NOTES

Solute transport in aggregated porous media: Theoretical and experimental evaluation. Soil Sci. Soc. Am. J. 44:1139–1146.
Snedecor, G.W., and W.G. Cochran. 1967. Statistical methods. The
Iowa State University Press, Ames, IA.
Steenhuis, T.S., J.Y. Parlange, C.J.G. Darnault, Y.J. Kim, O. McHugh,K.S. Kung, and M.S. Akhtar. 1999. A generalized theory for preferential flow. p. 180. In 1999 Annual meeting abstracts. ASA, Madison, WI.

57

Toride, N., F.J. Leij, and M.Th. van Genuchten. 1999. The CXTFIT
code for estimating transport parameters from laboratory or field
tracer experiments. Ver. 2.1. Res. Rep. No. 137. U.S. Salinity Laboratory, ARS–USDA, Riverside, CA.
Willmott, C.J., S.G. Ackleson, R.E. Davis, J.J. Feddema, K.M. Klink,
D.R. Legates, and C.M. Rowe. 1985. Statistics for the evaluationand comparison of models. J. Geophys. Res. 90:8995–9005.

DIVISION S-1—NOTESA MODIFIED UPWARD INFILTRATIONMETHOD FOR CHARACTERIZING SOIL
HYDRAULIC PROPERTIES
M. H. Young*, A. Karagunduz, J. Šimůnek,
and K. D. Pennell
Abstract
This note describes a modified upward infiltration method (UIM),
which combines laboratory experiments and inverse parameter estimation for determining soil hydraulic properties in the wetting direction. The laboratory method used a Mariotte system to impose aconstant head boundary condition on the bottom of a soil column,
allowing water to be taken up by the soil material under negative
pressure head. Tensiometers installed along the column measured thechange in soil pressure head before and after wetting front arrival.
The HYDRUS-1D code was used to obtain an optimal set of van
Genuchten parameters, using pressure head and cumulative flux data
as auxiliary variables in the objective function. Two soil types (a finesand and a sandy loam) were tested in triplicate in uniformly-packed
soil columns. The results of the uniform column experiments wererepeatable, and showed excellent fits between observed and predicted
data. Fitted parameters were used in forward simulations to independently predict water flow behavior in layered columns of the same
soil material. The forward simulations successfully predicted waterflow for sand-over-loam and loam-over-sand combinations in layered
columns. The relative simplicity of the experimental procedure andthe availability of appropriate numerical models renders the modified
upward infiltration method an alternative for determining wetting
hydraulic properties of soils.

T

he measurement of soil hydraulic properties, specifically soil water content (␪)—soil water pressure
head (␺) and hydraulic conductivity (K)—water content(␪) functions, is needed to predict the direction and rateof water movement in unsaturated soils. However, thepaired values of ␪(␺) and K(␪) are dependent upon the
direction of wetting or drying (Dane and Wierenga,
1975; Hillel, 1998). Experimentation required to estabM.H. Young, Division of Hydrologic Sciences, Desert Research Institute, Las Vegas, NV 89119; A. Karagunduz and K.D. Pennell, Schoolof Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332; J. Šimůnek, U.S. Salinity Laboratory, USDA–
ARS, Riverside, CA 92507. The work was conducted while M.H.
Young was at Georgia Institute of Technology, Atlanta, GA. Received
24 June, 2000. *Corresponding author (michael@dri.edu).
Published in Soil Sci. Soc. Am. J. 66:57–64 (2002).

