Irrigation Science (2020) 38:235–250
https://doi.org/10.1007/s00271-020-00666-3

ORIGINAL PAPER

A smart capillary barrier‑wick irrigation system for home gardens
in arid zones
A. Al‑Mayahi1J. Šimůnek4· S. Al‑Ismaily1· A. Al‑Maktoumi1· H. Al‑Busaidi1· A. Kacimov1· R. Janke2· J. Bouma3·

Received: 5 March 2019 / Accepted: 6 February 2020 / Published online: 23 February 2020
© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Abstract
New water-conserving irrigation technologies are vital in arid countries. We investigated the effects of (i) soil substrates
made of Smart Capillary Barrier Wick (SCB-W), consisting of silt loam blocks surrounded by sand-sheathes and irrigatedwith a sand wick cylinder (WC) as compared to a control (homogenous soil irrigated by the same wick system, HW), (ii) WC
diameters (2.54 cm vs. 1.27 cm), and (iii) 2-cm sand mulch layer on soil–water dynamics during wetting–drying cycles. Field
experiments with pots and HYDRUS (2D/3D) modeling were performed in two consecutive phases (with and without sandmulch). Analysis of variance at p < 0.05 was used to assess significant differences in measured water contents, θ, between thetwo substrates. For the wetting/drying cycles, the modeled and measured θ are in satisfactory/tolerable agreement, as documented by the model evaluation criteria, which are within acceptable ranges (the root mean squared error, RMSE 0.01–0.06;
Nash–Sutcliffe coefficient, NSE 0.51–0.97, and Willmott index, d = 0.97–1). SCB-W wets the soil substrate about two times
faster than HW during the wetting cycles (p < 0.05). Reducing the WC diameter prolonged the wetting time by 1 and 2 days
for SCB-W and HW, respectively, the same trend of two times faster wetting of SCB-W compared to HW was maintained.
SCB-W showed higher θ storage (by 44.3–52.4%) at the bottom part of the composite than HW (p < 0.05). The sand mulch
layer reduced evaporation and resulted in 20 and 38.9% higher θ during the drying cycle for both the bottom and top sensors,
respectively, in both substrates (p < 0.05). SCB-W could improve water conservation in home gardens.

Introduction
Home gardens play an essential role in sustainable development, landscaping, environmental quality, and citizens’
lifestyle—health in urban ecosystems (Jim and Chen 2006;
Communicated by Robert Schwartz.
Electronic supplementary material The online version of thisarticle (https​://doi.org/10.1007/s0027​1-020-00666​-3) contains
supplementary material, which is available to authorized users.
* S. Al‑Ismaily
esmaily@squ.edu.om
1

Department of Soils, Water and Agricultural Engineering,
Sultan Qaboos University, Al‑Khoud, Muscat 123, Oman

2

Department of Crop Sciences, Sultan Qaboos University,
Al‑Khoud, Muscat 123, Oman

3

Emeritus Professor Soil Science, Wageningen University &Research, Wageningen, The Netherlands

4

Department of Environmental Sciences, Universityof California, Riverside, CA 92521, USA

Uwajeh and Ezennia 2018; Glatron and Granchamp 2018).
Due to water scarcity and aridity, citizens are discouraged
from practicing home gardening, especially in the Middle
Eastern countries (Dare et al. 2017). Additionally, in many
arid countries, including Oman, gardens are over- irrigatedusing subsidized desalinated water (Al-Mayahi 2018; AlMayahi et al. 2019). In Muscat, the capital city of Oman,
about 45% of the total household water consumption (440 m3­year−1) is used for irrigating villa (household) gardens (Kotagama et al. 2016). Ninety percent of the household watersupply in Muscat comes from government-owned and operated desalination plants. The government subsidizes 61.5%(US$ 314 million ­year−1) of the total cost of water production. The residents pay for this water based on a block tariff
scheme; users pay different amounts for different consumption levels: about US$ 1.14 m­ −3 for the consumption of less3
−1than 23 m ­month and US$ 1.43 m−3 for the use greaterthan 23 m3 ­month−1 (Kotagama et al. 2016).
Finding water-saving technologies that conserve waterand prevent high evaporation rates is vital (Miller and Buys
2008). Recently, several irrigation techniques have been

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proposed and/or tested to optimize water use for crops,
including (i) capillary barriers (Khire et al. 2000; Ityel et al.
2011, 2014; Mohammad et al. 2014; Al-Maktoumi et al.
2014; Al-Harrasi 2017; Zhang et al. 2017; Wongkaew et al.
2018); (ii) capillary wick irrigation systems where water isapplied subsurface (Fultariya et al. 2017; Semananda 2017);(iii) automated irrigation systems (Syme et al. 2004; Nemali
and van Iersel 2006; Dabach et al. 2013; Nagarajan andMinu 2018) and (iv) intelligent automation-based systems(Jain and Kamalanathan 2018).
Al-Ismaily et al. (2013) discovered a formation of aunique three-dimensional cascade of a “smart” capillarybarrier (SCB) consisting of silt blocks sandwiched by asand-filled maze of horizontal and vertical cracks inside
the reservoir bed of Al-Khoud dam in Oman (Fig. 1a). The
smartness of this naturally formed capillary barrier is due toits ability to intercept and conduct via the cracks externallyapplied water (from precipitation or irrigation), which thenreduces evaporation from the blocks due to the capillarybarrier impedance by the sand proppants. Consequently, the
SCB system ensures a rapid wetting of the blocks when the
topsoil is ponded with water, conserves much more waterin the blocks at depths of 0.5 to 1.5 m when the reservoir
is empty, and hence leads to lush vegetation as comparedto soils lacking the SCB inside the dam (Fig. 1b, c). By
experimentally mimicking the SCB in pots and soil columns(Kacimov et al. 2017; Al-Harrasi 2017), soil composites
having SCB may preserve up to 30% gravimetric soil moisture in the first 3 days of desiccation as compared with a

