydraulic properties and, eventually,affect crop yields. The main goal of this study was to investigate two approaches that could mitigate thisadverse impact: (i) mixing WW with local freshwater (FW) and (ii) adjusting the irrigation managementand design to the deteriorated soil hydraulic properties. Four water qualities resulting from mixing FW and
WW at different ratios for irrigation of an avocado orchard planted on a clayey soil were considered: FW,
M1/3 (2/3 FW – 1/3 WW), M2/3 (1/3 FW ‐ 2/3 WW), and WW. Increasing the WW component in the irrigationwater reduces the soil's saturated hydraulic conductivity and inﬁltrability, and this reduction is depth
dependent. It also reduces the transpiration of trees and their trunk growth. This is supported by numericalsimulations that showed that irrigation with WW produced the reduction in plant transpiration relative toirrigation with FW. Numerical simulations indicated that irrigation management and design could be
adjusted to improve plant water uptake and reduce the differences due to water quality. The results
indicate that decreasing the irrigation rate and increasing the wetted area per tree could increase the water
uptake in WW‐irrigated soils to almost equal that in FW‐irrigated soils. This can be achieved by using
drippers with lower discharge and by using more drippers per dripline, more driplines per tree, and more
drippers per tree (concentric loops of driplines around each trunk).

1. Introduction
The global demand for food is expected to double by 2050 (Tilman et al., 2011). Because of the limited potential for additional arable land and rapid dwindling of freshwater (FW) resources, this challenge is formidable(Falkenmark & Rockström, 2006; Famiglietti, 2014; UN Environment, 2019). One way to increase crop
yields is to expand irrigated agriculture (Halliwell et al., 2001) and, therefore, to allocate new water sources
previously considered “marginal” (saline, treated efﬂuent, and desalinated water) to irrigation (Gleick, 2000;
Grant et al., 2012; Grattan et al., 2015; Tal, 2006). However, this must be done carefully while considering
potential long‐term risks on the hydro‐ecological functioning of soils (Assouline et al., 2015).
Irrigation with treated wastewater (WW) requires special attention as the use of WW may have drastic effects
on soils due to its unique combination of organic matter (OM), mainly dissolved organic carbon (DOC), andhigh concentrations of sodium (Na+). The high sodium concentration increases the sodium adsorption ratio(SAR) of water and soil solutions. This, in turn, increases the soil exchangeable sodium percentage (ESP) of
the soil (Qian & Lin, 2019), causing to aggregate slaking, clay dispersion, and soil swelling (Rengasamy &Olsson, 1993). These processes induce an overall soil structure degradation (Basile et al., 2012), thereby reducing the soil saturated hydraulic conductivity, eliminating large pores, and shifting the water retention curve(WRC) toward smaller pores (Bresler et al., 1982; Russo & Bresler, 1977a, 1977b). Clay dispersion was foundin many studies to be enhanced in the presence of DOC (Frenkel et al., 1992; Quirk & Schoﬁeld, 1955;
Tarchitzky et al., 1993, 1999), and therefore, changes in soil properties are expected as a result of irrigation
with WW (Assouline et al., 2015). These changes affect the main ﬂow processes in the soil (e.g., inﬁltration,
drainage, and evaporation) in the soil and, consequently, water and nutrient availability to plants. In addition, they were shown to reduce aeration and lower oxygen concentrations in the root zone (Assouline &Narkis, 2013).
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Table 1
Soil Physical and Chemical Properties of Pristine Soil at theExperimental Site
Clay

Silt

Sand

Calcite

(%)
46.7

(%)
24.5

(%)
28.8

(%)
9.8

Over the last two decades, studies have advanced knowledge regarding
the impacts of irrigation with WW on soil properties. Long‐term irrigation
with WW of clayey soils was shown to cause signiﬁcant deterioration of
CEC
ESP
EC
pH
soil physical and chemical properties (Aiello et al., 2007; Assouline &Narkis, 2011; Assouline et al., 2016; Lado et al., 2005, 2012; Levy &(meq/100 g)(%)(dS/m)
48.6
0.84
0.5
8.0
Assouline, 2011). Soil ESP in WW‐irrigated soils is often higher than the
SAR of the soil solution (Assouline et al., 2016; Levy et al., 2014), implying
the need for developing new approaches that address this issue and improve our understanding of soil chemical processes (Sposito et al., 2016).
A detailed quantitative description of the changes in soil physical and hydraulic properties, and consequently inﬁltrability, of a clayey soil following long‐term irrigation with WW can be found in Assouline
and Narkis (2011), Coppola et al. (2004), and Aiello et al. (2007). An interesting ﬁnding in Assouline andNarkis (2011) was the depth‐dependence of the extent of soil deterioration, suggesting that the long‐term
use of WW for irrigation will affect different zones in the soil proﬁle differently, depending on soil properties,
water quality, irrigation management, plant uptake, and climatic conditions.
The impact of long‐term irrigation with WW on soil hydraulic properties, and consequently, on soil air andwater regimes in the root zone, affects plant performances. Several studies reported (Assouline et al., 2015;
Noshadi et al., 2013) or predicted (Phogat et al., 2020) a systematic decrease in yields of orchards irrigatedwith WW. Following more than 10 years of continuous irrigation with WW yields from drip‐irrigated avocado and citrus orchards planted on clayey soils (~50% clay) dropped by approximately 20–30% relative tothosefrom orchards irrigated with local FW (Assouline et al., 2015). Mechanisms explaining the loss of productivity under WW irrigation are yet unknown and likely involve multifaceted interactions between water
quality and chemical, physical, and biological soil characteristics that affect plant functioning, including root
uptake. To support the required expansion of irrigated agriculture and to ensure its sustainability and proﬁtability when WW is used for irrigation, we need to seek for approaches that mitigate the adverse impacts ofwater quality on soil properties and yields. For example, Assouline (2019) has presented a simple physically
based method that relies on the inﬁltration capacity curve to adjust irrigation rate and duration to changes insoil inﬁltrability, thus contributing to the design of efﬁcient irrigation.
In this study, we propose to investigate two different approaches that mitigate the negative impact of WW
use for irrigation. The ﬁrst approach involves mixing WW with local FW to improve irrigation water quality.
This mixing will allow continuing to exploit WW as an additional source of irrigation water while enhancing
the sustainability and proﬁtability aspects of the irrigated agriculture that will rely on this type of water. The
second approach involves adjusting the irrigation management by considering the altered soil hydraulicproperties affected by the long‐term use of WW, especially on clayey soils. We present an analysis of the
impacts of different mitigation approaches on soil properties and plant response relying on ﬁeld data, laboratory experiments and analytical and numerical modeling and simulations.

