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Water Resources Research
RESEARCH ARTICLE
10.1002/2014WR015409

Special Section:
Eco-hydrology of Semiarid
Environments: Confronting
Mathematical Models with
Ecosystem Complexity
Key Points:
 Evaporation was quantiﬁed in a
drip-irrigated desert vineyard Speciﬁc focus was on spatial andtemporal variations across theinter-row
 Adapted HYDRUS (2-D/3-D) model
successfully simulated evaporation
patterns

Correspondence to:
A. Ben-Gal,
bengal@volcani.agri.gov.il
Citation:
Kool, D., A. Ben-Gal, N. Agam, J.
Simůnek, J. L. Heitman, T. J. Sauer, and
N. Lazarovitch (2014), Spatial anddiurnal below canopy evaporation in adesert vineyard: Measurements andmodeling, Water Resour. Res., 50,
doi:10.1002/2014WR015409.

Spatial and diurnal below canopy evaporation in a desert
vineyard: Measurements and modeling3, J. L. Heitman4, T. J. Sauer5, and N. Lazarovitch2

D. Kool1,2, A. Ben-Gal1, N. Agam2, J. Simůnek
1
Gilat Research Center, Agricultural Research Organization, Institute of Soil, Water and Environmental Sciences, Gilat,
Israel, 2Wyler Department for Dryland Agriculture, French Associates Institute for Agriculture and Biotechnology of
Drylands, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus,
Midreshet Ben-Gurion, Israel, 3Department of Environmental Sciences, University of California, Riverside, California, USA,
4
Department of Soil Science, North Carolina State University, Raleigh, North Carolina, USA, 5National Laboratory for
Agriculture and the Environment, Agricultural Research Service, United States Department of Agriculture, Ames, Iowa, USA

Abstract Evaporation from the soil surface (E) can be a signiﬁcant source of water loss in arid areas. In
sparsely vegetated systems, E is expected to be a function of soil, climate, irrigation regime, precipitation
patterns, and plant canopy development and will therefore change dynamically at both daily and seasonal
time scales. The objectives of this research were to quantify E in an isolated, drip-irrigated vineyard in anarid environment and to simulate below canopy E using the HYDRUS (2-D/3-D) model. Speciﬁc focus was onvariations of E both temporally and spatially across the inter-row. Continuous above canopy measurements,
made in a commercial vineyard, included evapotranspiration, solar radiation, air temperature and humidity,
and wind speed and direction. Short-term intensive measurements below the canopy included actual andpotential E and solar radiation along transects between adjacent vine-rows. Potential and actual E below the
canopy were highly variable, both diurnally and with distance from the vine-row, as a result of shading anddistinct wetted areas typical to drip irrigation. While the magnitude of actual E was mostly determined bysoil water content, diurnal patterns depended strongly on position relative to the vine-row due to variableshading patterns. HYDRUS (2-D/3-D) successfully simulated the magnitude, diurnal patterns, and spatial distribution of E, including expected deviations as a result of variability in soil saturated hydraulic conductivity.

Received 5 FEB 2014
Accepted 27 JUL 2014
Accepted article online 30 JUL 2014

1. Introduction
About half of the Earth’s land surface is currently considered water limited, and rising food demand is
expected to continue to increase pressure on water resources [Yermiyahu et al., 2007]. In water-limited environments, where evapotranspiration (ET) typically accounts for >95% of the water budget [Wilcox et al.,2003], evaporation from the soil surface (E) can be a substantial source of water loss. Studies on ET partitioning report that, for a range of different cover types, E accounts for 20–40% of ET on average [Kool et al.,2014]. In addition to concerns over diminishing water resources, the dynamics of E versus transpiration (T)also play an important role in climate processes through moisture, CO2, and energy exchange with theatmosphere [Scott et al., 2006; Lawrence et al., 2007]. Accurate understanding of E relative to T is therefore
critical to better identify productive and unproductive allocation of water as well as associated effects on cli~uelas et al., 2009].
mate and climate change [Newman et al., 2006; Pen
While the dynamics of E have been studied extensively for bare soils, quantiﬁcation of E in vegetated systems remains challenging [Kustas and Agam, 2014; Kool et al., 2014]. The presence of plants alters the microclimate and changes soil water status due to root water uptake [Kumar et al., 2013, 2014]. In row-crops andorchards, E differs depending on row orientation, row width, plant size and architecture, and water application as a result of under-canopy wind patterns [Heilman et al., 1994; Cammalleri et al., 2010], shade effects[Horton et al., 1984; Horton, 1989; Ham and Kluitenberg, 1993; Colaizzi et al., 2010; Pieri, 2010], and soil water
distribution across the inter-row [Agam et al., 2012]. Furthermore, the magnitude of E relative to ET varies
daily and seasonally depending on soil-plant interactions [Massman and Ham, 1994; Newman et al., 2006].
Drip-irrigated wine vineyards in desert areas are high value cropping systems that warrant separate assessment of E [Yunusa et al., 2004; Basile et al., 2012; Ortega-Farıas et al., 2012]. Surplus water in vineyards can