Irrigation Science (2020) 38:235–250

homogeneous control pot (10% gravimetric moisture), andat later time can maintain ivy and grass plants for more than
20 days of extreme summer conditions with air temperatureof 40–50 °C, and no rainfall or irrigation (Al-Maktoumi
et al. 2014).
Integrating the 3D water dynamics of the SCB with a capillary wick irrigation system may result in even more watersavings and therefore deserves further exploration. Such a
combination of SCB-wick of soil composites may have morepractical applications, specifically, in urban agriculture, i.e.,home gardening and landscaping. Several studies showed
the capillary wick system to reduce water use compared tosurface watering irrigation techniques, and this was attributed to its ability to eliminate the two main water loss pathways in irrigation, i.e., evaporation and runoff (Semananda
et al. 2018). Wick irrigation systems for potted ornamental
plants in Kenya reduced the net water use by 64% compared
to conventional overhead irrigation systems (Mungai et al.
2017). In Adelaide, Australia, the performance of wick irrigation was compared to precise surface irrigation for a system with a soil bed depth of 30 cm and tomatoes (Solanum
lycopersicum). The wicks resulted in a 50% reduction in thefrequency of irrigation events: 26 irrigation events by the
wicks, while the precision irrigation system required 40–50
irrigation events (Semananda et al. 2016). The yield of tomatoes under the former system was 62% to 73% higher thanunder the latter.
The wick system has several other advantages, besides
a higher soil water content in the root zone: improvements

Fig. 1  a Naturally morphed capillary barrier at the reservoir bed of Al-Khoud dam, b castor oil plants flourishing at the SCB site, and c deadherbaceous species elsewhere in the dam bed

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Irrigation Science (2020) 38:235–250

in plant growth and yield, retention of soil nutrients, andreduction in labor costs (Ferrarezi et al. 2015; Ferrarezi and
Testezlaf 2016; Semanada et al. 2016; Semananda 2017;
Fultariya et al. 2017; Mungai et al. 2017). For example, Leeet al. (2010) reported that a wick system, compared to theconventional overhead system, shortened working hours forwatering from 4 h to 5 min, decreasing the labor cost by
98%. Ferrarezi and Testezlaf (2016) showed that two wicks(one filled with pine bark and the other with coconut coir)outperformed the hydroponic system by increasing growth,yield, and quality of the lettuce plant, resulting in a 83%
higher leaf area, a 44% higher root volume, 58% and 24%
higher fresh and dry shoot masses, respectively, and a 35%
higher dry root mass. Nevertheless, there are disadvantagesof wicks: the root zone adjacent to the wick outlet is always
wet due to the continuous supply of water via capillarity(Muhammed 2015), and this may promote root diseases.
Despite the potential use of wick irrigation in urban gardening, few publications address the performance and designof the system, and its water use efficiency (WUE) and waterproductivity (WP) (Semananda 2018). Therefore, the objectives of this paper are to study the effects of (i) soil substrates
made of Smart Capillary Barrier Wick (SCB-W), consistingof silt loam blocks surrounded by sand-sheaths and irrigatedwith a sand wick cylinder (WC) as compared to a control(homogenous soil irrigated by the same wick system, HW);(ii) WC diameters (2.54 cm vs. 1.27 cm); and (iii) 2-cm sandmulch layer on soil–water dynamics during wetting–drying
cycles. Field experiments with pots and HYDRUS (2D/3D)(Šimůnek et al. 2016) modeling were carried out in two consecutive phases: phase II differed from phase I by having
the 2 cm sand mulch layer on top of the soil surface. We
hypothesized that the SCB-W would improve the soil–water
dynamics in the soil composite, increase water savings in thesoil, and reduce evaporative losses as compared to a control
that involved the wick irrigation system in homogenous silt
loam soil (HW).

Materials and methods
Analysis of soil properties used in SCB‑W
Two different types of soil, i.e., silt loam and sand, wereused to compose the SCB-W. The soil samples from bothtypes were air-dried and sieved (< 2 mm) and placed intoexperimental containers. Analyses were made for the soil
texture, bulk density, sand fractionation, electrical conductivity (EC), and saturated hydraulic conductivity ( Ks ) following standard procedures (Black 1965; Gee and Bauder1986; Soil Survey Staff 2014). The water retention ofsilt loam and sand was determined using a pressure plateapparatus. Specifically, θ FC was measured at the matric237

potential of − 1/3 bar (− 333 cm) and the θPWP at − 15 bar(− 15,000 cm). Obtained water retention data were used
to estimate soil hydraulic parameters of the parametricmodel of van Genuchten (1980) using the RETC code (vanGenuchten et al. 1991). A capillary rise experiment was conducted to determine the maximum capillary height and therate of the capillary rise in a transparent tube with a diameterof 2.54 cm filled with sand. This experiment guided us toselect the appropriate height of the wick cylinder used in the
SCB-W and HW systems and also to ensure that the flux ofthe imbibed water is adequate to wet the soil substrate andcompensate evaporative losses.