2. Methodology
2.1. Experimental Site
The experimental site is an avocado orchard planted in 2010 (Haas grafted on Fairchild rootstock) locatedin the Western Galilee, Northern Israel (32.9821°N, 35.0830°E). The soil is grumusol, containing approximately 50% clay, mostly smectite. Information on soil mechanical and chemical analyses of a pristine soil
is shown in Table 1. The climate is Mediterranean, with mild, wet winters followed by dry, hot summers.
The rainfall season in the region is between October and May, with a mean annual precipitation of
580 mm.
The tree density is 417 trees per hectare, with 6 m between tree rows and 4 m between trees within rows.
Each experimental plot comprised six rows (~100 trees), with the external rows considered as buffers. The
experimental ﬁeld design comprised four irrigation treatments involving high‐quality local FW, local
WW, and two mixing ratios between the two. The four treatments were as follows: FW, M1/3 (2/3 FW –1/3 WW), M2/3 (1/3 FW ‐ 2/3 WW), and WW. Chemical parameters of the resulting irrigation water in all
four treatments during the 2017 summer are presented in Appendix A and shown in Table A1 and Figure A1.
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The irrigation system in the ﬁeld consisted of two driplines per row, located 25 cm on opposite sides of thetrunk, with 1.6 L/hr pressure‐compensated drippers (Netaﬁm Inc., Israel) at a distance of 50 cm between
drippers. The same total amount of water of 930 mm is applied in all four treatments during the irrigationseason. Irrigation generally begins in May and ends in November, during the period where there is no rainin this region. Irrigation is applied every 2 days.
All horticultural treatments (e.g., pruning, pest and weed control, and fertilization) were performed uniformly in all treatments according to the recommendations of the Israeli Extension Service (Noy, 2006).
The orchard was drip irrigated with WW since 2010. Treatments using different water qualities resulting
from different mixing ratios between FW and WW began in April 2015.

3. Experimental Data
3.1. Field Data Collection
The soil at mid‐distance between the trees was sampled at three depth ranges: 0–20, 20–40, and 40–60 cm.
Sampling was carried out in April 2016, September 2017, and September 2019. To minimize the variability
inherent in solute distribution around drippers, the soil below the drippers was sampled. Consequently,
10 locations along the rows were sampled assuming that it is enough to average soil variability and toobtain the required amount of soil needed to run all planned experiments using the same batch. This
allowed a comparative analysis of the effects of irrigation water quality on soil properties. The disturbed
soil samples were air‐dried, crushed, and sieved. The soil fraction ≤2 mm of the aggregates was kept forthe study.
Mechanical and chemical analyses were carried out at the Extension Service Laboratory of the Ministry ofAgriculture at Neve Ya'ar (Western Galilee, Israel) using standard methods to provide the relevant soil characteristics (Methods of Soil Analysis, 1996). Dendrometers (PhyTech Ltd., Rosh Haayin, Israel) were
installed in March 2017 to monitor the impact of water quality on the trees' growth rate.
Irrigation with WW affects the soil hydraulic properties, which can be estimated from inﬁltration
experiments (Assouline & Narkis, 2011). The relative changes in these properties, resulting from the different water qualities, are thus estimated based on the analysis of inﬁltration experiments carried out on
the soil samples from the different treatments. Subsequently, the estimated hydraulic properties are used
as input in the numerical simulations for assessing the impact of irrigation water quality on water
uptake.
3.2. Inﬁltration Experiments
Cylindrical columns, 13 cm long and 5.2 cm inner diameter, were used to conduct inﬁltration tests using aminidisk tension inﬁltrometer (Decagon Devices, Inc., Pullman, WA, USA). A weighted amount of air‐driedsoil was packed to attain a 10‐cm thickness, at a bulk density of ~1.4 g/cm. A thin layer of 0.30 ± 0.05 cm ofsand was put on the top of the soil surface to ensure good contact between the porous plate of the inﬁltrometer and the soil surface. The air was allowed to escape freely at the bottom of the soil column, and theexperiment was stopped before the wetting front reached the bottom of the soil column. The inﬁltration
experiments were run under a capillary pressure of Hp ¼ −0.5 cm at the surface, to avoid ﬂow along possible
large voids resulting from packing or at the contact between soil aggregates and the wall of the column.
Considering the tight ﬁt between the cross sections of the column and the porous plate of the inﬁltrometer,
we assume one‐dimensional (1D) unsaturated ﬂow conditions during inﬁltration. The cumulative volume of
inﬁltrating water, V, was monitored versus time, t. The measured V(t) curves were expressed in terms of theV ðt Þ
cumulative inﬁltration depth, F ðt Þ ¼, where A is the cross‐sectional area of the column. The inﬁltration
A
experiments were run in two or three replicates. Tap water with EC of 0.3 ± 0.03 dS/m was used in all
experiments.
3.3. Computations and Modeling3.3.1. Analytical Expressions for Inﬁltration
Brutsaert (1977) proposed an approximate solution to the Richards equation (Richards, 1931) that has beenshown to perform quite well in reproducing F(t) curves (Selker & Assouline, 2017):
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S2F ðt Þ ¼ tK s þ
βK s(

