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Agricultural Water Management 98 (2011) 784–790

Contents lists available at ScienceDirect

Agricultural Water Management
journal homepage: www.elsevier.com/locate/agwat

Simulating root water uptake from a shallow saline groundwater resourcePeter J. Shouse a , James E. Ayars b,∗ , Jiří Šimůnek caU.S. Salinity Laboratory, Riverside, CA 92507, United States
San Joaquin Valley Agricultural Science Center, Parlier, CA 93648, United Statesc
Department of Environmental Sciences, University of California Riverside, Riverside, CA 92521, United Statesba r t i c l e

i n f oArticle history:
Received 28 May 2009
Accepted 23 August 2010
Available online 19 January 2011
Keywords:
Ground water
In situ use
Alfalfa
Drainage
Irrigation management
Crop water requirement
Numerical model
HYDRUS-1D

a b s t r a c t
Disposal of saline drainage water is a signiﬁcant problem for irrigated agriculture. One proposal to deal
with this problem is sequential biological concentration (SBC), which is the process of recycling drainage
water on increasingly more salt tolerant crops until the volume of drainage water has been reduced
sufﬁciently to enable its ﬁnal disposal by evaporation in a small area. For maximum effectiveness thisconcept will require crop water reuse from shallow groundwater. To evaluate the concept of sequential
biological concentration, a column lysimeter study was used to determine the potential crop water use
from shallow groundwater by alfalfa as a function of ground water quality and depth to ground water.
However, lysimeter studies are not practical for characterizing all the possible scenarios for crop water
use related to ground water quality and depth. Models are suited to do this type of characterization if
they can be validated. To this end, we used the HYDRUS-1D water ﬂow and solute transport simulation
model to simulate our experimental results. Considering the precision of the experimental boundary
and initial conditions, numerical simulations matched the experimental results very well. The modeling
results indicate that it is possible to reduce the dependence on experimental research by extrapolating
experimental results obtained in this study to other speciﬁc sites where shallow saline groundwater isof concern.
© 2011 Published by Elsevier B.V.

1. Introduction
Irrigated agriculture, especially in the western United States andspeciﬁcally California, is facing dual challenges for water resource
allocation. Firstly, water resources that historically have been dedicated to agriculture are dwindling because increased population
has drastically increased the demand for good quality drinking
water. Industry also wants its share, and environmental and recreational water use needs are ﬁnally being addressed (Postel, 1999).
Secondly, disposal of saline drainage water represents a substantial
economic and environmental liability. Traditional salinity control measures have employed the concept of leaching, enabled
by artiﬁcial subsurface drainage, to reduce the negative impact ofover-irrigation and to remove excess salts (United States Salinity
Lab Staff, 1954). Until half a century ago, saline drainage water
was discharged to rivers and streams without any considerationof the environmental impacts. This is no longer a viable option andalternative methods have to be developed (Ayars and Tanji, 1999).
Drainage water disposal from vast areas of cropland on the west

∗ Corresponding author.
E-mail addresses: pshouse@ars.usda.gov (P.J. Shouse),james.ayars@csiro.au (J.E. Ayars).
0378-3774/$ – see front matter © 2011 Published by Elsevier B.V.
doi:10.1016/j.agwat.2010.08.016

side of the San Joaquin Valley of California is now relegated to onfarm, because of increased regulation of receiving surface water
quality. As a result, irrigated agriculture in the West must ﬁnd
new water resources and reduce saline drainage water volumes
to remain sustainable and proﬁtable.
Worldwide, between 70% and 80% of the developed water supply is dedicated to irrigation (Postel, 1999). In the Central Valley of
California, surface irrigation is the predominant method of water
application, with an efﬁciency of 60–70% (Ayars and Schrale, 1989).
In the near term, irrigation efﬁciencies will have to increase to
between 80% and 85% to make up for demands on water resources
by municipalities, industry, the environment, and recreation. In thelonger term, irrigated agriculture will have to rely on poorer quality
water to meet crop water use demands, and at the same time reducesaline drainage water emissions to surface water sources. These
are formidable challenges. Solutions under study include source
control measures via improved irrigation management, more efﬁcient water application methods such as drip and/or sprinkler, anddrainage water reuse for irrigating salt tolerant crops (San Joaquin
Valley Drainage, 1990; Ghassemi et al., 1995). Additionally, irrigation management needs to be modiﬁed to maximize in situ
crop water use from the shallow groundwater and correspondingly
reduce irrigation water application and drainage water volumes tobe disposed (Ayars et al., 2006).

