J. Hydrol. Hydromech., 66, 2, 232–245
DOI: 10.1515/johh-2017-0055

Coupling DSSAT and HYDRUS-1D for simulations of soil water dynamics
in the soil-plant-atmosphere system
Vakhtang Shelia1*, Jirka Šimůnek2, Ken Boote1, Gerrit Hoogenbooom1
1Department of Agricultural and Biological Engineering & Institute for Sustainable Food Systems, University of Florida, Gainesville,
FL 32611, USA.
2
Department of Environmental Sciences, University of California Riverside, Riverside, CA, USA.
*
Corresponding author. Tel.: (1) 352-392-1864 ext 259. E-mail: vakhtang.shelia@ufl.edu

Abstract: Accurate estimation of the soil water balance of the soil-plant-atmosphere system is key to determining theavailability of water resources and their optimal management. Evapotranspiration and leaching are the main sinks ofwater from the system affecting soil water status and hence crop yield. The accuracy of soil water content and evapotranspiration simulations affects crop yield simulations as well. DSSAT is a suite of field‐scale, process‐based crop models
to simulate crop growth and development. A “tipping bucket” water balance approach is currently used in DSSAT for
soil hydrologic and water redistribution processes. By comparison, HYDRUS-1D is a hydrological model to simulate
water flow in soils using numerical solutions of the Richards equation, but its approach to crop-related process modelingis rather limited. Both DSSAT and HYDRUS-1D have been widely used and tested in their separate areas of use. The
objectives of our study were: (1) to couple HYDRUS-1D with DSSAT to simulate soil water dynamics, crop growth andyield, (2) to evaluate the coupled model using field experimental datasets distributed with DSSAT for different
environments, and (3) to compare HYDRUS-1D simulations with those of the tipping bucket approach using the samedatasets. Modularity in the software design of both DSSAT and HYDRUS-1D made it easy to couple the two models.
The pairing provided the DSSAT interface an ability to use both the tipping bucket and HYDRUS-1D simulation
approaches. The two approaches were evaluated in terms of their ability to estimate the soil water balance, especially soil
water contents and evapotranspiration rates. Values of the d index for volumetric water contents were 0.9 and 0.8 for theoriginal and coupled models, respectively. Comparisons of simulations for the pod mass for four soybean and four peanut treatments showed relatively high d index values for both models (0.94–0.99).
Keywords: System Modeling; Crop Growth; Soil water; Unsaturated zone; DSSAT; HYDRUS-1D.
INTRODUCTION
Accurate estimation of the soil water balance is importantfor determining the availability of water resources and theiroptimal management in agriculture, the major consumer ofwater. Process-based simulation models of complex systems
such as the soil-plant-atmosphere continuum are invaluable tools
for this purpose. They may be used to simulate in detail multiple
processes of the real system to predict various state variables at
every time step during the simulations. The processes of evapotranspiration and leaching are the main sinks of water in thesoil-plant-atmosphere system affecting soil water status. Theircorrect simulation is a prerequisite for successful simulations ofcrop growth and yield since the accuracy of soil water content
and evapotranspiration simulations affect the accuracy of simulated plant processes and crop yield. Comparing model simulation results with field observations, or with results of othermodels based on different conceptual approaches, may provide
invaluable information on the performance of the model andreveal their strong and weak parts. This is an essential step also
when selecting appropriate models for practical applications inwater resources analyses and for estimation of crop yield components. Comparing a physically-based soil-plant-atmospheremodel with a simpler model may provide information on how
the models perform compared to each other. If a simpler model
can simulate the required processes with sufficient accuracy,that model should be an attractive alternative to a more dataintensive, complex simulation model. Using a simple model
may also minimize the need for a comprehensive data collection effort (Ines at al., 2001; Pachepsky et al., 2006).

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Crop simulation models usually are based on mathematicalequations that describe the basic flow and conversion processesof carbon, water, and nitrogen, and that are integrated hourly or
daily to predict the time course of crop growth, nutrient uptake,and water use, and to predict final yield and other plant traits(e.g., Boote et al., 2010). The Cropping System Models (CSM)
in the DSSAT software (Decision Support System for Agrotechnology Transfer) are particularly well suited for simulating
various agricultural practices (Jones et al., 2003). DSSAT (version 4.6) integrates several crop system models, soil carbon andnitrogen models, a daily water dynamic model, and a range ofcrop/land management options to simulate crop growth/yieldand environmental impacts. DSSAT has been used widely andsuccessfully for crop yield simulations under different
management strategies, for optimizing the use of various
resources, for yield trend simulations under different soil andclimate scenarios, for crop yield risk analysis (Gijsman et al.,
2002b; Sarkar, 2009), for simulating corn yield and nitrogen
cycling in a 50-year corn production experiment (Liu et al.,2010), and for many other applications. The software has also
been applied to controlled tile drainage-subsurface irrigation
systems (Liu et al., 2011). Hence, DSSAT is particularly usefulfor predicting the short-, medium-, and long-term impacts ofspecific land management practices on crop yield, soil water
storage, and nitrate-N leaching losses (Boote et al., 2010;
Gheysari et al., 2009; Mullen et al., 2009).
The DSSAT Cropping System Model (CSM) (Jones et al.,
2003; Hoogenboom et al., 2015) is a suite of field-scale, process-based crop models that have the capacity to simulate both
crop and soil processes. They include the phenological devel-