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cause unnecessary or excessive vegetative growth, increased disease pressure, and low grape quality [Netzer et al., 2009]. Therefore, while grape production in desert areas is not feasible without irrigation, optimal
water application is essential both to conserve resources and to enhance fruit quality [Yunusa et al., 2004;
Basile et al., 2012]. Reported fractions of E/ET in drip-irrigated vineyards vary widely, ranging from 0.13 [Ferreira et al., 2012] to 0.41 [Yunusa et al., 2004], and little is known concerning the spatial or diurnal variability
below the canopy that affect ET partitioning. Since bare soil makes up the largest part of the vineyard surface area due to wide row spacing and vine training practices, the soil surface has a relatively large contribution to the energy and water balances [Ham et al., 1991; Heilman et al., 1994]. Detailed information
regarding E can support management decisions to improve water productivity and optimize vine growth[Ferreira et al., 2012].
Numerical models are ideally suited to study the importance of, and interaction among, the various transient variables that determine E. HYDRUS (2-D/3-D) [Simůnek et al., 2008] is a widely used numerical model
simulating water ﬂow in the soil, which has been previously used to study components of ET in, for example, cotton [Bufon et al., 2012] and pecans [Deb et al., 2013]. However, the model has never been validated
for accurate partitioning between E and T. Similar to other drip-irrigated row crops and orchards, the main
variables determining E in a drip-irrigated vineyard are expected to be soil water content and atmosphericboundary conditions, which are subject to shading and therefore not uniform across the inter-row. HYDRUS(2-D/3-D) has been found to successfully simulate wetting patterns under drip irrigation [Skaggs et al., 2004;
Lazarovitch et al., 2009; Hinnell et al., 2010] but requires adaptation to incorporate and allow for spatial variability in boundary conditions at the soil surface. The objectives of this study were to quantify E in an isolated, drip-irrigated vineyard in an arid environment and to employ HYDRUS (2-D/3-D) to simulate below
canopy E while considering variability in time and space.

2. Methods
2.1. Site Description
An experiment was conducted in a commercial vineyard under drip irrigation, located in Israel’s arid central
Negev highlands (30.7 N, 34.8 E). Long-term temperature averages range from 4.4 to 14.8 C in January and18.1 to 32.7 C in July. Average annual precipitation is <100 mm, falling erratically between November andApril (Israel meteorological service). The 10 year old Cabernet Sauvignon (Vitis vinifera L.) vineyard formed
an isolated irrigated area in a dry bare surrounding with ﬁeld dimensions of approximately 430 3 230 m(10 ha) in the north-south and east-west directions, respectively. Row orientation was approximately
north-south. The vines were planted 1.5 m apart, with 3 m spacing between rows, and were trained on a
vertical-shoot-positioned system, with 1 m cordon height. At full maturity, the canopy was 2 m high with
17% of the total soil surface area shaded beneath the canopy at solar noon.
2.2. Experimental Setup
A micrometeorological station was located at the south-east side of the vineyard with a fetch ranging from
200 to 500 m depending on the instantaneous wind direction. The prevailing wind direction at the site isnorth-west. Data were collected continuously as part of a larger experiment that spanned over the 2011–2012 growing seasons. Standard meteorological measurements above the canopy included wind speed
and direction (Wind Sentry, R.M. Young, Traverse City, MI), air temperature and relative humidity (HMP45C,
Vaisala Inc., Woburn, MA and 10-Plate Gill Radiation Shield, R.M. Young, Traverse City, MI), solar radiation (LI200SA Pyranometer, Li-Cor Biosciences, Lincoln, NE), and precipitation (TE525 tipping bucket rain gage,
Texas Electronics Inc., Dallas, TX). A water meter (WMR, Arad Ltd., Dalia, Israel) was used to monitor irrigation
events. As part of the energy budget, net radiation was measured at 5 m height (Q*7, Radiation and Energy
Balance Systems, Seattle, WA). Soil heat ﬂux was computed as a weighted average of measurements at ﬁve
positions across the inter-row, using ﬁve ﬂux plates (HFT1.1, Radiation and Energy Balance Systems, Seattle,WA) at 0.06 m depth, and accounting for heat storage using thermocouples at depths of 0, 0.015, 0.045, and0.06 m adjacent to each plate [Sauer, 2002]. Data were logged at 10 s intervals, and 15 min averages werestored using CR23X and CR5000 data loggers (Campbell Scientiﬁc Inc., Logan, UT). In addition, hourly windspeed, temperature, and humidity, at 2 m height, were retrieved from a weather station located 1 km to thenorth of the experimental site, in order to compute reference ET (ET0) according to the FAO Penman-Monteithmodel [see details in section 2.3.2, Allen et al., 1998].

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a

b

c

0

0.3

0.8

1.5

2.2

2.7

3m

Figure 1. Below canopy measurement setup for intensive observation periods (a) with a row of pyranometers in the back, (b) a row of
microlysimeters, and (c) a row of micropans in front.