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Research quantifying crop water use from shallow groundwater
as a function of crop salt tolerance, groundwater quality and depth
to groundwater has shown that the potential for meeting crop
requirements from shallow groundwater ranges to 50% of the total
irrigation requirement (Ayars et al., 2006, 1999). Perennial forage
crops, such as alfalfa and forage grasses, may offer a more continuous water uptake pattern from shallow groundwater because of
their long growing season and their deep and robust root systems(Ayars et al., 2009). Sensitivity to saline conditions may be the major
limitation to uptake of shallow groundwater by forage species.
Using alfalfa, a moderately salt tolerant forage species, as a test
crop, we quantiﬁed the effect of groundwater quality on the in situ
water use from a shallow water table (Ayars et al., 2009). While
our results conﬁrm results of other studies (Wallender et al., 1979;
Ayars and Hutmacher, 1994; Hutmacher et al., 1996), we need togeneralize or further extrapolate these site-speciﬁc results to othersoils and cropping systems. The most obvious way to extrapolatethese results is through simulation modeling.
Using simulation to extrapolate experimental ﬁndings to new
conditions is not a new concept (Otten, 1994; Heinen, 1997;
Hopmans and Bristow, 2002), but success without model calibration involving adjusting various model parameters is a rareoccurrence (Anderson and Woessner, 1992). We believed that if
we could simulate using a numerical model the results of wellconducted experiments where initial conditions are known, andboundary conditions are simple and controlled, then we shouldbe able to predict the results for similar conditions, but involving
different soils, crops, or weather, thus reducing our need to conduct experiments for every situation. By simulating well-controlled
experiments rather than ﬁeld observations we also reduce our riskof failure due to imprecise knowledge of initial and boundary conditions, soil heterogeneity, and other factors. If successful, simulation
can be used to evaluate the efﬁcacy of various crop, soil, and shallow
groundwater scenarios in managing irrigation water and minimizing drainage water disposal. The objective of this research was toillustrate this point using the initial and boundary conditions from
our alfalfa experiment (Ayars et al., 2009) and simulate results from
8 treatments over a 4-year period.

785

groundwater (T3), 4 dS/m groundwater (T4), 6 dS/m groundwater (T5), and 8 dS/m groundwater (T6). Tall column groundwater
quality treatments included: 2 dS/m groundwater (T3T) and 4 dS/mgroundwater (T4T). There were four replications of each treatment.
Low salt irrigation water (0.3 dS/m) was applied at the soil surface once or twice per week depending on the required depth ofapplication. As the depth of application increased it became necessary to use two irrigations a week to facilitate the irrigation in asingle day to all treatments. The depth of application was determined using the average weight loss between irrigation for all
lysimeters in a treatment. After a harvest and during periods of
low ETo there was only a single irrigation per week, and as thecrop water requirement increased the irrigation frequency wasincreased to twice a week. Climatic data collected at the California
Irrigation Management Information System (CIMIS) station locatedapproximately 1 km from the lysimeters were used to calculate reference crop evapotranspiration. Because of the atypical conditionsof a crop surrounded by dry land, an advection correction similar
to Skaggs et al. (2006a) was used.
We harvested the alfalfa every four to six-weeks during theseason, and the dry masses were used to calculate yield (kg m−2 ).
2.2. Simulation
We used the HYDRUS-1D model (version 3.00, Šimůnek et al.,2005; Šimůnek et al., 2008) for several reasons: (1) availability:
the latest version of the model can easily be downloaded from theinternet (http://www.pc-progress.com/en/Default.aspx?hydrus1d); (2) ease of use: the user friendly interface is intuitive withgood help ﬁles; (3) support: rapid customer technical support;(4) widely tested: it was used in hundreds of peer-reviewed
journal articles; (4) processes included: the current version has acompensatory root water uptake feature (Jarvis, 1989; Šimůnek
and Hopmans, 2009) where root water uptake reduced by wateror salinity stress in one part of the root zone is fully or partiallycompensated by root water uptake from a non-stressed part of theroot zone.
2.3. Model input