Coupling DSSAT and HYDRUS-1D for simulations of soil water dynamics in the soil-plant-atmosphere systemopment of crops and detailed growth components from emergence until harvest maturity on the basis of crop genetic coefficients, environmental (weather, soil) conditions, and management options. To simulate soil water flow and root water uptake
in each individual soil layer, DSSAT CSM uses a onedimensional tipping bucket approach (Ritchie, 1998) that considers the soil profile to consist of a number of soil layers thatare all homogeneous horizontally. The soil water balance model
currently uses a tipping bucket approach for simulating soilhydrologic cycle and water redistribution for all crop models in
CSM. The time step for the soil water balance and evapotranspiration (ET) calculations is daily, which matches the timing of
weather data inputs and daily plant dry matter growth (Ritchie,1998). Boote et al. (2008) concluded that the tipping bucket soil
water balance model in DSSAT generally works satisfactorily
when the soil water-holding properties (a drained upper limit,
DUL, and a lower limit of plant-extractable soil water, LL) are
estimated properly, and when the rooting depth and root length
distribution are predicted adequately. Nevertheless, the approach is inherently more approximate than the soil water flow
calculations used in many hydrological models such as HYDRUS-1D, which can estimate soil moisture distribution in thesoil profile with higher accuracy (Scanlon et al., 2002).
HYDRUS-1D (Šimůnek et al., 2008; 2016) is a widely usedhydrological model that simulates water flow in onedimensional variably-saturated soils using numerical solutionsof the Richards equation. The water flow equation incorporates
a sink term to account for water uptake by plant roots. Rootwater uptake and transpiration are calculated using the formulation of Feddes et al. (1978). Evapotranspiration can be calculated using the Penman-Monteith equation. As compared to
DSSAT, calculations in HYDRUS-1D are more physically
based by allowing flow to be driven by pressure head differences, thus allowing for capillary flow and hence both upward
or downward flow in the soil profile. The runoff calculations in
HYDRUS-1D are also less empirical than in DSSAT. WhileDSSAT uses the modified curve number method of the UnitedStates Department of Agriculture, Soil Conservation Service(USDA-SCS) (Williams, 1991), HYDRUS-1D determines
runoff as infiltration-excess water that is obtained by the numerical solution of the Richards equation for specified precipitation rates and soil hydraulic properties.
HYDRUS-1D has been used widely in many industrial andenvironmental applications, as well as for addressing many
agricultural problems (Šimůnek et al., 2016). Examples ofexisting agricultural applications include irrigation management(Bristow et al., 2002; Dabach et al., 2015), drip and sprinkler
irrigation design (Bristow et al., 2002; Gärdenäs et al., 2005;
Hanson et al., 2008; Kandelous et al., 2012), studies of root
water and nutrient uptake (Šimůnek and Hopmans, 2009; Vrugt
et al., 2001a,b), among many others.
Both DSSAT and HYDRUS-1D have been used widely andtested separately, as well as coupled with other models. Several
crop models describing relevant crop growth processes havebeen integrated successfully with hydrological models, such as
DSSAT-RZWQM (Ma et al., 2006), DSSAT-SWAP (Dokoohaki et al., 2016), SWAT-MODFLOW (Sophocleous et al.,1999), and WOFOST-SWAP (van Walsum, 2011). Similarly,
HYDRUS have been coupled successfully with existing cropand root growth models (e.g., Groenendyk et. al., 2012; Han etal., 2015; Hartmann et al., 2017; Li et al., 2014; Peña-Haro etal., 2012; Wang et al., 2014; Zhou et al., 2012). For example,
Peña-Haro et al. (2012) integrated WOFOST, a crop growthand production model, with HYDRUS-1D as well as with
MODFLOW-2000 (Harbaugh et al., 2000), the latter being asaturated flow model. The coupling was done using a combination of two different approaches: external coupling (throughinput/output data manipulation) and by using code wrapping.
Peña-Haro et al. (2012) used OMS3 for wrapping WOFOSTand PYTHON to write scripts. Groenendyk et al. (2012) coupled HYDRUS-1D with a generic crop growth model based on
the plant growth module used in the WEPP model and thenused the Ensemble Kalman Filter for assimilation of soil watercontent observations into the hydrologic model. The coupledapproach was then tested for an irrigated wheat cropping experiment conducted at Maricopa, Arizona. Similarly, Hydrus-1D
was coupled with MODFLOW-2000 for simulating hydrological processes at the large scale (Seo et al., 2007; Twarakavi etal., 2008).
The main objectives of this study were: a) to implement theflow routines from HYDRUS-1D into the DSSAT CSM cropmodels as a new option for simulating soil water dynamics, b)
to evaluate the coupled model using field experimental datasets
distributed with DSSAT for different environments, and c) tocompare the performance of the HYDRUS-1D approach withthe tipping bucket approach in DSSAT CSM using the samemeasured datasets. By integrating DSSAT with HYDRUS-1D,
crop production simulations of DSSAT can be improved byusing more accurate calculations of actual evaporation, transpiration, root water uptake, drainage, and water distributions inthe soil profile using the Richards equation.
MATERIALS AND METHODSDSSAT CSM
The Cropping System Model (CSM) within DSSAT V4.6
(Hoogenboom et al., 2015; Jones et al., 2003) contains various
crop models that are executed under the DSSAT shell. The cropmodels available are the CERES models for cereals (barley,maize, sorghum, millet, rice, and wheat), the CROPGRO
models for legumes (dry bean, soybean, peanut, and chickpea),
and additional models for root crops (cassava, potato) and othercrops (sugarcane, tomato, sunflower, and pasture). The
architecture differs from one model to another. Various controls
and management scenarios can be invoked within the shell tosimulate crop growth. The crop models can simulate single
cropping, seasonal and sequential cropping systems.
The soil water balance in DSSAT is based on a formulation
by Ritchie (1972, 1981a,b) using the concept of a drained upper
limit (DUL) and drained lower limit (LL) for available soil
water. The approach applies a simple water accounting
procedure to each layer in the soil profile (Porter et al., 2004;
Ritchie, 1985; 1998). Water from an upper layer cascades tolower layers, thereby mimicking the process of a series of linear
reservoirs. Infiltration is calculated as the difference between
rainfall/irrigation and runoff. Drainage between layers takes
place if soil water present in the layer exceeds its water holding
capacity. Drainage at the bottom of the soil profile is equal tothe drainage flux from the bottom layer. Upward flow can becaused by root water uptake due to transpiration and by soil
evaporation. Potential root water extraction depends onavailable soil water and the root length density of each layer inthe soil. Actual transpiration is calculated using reduction
factors as a function of the leaf area index and an energy
extinction coefficient. Infiltration and runoff from rainfall or
irrigation water are calculated using the USDA Soil
Conservation Service runoff curve number method (Williams,
1991). The profile is assumed to be well drained and without
any interaction with groundwater. Ritchie (1998) recommended
the use of more appropriate modeling approaches for poorly