10.1029/2020WR027781

 1=2 −1 )t Ks
1− 1þβS(1)where Ks and S are the soil saturated hydraulic conductivity and sorptivity, respectively, and β is aﬁtting parameter. The expression in Equation 1 was ﬁtted to the measured F(t) data of the 2017 soil
sampling for estimating Ks by parameter optimization, thereby providing quantitative estimates of theeffect of each treatment (FW, M1/3, M2/3, and WW) on each soil depth (0–20, 20–40, and 40–60 cm)
in terms of Ks.
3.3.2. Parameter Optimization for Estimating Soil Hydraulic Properties
The Richards equation (Richards, 1931) describes accurately the inﬁltration process through soils, provided
that the initial and boundary conditions and the soil hydraulic functions, namely, the WRC, θ(ψ), and thehydraulic conductivity function (HCF), K(ψ), are known. Richards equation for transient, 1D vertical water
ﬂow, is



∂θðψÞ
∂
∂ψ
¼
K ðψÞ
þ 1 − Sðψ; z; t Þ
(2)
∂t
∂z
∂z
where θ denotes the volumetric water content, ψ denotes the capillary head, z denotes the elevation(positive upward), t denotes the time, and S denotes the sink term accounting for water uptake by plantroots.
HYDRUS‐1D (Šimůnek et al., 2005, 2016) solves Equation 2 numerically. To mimic the inﬁltration
experiment with the inﬁltrometer, a constant capillary head was used as the upper boundary condition:(ψ ¼ Hp ¼ − 0.5 cm; t ≥ 0), free drainage was assumed as the lower boundary condition, S(ψ, Z, t) ¼ 0
with no plant uptake, and the initial constant capillary head was applied to the whole proﬁle(ψi ¼ − 200 cm; t ¼ 0).
For estimating soil hydraulic properties, we used the Matlab optimization toolbox, iterating with HYDRUS‐
1D, to determine the WRC and HCF if the inﬁltration function F(t) was known. This inverse procedure wasapplied, given the known initial and boundary conditions of the 1D inﬁltration tests and the measured F(t)data of the 2017 soil sampling, to estimate the WRC of the soil after long‐term exposure to irrigation with thefour different water qualities.
HYDRUS‐1D applies the model of van Genuchten (1980) for the WRC, with two shape parameters, α (L)and n,
θðψÞ − θrSe ðψÞ ¼¼
θs − θr!

1½1 þ ðαjψjÞn 

ð1‐1=nÞ

(3)and estimates the unsaturated HCF K (Se) using the pore‐size distribution model of Mualem(Mualem, 1976):"R Se ds#2
K ðSe Þ ¼ K s Se

0:5

0 ψ

R 1 ds(4)0 ψ

where Se is the saturation degree, θs represents the saturated water content, θr the residual water content,
and Ks the saturated hydraulic conductivity.
The values of α and n, corresponding to each treatment (FW, M1/3, M2/3, and WW) were estimated by optimization. In the optimization procedure, Ks, θr, and θs were set constant. We used the Ks values estimated by
Equation 1. The saturated water content, θs, was evaluated based on the bulk density of the packed soil column (Rogowski, 1971). The value of θr was estimated based on the speciﬁc surface area corresponding to theclay content in the soil, following the approach presented in Tuller and Or (2005).
3.3.3. 2D Numerical Simulation of Irrigation
The soil hydraulic properties determined from the inﬁltration tests were then applied to simulate theimpact of long‐term irrigation with FW and WW on the water regime and root water uptake at theASSOULINE ET AL.