Vineyard ET and sensible heat ﬂuxes were determined using an eddy covariance system (CSAT 3-D sonicanemometer, Campbell Scientiﬁc Inc., Logan, UT; with an open path infrared gas analyzer, LI-7500, Li-Cor
Biosciences Inc., Lincoln, NE) mounted 2 m above the plant canopy, facing north-west. Data were recorded
at 10 Hz using a CR5000 data logger (Campbell Scientiﬁc Inc., Logan, UT). Postprocessing of the eddy covariance data included despiking according to the algorithm developed by Goring and Nikora [2002], correction
for humidity and crosswind effects on sonic temperature [Schotanus et al., 1983; Liu et al., 2001], 2-D coordinate rotation correction [Tanner and Thurtell, 1969], frequency response correction [Massman, 2000], andthe correction for buoyancy effects described by Webb et al. [1980].
Below canopy measurements were conducted at six positions across the inter-row (Figure 1) during two
intensive observation periods (IOPs) toward the end of the season when the canopy was fully developed(3–5 and 22–24 July 2012). Below canopy E was measured using microlysimeters (MLs). The MLs were
100 mm deep with a diameter of 110 mm and were made of PVC to minimize heat conduction away fromthe surface [Evett et al., 1995]. Undisturbed soil cores were kept in the ﬁeld for less than 48 h to maintainrealistic bottom boundary conditions [Boast and Robertson, 1982] following the procedure described byAgam et al. [2012]. As MLs cannot be used during or immediately after irrigation due to changing boundary
conditions, measurements could only be taken on days following an irrigation event. Since irrigation wasapplied every other day, ML measurements were only conducted for 1 day during each IOP. The MLs were
pushed into the soil, excavated, capped to prevent losses other than evaporation, weighed and placed in apreformed hole with the same position relative to the vine-row as the original sample location. The installed
MLs were removed and weighed hourly from predawn to after sunset. The scale’s resolution was 0.1 g,
which corresponded to 0.011 mm. Two replicates for each position, with a total of 12 MLs, were sampledduring IOP1. Because of the high variability in E observed at the position directly under the vine during
IOP1, the ML setup for IOP2 was adjusted to one replicate for positions 0.8, 1.5, and 2.2 m, two replicates for
positions 0.3 and 2.7 m, and six replicates for the position directly underneath the vine. The MLs directly
under the vine represented distinctly wet and distinctly dry areas (each with three MLs). Variations in atmospheric conditions across the inter-row were investigated by measuring solar radiation (Rs) using pyranometers (CMP3, Kipp & Zonen, Delft, Netherlands) and potential E using micropans. The micropans were
designed similar to the MLs except that the cylinder was ﬁlled with water. A reference micropan was
installed in an open nonshaded area in the vineyard. Pyranometer data were logged at 10 s intervals, and15 min averages were stored using a CR10 data logger (Campbell Scientiﬁc Inc., Logan, UT). The micropans
were weighed hourly at a resolution of 0.05 g, corresponding to 0.006 mm, with two replicates for eachposition. Thermal images were acquired hourly using a thermal camera (FLIR T335, FLIR Systems Inc., USA).
2.3. Modeling2.3.1. Model Adaptation
The HYDRUS (2-D/3-D) model numerically computes evaporation and inﬁltration ﬂuxes across the surface interface where the ﬂux is limited either by a predeﬁned potential ﬂux or by the soil moisture

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conditions [Simůnek et al., 2008]. In its two-dimensional form, the surface boundary condition is given
as follows:
 
 


K K A @h 1K A ni   Epot
(1)
ij
iz


@xj
and
hA  h  hs

(2)

where K is the unsaturated hydraulic conductivity (m/h), KijA are components of a dimensionless anisotropy
tensor KA, h is the soil surface pressure head (m), xi (i 5 1, 2) are the spatial coordinates (x, z) in (m), ni arecomponents of the outward unit vector normal to surface boundary, Epot is the maximum or potential rateof either inﬁltration or evaporation based on atmospheric conditions (m/h), and hA and hS are the lower
and upper limits for h, respectively. The value of hA can be determined from the equilibrium conditions
between soil water and atmospheric water vapor pressure, whereas hS is generally set to zero, assuming
there is no surface ponding.
The standard version of HYDRUS (2-D/3-D) calculates actual E from Epot and soil moisture status for boundaries with an ‘‘Atmospheric’’ boundary condition (BC), for which potential ﬂuxes of precipitation and evaporation have to be speciﬁed. The standard version of HYDRUS (2-D/3-D) also allows one ‘‘Time-Variable Flux’’
BC to be treated as an atmospheric BC, i.e., with limited pressure heads (equation (2)). For this study, inwhich Epot was variable across the soil surface, HYDRUS (2-D/3-D) was modiﬁed so that it could treat up to
four different Time-Variable Flux BCs as Atmospheric BCs, i.e., with actual ﬂuxes limited by the soil moisture
conditions.
Water distribution in a drip-irrigated system can be considered either radially symmetrical around a dripper,
fully three-dimensional [Kandelous et al., 2011], or essentially two-dimensional due to close spacing andoverlapping of wetting from adjacent emitters. Concurrently, Epot at the soil surface is predominantly twodimensional, varying along the cross section between two rows. For simpliﬁcation purposes, the drip laterals were assumed to be line sources, thus allowing two-dimensional modeling. Hourly measurements of E
acquired during the IOPs were used to calibrate (ﬁrst IOP, 3–5 July) and validate (second IOP, 23–25 July)
the simulated ﬂuxes. Initial water proﬁle conditions of the domain were obtained by running presimulation
over a 3 month period before bud-break, using ET0 data as an atmospheric BC and zero root water uptake.
Total simulation time was 2784 h, from bud-break on 1 April until a week before harvest on 25 July.
2.3.2. Reference Evapotranspiration
Hourly reference ET (ET0) was used to estimate both potential transpiration (Tpot, section 2.3.5) and Epot (section 2.3.6). The FAO Penman-Monteith equation for hourly time steps is [Allen et al., 1998]:
ET0 5

37
0:408DðRn 2GÞ1c Th 1273
uðes 2ea ÞD1cð110:34uÞ

(3)

where ET0 is the ET rate (mm/h) from a well-watered reference surface, Rn is net radiation at the reference
surface (MJ/m2/h), G is soil heat ﬂux density (MJ/m2/h), Th is mean hourly temperature ( C), D is the saturation slope vapor pressure curve at Th (kPa/ C), c is the psychrometric constant (0.067 kPa/ C), es is the saturated vapor pressure at Th (kPa), ea is the average hourly actual vapor pressure (kPa), and u is average hourly
wind speed (m/s). The calculations were conducted according to the recommended procedure based ondata of Th, u, relative humidity (RH), and Rs from a nearby weather station. The reference surface is a hypothetical green grass to which other surfaces can be related, thus expressing climate parameters independent from soil and plant characteristics. As the weather station represented arid conditions rather than wellwatered conditions, computed ET0 underestimated real ET0. However, ET0 was not adjusted to better represent well-watered conditions, as the nonadjusted ET0 was expected to best characterize the natural evaporation demand of the climate [Allen et al., 1998; Temesgen et al., 1999].
2.3.3. Domain Parameters
The ﬂow domain (Figure 2) was chosen to represent a cross section from midrow to midrow, with time andspace-variable atmospheric boundary conditions (Neumann type) at the soil surface, zero ﬂux on the sidesof the domain and free drainage at the bottom of the proﬁle. The domain was discretized into 12,772