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Vakhtang Shelia, Jirka Šimůnek, Ken Boote, Gerrit Hoogenbooom

drained conditions where the oxygen stress may affect crop
growth.
Soil water balance processes include infiltration of rainfall
and irrigation, runoff, soil evaporation, crop transpiration, root
water uptake from the various soil layers, and drainage of waterfrom the soil profile below the root zone (Boote et al., 2008).
The soil is divided into a number of computational layers, up toa maximum of 20. The water content of each layer varies
between the lower limit of plant extractable soil water [LL(j)],the drained upper limit [DUL(j)], and the saturated soil watercontent [SAT(j)], where j is a soil layer number. If the watercontent of a given layer is above DUL, water will drain to thenext layer based on the tipping bucket approach, using a
profile-wide drainage coefficient (SWCON). If available, thesaturated hydraulic conductivity (Ks) of each soil layer can beentered to control vertical drainage from one layer to the next.
This feature allows the soil to retain water above DUL in layersthat have a sufficiently low Ks, and which case soil layers may
remain saturated long enough to cause root damage, reduced
root water uptake, anoxia-induced stress, and lower N fixation.
Water between SAT and DUL is available for root water uptake,subject to the anoxia-induced restriction that is triggered when
the air-filled pore space falls below 2% of porosity (this value
can be species-dependent). The DSSAT V4.6 software includes
pedotransfer functions that compute LL, DUL, and SAT fromsand, silt, clay, soil organic carbon content, and bulk density.
The pedotransfer functions are mostly those by Saxton et al.
(1986) as described by Gijsman et al. (2002a).
The default option for computing potential evapotranspiration (PET, equivalent to ETO) is the Priestley-Taylor method(Priestley and Taylor, 1972; Ritchie, 1985), primarily because it
is less demanding for weather data and does not require daily
wind speed or dewpoint temperature data as inputs. The FAO56 Penman-Monteith method is available if wind speed anddewpoint temperature are provided. The DSSAT crop models
partition PET into potential plant transpiration (EPO) andpotential soil evaporation (ESO) following Ritchie's (1972,1985) approach, which considers the portion of net radiationthat reaches the soil surface and can be used as latent energy toevaporate water from the soil surface if the soil is wet.
Actual soil evaporation (ES) and plant transpiration (EP) depend on the availability of water to meet potential rates. The
current DSSAT CSM models allow calculations of ES using
two soil evaporation methods: Suleiman-Ritchie (Ritchie et al.,2009) and the two-stage soil evaporation method of Ritchie(1985). The first approach is based on an equation derived fromdiffusion theory by Suleiman and Ritchie (Suleiman and Ritchie, 2003). The method is currently the default option for ES
computations in DSSAT CSM. The two-stage soil evaporation
method computes ES by assuming a constant rate during theenergy-limited stage (Stage 1) and a falling-rate stage (Stage 2)that begins after the first stage loss has been met, during which
ES declines with the square root of time. Since neither theSuleiman-Ritchie method nor the two-stage soil evaporation
method are considered to particularly accurate, improvements
are still being considered.
Root water uptake must be computed before actual canopy
transpiration (EP) is computed. Potential root water uptake,
RWU(j), from each soil layer is a function of the root lengthdensity (RLD) and the soil water content of that layer, and is
calculated using a simplified approximation of radial flow toroots (Ritchie, 1985). The total soil water content (SWTD) andthe total extractable water content (SWXD) in the soil profile,among other variables, are calculated in the soil water balance
module. SWTD is equal to the water contents summed over all

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soil layers, i.e., SWTD = ΣSW(j)×DLAYR(j), where SW(j) is thevolumetric water content in the jth layer and DLAYR(j) is the
thickness of the jth layer. SWXD is similarly equal to the extractable water contents summed over all soil layers, i.e.,
SWXD = Σ(SW(j)–LL(j))×DLAYR(j).
HYDRUS-1DHYDRUS-1D is a physically-based, detailed hydrologicalmodel that simulates the relationships between soil, water, andweather, while using a sink term to account for water uptake byplant roots. The core of the model is the Richards onedimensional equation, which combines the Darcy-Buckingham
law for the fluid flux with the continuity equation as follows:

∂θ
∂ 
∂h

=
K ( h ) − K ( h ) − S∂t ∂z ∂z
(1)where θ is the volumetric water content [L3L–3], h is the pressure head [L], K is the unsaturated hydraulic conductivity[LT–1], z is the vertical coordinate positive upward [L], t is time[T], and S is root water uptake [T–1].
HYDRUS-1D simulates soil water movement by considering spatial differences in the soil water potential in the soil
profile. The governing equation is solved numerically using animplicit finite element scheme, which can be applied to bothsaturated and unsaturated conditions. The soil hydraulic functions are described using the analytical functions of van
Genuchten-Mualem (Mualem, 1976; van Genuchten, 1980),
among other formulations. HYDRUS-1D also considers, as
needed, the effects of heat on water flow and the fate andtransport of solutes in soils. Numerical solutions are provided
for both flux or pressure head controlled boundary conditions at
the top and bottom boundaries. The Penman-Monteith equation
can be used to estimate potential evapotranspiration, ETp. The
HYDRUS-1D model uses the leaf area index (LAI) or the soil
cover fraction (SC) to separate potential evapotranspiration(ETp) into potential plant transpiration (Tp) and potential evaporation (Ep) of a partially covered soil. Reductions in Tp and Ep
are calculated using a physically-based approach. Reductions in
Tp are obtained by using the Feddes et al. (1978) approach
involving stress response functions, which depend on the typeof crop. Reductions in Ep are obtained directly from the numerical solution of the Richards equation by switching from a flux
to a pressure head boundary condition when some limiting
minimum pressure head is reached (e.g., –150 m). The effectsof salinity and water/oxygen stress on actual transpiration canbe considered to be either additive or multiplicative. Surface
runoff is evaluated as infiltration-excess water calculated using
the Richards equation for specified precipitation rates and soil
hydraulic properties. Alternatively, infiltration-excess water canaccumulate on the soil surface until a specified limit is reached,after which surface runoff is initiated. Field drainage to tile
drains can be simulated using the Hooghoudt or Ernst equations
for homogeneous and heterogeneous soil profiles, respectively.
The bottom flux is calculated according to the selected bottom
boundary conditions. Several water management scenarios canbe modeled with HYDRUS-1D. For example, irrigation scheduling can be considered at fixed times or using a number ofcriteria that can trigger irrigation, such as its timing and depthof application.

Coupling DSSAT and HYDRUS-1D for simulations of soil water dynamics in the soil-plant-atmosphere system

Coupling the two models
Accurate simulations of crop growth require proper integration of models for crop growth and water flow in the unsaturated zone. There are at least three different approaches for integrating independent external programs (Peña-Haro et al., 2012):
1) One could modify the original source code of the twomodels in order to have the coupled model within the same
executable file.
2) The models could interact through their input/output files.
However, if the variables of interest are not included in thedefault input or output files, this approach requires modifications to the original source codes of the two models.
3) The original code can be wrapped using a specialized
software such as OMS3 (David et al., 2002), OpenMI(Gregersen, 2007), or PYTHON (www.python.org). This approach allows one to have access to all variables stored in amemory that is common to all programs.
In order to implement the HYDRUS-1D water flow routines
into DSSAT, the routines were simplified first by removing thesource code related to other processes, such as vapor flow andsolute transport. After some reorganization, the remaining
source code was then included directly into DSSAT CSM (bothmodels are written in the Fortran language) to obtain one single
executable program. The modular structure of DSSAT CSM(Jones et al., 2003) requires that all rate processes are calculated
each day based on the state variables from the previous day,using a daily time step. Corresponding changes were implemented to inputs/outputs from HYDRUS-1D to DSSAT. Somevariables had to be passed between the main program and the
new subsystem. These data types were defined inside a newmodule and passed between the models.

