Simulating Root Water Uptake from a Heterogeneous
Vegetative Cover
M. S. De Silva1; M. H. Nachabe2; J. Šimůnek3; and R. Carnahan4
Abstract: A small body of knowledge exists on the root water uptake 共RWU兲 and evapotranspiration in humid environments having a
mixture of natural vegetative cover. In this paper, we assess the impacts of atmospheric conditions and land cover on RWU from a naturalvegetative cover. An intensive field investigation was carried out to monitor water table fluctuations along two flow transects comprising
an upland grass area and a lowland riparian zone. Calibration and validation of the soil hydraulic parameters using the two-dimensionalvariably saturated ground water flow model, HYDRUS-2D, confirms the reliability of the model to simulate satisfactorily the large-scale
daily fluctuation of RWU. Simulation results revealed that the actual RWU during the wet season is about 40% higher than RWU in thedry season due to high water table levels and temperatures prevailing throughout the wet season. Simulation results using HYDRUS-2D,
which was modified to accommodate variable surface boundary conditions and heterogeneous root distribution, showed that the RWU
from the riparian zone was 38 and 56% higher than RWU from the pasture land during the dry and wet seasons, respectively.
DOI: 10.1061/共ASCE兲0733-9437共2008兲134:2共167兲
CE Database subject headings: Finite element method; Models; Vegetation; Transpiration; Evapotranspiration.

Introduction
On average, about 70% of the mean annual rainfall over the continents is returned to the atmosphere as evapotranspiration 共ET兲
共Bedient and Huber 2002兲. Despite its significance in any waterbalance, little research has been carried out to simulate transpiration from catchments comprising heterogeneous vegetative covers
共Lawrence 2002; Nachabe et al. 2005兲. Transpiration, the evaporation of water from leaves’ surfaces, is driven by atmosphericconditions, and is limited by the ability of vegetations’s roots to
uptake available soil water. In most catchments, however, the root
water uptake 共RWU兲 varies both spatially and temporally. Spatial
variation in RWU is often attributed to the heterogenous root
distribution in the rhizosphere reflecting the different plant typecovering the landscape. Pasture and grassland have roots thatrarely exceed a meter, whereas wooded vegetation have longer
roots allowing access to water in deeper subsurface layers. Temporal variability in RWU stems from the continuous change inatmospheric conditions, such as solar radiation, cloud cover, andwind speed. In addition, the spatiotemporal variability of avail1

Assistant Professor, Dept. of Civil Engineering, Univ. of Ruhuna,
Ruhuna, Sri Lanka.
2
Associate Professor, Dept. of Civil and Environmental Engineering,
Univ. of South Florida, 4202 E. Fowler Ave., ENB 118, Tampa, FL 33620
共corresponding author兲. E-mail: nachabe@eng.usf.edu
3
Professor, Dept. of Environmental Sciences, Univ. of California, Riverside, CA.
4
Professor, Dept. of Civil and Environmental Engineering, Univ. ofSouth Florida, Tampa, FL 33620.
Note. Discussion open until September 1, 2008. Separate discussions
must be submitted for individual papers. To extend the closing date by
one month, a written request must be filed with the ASCE Managing
Editor. The manuscript for this paper was submitted for review and possible publication on May 17, 2006; approved on January 30, 2007. This
paper is part of the Journal of Irrigation and Drainage Engineering,Vol. 134, No. 2, April 1, 2008. ©ASCE, ISSN 0733-9437/2008/2-167–
174/$25.00.