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avocado root zone scale under drip irrigation. The simulated results do
not aim to reproduce real ﬁeld conditions but rather focus on the aspectof soil hydraulic properties, their difference under drip irrigation with
FW and WW, and their impact on water uptake and transpiration.
Therefore, we chose not to account for the effects of salinity and related
osmotic potential on root uptake and transpiration. This aspect of irrigation with WW was simulated and discussed in detail in several otherstudies (Assouline et al., 2015; Phogat et al., 2020; Russo et al., 2009),
which showed that increased salinity in WW could contribute to reducing plant water uptake. Another simplifying assumption was that theirrigation water (FW and WW) did not affect the root zone spatial distribution. This simpliﬁcation made it possible to study the impact of soil
hydraulic properties under the same conditions in the domain (a similar
irrigation amount and root uptake sink) and similar initial and boundary conditions.

Figure 1. The effect of water qualities (FW, M1/3, M2/3, and WW) on
inﬁltration in three soil proﬁle depths (0–20, 20–40, and 40–60 cm) (soilsampling from September 2017).

The 2D module of HYDRUS (2D/3D) (Šimůnek et al., 2016) was used
to simulate the ﬂow regime in FW‐ and WW‐drip‐irrigated soil proﬁlesduring 1 month in the 2017 irrigation season from DOY 212 to DOY
243. The 2D version of Equation 2 was solved, this time considering
the root water uptake term S(ψ, z, t). The same irrigation dose wasapplied in all simulated scenarios. Potential transpiration was estimated based on the Penman‐Monteith equation (Allen et al., 1998)
using climatic data for the simulated period, assuming evaporationfrom the soil surface to be negligible, and setting most of the upperboundary condition to no ﬂux. At the location of the drip line, thetime‐variable ﬂux boundary condition was set equal to the dripper discharge rate divided by the length of this boundary. The bottom boundary condition at a depth of 200 cm was set to free drainage. A
homogeneous soil proﬁle was assumed, characterized by the soil
hydraulic properties (WRC and HCF) corresponding to the 0‐ to
20‐cm layer of the FW‐ and WW‐irrigated plots, which is the layer thatis most affected by the water quality at this stage of the experiment.
An initial constant capillary head was applied to the whole proﬁle(ψi ¼ − 100 cm; t ¼ 0). Plant water uptake (transpiration), S(ψ, z,t), was simulated following the option in HYDRUS that adopts theFeddes et al. (1978) stress function with parameters for avocado. We
considered that the root system was distributed such that it spreads
out to maximum 100 cm from the tree and penetrates to a maximum
depth of 80 cm, with the maximum density at 25‐cm depth. The
numerical water balance error was up to 0.5% for the entire durationof the simulations.

The irrigation management and design in the experimental plots were chosen as the reference case: two
drip lines per row (located 25 cm from the tree at both sides), irrigation every 2 days and dripper discharge of 1.6 L/hr (2 DL, 48 h, and 1.6 L/hr). The numerical model allowed investigating these management and design characteristics and adjusting irrigation according to altered soil hydraulic properties
following the long‐term use of WW. Speciﬁcally, we tested: (1) applying different irrigation rates (by changing the 1.6 L/hr dripper discharge rate by a factor of 2 to get 3.2 and 0.8 L/hr discharge rates) for the
proportional (shorter or longer) duration, while achieving the same irrigation dose; (2) increasing thenumber of lateral drip lines per a tree row from 2 to 4, located 15 and 65 cm from the tree on both sides;and (3) changing the irrigation frequency from one irrigation every 2 days to daily irrigation. Different
combinations were considered and are described in Table 3. The impact of these changes was expressedin terms of cumulative transpiration (root water uptake), which was considered to be directly related toyield.