Data transfer between the models can be summarized as follows (Fig. 1): DSSAT CSM calculates crop growth by taking as
input the crop parameters and weather data. Among the output
that DSSAT CSM generates, the following parameters weretransferred to HYDRUS-1D as input: rooting depth, potential
transpiration and evaporation, and irrigation and precipitation.
Although DSSAT calculates root length distributions also, thecurrent version of the coupled model assumes the HYDRUS1D approach in which the root distribution decreases linearlywith depth from the soil surface to the bottom of the root zone.
HYDRUS-1D then calculates soil water contents and actual
transpiration and evaporation rates, integrated to a daily basis
using the variable time-steps of HYDRUS-1D, which are thensent back to DSSAT CSM. Temporal and spatial discretizations
are different in the two models and thus had to be synchronized.
While HYDRUS-1D uses variable time steps and water stressperiods are used, the structure of DSSAT CSM allows only
daily time steps. Consequently, the two models exchange information on a daily time interval, while HYDRUS-1D carriedout multiple time steps during this interval. Similarly as for the
temporal discretization, HYDRUS-1D usually uses a muchfiner spatial discretization than DSSAT CSM. HYDRUS-1D
outputs thus had to be averaged over multiple nodes, before
information could be entered into DSSAT CSM.
The coupled DSSAT-HYDRUS-1D model is now able tosimulate water flow using both the original tipping bucket
approach as well as the HYDRUS-1D based numerical solutionof the Richards equation. While HYDRUS-1D still focuses
mostly on soil water, the Cropping System Model (CSM) fromDSSAT deals nearly exclusively with plant related processes.

Fig. 1. Flowchart of the coupled DSSAT and HYDRUS-1D model.

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Vakhtang Shelia, Jirka Šimůnek, Ken Boote, Gerrit Hoogenbooom

Field experiments and data
We used data from eight treatments of six experiments released with DSSAT V4.6 (Hoogenboom et al., 2015) for soybean and peanut to evaluate the coupled model. The field
experiments were conducted between 1978 and 1990 by the
University of Florida (UF), Gainesville, Florida, and Iowa State
University (ISU), Ames, Iowa. The experimental field at UF
was located at the campus far, with soil being characterized as a
Millhopper fine sand soil (loamy, siliceous, hyperthemic
Grossarenic Paleudults). The soil profiles in the DSSAT soil
dataset (the SOIL.SOL file) are identified as IBSB910015 and
IBPN910015 for the soybean and peanut experiments, respectively. Table 1 shows the soil physical and hydraulic propertiesof nine layers of the soil profile. These data are needed alongwith other soil properties such as soil albedo (SALB, fraction),
the evaporation limit (SLU1, mm), the drainage rate (SLDR,
fraction day–1), and the runoff curve number (SLRO) to simulate soil water dynamics in DSSAT. The drainage rate, SLDR,
represents the fraction of water between the actual water content and the drained upper limit that drains from a soil layer in
one day. For the given soil, these values were SALB = 0.18,
SLU1 = 5.0 mm, SLDR = 0.5 day–1, and SLRO = 66.
Based on the particle size distribution and soil bulk density
data in Table 1, two soil layers 0–15 cm and 15–180 cm weredistinguished for the experimental field at UF, Gainesville,
consisting of sand and loamy sand, respectively. Parameters forthe van Genuchten-Mualem soil water retention functions (vanGenuchten, 1980) for these two soil textures were taken fromthe HYDRUS-1D soil catalog and then used in the DSSATHYDRUS-1D coupled model (Table 2).
The soil of the second experimental field at ISU wascharacterized as Nicollet clay loam (fine-loamy, mixed, Mesic
Aquic). This soil profile in the DSSAT soil dataset was identified
as IBSB910049. Since the IBSB910049 soil profile contains only
data necessary to run DSSAT, while lacking data on soil texture
and the saturated hydraulic conductivity, we assumed that theentire soil profile was homogeneous and used the HYDRUS-1D
default parameters for a clay loam soil (Table 2).
The soybean varieties “BRAGG” and “Williams-82” wereused in the field experiments at UF and ISU, respectively. Theirdefault cultivar coefficients are given in the soybean cultivar
file SBGRO046.CUL in DSSAT. The cultivar coefficients for
BRAGG are shown in Table 3. The peanut experiments werecarried out using the variety “Florunner”. Its cultivar-specificparameters as used in the DSSAT CSM and coupled DSSATHYDRUS-1D simulations are also listed in Table 3. Several
parameters of the soybean and peanut varieties had been calibrated earlier for the DSSAT CSM simulations. Specifically,
the BRAGG and Florunner cultivar coefficients were takenfrom the standard DSSAT V4.6 release, as previously calibrated for DSSAT V4.6 based on a full set of 7 and 18 treatments,
respectively. The peanut variety Florunner was calibrated forthe coupled DSSAT-HYDRUS-1D model using data from thefield experiment conducted at UF in 1989. Modified values ofseveral cultivar coefficients of this variety for the coupled model
are given in Table 3, together with those used for DSSAT CSM.
The datasets, distributed with DSSAT V4.6 and used forevaluation of the coupled model, were from a) soybean
experiments UFGA7801 (1978, treatment 1, irrigated, 206 mm
in 21 applications, and treatment 2, rainfed), IUAM8801 (1988,
treatment 1, rainfed) and IUAM9001 (1990, treatment 1,
rainfed), and b) peanut experiments UFGA8401 (1984,
treatment 1, irrigated, 365 mm in 16 applications, and treatment
2, rainfed, except irrigation, 243 mm in 9 applications, after
water deficit period was relieved), UFGA8601 (1986, treatment
1, rainfed), and UFGA8701 (1987, treatment 1, irrigated, 115
mm in 8 applications). The names of the experiments follow the
DSSAT file name convention and are constructed from 8 characters that include an institute code (2 characters), a site code (2characters), the year of the experiment (2 characters), and an
experiment number (2 characters). For example, UFGA7801 is
an experiment (01) conducted by the University of Florida (UF)at Gainesville (GA) in 1978 (78) and IUAM8801 is an experiment (01) conducted by the Iowa State University (IU) at Ames
(AM) in 1988 (88). Available measurements included total cropmass, leaf mass, stem mass, pod mass, grain mass, leaf areaindex (LAI), the V-stage (leaf number on main axis), specificleaf area, leaf nitrogen concentration, grain number, unit grainmass, pod number, shelling % and dates for anthesis, first pod,
first seed, and physiological maturity.