able soil moisture often enhances the variability in RWU. Recently, Vrught et al. 共2001a,b兲 simulated successfully RWU for asmall-scale single almond tree. Currently, however, a relatively
small body of knowledge exists that applies to watersheds having
heterogeneous natural vegetative cover. This is particularly important in Florida and the southeast United States where different
vegetation such as pasture, pine prairies, and wooded wetlands
coexist naturally within 1 km2.
In humid environments, such as Florida and the southeastern
United States, the water table is shallow, allowing roots to take upwater from both a thin unsaturated vadose zone and groundwater.
Typically, RWU from phreatophytes—deep rooted plants that extract water directly from the capillary fringe and the water table—
suppresses the local water table creating ground water dischargezones. This ground water discharge originates from surrounding
landscape and is driven by hydraulic gradients toward the locallysuppressed water table. Therefore, heterogeneity in vegetative
cover and associated RWU triggers local to medium scale 共from⬃10 to 1,000 m兲 subsurface flow in support of the variable RWU
over the landscape 共McWhorter and Sunada 1985; Nachabe et al.
2005兲. Indeed, a shallow water table in the discharge zone exhibits diurnal fluctuation: the water table, which declines rapidly during daylight hours due to ET, partially recovers at night by groundwater discharge. Diurnal fluctuation in water tables were reported
in numerous studies in the past 共Meyboom 1967; McWhorter andSunada 1985; Nachabe et al. 2005兲.
In Florida, minimum transpiration rates generally occur during
the coldest months of the year, whereas maximum rates coincidewith the summer season 共Hanson 1991兲. On clear days, the rate oftranspiration increases rapidly in the morning reaching a maximum in the early or mid afternoon. Additionally, in Florida’s flat
topography, a small change in elevation results in significant
variation in vegetative cover. Variation of topography and vegetative cover over short scales creates heterogeneous land hydrological response in terms of runoff, ET, and associated RWU.
In this study, water table fluctuations were monitored along
flow transects covering two types of vegetative landscapes in

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Fig. 1. Aerial photograph of study area showing distribution of observation wells

Lithia, west central Florida. As shown in Fig. 1, the vegetative
cover consisted of pasture upland and a riparian zone close to thecreek. Observation wells, rainfall gauges, and a total weather station were maintained at the site. These data were collected tocalibrate and set the boundary conditions for HYDRUS-2D, a
two-dimensional finite-element variable saturation flow model
共Šimůnek et al. 1999兲. Observation wells were distributed along aflow path to capture the subsurface flow between upland and lowland landscapes. This subsurface flow is often driven by the heterogeneous RWU and ET between the different landscapes. Basedon additional soil investigation, van Genuchten’s 共1980兲 model
was fitted to describe the soil hydraulic parameters. The optimization algorithm available with HYDRUS was adopted to finetune the soil hydraulic parameters until the observed water tablelevels agreed with the simulated water table levels. After successful calibration, the model was used to assess the impacts of variable atmospheric conditions and vegetation on RWU for thismixture of vegetation cover.
There were two objectives for this research. The first objective
was to demonstrate how, following calibration and validation, amultidimensional variable saturation flow model predicted RWUfrom a heterogeneous vegetative cover. One dimensional models
cannot capture lateral subsurface flow processes in the presenceof heterogenous vegetative cover. This was particularly importantin environments where landscapes and vegetative covers vary at
short scales. The second objective was to assess the impact ofvariations in atmospheric conditions and vegetation cover on
RWU in humid environments with a shallow water table using
field data collected at Lithia, west central Florida.

Materials and Methods
Site Description
The study site was in Lithia around the proposed Tampa Bay
Regional Reservoir in the southeast Hilsborough county in west
central Florida. The predominant soil in the area was Myakka finesand with pockets of Mulat fine sand, which were characterized
by the USDA Soil Conservation Service as deep poorly drainedand very poorly drained sands, respectively 共Carlisle et al. 1989兲.
The sands at this site were typical west central Florida soils withhigh permeability in the surface and subsurface layers 共Carlisle etal. 1989兲. The topography varied between +22.9 and +27.4 m
above national geodetic vertical datum 共NGVD兲. The surficial