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4. Results and Discussion
4.1. Impact of Irrigation Water Quality on the Inﬁltration Curves
Figure 1 depicts the effect of irrigation water quality on the measured
inﬁltration curves for four water qualities (FW, M1/3, M2/3, and WW)
and three soil depths (0–20, 20–40, and 40–60 cm). The error bars on the
FW and WW curves illustrate experimental standard deviations of thereplicates in all the experiments. At all soil depths, soil inﬁltrability is
reduced as the component of WW in the irrigation water increases andis signiﬁcantly lower in the WW‐irrigated soil. Mixing FW with 1/3 of
WW (M1/3) does not seem to have a signiﬁcant effect on soil inﬁltrability,with similar inﬁltration curves for the FW and M1/3 treatments at all
depths. The mixing of FW with WW reduces the negative impact of WW
on the inﬁltrability and, thereby, on soil hydraulic properties. The mixing
Figure 2. The effect of irrigation with FW (blue lines) and WW (red lines)
effect is stronger in deeper layers of the soil proﬁle. There is practically no
on inﬁltration in three soil proﬁle depths (0–20, 20–40, and 40–60 cm)
difference between the inﬁltration curves of irrigation water that contain(using soils from September 2017 sampling).
a component of FW at the 40‐ to 60‐cm depth. This, of course, couldchange as the period of consecutive irrigation seasons extends, because the effect of water quality with depth
can increase with the duration of the low‐quality water application.
The difference between the inﬁltration curves resulting from irrigation with FW and WW at the three depths
along the soil proﬁle is presented in Figure 2 to ease the comparison between the different curves. Two distinct groups of curves corresponding to two water qualities can be observed, indicating that water quality has
a larger impact than the soil depth. In both cases, the upper 0‐ to 20‐cm depth is more affected by water
quality. However, these ﬁndings may vary depending on site characteristics. For example, Assouline andNarkis (2011) showed that under different conditions (an older avocado orchard, more prolonged exposure
to irrigation with WW, and irrigation every 3 days), the most affected layer in the soil proﬁle was the 20‐ to
40‐cm layer.
The difference between the inﬁltration curves for the soil sampled in 2016, 2017, and 2019 from the upper
soil layer (the 0‐ to 20‐cm depth) resulting from irrigation with FW and WW are presented in Figure 3.
These results, too, reveal two distinct groups of curves corresponding to two water qualities. Additionally,
the results shown in Figures 2 and 3 indicate that the impact of water quality is stable over the seasons, thus
characterizing the soil‐plant system.
4.2. Impact of Irrigation Water Quality on Soil ESP and Ks at Different Depths
The depth distributions of ESP values corresponding to different soil proﬁles, as sampled in 2017, areshown in Figure 4 (left). An increase in the WW component in irrigation water leads to an apparent risein soil ESP. This trend was also simulated by Phogat et al. (2020) using
the multicomponent major ion chemistry module UNSATCHEM integrated into the HYDRUS‐1D software package (Šimůnek et al., 2016).
For the FW dominated water, ESP also tends to increase slightly withdepth. The impact of different water qualities on soil ESP is clear,and so is the beneﬁcial effect of mixing. It seems that the FW component has a more signiﬁcant impact on the upper soil layer (0–20 cm),while the effect of the WW component is more pronounced in the deeper layer (40–60 cm). In terms of ESP, the results show two distinctgroups: FW and the M1/3 soils in one group and M2/3 and WW inanother one.

Figure 3. Inﬁltration curves for the FW‐ (blue lines) and WW‐irrigated (redlines) soils sampled from the upper layer (0–20 cm) during 2016, 2017, and2019.

ASSOULINE ET AL.

The inﬁltration curves presented in Figure 1 were used to estimate
saturated hydraulic conductivities, Ks, characterizing the soil proﬁles,by means of Equation 1. The results are shown in Table 2 and in
Figure 4 (right). In the upper layer (0‐ to 20‐cm depth), Ks is higher
for the FW and M1/3 irrigated soils than for the M2/3 and WW soils.
Interestingly, the Ks values of the deeper layer (40–60 cm) are more
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Figure 4. Depth distribution of soil ESP (left) and corresponding estimated Ks (right) for different water qualities (FW,M1/3, M2/3, and WW).

affected by the FW component compared to the ESP values, which are more affected by the WWcomponent.
The relationship between Ks and ESP was found to be affected by the DOC concentration in the soil
(Assouline & Narkis, 2011), and more speciﬁcally, Ks was related to the ratio of (ESP/DOC) (Assouline
et al., 2016; Sposito et al., 2016). An empirical relationship was ﬁtted to data from several earlier experimentswith WW irrigation at different sites (Assouline et al., 2016). The data from this study are presented in
Figure 5 along those reported previously, as well as the ﬁtted relationship. The results from the mixing
experiment correspond to these previously reported data, and the overall trend is well represented by theﬁtted dashed curve. This provides additional support to the insight that interactions between monovalentcations and organic molecules play a major role in determining the intensity of the impact of irrigation withWW on soil properties, and especially on Ks.

Table 2Estimated van Genuchten (1980) Soil Hydraulic Parameters of Soil Irrigated With Four Water Qualities (FW, M1/3, M2/3,
and WW) at Three Soil Depths (0–20, 20–40, and 40–60 cm)
Water quality
FW