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Figure 2. Variable boundary conditions simulated in HYDRUS (2-D/3-D) with (1) potential ﬂux for position 2.7 m (A), (2) potential ﬂux
A 1 irrigation, (3) potential ﬂux for position 0 m (B) 1 irrigation, (4) potential ﬂux B, and (5) no ﬂux; and free drainage at the bottom of the
proﬁle. Dimensions are in meters.

triangular ﬁnite elements (6581 nodes) using an unstructured ﬁnite element mesh, with a ﬁner mesh close
to the surface [Warrick and Lazarovitch, 2007]. The roots were located throughout the complete depth overa 1 m cross section below the vine location, with a uniform relative distribution function of 0.45 in the top
0.4 m, and a linear decline to 0 toward the bottom of the proﬁle. Soil surface Areas 1 and 2 represent interrow position 2.7 m (Figure 1), where Area 2 falls within the wetted area of the dripper and Area 1 does not.
Similarly, Areas 3 and 4 represent inter-row position 0 m (Figure 1), with Area 3 within the wetted area ofthe dripper, and Area 4 outside the wetted area. Area 5 represents the midrow positions where E wasassumed to be zero.
2.3.4. Hydraulic Parameters
Soil hydraulic parameters of the van Genuchten-Mualem functions [Mualem, 1976; van Genuchten, 1980]
were estimated based on soil texture and bulk density measurements, using neural network predictions
with the ROSETTA package [Schaap et al., 2001; Table 1]. Analysis of the effects of spatial variability in soil
hydraulic conductivity was obtained by generating 40 realizations with a lognormal distribution of saturated
hydraulic conductivity (Ks) using a scaling factor (y) where the standard deviation of log 10(y) was 0.5, andcorrelation lengths x 5 0.05 m and z 5 0.05 m, respectively, following Lazarovitch et al. [2006].
2.3.5. Root Water Uptake Parameters
Root water uptake was simulated according to the water stress response function suggested by Feddes
et al. [1978]. The water stress response function was extended following Jarvis [1989] with a critical stressindex (xcrt) [Simůnek and Hopmans, 2009] to balance reduced water uptake from one part of the root zone
by increased uptake from a less stressed region of the root zone. The xcrt factor allows for water uptake
compensation proportional to the water stress response function, where xcrt 5 1 is noncompensateduptake and compensation increases as xcrt approaches zero. Visual observations of root distribution along
Table 1. Soil Texture and Hydraulic Propertiesa
Particle Size
Distributionb (%)
Sand

Silt

Clay

Soil Textureb

qbb (kg m23)

hs (m3 m23)-

hr (m3 m23)-

a (m21)n

Ks (m h21)

53.4

20.3

26.3

Sandy clay loam

1400

0.43

0.07

1.8

1.4

0.02

a

qb, soil bulk density; hs, saturated volumetric water content; hr, residual water content; a and n, empirical shape parameters; and Ks,
saturated hydraulic conductivity.
b
Measured values.

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the face of an exposed proﬁle indicated that virtually
all roots were located within a 0.5 m radius from the
Optimization
xcrt
h3 (m)
drip line. In the absence of root measurements, root
Min
0.1
230
water uptake parameters were obtained from the litMax
0.9
2120
erature and by optimization. Feddes parameter h1,
D
0.05
10
N
17
10
the pressure head below which roots extract water at
Final value
0.3
250
the maximum possible rate, was set to 20.5 m. The
a
Where h3 is the pressure head below which root waterpressure head below which root water uptake starts
uptake ceases, associated with the water stress response
to decline, h2, was set to 210 m, according to values
function [Feddes et al., 1978], and xcrt is the critical stress
reported for mature wine vineyards [Taylor and Ashindex [Jarvis, 1989]. The optimum values were found by running N simulation between Min and Max values over D intercroft, 1972]. Since root water uptake indirectly detervals for respective parameters.
mines evaporation through water availability, root
parameters, h3 and xcrt (Table 2), were chosen to
optimize agreement between the measured and simulated results for hourly E during the calibration IOP.
Potential transpiration (Tpot) was estimated hourly using the FAO-56 dual Kc procedure, where ET0 is multiplied by a crop-speciﬁc factor (Kcb). Late season Kcb was estimated according to the shade-Kcb model [Williams and Ayars, 2005], where the fraction shaded area at noon is considered the ground cover fraction(GCF). The Kcb can be estimated as Kcb 5 [0.02 3 GCF 1 0.07] following two independent studies for six
n-Toro et al., 2012]. The
different vineyards, assessing ﬁve different wine cultivars [Ferreira et al., 2012; Pico
Kcb was set to increase linearly until reaching a constant maximum value (the late season Kcb) for the last 40
days of the season.
Table 2. Root Water Uptake Parametersa

2.3.6. Boundary Conditions
The minimum allowed h at the surface (hA) was set to 2150 m, a default value in HYDRUS (2-D/3-D). This value
is much higher than expected hA under desert conditions, however, sensitivity analysis to hA revealed marginal
differences in E for hA < 2150 m, while computation time increased exponentially. The surface boundary was
divided into ﬁve sections (Figure 2) using the adapted HYDRUS (2-D/3-D) model with the option ‘‘Treat the timevariable ﬂux boundary as the atmospheric boundary condition, i.e. with limited pressure heads’’ applied to all timevariable ﬂuxes. The Atmospheric BC option was used to obtain more detailed E outputs for Area 4. In order toretrieve a full picture of changes in E over the gradient from wet to dry, ﬂuxes were calculated in adjacent 10 cm
strips. Simulations were run to assess E between 0 and 0.1 m, 0.1 and 0.2 m, and 0.2 and 0.3 m from the wettedarea (Area 3). Potential ﬂux was set to hourly reference ET0 calculated with below canopy RS measured for interrow positions 2.7 m (potential ﬂux A) and 0 m (potential ﬂux B), respectively (Figure 1). Irrigation events were
simulated over Areas 2 and 3, using measured irrigation data divided by respective surface areas, subtracted by
Epot, thus forcing E to equal Epot in the wetted areas during irrigation events.
2.3.7. Model Evaluation
Agreement between simulated and measured hourly E was assessed by means of the Nash-Sutcliffe efﬁciency (NSE) [Nash and Sutcliffe, 1970], and the root-mean-square errors (RMSE) [Willmott, 1982]:
XNðEobs ðt Þ2Esim ðt ÞÞ2
NSE512 Xt51
(4)
2
N 
E ðtÞ2E obs
t51 obs
"
#1=2
N
X
2
21
ðEobs ðtÞ2Esim ðtÞÞ(5)
RMSE5 N
t51