Experimental setup
The design of the SCB-W was based on findings of a sociological survey of home gardens in Muscat (Al-Mayahi et al.
2019). Commonly used plastic pots (the height of 37 cm,
the width of 40 cm, and the length of 40 cm) were therefore
used for the experiments. The final design of the SCB-W
involved a water reservoir at the bottom of the pot and thesoil composite at the top. The water reservoir in phase I
was maintained at a constant head of 3 cm by a float valve.
However, this technique required close monitoring and more
maintenance as the float valves were always affected by the
air/wind dust. Therefore, the experiment was improved byreplacing the float valves with a Mariotte bottle system(Fig. 2a). This system helped to control the head of waterand the supplied water volume. The Mariotte bottle works
on the same principle as a constant head method used todetermine Ks (Amoozegar and Wilson 1999). The head in
the reservoir was maintained at 3 cm, which supplied a total
volume of 3.5 L of water. The Mariotte bottle was tested in
the lab and the field and in both places, the head was always
maintained at the required level. A sturdy aluminum meshscreen was placed on the top of the water reservoir to support the soil substrate. The aluminum mesh was covered bya polyethylene mesh of a grid size of less than 0.05 mm toprevent soil particles from falling into the water reservoir(Fig. 3a). A polyvinyl chloride (PVC) wick cylinder (WC)
was perforated with 3 mm diameter holes (23 cm2 of perforation for a 263 cm2 total area of the cylinder, Fig. 3b),
wrapped with the same polyethylene mesh, placed at thecenter of the pot WCs, and then filled with a medium sand(Fig. 3c). Based on the capillary rise experiment (discussed
above), a height of 33 cm was found suitable for the WC.
That height sufficed to supply the reservoir water to thewhole profile of the soil composite.
The soil composite of the SCB-W was engineered to
mimic the SCB reported in the field study by Al-Ismaily
et al. (2013) and Al-Maktoumi et al. (2014). The SCB was
created in the pots using a stainless-steel template (28 cm
long, 15 cm wide, and 12 cm high) (Fig. 3d) that was filled

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Fig. 2  Schematic diagram forthe a SCB-W and b HW experimental set-ups for phase II

with air-dried silt loam and surrounded with a sheath of sandsimilar to that used to fill the WC (Fig. 3e). To ensure proper
packing (no air gaps and cavities), we utilized a light malletand plastic hammer. A 2 cm layer of sand mulch was placedon the top of the blocks. The template was then removed,
and the same procedure was repeated to make the upper
layer of two blocks placed on the sand layer perpendicular
to the bottom blocks. This type of stacking of the two layers makes the path of soil water through the sand sheath
more winding when our composite is subjected to typical13

rainfall, ponding or surface irrigation events. The soil ofthe control treatment pots (HW) consisted of homogeneous
silt loam. HW was irrigated with the same WC as SCB-W(Fig. 2b). Each SCB-W and HW substrate was equipped withfour ECH2O EC-5 sensors (Decagon Devices Inc., Pullman,
WA, USA), two at the top and another two at the bottom, tomeasure volumetric water content, θ, during the filling process (Figs. 2a, 3f). The sensors were connected to a datalogger Em50 (Decagon Devices Inc., Pullman, WA, USA). The
factory calibrations were used for the measurements of θ.

Irrigation Science (2020) 38:235–250

239

Fig. 3  Steps in assembling the SCB-W: a an aluminum mesh coveredby a polyethylene mesh, b a perforated WC covered by a polyethylene mesh, the top cylinder has a diameter of 2.54 cm and the bottom
cylinder of 1.27 cm, c filling the WC with medium sand, d stainlesssteel templates to create silt loam blocks, e making of silt loam blocks
and the surrounding sand using the template, and f the installation of
ECH2O EC-5 sensors in the silt loam blocks

Weather data including air temperature, relative humidity,
precipitation, solar radiation, and wind speed were collectedfrom a Meteorological Station located in the vicinity of theexperimental site (20 m away at coordinates: 23° 36′ 66′′
N 58° 9′ 49′′ E) at the Agricultural Experimental Station(AES), Sultan Qaboos University (SQU). These meteorological data were used to estimate reference evapotranspiration ­(ETo) using the Penman–Monteith equation (Allenet al. 1998). The estimated values of E
­ To were then used
as input data for the potential evaporation in the HYDRUS
atmospheric boundary conditions, while transpiration wasset equal to zero (no plants).

stage, water imbibed into the soil (both homogeneous andcomposite) laterally from the wick cylinder, with no impedance by a horizontal sand proppant layer. During this cycle,
both systems (i.e., the SCB-W and HW) were evaluated forthree criteria: water distribution, time to reach the equilibrium water content near saturation (θs= 0.47 cm3 ­cm−3), andthe amount of supplied water. After reaching a constant θregime and collecting corresponding data, the water supply
to the system was terminated, allowing for the drying cycle
to start. Both systems were evaluated for the rate of dryingand θ storage in the soil.

The effectiveness of the SCB‑W in conserving water

Numerical modeling of soil–water dynamics
and redistribution in the systems

The pot experiments were conducted to evaluate the effectof (i) the soil composite on water savings (i.e., SCB-W vs.
HW) and (ii) the size of the WC (i.e., an inner diameterof 2.54 cm vs. 1.27 cm) on the water distribution in thesoil composite of the two systems. Each treatment had three
replications, which were randomized. Two out of the three
replicates were equipped with ECH2O EC-5 sensors becauseof the limited number of sensors. The experiment was run
in two consecutive phases: phase II differed from phase I by
having a 2 cm layer of sand mulch on top of the soil surface.
Each phase consisted of a wetting and a drying cycle. During
the wetting cycle, water was supplied via wicking into thesoil substrate of the SCB-W and HW until the soil reached aconstant θ near full saturation. These values in both SCB-W
and HW were practically the same because, in the wetting