lish the functions in wetting and drying directions, andtheir intermediate values, often require specialized laboratory setups, which is why often times only the drying
functions are determined (Hillel, 1998).
In the past few decades, several transient methods
have been proposed for characterizing soil hydraulicproperties, including one-step outflow (Parker et al.,1985), multi-step outflow (Eching and Hopmans, 1993;
van Dam et al., 1994), and evaporation (Wind, 1968;
Šimůnek et al., 1998a). Each of these methods use thechange in column weight to infer changes in soil watercontent, and with the exception of Parker et al. (1985)and van Dam et al. (1994), either one or more tensiometers placed along the column to measure change in soil
water potential. A review of inverse estimation of hydraulic properties was done by Hopmans and Šimůnek (1999).
Experimental methods have been shown to work fora variety of soil textures undergoing drying. However,they do not yield hydraulic properties for soils undergoing wetting, and the transfer of drying curves to wetting
curves is not trivial. The UIM is one of a few methods
capable of obtaining wetting properties of soils. The
UIM was originally described by Hudson et al. (1996),
who showed that the method was robust for uniform,
sandy-textured soil samples. Wyckoff (1997) applied themethod to a variety of clayey-textured soils, including
those with swelling clays. Both studies used constantflux bottom boundary conditions, which reduces theusefulness of the flux as an optimization parameter because the flux is independent of the soil properties (Šimůnek and van Genuchten, 1997).
Other researchers have used variations of the UIM.
For example, Karkare and Fort (1993) and Demond etal. (1994) used standard Tempe cells, and reversed thegradient in a series of equilibrium pressure steps, sothat test solution in a graduated burette would be taken
up spontaneously into the soil. In these cases, soil waterpressure head was not measured, so gradients could
have existed in the column at the end of the step, yielding noncorresponding values of ␪ (inside the column)and ␺ (at the bottom boundary).
Controlling water intake by setting the bottom boundary pressure, coupled with intensive soil data collection
could improve on these methods. Recently, Šimůnek
et al. (2000) suggested using a tension-based UIM for
Abbreviations: UIM, upward infiltration method.

58

SOIL SCI. SOC. AM. J., VOL. 66, JANUARY–FEBRUARY 2002

Fig. 1. Schematic of experimental setup.

evaluating nonequilibrium flow features in structured
soils. Zurmühl (1998) described a fairly complex laboratory setup to reverse flow into and out of the soil column,
using a pump and two pressure valves, all controlled bya personal computer. As will be described below, ourexperiments use only a Mariotte system to control thebottom boundary.
The purpose of this research was to characterize thewetting hydraulic properties of soils using a constanthead bottom boundary. We (i) use the modified UIM to
characterize disturbed soil samples, (ii) optimize the van
Genuchten parameters with HYDRUS-1D to match experimental results, and (iii) use globally-fitted hydraulicproperties to predict water flow into layered columns.
Materials and Methods
Laboratory Procedures
Soils of two textures were used for this research: (i) Vinton
fine sand (sandy, mixed thermic Typic Torrifluvent) collected
at the Campus Agricultural Center, Tucson, Arizona, and (ii)
Appling sandy loam (clayey, fine, kaolinitic thermic Typic
Hapludult), collected near the J. Phil Campbell, Senior, Natural Resource Conservation Center (USDA–ARS), Watkinsville, GA. Particle-size analyses for the Vinton and Applingsoils showed percentage distributions of 96/3/1 and 77/14/9 forsand/silt/clay, respectively.
All column experiments were conducted using polycarbonate columns (15-cm long and 5-cm i.d.), outfitted with two
threaded access ports through which tensiometers were inserted. Soil was retained using acrylic end plates (model CL021, Soil Measurement Systems, Tucson, AZ). The interface
between the soil and inlet end plate was equipped with sponge
cloth (Spontex, Columbia, TN) cut into a circular discs(diam. ⫽ 6.2 cm) and placed inside the end plate. The sponge
was used because the material allowed rapid water uptake,slow gravity drainage, and very little resistance to flow. A
porous nylon filter (diam. ⫽ 7.4 cm; model R99SP04700, Osmonics, Inc, Minnetonka, MN), was then placed above thesponge and in direct contact with the soil. The sponge cloth
was weighed before and after the flow experiment so that an
accurate mass balance of water that entered the column could
be ascertained.
Tensiometers (model CL-029, Soil Measurement Systems,
Tucson, AZ), used to measure soil water pressure head, wereinserted through the column wall at 2.5 and 12.5 cm above
the bottom of the column and held in place using compression
fittings. The bottom location was chosen after observationsof Šimůnek et al. (1998a), who suggested monitoring as close
as possible to the input-output boundary. The top tensiometer
location was chosen close to the upper boundary to maximize
the amount of soil subjected to wetting before the wetting
front reached the top of the column. After calibrating thepressure sensors (model 136PC15G2, MicroSwitch, Richardson, TX), they were sealed to the open end of the tensiometer
body. Standard errors obtained from calibration were consistently close to ⫾0.7 cm. A data logger (model CR-23X, Campbell Scientific, Inc., Logan, UT) was used to collect and storepressure readings.
A Mariotte system was used to control water input to thecolumn. The water reservoir was 3.175-cm i.d. Using a pressure
sensor (model 136PC01G2, MicroSwitch) to monitor change
in water level and hence the volume of water flowing into thesoil column, a measurement error of ⫾0.34 cm3 water wasachieved. Test fluid consisted of deionized water containing
0.005 M CaCl2. Two head levels were set for most of the laboratory experiments, with the initial value close to ⫺0.8 kPa(⫺8 cm H2O) and the final value set at 苲 ⫺0.3 kPa (⫺3 cm
H2O). Head levels were set by placing the bubbling tube below
the bottom boundary of the soil column. Flow was initiated