2. Materials and methods
2.1. Experimental
A detailed account of the experimental procedures can be foundin Ayars et al. (2009) and Ayars and Shouse (2007), only a briefsummary will be presented here.
Alfalfa (Medicago savitia var SW9720) was grown in large
aboveground hydraulic pillow lysimeters made from 45-cm diameter polyvinyl chloride (PVC) pipes of two heights: 180 cm (shortcolumns) and 260 cm (tall columns) (Robbins and Willardson,
1980; Ayars and Hutmacher, 1994; Hutmacher et al., 1996).
By using a Marriotte bottle we could precisely control thewater table depth (at 120 cm for short columns and 200 cm fortall columns) and measure groundwater use directly. The hydraulicpillows were used to measure changes in water storage in each column. Crop evapotranspiration (ETc ) was calculated by summing the
groundwater use and storage changes. The lysimeters were locatedoutdoors in a ﬁeld at the USDA-ARS, San Joaquin Valley Agricultural
Sciences Center in Parlier, CA.
Each lysimeter was packed with Panoche clay loam soil (Typic
Torriorthents) to a bulk density of 1.4 Mg m−3 (Nielsen et al., 1973).
This soil dominates much of the cropland affected by shallow
groundwater in the central San Joaquin Valley, California. The soil
was initially non-saline (ECsw < 1 dS/m).
Short column groundwater quality treatments included: nogroundwater (T1), non-saline (0.3 dS/m) groundwater (T2), 2 dS/m

Main processes included in HYDRUS-1D are one-dimensional
water ﬂow, Eq. (1) (the Richards equation), and one-dimensional
solute transport, Eq. (2) (the convection–dispersion equation),through a homogeneous, isotropic soil:
∂
∂
=
∂t
∂z





K(h)

and
∂
∂(Rc)=∂z∂t∂h− K(h) − S,
∂z


D

∂c
− qc
∂z

(1)


− ,

(2)

where  is the volumetric water content (−), h is the soil waterpressure head (L), t is time (T), z is depth (L), K is the hydraulicconductivity (LT−1 ), R is a retardation factor accounting for sorption or exchange (−), c is the solute concentration of the liquid
phase (ML−3 ), D is the solute dispersion coefﬁcient (L2 T−1 ), q is
the Darcy–Buckingham volumetric water ﬂux (LT−1 ), S (T−1 ) is thesink or source for water, and  (ML−3 T−1 ) is the sink or source
for solutes. In our simulations  is considered negligible (Bresler
and Hoffman, 1986; Bresler et al., 1982), R is equal to 1 (i.e., noretardation as overall salinity was simulated), and S is associatedexclusively with root water uptake (Dudley and Shani, 2003).
The root water uptake sink term, S, has been deﬁned as the volume of water removed from a unit volume of soil per unit of time
(Šimůnek et al., 2005). Feddes et al. (1978) deﬁned S in terms ofAuthor's personal copy
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pressure head h to account for water stress and van Genuchten(1987) expanded that formulation by including dependence uponosmotic pressure to account for osmotic stress:

The transpiration rate for compensatory water uptake is now
given by

S (h, ) = ˛ (h, ) ˇ(z)Tp ,

Tac
Tp ω
=
Tp
⎩ Ta 1 = ω
Tp ωc
ωc

(3)

where Tp is the potential transpiration rate (LT−1 ), ˇ is the root
spatial distribution (L−1 ), ˛ is the root water uptake stress reduction function (−), h is the soil water pressure head (L), and  is theosmotic head (L). The stress reduction is a function of both waterand salinity stress. The functional form of the water stress reduction function proposed by Feddes et al. (1978) is a piecewise linear
function parameterized by four critical values of the water pressure
head (Skaggs et al., 2006b; Feddes and Raats, 2004), h4 < h3 < h2 < h1 ,˛h (h) =⎧h − h4⎪⎪⎪h⎪⎨ 3 − h4