Table 1. Soil physical and hydraulic properties of the experimental field at the University of Florida, Gainesville.
Depth
cm
0–5
5–15
15–30
30–45
45–60
60–90
90–120
120–150
150–180

Particle size distribution, %
clay
0.9
0.9
4.6
5.8
5.8
9.6
9.6
8.3
8.3

silt
11.8
11.8
6.4
5.4
5.4
4.2
4.2
3.6
3.6

sand
87.3
87.3
89
88.8
88.8
86.2
86.2
88.1
88.1

Bulk
density

Lower
limit

g cm–3
1.36
1.40
1.46
1.46
1.47
1.43
1.48
1.57
1.79

cm3 cm–3
0.023
0.023
0.023
0.023
0.023
0.021
0.020
0.027
0.070

Upper
limit,
drained
cm3 cm–3
0.086
0.086
0.086
0.086
0.086
0.076
0.076
0.13
0.258

Upper
limit,
saturated
cm3 cm–3
0.230
0.230
0.230
0.230
0.230
0.230
0.230
0.230
0.360

Saturatedhydraulicconductivity
cm h–1
7.40
7.40
15.80
28.00
28.00
27.60
17.50
0.30
0.10

Organic
carbon
%
0.9
0.69
0.28
0.20
0.20
0.09
0.03
0.03
0.03

Root
growthfactor
1.00
1.00
0.55
0.32
0.32
0.38
0.40
0.30
0.20

Table 2. Values of the van Genuchten-Mualem soil hydraulic parameters as obtained from the HYDRUS-1D Soil Catalog (Carsel andParrish, 1988) for two experimental fields soils (θr is the residual water content, θs is the saturated water content, α, n, and l are empiricalparameters, and Ks is the saturated soil hydraulic conductivity).
Soil ID
IBPN910015,IBSB910015
IBPN910015, IBSB910015
IBSB910049

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Depth
cm
0–15
15–180
0–202

Soil type by texture
Fine sandLoamy fine sandClay loam

θr
cm3/cm3
0.045
0.057
0.079

θs
cm3/cm3
0.43
0.41
0.442

α
1/cm
0.145
0.124
0.016

n

l

2.68
2.28
1.414

0.5
0.5
0.5

Ks
m/day
7.13
3.50
0.082

Coupling DSSAT and HYDRUS-1D for simulations of soil water dynamics in the soil-plant-atmosphere system
Table 3. Cultivar coefficients for the soybean variety BRAGG (SB) and the peanut variety Florunner (PN) used in the DSSAT-CSM andthe coupled DSSAT-HYDRUS-1D simulations.
#1

Coeff.
CSDL

SB
12.33

PN
11.84

Definitions
Critical short-day length, hour#
10

Coeff.
SIZLF

SB
170

PN
18.0

2

PPSEN

0.32

0.00

11

XFRT

1.0

0.92

3

EM-FL

19.5

21.2,
20.2

12

WTPSD

0.17

0.69,
0.65

4

FL-SH

10.0

9.2,
8.2

Slope of the relative response ofdevelopment to photoperiod withtime, 1/hour
Time between plant emergenceand flower appearance (R1),
photothermal days
Time between first flower andfirst pod (R3), photothermal days

13

SFDUR

24

40

5

FL-SD

15.2

18.8,
24.8

14

SDPDV

2

1.65,
1.6

6

SD-PM

37.6

74.3,
77.3

15

PODUR

10

24,
26

7

FL-LF

19

88,
90

16

THRSH

78

80

8

LFMAX

1.0

1.4

17

SDPRO

0.40

0.27

9

SLAVR

355

260,
264

Time between first flower andfirst seed (R5), photothermal
daysTime between first seed (R5) andphysiological maturity (R7),
photothermal days
Time between first flower (R1)and end of leaf expansion,
photothermal days
Maximum leaf photosynthesis
rate at 300 C, 350 vpm CO2, andhigh light, mg CO2/m2-s
Specific leaf area of cultivar
under standard growth conditions, cm2/g

18

SDLIP

0.20

0.51

The numerical time steps of HYDRUS-1D in the coupledmodel varied between 10–3 and 1 day, while the time step in
DSSAT CSM was always 1 day. The soil profile in HYDRUS1D was discretized into 1-cm soil layers in the top 30 cm, 2-cm
soil layers down to a depth of 150 cm, and 3-cm soil layers
until a depth of 180 or 202 cm.

Several statistical indicators were used to compare the performance of the original and coupled models and their capability to describe the experimental data. The indicators included
the Mean, the Root Mean Square Error (RMSE), the normalized
Root Mean Square Error (nRMSE) (Loague and Green, 1991),
the index of agreement (d) (Willmott et al., 1985), the Model
Efficiency (EF) (Nash and Sutcliffe, 1970), and the Mean Error(E).
The RMSE was computed using (P −O )RMSE =
n

i

2

i

(2)

n

where Pi and Oi are the simulated and observed values for the
ith measurement, respectively, and n is the number of observations.
The nRMSE was computed as:nRMSE =

RMSE
×100%
O

(3)

where O is the overall mean of observed values.
The d index was calculated using the equation:

 (P −O )nd = 1−(i =1

i

Fraction oil in seeds,g(oil)/g(seed)

while the Model Efficiency (EF) was computed using (P − O )EF = 1 − ( O −O )
n

2

i

i =1
n

i

2

(5)

i

i =1

 (P −O )
E=
n

i =1

i

i

n

(6)

A high value of the d index ( 0 ≤ d ≤ 1 ) and a low value of
RMSE indicate a good fit between the simulated and observed
values. Since EF has no unit, this criterion can be used to compare the accuracy of model outputs for different variables. Itsvalue can range from −∞ to 1. The closer the model efficiency
is to 1, the more accurate the model, with EF = 1 corresponding
to a perfect match between simulated values and observed data.
A value of zero for the efficiency E indicates that the predictions are as accurate as the mean of the observed data, whereas
an efficiency less than zero suggests that the observed mean is abetter predictor than the model. E is an indicator of whether themodel predictions tend to underestimate (if negative) or overestimate (if positive) the measured data.
RESULTS AND DISCUSSION
The two approaches for soil water modeling in DSSAT were
evaluated based on their ability to accurately estimate the soil
water balance, the soil water content by soil layer, potential andactual evapotranspiration rates and, subsequently, crop growth,which was characterized using both total and individual organ
biomass accumulation.