water table aquifer is only 4.6 m deep and is poorly connected tothe intermediate aquifer below 共Thompson 2003兲. The region of
the study area has a humid subtropical climate with a cool dry
winter and a warm summer rainy season constituting a strong
climate cycle. The average yearly rainfall is 1.57 m. Part of thesite has been utilized for cattle ranching and farming in the past.
Two flow paths 共transects兲 on the eastern and western sides ofLong Flat Creek were delineated from topographic maps for thearea 共see Fig. 1兲. Observation wells were distributed along thetransects to monitor water table variability with time. Observation
wells from PS1 to PS5 constituted the eastern transect and wellsfrom PS39 to PS43 constituted the western transect. The transects
were separated by Long Flat Creek, which runs through the
middle of the forested riparian zone in Fig. 1. The eastern transect
was 295 m long with an average slope of 1.3% and a total topographic relief of 3.94 m. The western transect was 300 m longwith an average slope of 1.2% and a total relief of 3.45 m. The
topography of the transect, including location and elevation ofeach observation well, were surveyed with total station surveying
equipment 共TOPCON, Tokyo Optical Instruments, Tokyo兲.
As shown in the aerial photograph in Fig. 1, each transect hada mixture of vegetation. Typically, the upland area 共e.g., area
PS1–PS4 on the eastern transect兲 was covered with pasture grass.
The vegetative cover changed into a mixture of deep rooted trees
in the riparian forested zone close to the creek 共e.g., area between
PS4 and creek兲. A variety of species could be identified including
Bahia grass, cabbage, scrubs, oak, and maple trees.
Field Data
In order to obtain independent data sets to calibrate and validate
the model, an extensive fieldwork was carried out. A high resolution 共±2 mm兲 pressure transducers 共Instrumentation Northwest,
Inc PS9800兲 was placed in each observation well to record watertable variation. The pressure transducers were connected to dataloggers that recorded water table continuously at 5 min intervals.
Although gaps existed, graphical presentation and screening ofdata sets did not reveal outliers for the duration of this study
which extended from October 2001 until July 2002. All pressuretransducers were calibrated before installation. Also readings
from pressure transducers were compared routinely with manual
observations of water table levels.
In addition, undisturbed soil samples were collected using hollow stem auger and split-spoon sampling methodology at two
locations on the eastern transect 共PS1 and PS5兲 and three locations in the western transect 共PS41, PS42, and PS43兲. Altogether
twenty samples at various depths were collected and analyzed in
the laboratory 共Thompson 2003兲. Samples were sealed with duck
tape to preserve the water content of the samples. Since the textural analysis test is destructive, the conductivity test was done
before the texture test. The falling head permeability test was
utilized to estimate the saturated hydraulic conductivity. Porosity,
sieve analysis, and hydrometer analysis were also conducted todetermine textural content 共Dane and Topp 2002兲. These investigations on both transects revealed that soil texture and otherphysical properties were fairly uniform down to an elevation of
21.3 m 共above NGVD兲 for the eastern transect and 20.9 m 共above
NGVD兲 for the western transect, and can thus be represented by
one set of soil hydraulic parameters down to this depth. Further,because the underlying layer consisted of clay, this layer could betreated as no-flow boundary in the numerical model.
The land surface boundary was driven by atmospheric conditions, namely rainfall and potential evapotranspiration. At this