where N is the number of observations, t is time (h), and subscripts ‘‘obs’’ and ‘‘sim’’ refer to observed andsimulated E (mm/h), respectively. The model was optimized to obtain the largest NSE and lowest RMSE formeasured and simulated average E directly underneath the vine (Areas 3 and 4, Figure 2) for the calibration
IOP. Resulting NSE and RSME of E from the second IOP were used for validation.

3. Results
3.1. Field Conditions
Total precipitation at the vineyard from November 2011 to March 2012 (the rainy season prior to the 2012
growing season) was 48.3 mm. The last rainfall event (2.5 mm) occurred on 16 March. The growing season

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RS (W m-2)

(a)
1000
500

Temperature (°C)
Winspeed (m s-1)

(b)

RH
RH

T
T

40

100
80

30

60
20

40
10

20

0

0

5

N

NW

W

SW

S

SE

E

Relative humidity (%)

0

(c)

NE

4

3
2
1

0
01/07

06/07

11/07

16/07

21/07

26/07

Figure 3. Field conditions for 1–25 July 2012. (a) Solar radiation (RS). (b) Temperature (T) and relative humidity (RH) at 3 m height. (c) Wind
speed at 3 m height; symbols and colors represent different wind directions: north (N), northwest (NW), west (W), southwest (SW), south(S), southeast (SE), east (E), and northeast (NE). Days that were used for calibration and validation of the model are marked by gray bars.

started with bud-break on 1 April and data were collected until 26 July, shortly before harvest. No precipitation occurred during the growing season. Analysis of below canopy E is reported for the month of July, during which the canopy was fully developed. This period coincided with veraison in the vineyard. The
atmospheric conditions in July were characterized by clear days with little variation from day to day (Figure3). Generally, there were no clouds, resulting in a very regular pattern of RS (Figure 3a). Air temperature ﬂuctuated between about 35 C during the day and 20 C at night, and relative humidity between 25% and90%, respectively (Figure 3b). Wind direction was predominantly northwest and wind speeds tended toincrease during the day, typically reaching a peak of about 4 m/s between 16:30 and 17:00 in the afternoon(Figure 3c).
Similar to the atmospheric conditions, ET0 followed a regular diurnal pattern averaging 7.3 mm/d (Figure 4)
with average peak values of 0.79 mm/h. Irrigation was applied three times a week at 2 or 3 day intervals
with an average application of 7.5 mm per event. Average ET amounted to 3.08 mm/d, with average peakvalues of 0.43 mm/h, and was strongly affected by irrigation events (Figure 4). On days immediately following irrigation, ET reached highs of up to 0.61 mm/h, dropping to lows of 0.21 mm/h on the second day without irrigation.
3.2. Below Canopy Conditions
3.2.1. Shade and Temperature Patterns
Distinct diurnal patterns of shaded and sunlit soil surface, dependent on solar position and vineyard architecture, are illustrated in Figure 5. A thermal image acquired at 12:35 on 23 July 2012 (Figure 6) shows large
heterogeneity in soil surface temperature; which is an indicator both of soil water status and available
energy at the soil surface. The soil surface temperature is expected to be inversely related to the evaporation rate. At the time of acquisition, the central part of the inter-row was sunlit, and temperatures exceeded60 C in the center (red tints in Figure 6) with temperatures of about 45 C (yellow tints) in the area whereshade had just receded. Below the vine, distinct wet (28 C) and dry (34 C) areas could be observed.
3.2.2. Below Canopy Radiation and Evaporation
Diurnal below canopy radiation (RS_below) and micropan evaporation (Epot_below) resembled the patterns ofsoil surface shading under the canopy (Figure 7). The reduction in RS_below, compared to the above canopy
RS (RS_ref), was as great as 90% during periods of shading, while Epot_below decreased by about 50%

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ETET

Irrigation
Series2

0.0

0.8

0.5

0.6
1.0

0.4

1.5

0.2

0.0
01/07

06/07

11/07

16/07

21/07

2.0
26/07

Irrigation (mm h-1)

ET0
ET0

1.0

10.1002/2014WR015409

Figure 4. Hourly ﬂuxes for 1–25 July 2012. Evapotranspiration (ET) is driven by reference ET (ET0) and irrigation.

05:14

06:14

07:14

08:17

09:12

10:15

11:13

12:15

13:15

14:14

15:19

16:18

17:17

18:14

19:14

Figure 5. Hourly changes in shade patterns across the inter-row, facing north.

compared to that measured outside the vineyard (Epot_ref). The position directly underneath the vine experienced the greatest reduction in both RS_below and Epot_below, as shading occurred around solar noon, thetime of peak radiation. Cumulative daily Epot_below ranged from 6.5 to 8.0 mm across positions for 4 July andfrom 7.0 to 8.9 mm for 23 July. Evaporative demand was about 20% lower at the below vine position

Figure 6. RGB image with overlaid thermal image of the vineyard inter-row that were acquired simultaneously on 23 July 2012 at 12:35.
The legend refers to temperature ( C) and circled numbers represent average temperatures for respective surfaces.