M1/3

M2/3

WW

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Soil depth

θs

(cm)
0–20
20–40
40–60
0–20
20–40
40–60
0–20
20–40
40–60
0–20
20–40
40–60

3

θr
3

3

3

(cm /cm )0.47(cm /cm )
0.15

0.47

0.15

0.47

0.15

0.47

0.15

α

n

Ks

(1/cm)
0.034
0.034
0.025
0.026
0.026
0.023
0.027
0.018
0.026
0.029
0.023
0.023

1.70
1.72
1.74
1.74
1.73
1.76
3.03
1.69
1.74
1.58
1.64
3.12

(mm/h)
5.68
6.10
4.96
5.14
4.86
4.76
1.68
3.14
4.89
2.75
3.01
1.25

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4.3. Impact of Irrigation Water Quality on Soil
Hydraulic Properties
The van Genuchten parameters for the soil WRC, α and n, for the different
treatments (FW, M1/3, M2/3, and WW) were estimated based on the inﬁltration data. The Ks values previously determined using the analyticalsolution for cumulative inﬁltration (Equation 1) were used as prescribedvalues in the optimization procedure. The results are shown in Table 2.
Increasing the WW component induces an increase in the air‐entry valueof the soil, as reﬂected by a smaller α value, and a decrease in n. A decreaseof α with depth (increase in the air‐entry value of the soil with depth) is
also observable. In two cases, (M2/3: 0–20 cm) and (WW: 40–60 cm), theresulting WRC are characterized by high n values (>3.0) that differ significantly from the relatively stable values in most of the layers. These two
Figure 5. The Ks (ESP/DOC) relationship for the soil in the experimental
distinct values are correlated with the lowest two Ks values (<2.0 mm/
site compared to previously reported data in Assouline et al. (2016) andhr). These high n values could result from the imposed low Ks values
the empirical relationship ﬁtted to these data (dashed line).
but could also result from local minima in the optimization procedures
applied (for Ks and then for α and n). The resulting hydraulic functions,ψ (Se) in Equation 3) and K (Se) in Equation 4, corresponding to the 20‐ to 40‐cm layer are depicted in
Figure 6 to illustrate the effect of water quality on soil hydraulic properties. Increasing the WW component
in the irrigation water moves basically the ψ (Se) and K (Se) functions to the right, indicating that the exposure to WW induces a “heavier” soil. In terms of K (Se), and as shown previously for the ESP values and theinﬁltration curves, the results show a distinct group for the FW and the M1/3 soils, and another one for theM2/3 and WW soils.
An important point to bear in mind with regard to the impact of irrigation water quality on the soil hydraulic properties is the large intraseasonal and interannual variability that characterizes the quality of WW for
irrigation (Figure A1). This water quality variability, coupled with high variability in annual rainfall, especially in semiarid regions, induces partial leaching of the root zone and indicates that precise simulations ofchemical interactions between water components and the clay fraction are a challenge. This variability
could explain why some studies reported that irrigation with WW had no detrimental effects on tree growthand productivity (Minhas et al., 2015; Morgan et al., 2008; Parsons et al., 2001) and soil physicochemical
properties (Andrews et al., 2016). It is important to note that these studies are in full disagreement with

Figure 6. The water retention curves (left) and the hydraulic conductivity functions (right) for the 20‐ to 40‐cm soil layer(corresponding to the parameters in Table 2) illustrating the impact of water qualities (FW, M1/3, M2/3, and WW) on soil
properties, as estimated using the HYDRUS‐1D inverse procedure.

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the results presented here and in previous studies (Assouline &Narkis, 2011, 2013; Assouline et al., 2016; Bardhan et al., 2016; Lado
et al., 2012; Noshadi et al., 2013; Paudel et al., 2016; Phogat et al., 2020;
Qian & Lin, 2019).
4.4. Simulated Water Regime in FW‐ and WW‐Irrigated Soils

Figure 7. Simulation results in terms of actual transpiration (tree water
uptake) for irrigation with FW and WW along with applied irrigationdose and estimated potential transpiration.

HYDRUS‐2D was utilized to simulate the impact of irrigation with thetwo end members of the water quality range considered herein, FW andWW, on transpiration (plant water uptake) during 1 month in the irrigation season of 2017. The hydraulic properties representing the FW andWW soils at the 0‐ to 20‐cm layer (Table 2), which is the layer that is mostaffected by the water quality at this stage of the experiment, were applied.
The results are depicted in Figure 7 in terms of cumulative actual transpiration for the simulation period, along with a corresponding amountof applied irrigation water and estimated cumulative potential transpiration estimated based on climatic data at the site. The applied irrigationdose is higher than estimated potential transpiration, representing realistic irrigation management for uptake efﬁciency and salt leaching (notsimulated here) from the root zone.

The transpiration in the FW‐irrigated soil is higher than in the WW‐irrigated soil by approximately 10%.
This, as explained previously, illustrates only the impact of soil hydraulic properties (a capillary component)and does not account for additional effects of irrigation with WW on transpiration, such as salinity (an osmotic component) and spatial distribution of the root system (which can be assumed as additive effects).
Under ﬁeld conditions, soil hydraulic properties, salinity, and the size of the root system have mutual andcomplex effects on the plant performance. The integral effect may be represented by the dendrometer readings. Measurements of trunk growth carried out using dendrometers installed on trees irrigated with different water qualities during 2019 (Figure 8) reveal a signiﬁcant impact of water quality on tree performance.
The trunk growth is maximal for irrigation with FW. Increasing the WW component in mixed irrigationwater reduces trunk growth, which is minimal for irrigation with WW. Trunk growth was shown to bestrongly related to tree transpiration and yield (Silber et al., 2019, 2013). The reduction in simulated actual
transpiration due to changes in soil hydraulic properties induced by irrigation with WW indicates that such
irrigation contributes to a decrease in tree performances and, consequently, to the drop in yield. The simulated decrease in actual transpiration of about 10% and an estimated additional decrease in actual transpiration due to higher osmotic pressure related to higher WW salinity (Assouline et al., 2015; Russo et al., 2009)
could explain the drop in yields observed in avocado orchards after long‐term irrigation with WW (Assouline
et al., 2015; Phogat et al., 2020).
4.5. Adjusting the Irrigation Management

Figure 8. The impact of water qualities (FW, M1/3, M2/3, and WW) on
trunk growth during 2019.