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site, rainfall was monitored using two tipping buckets rainfall
gauges that recorded data at 20 min intervals. Due to a technicalproblem with the data logger on the evaporation pan at the site,
the required data to estimate potential ET had to be imported fromthe Bowling Green weather station about 26 km south east of thesite. The South West Florida Water Management District, which
maintains this weather station, uses the energy-budget-basePenman–Monteith method to calculate daily potential ET rates.
Finite-Element Model
A two-dimensional finite-element model 共Šimůnek et al. 1999兲
was used for this study. The model is widely accepted in both theresearch and the engineering communities and has been extensively verified by Šimůnek et al. 共1999兲 by comparing model
results with available analytical solutions for solute transport andwith other numerical models for water flow. Further, an independent team of hydrologists tested the performance and capabilities
of HYDRUS-2D and found the model reliable. The results of
their findings were published in the Software Spotlight sectionof Ground Water 共Diodato 2000兲. Early simulations with
HYDRUS-2D showed that the model was appropriate for variable
saturation flow modeling in irregular geometric domains 共Hernandez et al. 2003兲. However, research utilizing HYDRUS-2D for
simulating large scale RWU had not been previously reported.
The HYDRUS-2D model uses the Galerkin type linear finiteelement method for space discretization and a finite difference
method for temporal discretization of the two-dimensional variably saturated flow 共Richards兲 equation written as冋 冉⳵ ␪共x,z,t兲 ⳵⳵ h共␪兲
=
−1
K共␪兲
⳵t
⳵z
⳵z

冊册 冋 冉 冊册+⳵⳵ h共␪兲
K共␪兲
⳵x
⳵x

−S
共1兲

where ␪共x , z , t兲⫽water content in the soil 关L / L 兴; h共␪兲⫽matricpotential head 关L兴; z⫽depth dimension 关L兴; x⫽horizontal dimension 关L兴; K共␪兲⫽unsaturated hydraulic conductivity function
关L/T兴; t⫽time 关T兴; and S⫽sink term representing the root water
uptake 关1/T兴.
3

3

Root Water Uptake and Soil Parameters
Generally, two approaches can be used to model the uptake of soil
water by roots 共Hillel 1998兲. The first approach, recognized as the
single root method, simulates RWU as convergent radial flow
towards individual roots treated as cylindrical sinks. The second
approach is macroscopic and regards the root system in its entirety as a diffuse sink that permeates each depth layer at varying
density. In this study we use the second macroscopic approachand adopt the ␣-model defined by Feddes et al. 共1978兲 to simulate
the RWU. The actual root water uptake term S in Eq. 共1兲 is defined as
S共h兲 = ␣共h兲S p

共2兲

in which S p⫽maximum RWU rate 关T−1兴 and ␣共h兲⫽dimensionlessvariable function of soil water pressure head and characterized byfour parameters as shown in Fig. 2. The RWU is assumed zeroclose to saturation 共i.e., wetter than some “anaerobiosis point” P0
to represent very low oxygen environment兲. The root water uptake is also zero for pressure heads less than the wilting point
共P3兲. Reduction of the RWU close to soil saturation and below thewilting point is caused by reduction of oxygen availability and theunsaturated hydraulic conductivity, respectively. Water uptake isFig. 2. Schematic of plant water stress response function ␣共h兲
共adapted from Feddes et al. 1978兲

considered optimal between pressure heads Popt and P2. Rootwater uptake is also assumed to vary linearly between P2 and P3
and between P0 and Popt. To allow P2 to act as a function ofpotential transpiration, the model uses two parameters P2H andP2L, which vary according to the potential transpiration rates
R2H and R2L. During the simulations following parameter values
were adopted from HYDRUS 2D 共Šimůnek et al. 1999兲: P0= −0.1 m, P3 = −80 m, Popt= −0.25 m, P2H = −3 m, P2L = −10 m,
R2H = 0.005 m / day, and R2L = 0.001 m / day. To account for theheterogeneous vegetative cover, different root depths were assigned for the pasture land and the forested area in the model.
Initially, root depth was assumed 70 cm for grass and 200 cm forthe forested area. The root depth of 200 cm was obtained byexamining roots of pushed over trees at the site. Because of theuncertainty in root depth, however, we allowed these parameters
to be iteratively adjusted during calibration and optimization.
Soil hydraulic properties characterizing soil water retention
and permeability were assumed to be described by an analyticalmodel of van van Genuchten 共1980兲 as follows:␪共h兲 − ␪r1=
␪s − ␪r
共1 + 兩␣h兩n兲m