The van Genuchten–Mualem (VG-M) parameters (vanGenuchten 1980) of silt loam and medium sand are presented in Table 1, as estimated by the ROSETTA software(Schaap et al. 2001) from the laboratory analysis of the soil
texture and bulk density.
HYDRUS (2D/3D) (Šimůnek et al. 2016) was used
to simulate the pot design presented in Fig. 2a, b. In
HYDRUS, the origin (O) of the Cartesian coordinate system was at the bottom corner of the pot, i.e., at the intersection of three perpendicular no-flow planes, which are noflow boundaries (walls of the pot). In the experiment, there
were four sensors in the middle of the four blocks, as discussed above. In HYDRUS, two “observation points” wereplaced in the middle of the bottom (x = 20 cm, y = 10 cm,
z = 7 cm) and top (x = 10 cm, y = 20 cm, z = 21 cm) blocks

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Table 1  Soil properties for silt loam and medium sand: θr and θs are
the residual and saturated water contents, respectively; α and n are
shape parameters for the van Genuchten–Mualem equation; and Ks
Soil type

Silt loam
Medium sandSoil particles (%)
Sand

Silt

Clay

22
96

56
2

22
2

and L denote the saturated hydraulic conductivity and pore connectivity parameter, respectively

Bulk density(g cm−3)θr ­(cm3 ­cm−3)

θs ­(cm3 ­cm−3)

α ­(cm−1)n (–)

Ks (cm day−1)

L (–)

1.44
1.58

0.11
0.04

0.47
0.4

0.006
0.031

1.61
3.63

14.7
1191

0.5
0.5

Values of θr, θs, and Ks were measured while α, n and L were obtained by ROSETTA​

and matched the locations of the sensors in the physicalexperiment. Hysteresis in the soil–water retention curve
was employed to improve the performance of HYDRUS
simulations following the procedure described by Šimůnek
et al. (2008). We specified initial conditions associatedwith the main wetting and drying retention curves for simulations of the wetting and drying cycles, respectively. We
assumed that 𝜃sd = 𝜃sw , 𝜃rd = 𝜃rw , nd = nw and 𝛼 w = 2𝛼 d where
w and d indicate wetting and drying, respectively. Additional information about initial and boundary conditions,
iteration criteria, time-variable boundary conditions, anddomain discretization can be found in the Electronic Supplementary Material, section S1.

Sensitivity analysis
Sensitivity analysis using the “one at a time” method(Rocha et al. 2006) was carried out to determine the mostimportant VG-M soil hydraulic parameters of silt loam
and medium sand affecting the simulated θ. The results
were evaluated by calculating the root mean squared
error (RMSE) between water contents θ simulated using
the original soil hydraulic parameters obtained fromROSETTA and their 5% decrements. Our results indicatedthat θ was most sensitive to α and n VG-M parameters of
the silt loam in both HW and SCB-W systems during thewetting and drying cycles, as compared with the medium
sand (Table S2). This concurs with the results of Abbasi
et al. (2003), Rezaei et al. (2015), and Rocha et al. (2006).

Calibration of the models
The HYDRUS (2D/3D) model was evaluated and calibrated by finding optimal values only for the most sensitive parameters (α and n) of the silt loam that minimized
the RMSE, and maximized the Nash–Sutcliffe model
efficiency coefficient (NSE) and the Willmott index (d)between observed and simulated θ as follows:

13

�

∑N �

RMSE(𝛼, n) =

𝜃obs (ti ) − 𝜃sim (ti , 𝛼, n)

�2
,

(1)

�2
(𝜃obs (ti ) − 𝜃sim (ti , 𝛼, n)
�2 ,
∑N �
𝜃obs (ti ) − 𝜃 obs (ti )
i=1

(2)

i=1

N
∑N �
i=1

NSE(𝛼, n) = 1 −d(𝛼, n) = 1 − ∑�2∑N �𝜃sim (ti , 𝛼, n) − 𝜃obs (ti )
i=1
�,
� �
�
�
N
𝜃
(t
)
−
+
𝜃
(t
)
𝜃
(t
)
𝜃
(t
,
𝛼,
n)
−
�
�
�
�
obs
i
obs
i
obs
i
sim
i
i=1 �
� �
�
�

(3)

where N is the number of observations at the top and bottom point sensors for the caseand HYDRUS( of observed)
simulated 𝜃 , 𝜃obs (ti ) and 𝜃sim ti , 𝛼, (n )denote observed and( )simulated water contents at the time ti , respectively. 𝜃 obs ti
is the mean of observed water content. Other VG-M parameters of the silt loam (i.e., θs and Ks) were fixed to measured values. For the medium sand, θs, and Ks were fixed
to the measured values, while α and n were fixed equal
to the values estimated by ROSETTA due to difficulties
in measuring these two parameters. θr for both soils wereapproximated by θPWP, which is, obviously, an upper boundof the real θr. Optimization was done using two empiricalmethods. In the first method, the ranges of parameters α(0.0031 < α < 0.0368) and n (1.45 < n < 2.42) were selectedbased on their reported values in the literature (Bradford
et al. 2006; Karimov et al. 2018; Leij 1996; Liao et al. 2014;
Schneider-Zapp et al. 2010; Sillers and Fredlund 2001; Yangand You 2013; Carsel and Parrish 1988; Schaap et al. 2001;
Zhang and Schaap 2017). Then, within the selected ranges,
13 sets of α and n were obtained by varying the originalvalues (α0, n0) obtained from ROSETTA using the texture
and bulk density as inputs. We varied α and n from (α0, n0)
by ± 50% and ± 5%, respectively, unless these increments
and decrements of (α, n) went outside the bounds of theranges mentioned above. Next, each dyad of these VG-M
parameters was used in HYDRUS (2D/3D) simulations.
RMSE, NSE, and d were then calculated for each set. The