h3 > h > h4

h − h1⎪⎪⎪h⎪⎩ 2 − h1

h1 > h > h2

h2 ≥ h ≥ h 3

10h ≤ h4or(4)h ≥ h1and this is the function we used in our simulations (Šimůnek et al.,2005).
We used a salinity stress reduction function consistent with
the Maas and Hoffman (1977) model for crop salt tolerance. The
effects of salinity stress on root water uptake were described using
a piecewise linear (threshold-slope) function:˛ () =1
1 + b( − a)00≤≤aa <  < a − 1/b ,a − 1/b ≤ (5)where the threshold a and the slope b are adjustable parametersthat mirror the terminology used for the Maas–Hoffman parameters A and B (Maas and Hoffman, 1977). Note, however, that theparameter sets are not necessarily the same: A and B parameterize
total yield reductions as a function of average root zone salinity,
whereas a and b parameterize local reductions in the root water
uptake rate as a function of osmotic head (Skaggs et al., 2006a,b,c).
It should also be pointed out the parameter sets would be thesame if and only if the uptake process would be homogeneous overthe entire root zone. Piecewise Feddes et al. (1978) and Maas andHoffman (1977) functions were used, rather than the S-shape functions of van Genuchten (1987), since HYDRUS provides parameters
for these functions for many agricultural crops.
We used the multiplicative model for combining the effects ofwater and salinity stresses:
˛(h, ) = ˛a (h)˛ ()(6)according to a recent review of root water uptake modeling (Skaggs
et al., 2006c).
Šimůnek et al. (2005) and Šimůnek and Hopmans (2009)
recently implemented into HYDRUS-1D the compensatory root
water uptake model of Jarvis (1989), modiﬁed by using soil waterpressure head as the controlling variable. To formulate a compensatory uptake model, they deﬁned the dimensionless water stressindex, ω:Ta=Tp˛h (h)˛ ()b(z) dz ≡ ω,

(7)

LR

where Ta is the actual non-compensated transpiration rate (LT−1 ),Tp is the potential transpiration rate (LT−1 ), ˛h (h) is the water stress
reduction factor (−), ˛ () is the salinity stress reduction factor (−).
The dimensionless water stress index, ω, sometimes also called theroot adaptability factor (Jarvis, 1989), provides a measure of total
plant stress. A value of ω equal to 1 indicates that there is no stressin the soil root zone and that the actual transpiration rate Ta is equal
to the potential transpiration rate Tp .

⎧ T 1
⎨ a =1

ωc < ω ≤ 1,(8)