共3兲

1/m m 2
K共␪兲 = KsS0.5
e 关1 − 共1 − Se 兲 兴

共4兲

Se共h兲 =

where Se⫽effective water content, Ks⫽saturated hydraulic conductivity, ␪r and ␪s denote the residual and saturated water contents, respectively; and ␣, n, and m共=1 − 1 / n兲 are empiricalparameters. Hydraulic characteristics defined by Eqs. 共3兲 and 共4兲
thus contain five unknown parameters: ␪r, ␪s, ␣, n, and Ks.
Parameter Optimization
Inverse methods are typically based upon the minimization of asuitable objective function, 共␾兲, which expresses the discrepancy
between some observed values and the predicted system response.
In our model calibration, we minimized the objective functionthat contained weighted residuals between the measured andsimulated water levels in the observation wells in each transect
共Šimůnek et al. 1998兲:nqj

␸共b,q,p兲 =wij关q*j 共x,ti兲 − q j共x,ti,b兲兴2
兺
i=1

共5兲

The terms on the right-hand side represents weighted deviations
between the measured and calculated pressure heads at different
times and locations in the flow domain. The term nqj is the number of measurements in a particular measurement set; q*j 共x , ti兲
represents specific measurements at time ti for the jth measurement set at location x共r , z兲; q j共x , ti , b兲 are the corresponding

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Fig. 3. Finite-element meshes for the geometric domains in the 共a兲 eastern and 共b兲 western transects

model predictions for the vector of optimized parameters b 共e.g.,
␪r, ␪s, ␣, n, and Ks兲; and wi,j are weights associated with a particular measurement set or point, respectively.
Levenberg Marquardt nonlinear minimization method 共Marquardt 1963兲, a weighted least-squares approach based on Marquardt’s maximum neighborhood method which combines the
Newton and steepest descend methods and generates confidence
intervals for the optimized parameters is used to minimize theobjective function.
Modification of the HYDRUS-2D CodeThe original HYDRUS-2D code simulates a single atmosphericboundary condition such as prescribed precipitation or ET rates
for a particular time. The transpiration rate is distributed over theentire root zone to obtain potential local root water uptake rates,
S p共x , z兲. Also HYDRUS-2D assumes a single root zone is normalized so that it corresponds with the surface area with prescribedatmospheric boundary conditions. For the purpose of this study,
the HYDRUS-2D code was modified so that it simulates atmospheric conditions continuously in time 共e.g., precipitation followed by ET兲. Also the code was modified to allow variablerooting depths reflecting the different vegetation in the transect.
This last modification was necessary to simulate root water uptake rates for the upland with shallow rooted vegetation 共grass兲and the lowland with deep rooted vegetation 共trees兲.

Results and Discussion
The finite-element meshes are shown in Fig. 3 for the eastern andwestern transects. The generated meshes consisted of 5,137 nodes
and 11,023 computational elements on the eastern transect 关Fig.
3共a兲兴 and 5,113 nodes and 9,795 elements on the western transect
关Fig. 3共b兲兴. We intentionally refined the resolution of the mesharound the creek and close to the land surface to capture the steep
hydraulic gradients that were expected to develop from imposing
the boundary conditions. Numerical convergence of finiteelement simulations is usually dependent on the finite-element
discretisation. The finite-element mesh in Figs. 3共a and b兲 provided stable numerical simulations for this study, because further
mesh refinement through adding more computational elements
increased computational time without significant change in simulations’ results. The upper geometry of the mesh constituted theland surface as determined from the survey for the site. We used
the meshes in Fig. 3 for all simulations including model calibration and validation.