Irrigation Science (2020) 38:235–250

optimal α and n were those with the lowest RMSE and thehighest NSE and d.
For the second method, three dyads (α, n) were usedfrom the literature (Carsel and Parrish 1988; Schaap et al.
2001; Zhang and Schaap 2017): ((0.005, 1.663), (0.008,
1.780), (0.020, 1.470)). Next, the Interpolation routine of
Wolfram’s (2003) Mathematica was used. The corresponding function (quadratic parabola) has been used to generate
n-values for selected values of α. Then the dyads wereused to run HYDRUS for one case only: HW, 2.5 diameter
WC during the wetting cycle of phase I. We computed thevalues of θ at the bottom and top sensor points. After that,the experimental θ from the sensors was used to calculate RMSE, NSE, and d for the three dyads (α, n). Then
we recurred back to Mathematica. We used the Nonlinear
Fit routine with a second-order polynomial interpolant.
Optimal values of α and n were obtained by minimizing
RMSE and maximizing NSE and d. Finally, the optimized
parameters based on the first method of α and n for eachcycle and HW and SCB-W in both phases were used to run
the final simulations.

Statistical analysis
A three-way repeated-measures ANOVA was conductedto compare the effects of soil substrate (SCB-W vs. HW),
size of the WC (2.54 cm vs. 1.27 cm), and the additionof 2 cm sand mulch layer at the top of soil surface on themeasured θ ­(cm3 ­cm−3) during wetting and drying cycles.
P values < 0.05 were considered significant. The analysis

241

was carried out in Datadesk 8.1 (Data Description Inc.
Ithaca, NY, USA).

Results and discussion
Soil properties
The analysis of the particle size fractions of sand usedin this study showed that the soil is well-sorted medium
sand with d50 = 0.17 mm based on the Wentworth (1922)
and Blott and Pye (2012) scale. Table 1 summarizes soil
physical properties. The contrast in soil textures between
silt loam and medium sand is expected to create the required
capillary barrier effects (Hillel and Baker 1988; Baker andHillel 1990; Al-Harrasi 2017). Figure 4 illustrates the capillary rise in a column (diameter = 2.54 cm) of medium sand,with a zero hydrostatic pressure at the bottom. The heightof the capillary rise between t = 0.016 and 1.01 h of imbibition increased from 8 to 35 cm at a rate of 27.16 cm h­ −1(= 651.84 cm day−1). The maximum (equilibrium) heightwas 52 cm.
The soil water retention curves of silt loam and medium
sand are shown in Electronic Supplementary Material, Figure S5. The water content values at full saturation (θs), field
capacity (θFC), and the permanent wilting point (θPWP) for
silt loam were 0.47, 0.33, and 0.11 c­ m3 ­cm−3, respectively.
For medium sand, θs, θFC, and θPWP were 0.30, 0.11, and0.04 ­cm3 ­cm−3, respectively. The retention curves for thisparticular soil matched the retention curves obtained by

Fig. 4  A capillary rise (imbibition) in a vertical column (diameter of 2.54 cm) filled withan initially dry medium sandfrom the column’s bottom kept
at zero pressure. The zoomed
graph (the top panel) represents
a capillary rise over the first
one hour13242

Walker and Skogerboe (1987), Saxton and Rawls (2006),
and Yang and You (2013). The obtained θs and θPWP wereused as input parameters for the VG-M model in HYDRUS(2D/3D).
The EC of the saturated extract for the silt loam and thesand were 1.55 dS ­m−1 and 0.99 dS ­m−1, respectively.

The effectiveness of the SCB‑W in saving waterPhase I experiment
Average θ and cumulative water intakes of the SCB-W and
HW with a WC of 2.54 cm and 1.27 cm diameters during thewetting and drying cycles of phase I are shown in Fig. 5a–d.
The wetting cycle lasted 14 days from April 20th to May 3rd,
2018, while the drying cycle lasted 30 days from May 3rd toJune 2nd, 2018. The results of the wetting cycle have shownthat SCB-W wets the soil substrate two times faster than
HW. The size of the wick cylinder also affects the wetting
rate. The 2.54 cm WC wets the soil faster than the 1.27 cm
WC for both soil substrates. Specifically, the soil substratesof the SCB-W and HW systems with a WC of 2.54 cm diameter reached the equilibrium water content near saturation(0.47 cm3 cm−3) in 2 and 4 days, respectively (Fig. 5a, b).
For a 1.27 cm diameter WC, the equilibrium water content
was reached in 3 and 6 days in the SCB-W and HW systems, respectively (Fig. 5c, d). The faster wetting rate of the
SCB-W substrate can be attributed to either higher Ks of thewick (sand’s Ks is 85 times greater than that of silt loam, see
Table 1) or other factors (e.g., the entrapped air).
The ANOVA results are presented in Table 2. In the wetting cycle, the bottom sensor showed a significant interaction
effect between the soil substrate and the time of wetting,which affects soil water dynamics (p < 0.05). In contrast
to that, no significant interaction effects were recorded forsoil substrate and the time of wetting for the top sensors(p = 0.858). However, for the top sensor, the wick diameterand time showed a significant interaction (p < 0.05). Thismeans that differences in the substrates, as well as wicksizes, have resulted in differences in wetting the soil overtime. Figure 6a clearly illustrates that SCB-W wets the soil
faster than HW, while Fig. 6b shows faster wicking/wettingof the substrates with an increase in the diameter size of
the WC.
Sand facilitates the propagation of the ascending water
into the blocks from all sides, i.e., sand increases the blocks’
surface area exposed to wetting. Mein and Farrell (1974)
demonstrated analytically using the Green–Ampt model thatpore water rises lower but faster in sandy textured soils thanin fine soils. The HW silt loam is wetted through the wick
cylinder only, which makes saturation time longer.