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8

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Rs_ref1000(a)

Rs_below(b)

Epot_ref(c)

Epot_below

E

.

(d)

800

0m

600
400
200
0
1000
(e) 06:00 12:00 18:00-20000:00
800

(f)

(g)

(h)

0.3m

600

Flux density (W m-2)

400
200
0
1000
(i) 06:00 12:00 18:00-20000:00
800

(j)

(k)

(l)

1.5m

600
400

200
0
1000
(m) 06:00 12:00 18:00-20000:00
800

(o)

(n)

(p)

2.2m

600
400
200
0
1000
(q) 06:00 12:00 18:00-20000:00
800

(r)

(t)

(s)

2.7m

600
400
200
0
00:00 06:00 12:00 18:00

00:00 06:00 12:00 18:00 00:00 06:00 12:00 18:00

4 July 2012

00:00 06:00 12:00 18:00

23 July 2012

Figure 7. Hourly changes in radiation and actual and potential evaporation ﬂuxes from reference measurements and for ﬁve positions
across the inter-row: (a–d) 0 m, (e–h) 0.3 m, (i–l) 1.5 m, (m–p) 2.2 m, and (q–t) 2.7 m for 4 and 23 July 2012. RS_ref is solar radiation above
the canopy, RS_below is solar radiation at the respective position below canopy, Epot_ref is potential evaporation outside the vine-row,
Epot_below is potential evaporation below the canopy, and E is actual evaporation below the canopy where each line represents one
microlysimeter.

Epot (mm h-1)

compared to the midrow position. The diurnal patterns of E were similar to Epot_below, but less pronounced,
and both E and Epot_below lagged behind RS_below. Actual E was concentrated mostly in positions 0 and
2.7 m, close to the location of the dripper (2.9 m). On 4 July, cumulative E directly underneath the vine
equaled 2 mm according to MLs located at random distances from the drippers. On 23 July, MLs werelocated at distinct dry and wet areas below the vine (position 0, with ‘‘wet’’ MLs deployed immediately
under a dripper and ‘‘dry’’ MLs 0.25 m from the dripper; distance between drippers was 0.5 m). Average
cumulative E amounted to 0.7 and 4 mm/d for the dry and wet MLs, respectively. The daily sums for positions 0.3–2.2 m were negligible. Overall daily E was about 0.26 mm on 4 July and 0.52 mm on 23 July,
assuming positions 2.7 and 0 m each represented 0.3 m of the inter-row and that
ET0
below vine micro-pan
the remaining 2.4 m did not contribute
below vine ET0
Pan
1.0
to E ﬂuxes.
0.5
0.0
0:00

12:00

0:00

12:00

0:00

Figure 8. Estimated potential evaporation (Epot) with reference evapotranspiration (ET0) using below canopy radiation and micropan evaporation for 4–5
July 2012.

KOOL ET AL.

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V

3.3. Modeling Results
3.3.1. Model Parameterization
Optimization of root water uptake parameters xcrt and h3 (Table 2) was limited toevaporation data from the position
directly underneath the vine (Areas 3 and9(mm h -1)

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10.1002/2014WR015409

0.6

ET_modeled
ET_modeled

E_modeled
E_modeled

0.5

ET_eddy
covariance
ET_9:00-15:00

E_ML
E_ML

T_modeled
Series4

0.4
0.3
0.2

0.1
0.0
19/7

20/7

21/7

22/7

23/7

24/7

25/7

26/7

Figure 9. Evapotranspiration (ET), transpiration (T), and evaporation (E) ﬂuxes, where (_modeled) are ﬂuxes simulated using HYDRUS (2-D/
3-D) and (_ML) and (_eddy covariance) are microlysimeter and eddy covariance measurements, respectively. Gray bars indicate irrigation
periods.

4, Figure 2). The optimized values resulted in an NSE of 0.44 and a RMSE of 0.06 mm/h for modeled versus
measured hourly E during the calibration IOP. Linear regression resulted in an R2 of 0.74 with an interceptthat did not differ signiﬁcantly from 0 (p > 0.05). The results were more sensitive to xcrt than to h3 (Table 2).
The Kcb was computed to be 0.40 for the month of July which corresponds to 17% shading of total vineyard
surface area at noon (see section 2.3.5). Estimated below canopy ET0, using below canopy radiation data,
was compared to micropan data (Figure 8) to evaluate whether the method could be used as input for Epotin simulations. Data comparison resulted in an R2 of 0.86 and a slope of 1.01. The intercept was not signiﬁcantly different from 0 (p > 0.05).

Evaporation (mm h-1)

Evaporation (mm h-1)

Evaporation (mm h-1)