ω < ωcwhere Ta is the actual compensated transpiration rate (LT−1 ), and0 < ωc ≤ 1. Transpiration occurs at the potential rate (Tac = Tp ) only
when total root zone stress is low, i.e., when ω is greater than somecritical value ωc . For actual root water uptake to remain equal tothe potential rate, uptake is increased throughout the root zone by
a compensatory factor of 1/ω. Although on a relative basis uptake
is increased uniformly throughout the root zone, in absolute terms
the biggest increase occurs in those parts of the root zone wherestress is low. Root water uptake from stressed parts of the rootzone is then compensated by uptake from less stressed parts. When
stress becomes higher and ω < ωc , compensation is only partial,with uptake increased throughout the root zone by a factor of 1/ωc
(Skaggs et al., 2006a,b,c; Šimůnek and Hopmans, 2009). Notice that
Jarvis (2010) recently showed that his 1989 model (Jarvis, 1989) can
be considered as a simpliﬁed, dimensionless form of the physically
based de Jong van Lier et al. (2008) model, which uses a conceptof the matric ﬂux potential to describe radial ﬂow to roots. Consequently, the dimensionless parameters in the empirical model ofJarvis (1989) can be explicitly related to measurable system properties such as root length density and soil hydraulic characteristics(Jarvis, 2010).
2.4. Model parameters
We assumed the soil proﬁles were a uniform and isotropic
clay loam soil with a bulk density of 1.4 Mg/m3 (either 180 cm
or 260 cm deep). The soil hydraulic properties were taken fromthe HYDRUS soils catalog for a generic clay loam soil, for which
the van Genuchten–Mualem (van Genuchten, 1980) parameters
were as follows: the saturated water content  s = 0.41, the residual water content  r = 0.095, the retention curve shape parameters
˛ = 0.019 cm−1 and n = 1.31, the saturated hydraulic conductivity
Ks = 6.24 cm/d, and the tortuosity and pore connectivity parameter l = 0.5. Soil longitudinal dispersivity was assumed to be 10 cm.
The parameters for the Feddes water stress reduction function(Feddes et al., 1978) were h1 = 0 cm, h2 = −10 cm, h3 = −5000 cm,
and h4 = −17,000 cm. The salinity stress reduction parameters werea = 2 dS/m and b = 7, with a critical stress index, ␻c = 0.25 (Jarvis,
1989).
For water ﬂow upper boundary condition we used an atmospheric boundary, which was the daily Penman–Montieth ET0 fromCIMIS weather station data and measured precipitation and irrigation. The solute ﬂux at the top was deduced from the irrigationwater salinity (0.3 dS/m) and the rate of irrigation. The free drainage
bottom boundary condition was used for both water ﬂow andsolute transport for the treatments without a groundwater table(T1). For the treatments with groundwater, a constant pressure
head (corresponding to the position of the water table) and a constant concentration, equal to the concentration of the groundwater
treatment (e.g., T2 = 0.3 dS/m, T3 = 2 dS/m), were used as bottom
boundary conditions.
The initial conditions for water ﬂow for 2002 were considered
to be uniform with depth and equal to −50 cm pressure head,
except in the vicinity of groundwater where hydrostatic conditions
were assumed. The initial conditions for solute transport for 2002
was a uniform salinity of 0.3 dS/m, except for runs with the watertables for which the initial concentrations were taken to be theconcentrations of the groundwater. The growing season and therefore the simulation season was approximately 250 days from day

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of the year (DOY) 45 to DOY 295 (mid-February to mid-October).
For subsequent years, the initial conditions were determined fromthe previous year’s pressure head and salt concentration distributions, so any salt build-up or residual water stress was taken into
account. The off season was not simulated since all major processes
in the Central Valley usually occur during the growth season. During the off season the land is either fallow or the plants are dormant.
Any precipitation is usually signiﬁcantly lower that potential E and,
consequently, there are no major changes in the water content orsalinity proﬁles during the off season.
3. Results and discussion
Table 1 summarizes the experimentally measured crop water
use as well as simulation results for each treatment. The presenceof a shallow groundwater table in T2 led to a higher water use by thealfalfa crop, implying that the crop was under-irrigated in T1. The
alfalfa may have undergone a substantial water deﬁcit during partof the growing season reducing water use. Table 1 also shows thatthe total range of measured water use values was within 20–25%
of the mean value. With a few exceptions, the simulated values forwater uptake fell within the range of the measured values for eachtreatment. One notable exception is the T1 in 2004. The simulated
value is 1000 mm less than the measured value. The reason for thisdiscrepancy is not known, but in that year the experimental water
use for T2 was also higher than other years by about 1000 mm.
This leads us to speculate that weather conditions, disease or insectpressures were more favorable in 2004 for the growth and yield ofalfalfa than in other years. According to Ayars et al. (2009), the yieldvs. water use function was linear with no apparent abnormalities,
further adding to the circumstantial evidence that this was indeed
an exceptional year for alfalfa. Table 1 also shows that, with the
exception of T2, water use was reduced during the course of theexperiment, implying that perhaps salinity may have been building up in the root zone, and reducing root water uptake. Ayars et al.
(2009) show some soil salinity data at the end of the experimentthat do indicate that salinity did build up with time. This is dueto the fact that there was no leaching. The higher groundwatersalinity treatments were affected to a greater extent than the lower
groundwater salinity treatments. The simulation of these measured
conditions conﬁrms that the root water uptake reduction functions
are performing well enough to simulate the experimental results.
The general results from measurement and simulation of water
uptake from the groundwater are shown in Table 2. As with thetotal crop water use, the simulation results for the ground water
contribution were within the range of measured values, indicatingthat the simulations were as accurate as the measurements.
Fig. 1 shows for T1 during the 2002 season (DOY 50-284) thetime course of cumulative ET0 (CIMIS data), measured ETa , simulated ETa , and irrigation. ET0 for the 2002 growing season wasapproximately 3000 mm, however the ETa measured during theseason was around 66% of that amount. Regardless, the HYDRUS
simulation of T1-2002 showed a very good approximation of themeasured values. The simulation was able to follow the general
dynamic of the measurements, starting slowly, leveling off after
DOY 75, increasing after DOY 150 during increased irrigation, andthen paralleling the irrigation rate until the end of the season. Similar simulation results were obtained for T1 during 2003 and 2005.
As noted before, 2004 was an odd year, alfalfa growth and water use
were higher in that year than other years, especially for T1 (Ayars
et al., 2009). At this time we cannot explain the increase in thewater use and yield, and we are not sure if this is just a local phenomenon or if the 2004 San Joaquin Valley alfalfa crop in general
was affected.
Simulation and measurement results for T2-2002 are shown in
Fig. 2. As with T1-2002 simulations, major features of the mea-