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Irrigation Science (2020) 38:235–250

At the end of the wetting cycle, average cumulative water
intakes were almost the same, approximately 40–42 L, forall treatments (Fig. 5a–d).
However, a significant effect of the soil substrate on themean θ at the bottom (p < 0.05) layer was observed in thedrying cycle (Table 2). The sensor in the bottom layer of
SCB-W showed a significantly higher θ (44.3%) over thewhole drying cycle than in HW (p < 0.05) (Table 2 andFig. 6c). The bottom sensor showed that bottom blocksof the SCB-W pots dried at a slower rate, 0.009 cm3 ­cm−3­day−1. For example, in 15 days, θ in the bottom blocks of
the SCB-W system dropped by only 0.14 cm3 ­cm−3 fromnear saturation (from 0.47 to 0.33 cm3 ­cm−3) (Fig. 5b, d).
During the same time, θ in the bottom part of HW dropped
from 0.47 to 0.18 cm3 ­cm−3. Even after 25 days of the drying cycle, θ in the bottom blocks of the SCB-W system was
0.27 cm3 ­cm−3, which is just 0.06 cm3 ­cm−3 below the field
capacity of silt loam (θFC= 0.33 cm3 ­cm−3) (Fig. 6c) despiteof the continuous drying and the harsh weather conditions
recorded for April and May with high air temperatures ranging between 40 and 45 °C and low RH of air in the rangeof 20–40%. Consequently, the reference evaporation rate
ranged from 0.63 to 1 cm day−1 (Electronic Supplementary Material, section S2). On the other hand, θ dropped
sharply in the HW to reach 0.18 cm3 ­cm−3 during the sametime under the same conditions. However, the top part of
the SCB-W was drying faster than the same part of the HWover time (p < 0.05) (Table 2 and Fig. 6e). Consequently, themean θ measured by sensors in SCB-W was significantly
lower than those in HW (p < 0.05) (Table 2).
The water contents under the HW treatment attained ahigher equilibrium level before the drying phase, but afterthat declined more quickly compared with the SCB-W treatment. For example, after 15 days from the beginning of thedrying cycle, the top block sensor of the SCB-W indicatedthat θ has dropped from near saturation to 0.15 cm3 ­cm−3,
whereas for HW θ has dropped to 0.18 cm3 ­cm−3 (Fig. 5a–d).
The horizontal sand layer caused a discontinuity of watersupply between the bottom and top blocks that reduced
evaporative losses from the soil surface of the SCB-W. This
resulted in two things: first, more water was saved in the bottom blocks of SCB-W, and second, faster drying of the top
blocks of SCB-W as compared to the top part of HW where
evaporated water was replenished from the bottom part.
The diameter size of the wick cylinder has no effect onθ during the drying stage as no water is wicked through thecylinder.
Phase II experiment
The wetting and drying cycle of phase II lasted for 9 days
(5th to 14th of June 2018) and 28 days (June 14th to July
12th, 2018), respectively (Fig. 7a–d). As with phase I, theIrrigation Science (2020) 38:235–250

Fig. 5  Measured vs. simulated θ ­(cm3 ­cm−3) for the wetting and drying cycles and cumulative water intake (L) for the wetting cycle during phase I: a HW with a 2.54 cm WC, b SCB-W with a 2.54 cm

243

WC, c HW with a 1.27 cm WC, and d SCB-W with a 1.27 cm WC.
Error bars are standard deviation

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244
Table 2  Three-way repeatedmeasures ANOVA for theeffects of soil substrate, sizeof WC, and sand mulch layeron the measured θ ­(cm3 ­cm−3)
during wetting and drying
cyclesIrrigation Science (2020) 38:235–250
Location of sensor
Bottom sensor point during wetting cycle

Top sensor point during wetting cycle

Bottom sensor point during drying cycle

Top sensor point during drying cycle

SCB-W system again demonstrated a two times faster rate ofwetting than the HW system, regardless of the diameter ofthe WC (Fig. 7a–d). Similar to phase I, reducing the diameter of the WC from 2.54 to 1.27 cm resulted in prolonging
the time to reach the equilibrium water content near saturation by 1 and 2 days for the SCB-W and HW systems,
respectively (Fig. 7a–d).
However, in phase II, cumulative water intakes for all
treatments were 15–22 L lower than those in phase I. This isbecause the substrates in phase II still had relatively higher
θ stored from phase I. Substrates with the 2.54 cm WC consumed about 27 L (Fig. 7a, b) at the end of the wetting cycle
whereas substrates with the 1.27 cm WC consumed around
16.5–18 L (Fig. 7c, d). The initial condition of θ in the soil
substrates of phase I was 0.11–0.19 cm3 ­cm−3 (Fig. 5a–d),
whereas it was 0.16–0.25 cm3 ­cm−3 for phase II (Fig. 7a–d).
The addition of a 2 cm sand mulch layer on the top of thesoil substrates resulted in a significant decrease of evaporation, hence saving more water in the soil, particularly in the
top layers of the SCB-W and HW (p < 0.05) (Table 2). The
bottom sensors, as well as the top sensors in both substrates,

13

Effects

p value

Soil substrate
Wick diameter
Sand mulch layer
Soil substrate × time
Wick diameter × time
Sand mulch layer × time

0.182
0.507
0.508
< 0.050.085
0.124

Soil substrate
Wick diameter
Sand mulch layer
Soil substrate × time
Wick diameter × time
Sand mulch layer × time