3.3.2. Evapotranspiration Partitioning
Modeling results with the optimized parameters are shown along with measurements in Figure 9. Total
below canopy E computed by HYDRUS (2-D/3-D) was compared to an average of ML measurements
weighted according to representative surface area. In addition, modeled ET was compared to eddy covariance measurements. An energy budget over 30 min intervals assessed for day-time data over the wholeseason (data not shown) indicated that eddy
covariance latent and sensible heat were fairly
Model calibration: 4 July 2012.
1.0
linear with measured net radiation and soil heat
ﬂux, with an R2 of 0.74, a slope of 1.13 and an
0.8
ML_randomintercept of 255 W/m2. A stationarity test for
Area 30.60m
eddy covariance ET indicated some uncertainty
Area 4
0.4
for the data between 15:00 and 9:00 the follow0.2
ing morning. Statistical comparison was thus
0.0
limited to time periods between 9:00 and 15:00.
00:00
06:00
12:00
18:00
Good correlation was found between measured
-0.2
Model
1.0 validation: 23 July 2012.
and modeled ET and E. Total modeled ET for the
0.8
ML_wetperiods between 9:00 and 15:00 over the whole
ML_dry0.6
week was 96% of measured ET for the corre0m
sponding period. While the model slightly
0.4
underestimated ET for most days, it overesti0.2
mated ET on irrigated days. As a result, modeled
0.0
and measured ET values were more similar on
1.0-0.2
Area 1days following an irrigation event. Comparison
0.8
Area 2of the ET data resulted in an NSE of 0.62, RMSE
2.7m
0.6
of 0.05 mm/h and an R2 of 0.65, with a slope of
1.04 and an intercept of 0 mm/h. The results for0.4
E gave an NSE of 0.73, RMSE of 0.02 mm/h and
0.2
an R2 of 0.76, with a slope of 0.69 and an inter0.0
cept of 0.01 mm/h. The cumulative modeled E
00:00
06:00
12:00
18:00
-0.2
for 23 July was about 20% less than measured E.
Figure 10. Model calibration and validation for 40 simulations, where
each simulation had a different random distribution of saturated
hydraulic conductivity. Microlysimeter (ML) measurements were collected from random, wet, and dry positions across areas 1 and 2 and 3
and 4 (see Figure 2).

KOOL ET AL.

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3.3.3. Below Canopy Variability
Individual ML measurements were plottedagainst results of 40 different realizations, where
each simulation had a different randomized soil

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10.1002/2014WR015409

proﬁle Ks distribution (Figure 10). The optimization was centered on the position
0.6
below the vine, where Areas 3 and 4 represent wet and dry areas, respectively (Figure
0.42). An average of the two areas was compared to MLs located at the same position
0.2
at random distances from the drippers (Figure 10, 4 July). For the validation period, the0.0
wet and dry areas were measured sepa0:00
6:00
12:00
18:00
rately for the position underneath the vine,
23 July 2012
-0.2
allowing for direct comparison (Figure 10,
Figure 11. Simulated variability in evaporation as a function of distance23 July). The Ks distribution did not seem tofrom wetted area (0 m). Microlysimeter (ML) measurements were collectedaffect simulations for the wet surfaces
from distinctly wet and dry areas below the vine.
(Areas 2 and 3) very much over the period
before noon. Over the second half of theday, however, simulations for Areas 2 and 3 differed by as much as 100%. Simulated peak E values in theareas further away from the dripper, Areas 1 and 4, varied between 0.2 and 0.4 mm/h. While variability insoil properties explained some of the variability in ML measurements, it appeared that modeled results
overestimated E in the dry areas below the vine. When Area 4 was divided into three sections of 0.1 m (Figure 11), a strong decline in E with distance from wetted area (Area 3, 0 m) was observed. Compared to thatat 0 m, E decreased to 81%, 45%, and 2% at distances of 0–0.1, 0.1–0.2, and 0.2–0.3 m, respectively. The
results from the drier MLs were equivalent to simulated evaporation originating from areas between 0.1
and 0.3 m away from the wetted surface.
0.8

Evaporation (mm h-1)0m
0-0.1 m0.1-0.2 m
0.2-0.3 m
ML_wetML_dry4. Discussion
The ﬁeld conditions shown in Figure 3 are typical for summer in the central Negev [Bruins, 2012]. The winds
in the afternoon were caused by the so-called sea-breeze effect as a result of relative proximity to the Mediterranean Sea [Breckle et al., 2008]. As the canopy was fully grown in July and the atmospheric conditions
showed little variability from day to day, ET was mostly a function of irrigation. This resulted in very similar
conditions for the two IOPs, as evident by comparing both hourly ET and Epot for 4 and 23 July (Figures 4
and 7). Similar results were found for Cabernet Sauvignon [Pellegrino, 1987] and Tempranillo [Intrigliolo
n-Toro et al., 2012] vineyards, where ET showed little variability starting shortly before veraiet al., 2012; Picoson until harvest.
Variability in below canopy Epot could largely be explained by canopy shading effects which changed with
distance from the vine-row and time of day. This is consistent with ﬁndings by Ham and Kluitenberg [1993],
who concluded that RS could be used to explain most of the diurnal and positional variation in the soil
energy balance beneath a soybean crop. While the shading effect on Epot was also visible for E, measurements taken from the same position relative to the vine-row showed large irregularity. Discrepancies wereas much as a factor of 6 between wetter and drier samples, each directly underneath the vine but at different distances from drippers. Similar variability was reported by Zhang et al. [2011] who found that daily values of E in a Merlot vineyard depended more on soil water content than on Rs or vapor pressure deﬁcit. A
more pronounced effect of Epot on wetter areas was also observed by Yunusa et al. [2004], who reported distinct spatial patterns in daily E in a Sultana vineyard on days with rainfall, where under-vine E was lower
than the positions 1 m away, but higher than midrow positions. They observed a similar but less pronounced spatial pattern on days with irrigation, while on dry days the differences between positions were
negligible but the variability in the measurements was large, averaging 0.4 mm/d with standard errors of
up to 4 mm/d. Both Zhang et al. [2011] and Yunusa et al. [2004] looked at spatial variability in daily E in vineyards with an east-west orientation where diurnal patterns in shading tend to be less variable. Two studies[Heilman et al., 1994; Holland et al., 2013] conducted in vineyards with a north-south orientation focused on
diurnal patterns but did not include spatial variability. They reported a similar diurnal pattern for E, which
increased sharply in the morning, peaked in the afternoon, and continued for 1 or 2 h after sunset. No studies were found reporting both diurnal and spatial patterns of E. While Epot and ET were relatively similar forthe two IOPs in the current study, total E differed by 100%, resulting in estimated ratios of E/ET of 0.08 and