The selection of HYDRUS-2D for this study was justified for
two reasons. First, the model uses triangular finite-element meshwhich captures the irregular shape of the land surface topography
forming the upper boundary of the flow transport domain. Secondly, the model accommodates system-dependent boundary conditions which are adjusted continuously depending on the statevariable during the simulation. These boundary conditions cannot
be specified a priori. Root water uptake is considered a systemdependent condition because it depends on soil water content
which varies continuously in time. The root water uptake is equal
to the potential transpiration when the soil is wet, but the root
water uptake can be limited by the lack of moisture in the rootzone when the soil is dry 共Šimůnek et al. 1999兲.
Model calibration consisted of adjusting iteratively the soil
hydraulic parameters, so that simulated water levels agreed withmeasured water levels in the observation wells to a desired levelof accuracy. Because model calibration, or parameter optimization, was an indirect approach for estimating soil hydraulic parameters from transient flow data, independent data were used tovalidate the calibrated soil hydraulic parameters. It was importantto validate the model with a different data set to enhance confidence in its performance. This two-step procedure is often called
historical validation. In this procedure, the existing data set isdivided into two parts with the first 共calibration兲 part being used
to calibrate the model and to estimate all necessary parameters,
whereas the second 共validation or verification兲 part of the data set
serves to compare predicted and measured data values using theparameters calibrated against the first part of the data set 共Šimůnek and de Vos 1999兲. Thus if simulated water levels are in acceptable agreement with measured water levels for this additional
data set, then the model is considered to be validated for given
conditions. Once validated, the model can be used to simulate
non-measured conditions. The calibration and validation procedure basically consists of three basic steps: data compilation,model calibration, and model validation.
Data Compilation
Due to air entrapment and encapsulation in the unsaturated zone
during rainfall, unusually high rises in water table levels wereobserved. During rainfall and infiltration, these rapid jumps inwater table levels were attributed to compression of the air phasebetween the advancing moisture front and the shallow watertable, as revealed by Freeze and Cherry 共1979兲 and Nachabe et al.
共2004兲. The compressed air phase puts pressure on the water table
causing the water level in the vented observation well to rise
rapidly as explained by Sabeh 共2004兲 for this site. Thus, for a

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Fig. 4. Observed 共dots兲 and simulated 共solid line兲 water levels with precipitation for the 共a兲 eastern and 共b兲 western transects. Vertical dashed line
shows the cutoff period used for calibration.

short period during and following a storm, water level in an observation well may not reflect the actual position of the watertable. Because HYDRUS-2D is a one phase flow model which
does not simulate the compression of the air phase, periods withno rain were utilized for model calibration. Further, the available
zero rainfall periods were scrutinized graphically to avoid erratic
records. From the available raw data, daily values of water levels,
precipitation, and ET were prepared.
Calibration and Parameter Optimization
The measured soil hydraulic parameters such as porosity, conductivity, and texture were used to initialize van Genuchten’s soil
hydraulic parameters for the calibration. Initial guesses of the
shape parameters were obtained using pedotransfer functions
from Rosetta 共Schaap et al. 2001兲, a submodel available in
HYDRUS-2D. The optimization algorithm was used to fine-tuneroot depth and soil hydraulic parameters 共␪r, ␪s, Ks, ␣, and n兲
until the simulated water levels agreed with observed waterlevels.
The calibration of HYDRUS-2D was based on quantitative
comparisons between the simulated and measured water table levels. The value of the correlation coefficient 共r2兲 was 0.99953 forthe eastern transect and 0.99653 for the western transect. Thiscoefficient is a quantitative criterion of the goodness of fit of themodel and reflects similar and/or dissimilar trends between ob-

served and simulated water levels. The high 共r2兲 coefficient suggested a rather good fit, as demonstrated in Figs. 4共a and b兲 showing the variation of observed and simulated water levels.
Calibrated soil hydraulic parameters are given in Table 1. The
calibrated soil hydraulic parameters were different for eachtransect. For example, the saturated hydraulic conductivity 共Ks兲on the western transect was 2.88 m / day, whereas it was only
0.080 m / day on the eastern transect. These results, however, wereconsistent with permeability and textural analysis on thesetransects. The measured average 共Ks兲 using the falling head permeability test for the eastern and western transects were 0.31 and1.38 m / day, respectively. The difference between the measured
and calibrated 共Ks兲 can be explained by considering the anisotropy effect. The falling head permeability test measures only the
Table 1. Calibrated Soil Hydraulic Parameters
TransectVariable
Residual water content, ␪r共m / m 兲
Saturated water content, ␪s共m3 / m3兲
Empirical shape parameter ␣ 共1/m兲
Empirical shape parameter nSaturated hydraulic conductivity, Ks 共m/day兲3