0.085
0.066
0.213
0.858
< 0.05< 0.05

Soil substrate
Wick diameter
Sand mulch layer
Soil substrate × time
Wick diameter × time
Sand mulch layer × time

0.018
0.791
0.282
< 0.051< 0.05

Soil substrate
Wick diameter
Sand mulch layer
Soil substrate × time
Wick diameter × time
Sand mulch layer × time

< 0.050.784< 0.05< 0.051< 0.05recorded 20 and 38.9%, respectively, higher θ throughout
the drying cycle of phase II than those of phase I (p < 0.05)(Table 2, Fig. 6d, f).
The rate of drying for the HW system and the top partof the SCB-W system in phase II was 1.4 times slower
compared to phase I. Rates of drying for the HW system and the top part of the SCB-W system in phase I andphase II were 0.02 cm3 ­cm−3 ­day−1 and 0.014 cm3 ­cm−3­day−1, respectively (Figs. 5a–d, 7a–d). As a result, θ inthe HW system and the top part of the SCB-W system
dropped by 0.21 cm3 ­cm−3 from near saturation (from 0.47
to 0.26 cm3 ­cm−3) during the 15 days from the beginningof the drying cycle. After 22 days of drying and thereafter,
θ in the HW soil substrate and the top blocks of SCBW
soil substrate remained constant at 0.20 cm3 ­cm−3. During the same period, θ of the bottom blocks in the SCB-W
substrate dropped by only 0.15 cm3 ­cm−3 and was 0.32 cm3­cm−3 (Fig. 7b, d). After 28 days of the drying cycle, θ ofthe HW substrate and top part of the SCB-W substrate
was 0.19 cm3 ­cm−3 whereas θ of the bottom blocks of the
SCB-W substrate was 0.27 cm3 ­cm−3.

Irrigation Science (2020) 38:235–250

245

a 0.5

b 0.5

0.4

ϴ (cm3 cm-3)

ϴ (cm3 cm-3)

0.45

0.35
HW
SCB-W

0.3
0.25
0.2
0

1

2

3

4

5

6

7

8

9

0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0

WC (1.27cm diameter)
WC (2.54cm diameter)

0

10

1

2

3

4

c

d

0.5

HW
SCB-W

0.4

ϴ (cm3 cm-3)

ϴ (cm3 cm-3)

0.45
0.35
0.3
0.25

0.1

10

0.4

Without Sand layer

0.3
0.25

20

25

0.1

30

0

5

10

Time (day)

15

20

25

30

Time (day)

e

f
0.5

0.5

0.4

HW

0.35

SCB-W

0.3
0.25
0.2

ϴ (cm3 cm-3)

0.45

0.45

ϴ (cm3 cm-3)

9

0.35

0.2

15

8

With Sand layer

0.15
10

7

0.5

0.2

5

6

0.45

0.15
0

5

Time (day)Time (day)0.4With Sand layer

0.35

Without Sand layer

0.3
0.25
0.2

0.15

0.15

0.1
0

5

10

15

20

25

30

Time (day)

0.1
0

5

10

15

Time (day)

20

25

30

Fig. 6  Comparison of mean θ ­(cm3 ­cm−3) (n = 8) with time (day) andthe effects of a soil substrate during the wetting cycle; bottom sensor
point, b the size of WC during the wetting cycle; top sensor point, csoil substrate during the drying cycle; bottom sensor point, d additionof the 2 cm sand mulch layer on the top of the soil surface during thedrying cycle; bottom sensor point, e soil substrate during the drying
cycle; top sensor point, f addition of the 2 cm sand mulch layer on the
top of the soil surface during the drying cycle; top sensor point. Error
bars are standard deviations

HYDRUS (2D/3D) modelingand simulated θ is acknowledged when the values of NSE
and d are close to 1, while the RMSE value is close to 0
(Rezaei et al. 2015; Brunetti et al. 2016; Silva Ursulino
et al. 2019). In this study, the values of RMSE, NSE, andd before optimization were in the range of 0.04 to 0.16,− 8.45 to 0.94, and 0.88 to 0.99, respectively (Table S4).
After optimization, RMSE ranged between 0.001 and 0.06,
NSE between 0.51 and 0.97, and d between 0.97 and 1.

Model evaluation and calibration
Optimized values of α and n for silt loam in the HW
and SCB-W and their corresponding model evaluation
parameters between simulated and measured θ are shownin Table 3. An acceptable agreement between measured

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Fig. 7  Measured vs. simulated θ ­(cm3 ­cm−3) for the wetting and drying cycles and cumulative water intake (L) for the wetting cycle during phase II: a HW with a 2.54 cm WC, b SCB-W with a 2.54 cm

13

Irrigation Science (2020) 38:235–250

WC, c HW with a 1.27 cm WC, and d SCB-W with a 1.27 cm WC.
Error bars are standard deviations

Irrigation Science (2020) 38:235–250
Table 3  Summary of optimized
α ­(cm−1) and n (–) for silt loamin the HW and SCB-W andtheir corresponding model
evaluation parameters ofagreements between simulated
and measured θ

247
Cycle
First empiric method
Phase I
WettingDryingPhase II

WettingDryingSecond empiric method
Phase I
Wetting

Soil composite

αn

RMSE ­(cm3­cm−3)NSEd

HW (2.54 cm)
HW (1.27 cm)
SCB-W (2.54 cm)
SCB-W (1.27 cm)
HW (2.54 cm)
HW (1.27 cm)
SCB-W (2.54 cm)
SCB-W (1.27 cm)
HW (2.54 cm)
HW (1.27 cm)
SCB-W (2.54 cm)
SCB-W (1.27 cm)
HW (2.54 cm)
HW (1.27 cm)
SCB-W (2.54 cm)
SCB-W (1.27 cm)