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0.17, respectively. Keeping in mind the high variability in E between individual samples this difference is notthat surprising. However, both values are relatively small compared to fractions found in the literature [Koolet al., 2014]. This is likely due to the relatively small wetted surface fraction that is typical for deﬁcit dripirrigated systems. Larger fractions may occur at the beginning of the season when the canopy is just starting to develop.
Optimization of root water uptake parameters using E from wet areas proved to be the most effective as
both modeled and measured E from wet areas were more uniform compared to drier areas. The variable
BCs in the model require transient values for Epot in order to allow long-term simulations. While micropan
measurements provided spatial and diurnal Epot, these data were only applicable to the short IOPs. To allow
continuous estimates of Epot for the entire modeled period, Rs in the ET0 computations was replaced withbelow canopy Rs for each position, assuming that RS_below is largely responsible for positional differences inthe energy balance [Ham and Kluitenberg, 1993]. Given the difference in measurement technique: themicropan is an in situ measurement with a free water surface, while ET0 is calculated from weather data
gathered nearby, below canopy ET0 and micropan measurements correlated surprisingly well. Small differences between them can be attributed to variations in onset of shading, i.e., the micropans covered a larger
area than the pyranometers and therefore shade reached the pans ﬁrst. The use of above canopy RH, Th,and u for computing below canopy ET0 may have added uncertainty to ET0 estimates [McVicar et al., 2012],
though the wide row spacing and narrow canopy likely minimized these discrepancies. The relatively good
agreement indicates that using ET0 with below canopy RS is a viable option to estimate below canopy Epot,
although more general use would require validation for other environments. The calculated Kcb of 0.4
seemed reasonable; other studies reported Kcb values of 0.4 [Allen et al., 1998], 0.43 [Poblete-Echeverrıa et al.,2012], and 0.46 [Carrasco-Benavides et al., 2012] for wine vineyards between veraison and harvest or
stage III.
The HYDRUS (2-D/3-D) results compared well with ET data, particularly on nonirrigated days. It appears thatafter irrigation, it took until the next day for the ET to recover to its nonstressed high. The model is unable
to account for this since root water uptake in HYDRUS is determined purely by water availability, while lagin recovery is likely caused by additional plant physiological responses [Galmes et al., 2007]. This caused an
overestimation in modeled ET on days with irrigation, and a slight underestimation of ET on the following
days. The major concern of this study is quantiﬁcation of E and not ET. That said, the independently measurable ET allowed preliminary evaluation of the model. Some consideration of possible contribution to ET estimation using the modiﬁed model is possible. In the case of a drip-irrigated arid vineyard in the current
study, ET was largely dictated by available water (irrigation), making the improvements in ET due to themore accurate assessment of E marginal. Cumulative ET estimation likely was improved with the more accurate assessment of E, but to be valid on a daily or hourly base, more detailed modeling of plant processes isrequired. The contribution of the improved E estimation would possibly be more signiﬁcant in environments where water is less limiting.
The HYDRUS (2-D/3-D) results were well correlated with measured E from the wet area. The drier areas weremuch more variable and therefore more difﬁcult to compare. Soil properties are known to cause large variability in vineyard yield [Bellvert et al., 2012; Kerridge et al., 2013]. Simulating a range of possible Ks distributions was therefore very helpful to identify the range of variability in E that can be expected in the ﬁeld
because of natural variations. The model showed distinct differences between wet and dry areas, with anabrupt decline in E at 0.2 m compared to 0.1 m away from the dripper. These abrupt changes made it difﬁcult to obtain representative ML measurements for the transient zone from wet to dry. Likewise, a very ﬁnemesh was required to model these abrupt changes in HYDRUS (2-D/3-D). A ﬁne vertical discretization close
to the surface proved to be particularly important to acquire reasonable values for E [Vanderborght et al.,2005]. A limitation of HYDRUS (2-D/3-D) is that heat and water ﬂuxes are not fully coupled, neglecting water
vapor ﬂuxes [Saito et al., 2006]. Since the van Genuchten-Mualem hydraulic functions may be inaccurate invery dry soils, the use of their model could be an additional source of error if applied under particularly dry
conditions. In such cases, modiﬁed functions, for example Fayer and Simmons [1995], which allow the soil tobecome air dry, could improve prediction of late stage E. In spite of this, the results from this study indicatethat HYDRUS (2-D/3-D) can derive E ﬂuxes, including consideration of spatial variability, as determined bysoil hydraulic properties and soil water content. The model can therefore potentially be used to evaluate E
under alternative conditions or management strategies. This includes operational parameters such as drip

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emitter distance, mulching, emitter depth, and irrigation scheduling. The ability of the model to simulate
solute transport also poses opportunities to study effects of E on soil salinity.

5. Conclusions
The objectives of this study were to quantify E and to evaluate HYDRUS (2-D/3-D) simulation of below canopy E while considering variability in time and space. In a drip-irrigated vineyard, it was found that below
canopy Epot and E are highly variable both diurnally and with distance from the vine-row. While the magnitude of E was mostly determined by water content, diurnal patterns depended strongly on canopy shading.
Large reduction in Epot was observed directly under the vine at noon. HYDRUS (2-D/3-D) successfully simulated the magnitude and diurnal patterns of E including spatial variability due to uneven water content andsoil saturated hydraulic conductivity and therefore should prove useful in simulating E under different conditions or management scenarios.
Acknowledgments
This research was supported by
Research Grant No. US-4262-09 from
BARD, the United States-Israel
Binational Agricultural Research andDevelopment Fund, and was partially
supported by the I-CORE Program of
the Planning and Budgeting
Committee and the Israel Science
Foundation (grant no. 152/11). We areparticularly grateful for technical andﬁeld support from the Gilat ResearchCenter’s Eugene Presnov and theJacob Blaustein Institutes for Desert
Research’s Yuval Shani. We also thankYoav Rabani for graciously providing
his vineyard as an experimental site.

KOOL ET AL.

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