3

Eastern

Western

0.021
0.399
1.115
2.453
0.080

0.1098
0.350
0.860
3.709
2.881

JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING © ASCE / MARCH/APRIL 2008 / 171

Fig. 5. Seasonal variation of the potential and actual RWU

vertical conductivity and represents local point values. On theother hand, the calibrated parameters represent average effective
values over the entire domain. The calibrated saturated water content was larger on the eastern transect 共␪s = 0.399 for eastern and␪s = 0.35 for western兲. The best fit of the n parameter for theeastern transect was lower than that for the western transect. Further, the parameter ␣ for the western part was lower than that for
the eastern part.
These deviations were mainly due to the presence of different
soil textural classes in the two transects as revealed by soil texture
analysis. Fine textured soils dominated the eastern transect,whereas the western part contained more coarse textured soils.
Textural analysis, indeed, suggested that soils could be classified
as sandy clay on the eastern transect and fine sand on the westerntransect 共Thompson 2003兲. Variability in calibrated parametersreflected the soil textural differences observed at the site.
Validation
Water level observations from October 1, 2001 to June 27, 2002
and from January 20, 2002 to June 19, 2002 were then used tovalidate the model for the eastern and western transects, respectively. The validation period for the eastern transect included both
dry 共October–April兲 and wet 共May–September兲 seasons. Due tolack of data on the western transect, however, the time periodused 共January–June 2002兲 could not capture the seasonal variability. The same finite-element mesh and time steps were utilized,
but a different, significantly longer time record of surface boundary conditions was used for validation 共see Fig. 4兲. As for model
calibration, boundary conditions were obtained from available
rainfall and ET data. The calibrated soil hydraulic parameters
were used in the model, and the simulated water levels werecompared with the observed water levels.
The simulated water table levels reproduced well the observed
water table levels as shown in Figs. 4共a and b兲. During the dry
season, the correspondence between the observed and simulated
water levels was exceptionally good, especially for the easterntransect. Although we used water level data for periods with zerorainfall for the model calibration, our results show that we can use
the soil hydraulic parameters calibrated during these periods andreadily predict also the water level fluctuations after storms. But
water level fluctuations following a heavy storm event were lessaccurately predicted. The reasons for the less accurate estimate ofwater level fluctuations during storm events could be due to hys-teresis and air compression during infiltration 共Sabeh 2004兲 or to
the fact that Ks was calibrated with a record that did no containrain. Therefore, the calibrated Ks could not capture the influencesof any macropores that tend to increase the vertical hydraulicconductivity 共Nachabe 1995兲. The inability of HYDRUS to modelsurface flow was not a factor for these simulations because no
runoff was observed at this site.
Seasonal Variation of Root Water Uptake
The transpiration trend for a vegetative cover follows the seasonal
cycle of rainfall, solar radiation, and air temperature. The humid
subtropical weather at Lithia in west central Florida is characterized by a warm wet season from April to September and a mild
relatively dry season from October to March. Over 75% of therain occurs in the wet season.
Fig. 5 shows the variability of potential and simulated transpiration at the site. As expected, the potential transpiration reflected
the climatic seasonality recognized in Florida. Over the course ofa year, the cyclic, almost sinusoidal, variability in potential transpiration was attributed to changes in temperatures from the cooldry season when average potential ET hovered at 2.59 mm/ day tothe hot season with an average potential ET of 4 mm/ day. The
actual RWU followed the same pattern of seasonality, but wasalso modified by availability of soil moisture which depended on
precipitation patterns. Immediately after storms, RWU was at potential due to the increase in water table levels and associated soil
moisture in the root zone. With no rainfall, however, the gradualdecline in water table levels was accompanied by a reduction in
RWU over time. The lowest RWU was in November and December when both potential transpiration and rainfall were low. When
we move from the dry to wet season, both potential transpiration
and RWU increased. On average, the actual RWU during the wet
season was 40% higher than RWU in the dry season. Simulations
results suggested that HYDRUS was able to capture both shorttime scale variability in RWU associated with arrival of storms,
and long term seasonal variability in RWU attributed to seasonality in climate and potential ET.
Spatial Variability in Root Water Uptake
The spatial vegetative cover has a direct effect on the hydrology
through its influence on ET and RWU 共Dunn and Mackay 1995兲.
As shown in Fig. 1, the simulated site had a mixture of upland