2i

 i =1 Pi − O + Oi − OnSeed filling duration for pod
cohort at standard growth conditions, photothermal days
Average seed per pod understandard growing conditions,#/podTime required for cultivar to reach
final pod load under optimal
conditions, photothermal days
Maximum ratio of(seed/(seed+shell)) at maturity,
Threshing percentage
Fraction protein in seeds,g(protein)/g(seed)

and the Mean Error (E) as

Statistical indicators

i =1

Definitions
Maximum size of full leaf (threeleaflets), cm2
Maximum fraction of daily
growth that is partitioned to seed
+ shellMaximum weight per seed, g

)

2

(4)

237

Vakhtang Shelia, Jirka Šimůnek, Ken Boote, Gerrit Hoogenbooom

Soil water balance
Since coupling added new functionality to the soil water
simulations in DSSAT, the performance of the original andcoupled models was evaluated first in terms of their ability to
predict soil water contents (total and extractable) in the entire
soil profile and particular soil layers during the crop growing
season. Both irrigated or rainfed treatments were considered in
the analysis.
For the UFGA7801 soybean experiment, the initial total water content in the soil profile 180 cm deep was 214 mm (equivalent to field Capacity in DSSAT-CSM) for both treatments(treatment 1 was irrigated and treatment 2 was rainfed). Simulations with both models showed a similar tendency of describing
the dynamics of the total soil water content (SWTD) (Fig. 2a,b).
Similar simulated trends can also be seen for extractableamount of soil water (SWXD) (Fig. 2c,d). The extractable soil
water as obtained with the coupled model was in general higher
than when simulated with the original model. For example, one
can see that the SWXDs simulated using both models werehigher than those measured in treatment 2 of the 1978 soybean
experiment (Fig. 2d). Values of the d index were 0.95 and 0.90
and those of RMSE were 26.5 mm and 41.3 mm for the original
and coupled models, respectively.
The soybean experiments at Ames, Iowa in 1988 and 1990
included data about the Williams-82 soybean variety grownunder rainfed conditions. The 1988 season showed a major
drought, which allowed us to contrast its results with the cooland rainy season of 1990. Soil water contents during the 1988
experiment were measured with a neutron probe. Initial conditions and water content distributions with depth were different
in the 202-cm deep soil profile for these soybean experiments.
For treatment 1 of the IUAM8801 experiment and treatment 1of the IUAM9001 experiment, the total water contents (SWTD)
were 628 mm and 574 mm, respectively, with water contents indifferent layers being close to the drained upper limit.
Simulations by both models showed a similar tendency ofdescribing soil water content dynamics for the Iowa experiments (Fig. 3). On 103 DAP (days after planting) of the 1988

growing season, a large 142-mm rainfall event occurred (Fig.
3a). The original and coupled models reacted from this byincreasing the total water content by 80 mm and 136 mm, respectively (Fig. 3a). While the tipping bucket approach limited
infiltration as determined by the SCS curve number, the watercontent of the near-surface soil layer, and the total soil drainage
coefficient (SWCON) (see Table 2), the HYDRUS-1D approach, which considers not only gravity but also capillarity,
allowed larger infiltration. Both modeling approaches produced
similar results for the cool and rainy season of 1990, while thecoupled model slightly overpredicted water contents during
most of the season (Fig. 3b,d).
Comparisons between volumetric water contents measured
in the different layers during the 1988 soybean experiment andsimulated using the DSSAT CSM and the DSSAT-HYDRUS1D models are illustrated in Fig. 4. In most cases, the d index
was approximately 0.9 for the original model and 0.8 for the
coupled model. The RMSE values for the original and coupledmodels were 0.08 and 0.05, respectively. The coupled model
simulated more soil water depletion in the deeper layers during
the early part of the season. The two modeling approaches
again showed different responses to a large rainfall amount(142 mm) on 103 DAP. The original model failed to re-saturate
the soil profile because of excessively high runoff caused byincorrect application of the SCS runoff number to a cracking
clay soil, while the coupled model simulated a much large
increase in the total water content on day 103 due to better
simulations of infiltration.
The initial total water content (214 mm) of the 180-cm deep
soil profile, and the water contents at depths of 5, 15, 30, 45,
60, 90, 120, 150, and 180 cm, were the same in all three peanut(UFGA8401, UFGA8601, and UFGA8701) experiments at
Gainesville, FL when the simulations started on 19, 5, and 2days before planting, respectively. The coupled model showed
lower water contents at the beginning of all simulations throughabout 40–50 days after planting (DAP) in both treatments of the
UFGA8401 experiment (Fig. 5a,b). This was expected forsandy soils that have very low water retention properties.

Fig. 2. Total water contents (SWTD) (a,b) and extractable water contents (SWXD) (c,d) in the 0–180 cm soil profile as simulated with the
DSSAT CSM and DSSAT-HYDRUS-1D models for an irrigated treatment 1 (left) and rainfed treatment 2 (right). Also shown are measured total plant extractable soil water (d) and irrigation and rainfall amounts (a, b) during the BRAGG soybean growing season at Gainesville, FL, in 1978.

238

Coupling DSSAT and HYDRUS-1D for simulations of soil water dynamics in the soil-plant-atmosphere system

Fig. 3. Total water contents (SWTD) (a,b) and extractable water contents (SWXD) (c,d) in the 0–202 cm soil profile as simulated with the
DSSAT CSM and DSSAT-HYDRUS-1D models for 1988 (left) and 1990 (right). Also shown are rainfall amounts during the Williams-82
soybean growing season at Ames, IA, in 1988 (a) and 1990 (b).

Fig. 4. Measured and simulated water contents of six soil layers as simulated with the DSSAT CSM and DSSAT-HYDRUS-1D models
during the Williams-82 soybean growing season at Ames, IA, in 1988.

After this initial period, water contents simulated with thecoupled model were slightly higher than those simulated withthe original model. The two models adequately reacted to precipitation and showed similar water content dynamics, albeit atdifferent levels (Fig. 5).

Daily and seasonal evapotranspiration
The performance of the two models was also evaluated interms of their ability to predict potential and actual evapotranspiration rates. Potential evapotranspiration dynamics and theirvalues were the same for both models (Figs. 6a,b and 7a,b).

239

Vakhtang Shelia, Jirka Šimůnek, Ken Boote, Gerrit Hoogenbooom

Fig. 5. Total water contents (SWTD) (a,b) and extractable water contents (SWXD) (c,d) as simulated with the DSSAT CSM and DSSATHYDRUS-1D models for treatments 1 (left) and 2 (right). Also shown are rainfall and irrigation amounts for the two treatments of the
Florunner peanut experiment at Gainesville, FL in 1984 (a,b).