Fig. 1. Measured and simulated water use for T1-2002.

sured ET are preserved in the simulations, although the ET wasover-predicted between DOY 100 and DOY 150. After DOY 150 thesimulated ET was parallel with the measured ET indicating the accuracy of the simulation during this period. Simulated groundwater
use also tracked the measured values until DOY 200, where there
was a slight divergence until the end of the season.
The data and simulations shown in Fig. 1 and Table 1 imply thatthe alfalfa crop in T1 may have been undergoing moderate waterdeﬁcit compared to T2 in Fig. 2 and Table 1. One reason for simulating experimental results is that insight into factors not measured
during the experiment can be gained by looking at speciﬁc simulation output. Considering the case of contrasting water use between
T1 and T2, we speculate that T1 may have been affected by waterdeﬁcit during part of the growing season. But can our model substantiate our theory? We know from Figs. 1 and 2 that we havegood simulation results for these treatments during 2002 (for 2003,
2004, and 2005 also, data not shown). HYDRUS allows us to lookinside the soil column at the average root zone soil water pressure
head during the season (Fig. 3). By comparing the time course of theFig. 2. Measured and simulated water use for T2-2002.

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Table 1
Measured and simulated overall water uptake for 8 treatments (in columns) and 4 years (rows).
Year

Data set

2002

Measured
Simulated
Measured
Simulated
Measured
Simulated
Measured
Simulated

2003
2004
2005

Treatments
T1 (mm)T2 (mm)T3 (mm)T4 (mm)T5 (mm)T6 (mm)T3T (mm)

T4T (mm)

1924 ± 230
1940
1947 ± 210
1752
2805 ± 250
2080
1236 ± 150
1117

2379 ± 210
2457
2688 ± 350
2655
3467 ± 500
3036
2403 ± 200
2271

2502 ± 250
2800
2507 ± 225
2513
2126 ± 125
2103
1384 ± 200
1212

2269 ± 125
2510
2299 ± 175
2102
1657 ± 150
1559
1074 ± 250
979

2261 ± 350
2109
2363 ± 325
1904
1101 ± 200
1297
767 ± 150
898

1907 ± 112
2023
1979 ± 150
1685
784 ± 10822
660 ± 15625

2358 ± 250
2485
2475 ± 285
2342
2368 ± 200
2205
1667 ± 100
1518

2282 ± 75
2484
2286 ± 175
2105
1425 ± 300
1521
1129 ± 75
947

Fig. 3. Simulated average root zone soil pressure heads for T1 and T2-2002.
Fig. 4. Measured and simulated water use for T4-2004.