0.01
0.01
0.02
0.01
0.01
0.003
0.03
0.02
0.01
0.01
0.02
0.02
0.01
0.01
0.02
0.02

1.77
1.69
1.85
1.69
1.61
1.45
2.34
1.93
1.77
1.69
1.93
2.09
1.69
1.77
2.01
2.09

0.05
0.05
0.02
0.01
0.04
0.05
0.04
0.02
0.01
0.03
0.03
0.05
0.04
0.06
0.02
0.02

0.74
0.78
0.92
0.94
0.89
0.77
0.73
0.81
0.97
0.90
0.76
0.58
0.63
0.51
0.94
0.91

0.97
0.98
0.99
1.00
0.99
0.98
0.98
0.99
1.00
0.99
0.99
0.97
0.98
0.97
1.00
1.00

HW (2.54 cm)
Obj. RMSE
Obj. NSE
Obj. d

0.01
0.01
0.01

1.85
1.85
1.84

0.00
0.00
0.03

1.00
1.00
0.73

1.00
1.00
0.99

RMSE root mean square error, NSE Nash–Sutcliffe model efficiency coefficient, d Willmott coefficient, Obj
objective function for optimization. Optimization was performed on the bottom sensor point for all treatments except SCB-W (1.27 cm) during phase

Modeling of phases I and II
The simulated θ as a function of time for all models inboth phases was in a close agreement with the measured
θ of the wetting cycles as discussed above and as shown in
Figs. 5a–d, 7a–d. However, there was a 0.14 m3 ­m−3 difference in the accuracy between the simulated and measured θ in the drying cycles, which may have arisen due tosensors’ inaccuracies. ECH2O EC-5 devices require a sitespecific calibration and may even need individual calibrations because of a sensor to sensor variability. Devices are
influenced by the EC of the soil and clay content (Bogena
et al. 2007; Rosenbaum et al. 2010, 2011; Schwartz et al.
2013). The discrepancy may also be attributed to the simplifications used in the HYDRUS modeling, mainly in (i)the description of hysteresis and (ii) assumption made thatthe soil is homogeneous (we ignored all possible local heterogeneities due to the packing process). The model also
did not consider the effects of diurnal variation of temperature in the field which might affect the movement ofwater, especially through water viscosity, surface tension,and contact angle, which are involved in Ks, n, and α (Grant
and Bachmann 2002; Or and Tuller 2005). The temperatureeffect is, however, generally very small and not measurable.

Another reason for the discrepancy may also be attributed
to the errors resulted from averaging of replicates in theexperiments. The limitations of the modeling approach, as
discussed, illustrate the importance of executing the typesof experiments as described, which themselves have various
constraints (e.g., budget, technical, and time).

Concluding remarks
The SCB-W engineered system is a promising technique forhome gardens in arid zones. HYDRUS (2D/3D) simulationsof θ described the wetting cycle well, but modeled drying
with varying degrees of accuracy (as much as a 0.14 m3 ­m−3
difference). Therefore, hysteresis is very difficult to incorporate into simulations. The field experiments and numericalmodeling have shown the advantages of SCB-W over thecontrol treatment (HW) during the wetting and drying cycles
in phases I and II.
First, during the wetting cycle of both phases, SCB-W
showed its ability to wet the soil substrate two times fasterthan HW. Second, reducing the diameter of the wick cylinder(WC) from 2.54 to 1.27 cm resulted in prolonging the timeneeded to attain equilibrium of θ near saturation by 1 day for

13

248

the SCB-W system and 2 days for the HW system. However,even with this delay, SCB-W still wets the soil substrate
two times faster than HW. This faster wetting dynamic by
SCB-W is, apparently, due to the sand proppant surrounding
the blocks. This extra proppant increased blocks’ surface
area in contact with the almost saturated sand during thewetting cycle that intensifies the process of imbibition into
these blocks.
Third, SCB-W stored a significantly higher quantityof soil water (44.3–52.4% more) in the bottom layer than
HW throughout the drying cycle due to the capillary barrier, which caused a discontinuity in the media. Ultimately,
the capillary barrier reduced evaporative losses and hence
yielded higher soil–water storage. This difference in stored
water was maintained even after more than 25 days of continuous drying under extreme summer conditions in the open
field with air temperatures ranging between 40 and 45 °C,
dry air with RH ranging between 20 and 40%, and high reference evaporation rates of 0.63–1 cm day− 1. The placementof a 2 cm sand mulch layer on top of the soil significantly
reduced evaporation and yielded a 20–38.9% higher θ inboth substrates over the course time of the drying cycle ofphase II as compared to phase I. Even with this improvement, SCB-W stored more water, especially in its bottom
layer, than HW. These results suggest that the applicationof SCB-W for saving water in home gardens, particularly
in desert climate countries, would be beneficial. Future
research should focus on testing the ability of water-saving
by SCB-W with cultivated plants.
Acknowledgements This work was funded by a grant from the Sultan
Qaboos Higher Center for Culture and Science—Diwan of Royal Court
and the Research Council of Oman (TRC) (RC/AGR/SWAE/17/01).
The authors appreciate the additional in-kind support from SQU,
Oman. Special thanks also go to Ms. Marwah Al-Batashi, Mr. Mohammed Al-Belushi, Mr. Hamid Al-Handhali, and Mr. Khalifa Al-Hinai
for their help in the experiments. Thanks to Dr. Michel Claereboudt
for guidance with the statistical analysis. Helpful comments, critique,and editing by three anonymous referees and editors are appreciated.

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