Fig. 6. Potential evapotranspiration (EOAA) (a,b) and actual evapotranspiration (ETAA) (c,d) rates as simulated with the DSSAT CSM andthe DSSAT-HYDRUS-1D models for Treatment 1 (left, irrigated) and Treatment 2 (right, rainfed) of the BRAGG soybean experiment at
Gainesville, FL, in 1978.

Actual evapotranspiration rates differed significantly between the two models. The coupled model showed less variations in its predictions, although the trend was similar comparedto the original model (Figs. 6c,d and 7c,d). For both
UFGA7801 soybean treatments during full canopy, evapotranspiration values varied between 2.5–5 mm/day and 3–7 mm/day
for the coupled and the original models, respectively.
The coupled and original models produced comparable values for the evapotranspiration rate at the beginning of the growing season (about 3 mm and 3.5 mm, respectively) and at theend of the season (1.5–2.5 mm and 1–3 mm, respectively), forirrigated treatment 1 (Fig. 6c). Differences were more significant in the case of the treatment 2 (rainfed): about 2.5 mm and
0.5–1 mm at the beginning of the season and 1–2 mm and240

0.5–1 mm at the end of the season for the coupled and originalmodels, respectively (Fig. 6d). However, the seasonal patternsof the coupled model differed from the original CSM. The
coupled model had higher evapotranspiration (ETAA) rates
during the early season when LAI values were still low, but
lower ETAA rates in mid-season with its higher LAI values.
DSSAT CSM results showed differences in the average
daily evapotranspiration rates between for the irrigated andrainfed treatments of the UFGA8401 peanut experiment (Fig.
7c,d), for only 24 out of 143 days of simulations, withdifferences that varied between 1.4–3 mm/day. The coupledmodel showed differences in evapotranspiration between the
two treatments during 80 days of the 143 simulation days, withmaximum differences of about 2 mm (Fig. 7c,d). By comparison,Coupling DSSAT and HYDRUS-1D for simulations of soil water dynamics in the soil-plant-atmosphere system

Fig. 7. Potential (EOAA) (a,b) and actual (ETAA) (c,d) evapotranspiration rates and extractable soil water (SWXD) (e,f) as simulated with
the DSSAT CSM and the DSSAT-HYDRUS-1D models for Treatment 1 (left, irrigated) and Treatment 2 (right, rainfed) of the Florunner
peanut experiment at Gainesville, FL, in 1984.

Fig. 8. Simulated and observed crop mass for the BRAGG (a,b) soybean experiments at Gainesville, FL in 1978, and LAIs and seed mass
for the Williams 82 (c–f) soybean experiments at Ames, IA in 1988 and 1990, respectively. Simulations were carried out using the DSSAT
CSM model with its tipping bucket approach and the coupled DSSAT-HYDRUS-1D model.

241

Vakhtang Shelia, Jirka Šimůnek, Ken Boote, Gerrit Hoogenbooom

the original model did not show any differences for the twotreatments after 98 DAP, although 48 mm irrigation wasapplied on day 98 after planting and 139 mm during the periodof 98–114 DAP, while the coupled model simulated more
evapotranspiration for irrigated treatment 1 until 115 DAP.
Fig. 7 shows results obtained with the two modelingapproaches for the time interval of 25–115 DAP out of 0–143
DAP when differences were found between the irrigated andrainfed treatments. Similarly as for the soybean example, thecoupled model tended to produce lower values than the CSM
approach during much of the season.
Crop growth simulations
The comparison between simulated and observed yield components during the growing season indicated the different per-formance of the DSSAT-HYDRUS-1D and original CSM models. Of the three soybean experiments, the coupled model performed worst for treatment 2 of the UFGA7801 experiment,
which showed a very low value of the d index (0.47) for pod
mass, compared to 0.91 for the original model (Table 4).
The EF values were negative for this treatment as well. The
original and coupled models performed well for treatment 1,
producing relatively high values (0.99) of the d index for pod
and stem mass, and with slightly better d index values for theoriginal model for crop mass and LAI (0.99 and 0.95,
respectively) than the coupled model (0.98 and 0.95,
respectively). The coupled model performed better for the
IUAM9001 experiment (Table 4, Fig. 8d,f) in terms of all
statistical measures than for the IUAM8801 soybean
experiment (Table 4, Fig. 8c,e).

Table 4. Comparisons of observed and simulated crop mass, pod mass, stem mass, and LAI using the original DSSAT CSM model (CSM)
and the coupled DSSAT-HYDRUS-1D model (HYD) for three soybean experiments and their statistical indicators.
Mean (Obs.)

Mean (Sim.)CSM HYD

Biomass component

Trt #Crop mass, kg/ha
Pod mass, kg/ha
Stem mass, kg/ha
LAI
Crop mass, kg/ha
Pod mass, kg/ha
Stem mass, kg/ha
LAI

1
1
1
1
2
2
2
2

4485
2336
2069
2.85
2960
655
1714
2.61

4501
2280
2099
2.89
3054
851
1714
2.68

4722
2514
2107
3.1
3541
2510
2093
3.0

Crop mass, kg/ha
Pod mass, kg/ha
Stem mass, kg/ha
LAI

1
1
1
1

4220
2043
1966
2.98

3854
1568
1888
2.81

4280
2340
1769
2.94

Crop mass, kg/ha
Pod mass, kg/ha
Stem mass, kg/ha
LAI

1
1
1
1

4267
2304
2001
3.33

4511
2577
2055
3.31

4553
2575
2082
3.36

RMSE
nRMSE
CSM HYD CSM HYD
UFGA7801
478
731
10.7
16.3
227
317
9.7
13.6
220
273
10.6
13.2
0.4
0.6
14.3
22.6
468
1191
15.8
39.3
263
2010
40.1 232.4
253
596
14.8
34.6
0.56
1.2
21.3
51.1
IUAM8801
525
872
12.4
20.6
530
678
26.0
33.2
216
301
10.9
15.3
0.39
0.98
13.3
32.7
IUAM9001
454
478
10.6
11.2
396
393
17.2
17.1
184
196
9.2
9.8
0.23
0.22
6.9
6.7

d

EF

E

CSM

HYD

CSM

HYD

CSM

HYD

0.99
0.99
0.99
0.98
0.97
0.91
0.98
0.96

0.98
0.99
0.99
0.95
0.86
0.47
0.91
0.85

0.96
0.98
0.96
0.93
0.89
0.57
0.92
0.84

0.83
0.94
0.96
0.92
0.20
–13.38
0.47
0.4

16
–56
30.87
0.04
94.3
196.4
0
0.07

236
178
38.6
0.22
510
1644
374
0.74

0.99
0.96
0.99
0.99

0.98
0.96
0.99
0.94

0.97
0.86
0.98
0.95

0.91
0.78
0.95
0.72

–366
–474
–78.6
–0.17

59.8
297
–197
–0.04

0.99
0.99
0.99
0.99

0.99
0.98
0.99
0.99

0.98
0.95
0.98
0.99

0.98
0.95
0.98
0.99

244
272
53.7
–0.03

285
270
80.5
0.03

Table 5. Comparisons of observed and simulated crop mass, pod mass, stem mass, and LAI using the original DSSAT CSM model (CSM)
and the coupled DSSAT-HYDRUS-1D model (HYD) for three peanut experiments and their statistical indicators.
Mean (Sim.)CSM HYD

Biomass component

Trt #Mean (Obs.)