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Based on the results presented above in terms of soil hydraulic properties,
WW‐irrigated soils act as “heavier” more clayey soils, although theamount of clay remained unchanged (compared to the FW‐irrigatedsoils), displaying lower inﬁltrability, lower saturated hydraulic conductivity, and higher air entry value (lower α). These altered properties would
normally necessitate to change the design of the irrigation system to adapt
it to the “new soil.” The numerical solution of the ﬂow equations via
HYDRUS was further used to investigate the changes in irrigation management and design that could or should be made to mitigate the negative
effects of WW irrigation on soil hydraulic properties and improve plantwater uptake. Several irrigation designs and management parameters
were considered: irrigation frequency, dripper discharge, and the numberof drip lines per row. Seven irrigation schemes involving changes in theseparameters were simulated (Table 3). In all the simulations, the same irrigation dose was applied. The results, expressed in terms of the ratio of
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Table 3
Simulated Irrigation Scenarios
Irrigation scheme
1. 2 DL; 48 hr; 3.2 L/hr
2. 2 DL; 48 hr; 1.6 L/hr (ref.)3. 2 DL; 48 hr; 0.8 L/hr
4. 2 DL; 24 hr; 1.6 L/hr
5. 2 DL; 24 hr; 0.8 L/hr
6. 4 DL; 24 hr; 0.8 L/hr
7. 4 DL; 48 hr; 1.6 L/hr
8. 4 DL; 48 hr; 0.8 L/hr

Description
2 drip lines per row; irrigation every 48 hr; 3.2 L/hr dripper discharge
2 drip lines per row; irrigation every 48 hr; 1.6 L/hr dripper discharge
2 drip lines per row; irrigation every 48 hr; 0.8 L/hr dripper discharge
2 drip lines per row; irrigation every 24 hr; 1.6 L/hr dripper discharge
2 drip lines per row; irrigation every 24 hr; 0.8 L/hr dripper discharge
4 drip lines per row; irrigation every 24 hr; 0.8 L/hr dripper discharge
4 drip lines per row; irrigation every 48 hr; 1.6 L/hr dripper discharge
4 drip lines per row; irrigation every 48 hr; 0.8 L/hr dripper discharge

actual transpiration (plant water uptake) relative to potential transpiration, are depicted in Figure 9. The
reference scheme is the management in the experimental plots as described above: the (2 DL, 48 hr, and
1.6 L/hr) scheme (marked by a shaded rectangle).
The impact of the different schemes in terms of the spatial distribution of the water content in the simulation
domain is illustrated in Figure 10 for the reference scheme (Scheme 2: 2 DL, 48 hr, and 1.6 L/hr) and for theone presenting the best result (Scheme 7: 4 DL, 48 hr, and 1.6 L/hr), for the FW and the WW soils properties.
The water content distribution corresponds to the end of the application of the irrigation dose toward theend of the simulation period (DOY 240). The area limited by the dashed lines represents the root zone.
For the reference scheme, the WW‐irrigated soil is wetter (in agreement with the shift to a “heavier soil”),
closer to saturation below the dripper, and more water drains below the root zone in that proﬁle, comparedto the FW‐irrigated one. This indicates lower irrigation efﬁciency (as shown in Figure 9 in terms of the lower
water uptake) and possible problems of aeration in the WW‐irrigated proﬁles. Increasing the wetted area by
doubling the number of driplines (Scheme 7) reduces the differences in the spatial distribution of the watercontent between the FW and WW treatments and improves the irrigation efﬁciency in the WW‐irrigatedtreatment.
Several interesting and valuable insights can be gained from these results regarding approaches to adjust irrigation to the WW quality to reduce damages:
1. There is practically no gain in increasing the dripper discharge(Scheme 1 compared to Scheme 2, Figure 9) if the current setup of
two drip lines and irrigating every 2 days is maintained. However,using drippers with a lower discharge increases transpiration andreduces the difference between FW‐ and WW‐irrigated soils(Scheme 3 compared to Scheme 2, Figure 9), thus achieving somemitigating effect.
2. Increasing the irrigation frequency, namely, moving from one irrigation every 2 days to daily irrigation, has a signiﬁcant negative impact,
especially on the WW‐irrigated soil (Schemes 4 and 5 compared to
Scheme 2, Figure 9). The difference between actual transpiration in
the FW‐ and WW‐irrigated soils is maximal for this irrigation frequency (Schemes 4 and 5, 24 hr), independently of the dripper discharge used (1.6 L/hr in Scheme 4 and 0.8 L/hr in Scheme 5).
3. Increasing the number of driplines per row (here it was doubled,
Schemes 6 through 8) provides the best result among the simulatedcases: actual transpiration increases in both FW‐ and WW‐irrigatedsoils, and the difference between the two is signiﬁcantly reduced,
achieving the strongest mitigating effect for the schemes under consideration. Practically similar results are obtained for Schemes 7 (4 DL,
48 hr, and 1.6 L/hr) and 6 (4 DL, 24 hr, and 0.8 L/hr).
Figure 9. The effect of irrigation schemes and water qualities (FW andWW) on simulated relative (actual/potential) transpiration.