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Fig. 6. Variation of the potential and actual RWU in the lowland and upland zones

vegetation with pasture grass and shrubs and a lowland riparianzone with trees. Obviously, vegetation in the riparian zone hashigher biomass and deep penetrating roots allowing the plants toaccess moisture from the deeper layers in the soil profile. Fig. 6
shows the variation of the RWU rates for lowland and uplandalong with the potential ET rates. Results in Fig. 6 reveal thatvegetation in the riparian zone did not suffer any moisture stressthroughout the period of simulation. The RWU was equal to thepotential almost all the time of the year. The position of thesevegetation in the landscape—along subsurface drainage pathways
and proximity from creek—in addition to their deep penetratingroots kept RWU at potential throughout the seasons of the year.
That was not true of the upland pasture grass areas. The RWU forthese areas was close to the potential for few days after a rainfall
storm, but declined gradually afterward. It seems these shallow
rooted plants were more vulnerable to rainfall and moisture availability.
The average RWU for the lowland vegetated was 2.59 mm/
day during the dry season and 4.08 mm/ day during the wet season. Those variations reflected the potential ET seasonal variability in Florida. The corresponding values for upland was
1.87 mm/ day for the dry and 2.61 mm/ day during the wet season. Obviously the differences in RWU for pasture grass andforested areas were attributed to heterogeneity in rooting depth
and available soil moisture. In this study area, the dry season isassociated with low temperatures and low rainfall whereas thewet season had high temperatures. Therefore, the RWU during thewet season is high not only due to high water table levels, but also
due to elevated temperatures. With plentiful water and elevatedtemperatures, a plant consumes more water.

Conclusion
The overall objective of this research was to demonstrate theutility of modeling to assess the impact of variations in weather
conditions and vegetation cover on the RWU in humid environments using field data collected at Lithia, west central Florida.
Calibration and validation of the soil hydraulic parameters using
two-dimensional variably saturated model, HYDRUS-2D, confirmed the reliability of the physically based model to simulate
satisfactorily the large scale daily fluctuation of the actual RWU
in humid environments with shallow water table. Simulation ofwater table level fluctuations during the dry season was exceptionally good. During the wet season the correspondence between

simulated and measured water levels was fairly good, providing
the ability of the initial pressure head conditions to represent thewater level on the initial day. Simulation results revealed that theRWU during the wet season is about 40% higher than during thedry season. This difference is not only due to high water levelelevations caused by precipitation, but also due to the high temperatures prevailing during the wet season which increases potential ET. Simulation results, obtained using HYDRUS-2D modified
to compare the RWU from heterogeneous natural vegetation
cover, showed that the RWU from the riparian zone is 38 and
56% higher than from the pasture land during the dry and wet
seasons, respectively. Though the presented results are very promising in demonstrating the capability of the HYDRUS-2D model
to simulate large scale ET, the model needs to be further modified
for large rainfall events to accommodate runoff and overland flow
to be able to describe main processes relevant to hill slope hydrology.

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