Crop mass, kg/ha
Pod mass, kg/ha
Stem mass, kg/ha
LAI
Crop mass, kg/ha
Pod mass, kg/ha
Stem mass, kg/ha
LAI

1
1
1
1
2
2
2
2

6781
3290
2588
3.74
6686
3079
2697
3.59

5806
2738
2236
3.39
5370
2406
2146
3.08

6035
2544
2445
3.71
6031
2522
2452
3.7

Crop mass, kg/ha
Pod mass, kg/ha
Stem mass, kg/ha
LAI

1
1
1
1

8363
2711
3694
5.19

6991
2853
2675
3.82

8251
3187
3281
4.9

Crop mass, kg/ha
Pod mass, kg/ha
Stem mass, kg/ha
LAI

1
1
1
1

7336
3103
2649
3.89

7175
2873
2826
4.13

8011
2675
3454
5.1

242

RMSE
nRMSE
CSM HYD CSM HYD
UFGA84001
1197
974
17.7
14.4
728
1029
22.1
31.2
472
388
18.2
15.0
0.54
0.41
14.6
11.1
1654 1076
24.7
16.1
838
862
27.2
28.0
799
478
29.6
17.7
0.75
0.67
20.9
18.8
UFGA86001
1641
734
19.6
8.8
288
628
10.7
23.2
1164
631
31.5
17.1
1.77
1.11
34.1
21.5
UFGA8701
514
890
7.0
12.1
458
649
14.8
20.9
300
973
11.3
36.7
0.61
1.37
15.7
35.2

d

EF

E

CSM

HYD

CSM

HYD

CSM

HYD

0.98
0.97
0.97
0.97
0.96
0.95
0.92
0.94

0.99
0.94
0.98
0.99
0.98
0.96
0.98
0.96

0.92
0.89
0.89
0.91
0.85
0.84
0.72
0.81

0.95
0.78
0.93
0.95
0.93
0.83
0.90
0.85

–975
–552
–351
–0.35
–1316
–673
–552
–0.52

974
–746
–143
–0.04
–654
–558
–245
0.11

0.96
0.99
0.87
0.81

0.99
0.98
0.96
0.92

0.87
0.98
0.57
0.41

0.97
0.91
0.87
0.77

–1371
141
–1019
–1.37

–112
475
–413
–0.3

0.99
0.99
0.99
0.96

0.99
0.98
0.89
0.86

0.98
0.95
0.94
0.83

0.95
0.90
0.32
0.16

–161
–230
177
0.24

675
–427
805
1.23

Coupling DSSAT and HYDRUS-1D for simulations of soil water dynamics in the soil-plant-atmosphere system

Fig. 9. Simulated and observed crop mass (a,b), LAIs (c,d) and pod mass (e,f) for the Florunner peanut experiments at Gainesville, FL in
1984, 1986, and 1987. Simulations were carried out using the DSSAT CSM model with its tipping bucket approach and the coupled
DSSAT-HYDRUS-1D model.

In both treatments of the UFGA8401 peanut experiment, thecoupled model performed better with higher values of both thed index and EF, and lower values of nRMSE, as compared tothe original model (Table 5, Fig. 9a,b). Treatment 1 of the
UFGA8601 peanut experiment showed again better performance of the coupled model for crop mass, stem mass, and LAI.
But the coupled model performed slightly worse for pod mass,
for which the d index of 0.98 was a fraction lower than 0.99 forthe original model, while the EF of 0.91 was also lower than
0.98 for the original model (Table 5, Fig. 9c,e). For treatment 1of the UFGA8701 peanut experiment, the original model performed better with higher values of the d index and EF. Bothmodels had the same d index value (0.99) for crop mass, although the RMSE for the original model was better as comparedto the coupled model: 514 kg/ha and 890 kg/ha, respectively(Table 5 and Fig 9d,f).
The various DSSAT CSM and DSSAT-HYDRUS-1D simulations of soybean and peanut crop production overall showed
differences when applied to water-limiting conditions using
several different datasets for two locations. As shown above,the coupled model in general predicted less evapotranspirationand higher water contents in the soil profile, which resulted inless water stress, higher LAI values and more biomass than theoriginal DSSAT CSM model. While the simulations as suchshowed the great potential of the coupled model, several improvements may be possible in the analysis of the experiments
discussed in this study. These include getting better soil texture

data (and hydraulic property information) especially for thesoybean experiment at ISU, improving root length distribution
data, and improving the partitioning of potential ET into potential evaporation (Ep) and transpiration (Tp). Availability of suchinformation should further improve the reliability and accuracyof the predictions.
CONCLUSIONSHYDRUS-1D was coupled in this study with the Cropping
System Model (CSM) of DSSAT V4.6 to simulate soil water
dynamics. Simulations with the coupled model were comparedwith results obtained with the original model that uses a tipping
bucket approach for soil water modeling. Performance of thetwo models was evaluated in terms of simulated soil watercontents, evapotranspiration, and various crop yield components. Results showed that the coupled model provided satisfactory simulations of soil water content changes as well as theplant growth. However, the two models provided different
predictions for crop, pod, seed, and stem mass, and the crop
LAI, especially under water-limited conditions for soybean in
Iowa and Florida and peanut in Florida. The coupled model, inthat case, simulated insufficient yield reductions under drought,
possibly related to its lower evapotranspiration rate during thefull canopy phase. The coupled model produced significantly
different results for actual evapotranspiration rates, being higher during an early canopy phase, but lower during the full cano-243

Vakhtang Shelia, Jirka Šimůnek, Ken Boote, Gerrit Hoogenbooom

py phase when LAI was high. These differences in evapotranspiration rates contributed to differences in simulated soil watercontents at different times of the season, even though the dynamics of water movement by the coupled model was simulated well. Each of the two approaches to soil water modelinghas particular advantages and disadvantages, which should betested in further studies against more comprehensive datasets
related to the soil and plant water regimes.
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Received 23 September 2017
Accepted 11 December 2017

Note: Colour version of Figures can be found in the web version of this article.

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