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Both the use of drippers with lower discharge rates and an increase in thenumber of driplines that increases the wetted area per tree tend to reduce
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Figure 10. Spatial distribution of the water content in the simulation domain for the reference scheme (Scheme 2: 2 DL,
48 hr, and 1.6 L/hr) and for the one presenting the best result (Scheme 7: 4 DL, 48 h, and 1.6 L/hr), for the FW and the
WW soils properties.

the local irrigation rate. This can also be achieved by increasing the number of drippers per line (drippers
every 25 cm instead at every 50 cm as it is done currently) or by using concentric loops of driplines
around the trunk. The promising results described above are in agreement with the physically based
analysis presented in Assouline (2019).
From a practical point of view (the pressure available in the system and clogging risks in drippers), the use ofdrippers with higher discharge (1.6 L/hr) is preferable. Therefore, the design aiming at a lower irrigation rateby increasing the number of drippers per tree and widening the wetted area, such as by reducing the distance
between drippers along a dripline, increasing the number of driplines per tree, or using concentric loops ofdriplines around each trunk, seems to be preferable. Therefore, we suggest this approach should be further
investigated in future experimental studies evaluating irrigation managements and designs adjusted to thelong‐term effect of irrigation with WW on soil hydraulic properties.

5. Conclusions
Several insights can be gained from the above results:
1. On the one hand, the long‐term use of WW for irrigation may deteriorate soil properties, especially if theclay content is relatively high. This, in turn, affects plant water uptake and lead to lower yields. On theother hand, we need to treat and recycle WW and use it whenever possible and appropriate. The resultspresented herein show that the M1/3 water quality (mixing 2/3 FW with 1/3 WW) allows for using WW
without causing damage to soil hydraulic properties that are evident after few years of irrigation andwithout signiﬁcantly affecting plant uptake and plant growth. Mixing FW with WW at the M1/3 ratio
allows for beneﬁting from the use of WW, namely, by reducing FW
demand, recycling nutrients, and minimizing the discharge of pollutants into waterways (Hanjra et al., 2012; Hassena et al., 2018) while
Table A1
preventing hazards.
Representative Range of Chemical Variables of FW and WW Irrigation
Water Between June to September 2017
2. The Shavei Tsion orchard was irrigated with WW before the experiment presented here was carried out. The inﬁltration experiments on
ParameterFWWWsoil samples from plots irrigated with FW or M1/3 indicate thatpH
7.4–7.5
8.1–8.2−
damages to soil hydraulic properties induced by irrigation with WW
Cl (ppm)
52–87
209–239
may be reversible. This needs to be investigated more thoroughly in
EC (dS/m)
0.8–1.0
1.5–1.6+
Na (ppm)
17–30
174–178
the future.
2+
2+
73–132
113–126
Ca + Mg (ppm)
3. It seems that the irrigation management and design can and should be
SAR (−)
0.4–0.6
4.0–4.2
adjusted to account for the changes in soil hydraulic propertiesASSOULINE ET AL.

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10.1029/2020WR027781(especially in ﬁne‐textured soils) resulting from long‐term irrigation
with WW. The best approach seems to be to reduce the irrigation rateand increase the wetted area per tree. This can be achieved by using
more drippers per dripline, more driplines per tree, more drippers
per tree (concentric loops of driplines around each trunk), and drippers with lower discharge. Increasing the irrigation frequency leads
to the worst performances in terms of plant water uptake.

Appendix A
The range of the main chemical characteristics representing the FW andWW irrigation water quality during the summer of 2017 is presented inTable A1.
The quality of the irrigation water that affect the soil properties in the context of WW irrigation is represented by the SAR, the dissolved organic
content (DOC), and the residual sodium carbonate (RSC) (Hopkins
et al., 2007), the latter evaluated as follows:
RSC ¼ HCO3 − þ CO3 2 − − Ca2 þ þ Mg2 þ (A1)RSC provides information about the potential of sodicity hazard. When
RSC is positive, calcite is predicted to precipitate, lowering the Ca concentration in the soil solution and increasing SAR (Sposito, 2008). The SAR,
DOC, and RSC values of the irrigation water along the irrigation seasonof 2016 are depicted in Figure A1. The effect of mixing on the irrigationwater is evident, especially in terms of SAR. WW is characterized by an
RSC that indicates the highest sodicity potential. This decreases graduallywith the mixing, to be the lowest for FW. It is interesting to note the large
variability in irrigation water qualities during the season. RSC for WW
increased gradually during the season and reached the maximum value
in August.
Figure A1. Parameters of water qualities (SAR, DOC, and RSC) for
different irrigation waters (FW, M1/3, M2/3, and WW) during theirrigation season of 2016. The DOC data for irrigation water M1/3 in June ismissing.

Acknowledgments
The authors are grateful to Hadar
Cohen, Rami Bar‐Ziv, Menashe Levy,
Yael Bar‐Noy, and Nimrod Wolf, fromthe Acre Experimental Station and theNorthern R&D, and the avocado teamof Shavei Tsiyon orchard for theirstrong support and technical assistance.
This research was partly supported by
the Israeli Board of Avocado.

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Data Availability Statement
The data from the experiments can be found in the Mendeley repository(https://data.mendeley.com/datasets/5sm6tj3zgk/draft?a¼aba016b7-af0f4c85-bace-28546032af6d).

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