average pressure head, we can see substantial differences between
T1 and T2. While T2 never had pressure heads less than −4000 cm,
T1 had pressure heads less than −10000 cm for a major part of thegrowing season. The speciﬁc values of the simulated pressure heads
are less important than the relative differences between the twosimulated treatments. Skaggs et al. (2004) showed the accuracy of
the HYDRUS model for simulating water content distributions during and after drip irrigation, which leads us to believe that thesesimulated results are reasonable.
To illustrate the effect of salinity on water uptake, Fig. 4 shows
simulation and experimental results of crop water use from T42004 with 4 dS/m salinity in the groundwater. The salinity is twice
the Maas–Hoffman (1977) threshold value for alfalfa salt tolerance.
Moreover, during the 3 years of the experiments there was noleaching, so that salts were building up in the lysimeter. Therefore reductions in water uptake were to be expected. The seasonal
ET0 for 2004 was 3100 mm, and T4 ET was reduced by approxi-

mately 50% due to the salinity of the groundwater and in the soil
proﬁle. Our simulation of T4-2004 slightly over-estimated the crop
ET for DOY’s 70 to 175, most likely due to a mismatch of initialsoil water storage between the simulation and the experiment.
For the remainder of the season, simulated ET tracked measured
ET very well. Simulated groundwater use under-estimated actual
groundwater use for approximately the same duration as the ET
was over-estimated. This leads us to think that these two simulation results are linked and that one will compensate for the other,but both simulated variables were on track during the latter part ofthe season, indicating that the mismatch of initial conditions only
had a transient effect and that after some time the mismatch had
less effect on the results (Skaggs et al., 2006b).
To look more closely at the effect of soil and groundwater salinity on root water uptake by alfalfa, we again employ the HYDRUS
output. This time we look speciﬁcally at the root water uptake dis-

Table 2
Measured and simulated water uptake from the shallow groundwater for 8 treatments (in columns) and 4 years (rows).
Year

2002
2003
2004
2005

Data set

Measured
Simulated
Measured
Simulated
Measured
Simulated
Measured
Simulated

Treatments
T1 (mm)T2 (mm)T3 (mm)T4 (mm)T5 (mm)T6 (mm)T3T (mm)

T4T (mm)

–
–
–
–
–
–
–
–

1385 ± 250
1297
1438 ± 200
15332874 ± 350
2299
1973 ± 220
1922

1135 ± 180
15001327 ± 205
1393
1626 ± 1551625
808 ± 200826

978 ± 175
975
1089 ± 145
1048
1009 ± 150
1215
510 ± 200
701

650 ± 300
594
763 ± 120832
655 ± 150
991
767 ± 150620

408 ± 112484
523 ± 110559
414 ± 50512
101 ± 15348

812 ± 125950
986 ± 85
1209
1889 ± 150
1931
1190 ± 100
1276

782 ± 105948
743 ± 107801
971 ± 250
1089
514 ± 75
710

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water from the groundwater). Consequently, the compensated root
water uptake model predicted cumulative ET almost identical to themeasured ET (Fig. 4).
Our simulation results are impressive for several reasons: (1)we used internally (within HYDRUS) provided parameter sets forboth water ﬂow and solute transport (e.g., soil hydraulic parameters from HYDRUS soil catalog); (2) we used external atmosphericboundary conditions for the simulated location; (3) we used standard parameter sets for compensatory root water uptake, water
stress, and salt stress reduction functions taken from HYDRUS
database or appropriate literature; and (4) we did no calibration of
the model. By choosing to simulate lysimeter experimentals withknown, precisely controlled boundary and initial conditions, we
effectively reduced the effects of soil spatial heterogeneity inherent in ﬁeld studies that could blur the interpretation of the results.
As a result we are well equipped to extrapolate our experimental
results to other speciﬁc soils, crop and weather conditions.
4. Concluding remarks
Simulation model development has matured enough in recent
years to be helpful in reducing our dependence on experimental
research for solving real world problems. Problems facing irrigatedagriculture in semi-arid climates are immense. Dwindling water
resources and saline drainage water disposal are two problems
that can be partially addressed using simulation modeling. Throughsimulations one can extrapolate experimental results to speciﬁcsites where saline shallow groundwater is of particular concern.
Our simulations have provided insight into the dynamic characterof using shallow groundwater as a resource as well as the ramiﬁcations of drainage water reuse. The next challenge is to use oursimulations to design shallow groundwater management systems
that reduce drainage water volumes and minimize salt redistribution into the root zone.
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Fig. 5. Simulated root water uptake and salt distributions for T4-2002 and 2005 at
the end of the growing season.

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