59

NOTES

Table 1. Experimental conditions for laboratory experiments.

⌰⫽

Initial vol.
Final vol.
Replicate Bulk density water content water content

Soil
VintonVintonVintonAppling
Appling
Appling
Appling/Vinton†
Appling/Vinton
Appling/Vinton
Vinton/Appling
Vinton/Appling
Vinton/Appling

1
2
3
1
2
3
1
2
3
1
2
3

Mg m⫺3
1.52
1.49
1.51
1.58
1.56
1.57
1.52 (1.56/1.44)
1.52 (1.56/1.44)
1.52 (1.54/1.48)
1.52 (1.47/1.56)
1.52 (1.48/1.57)
1.52 (1.45/1.55)m 3 m ⫺3
0.027
0.027
0.033
0.060
0.056
0.066
0.049
0.049
0.048
0.044
0.044
0.048

K(␪) ⫽ Ks ⌰1/2(1 ⫺ (1 ⫺ ⌰1/m)m)2

by placing the upper soil boundary under vacuum (苲 ⫺0.1 to⫺1.0 kPa) with a syringe for 1 to 5 s or until the spongematerial began absorbing water. Flow was then sustained in
virtually all cases. A schematic of the experimental setup is
shown in Fig. 1.
Column experiments were run with uniform and layeredsoil configurations. Experiments were conducted in triplicate
for uniform material for each of two soils, and for layered
columns, with Vinton soil placed above Appling soil (i.e.,
Vinton/Appling), and vice versa (i.e., Appling/Vinton). Eachlayer was 7.5-cm thick. Repacked columns were used to better
observe the wetting behavior in the soil, without potential
uncertainties introduced by nonequilibrium flow into aggregates. Table 1 lists the soil bulk densities, and initial and final
water contents for each experiment.
Analytical Procedures
Data were analyzed using the HYDRUS-1D code (Šimůnek
et al., 1998b), which numerically solves Richards’ equation forwater flow in variably-saturated porous media. The governing
flow equation is solved using Galerkin linear finite element
schemes for time-dependent or time-independent boundary
conditions. Richards’ equation for one-dimensional flow is:

冢

冢

冣冣

[1]

where t is time, ␪ is volumetric water content (L3 L⫺3 ), ␺ isthe soil pressure head (L), and z is the spatial coordinate (L),positive upward. Initial and boundary conditions are, respectively:

␺(z) ⫽ ␺i

冤

for t ⫽ 0, 0 ⱕ z ⱕ L冥⳵␺
⫹ K(␺) ⫽ 0⳵zq ⫽ K(␺)[2]

for t ⬎ 0, z ⫽ L [3]h ⫽ h1for 0 ⬍ t ⱕ t1, z ⫽ 0h ⫽ h2for t1 ⬍ t ⱕ t2, z ⫽ 0[5]where ⌰ is the relative volumetric water saturation; subscripts
r and s refer to residual and saturated volumetric water contents, respectively; ␣ (L⫺1 ) and n (–) are empirical fitting
parameters; and m ⫽ (1 ⫺ 1/n ). The Mualem–van Genuchten
function (Mualem, 1976; van Genuchten, 1980) was used torepresent hydraulic conductivity, K(␪):

0.280
0.320
0.310
0.250
0.261
0.249
0.281
0.277
0.243
0.283
0.280
0.276

† First soil type listed indicates upper layer, and second soil type indicatedbottom layer.

⳵␪
⳵
⳵␺
K(␺)
⫽
⫹1
⳵t
⳵z
⳵z

␪ ⫺ ␪r1⫽
␪s ⫺ ␪r (1 ⫹ (␣ ␺)n)m[4]

where ␺i is the initial soil water pressure head (L), q is theDarcy flux (L T⫺1 ), and h is the head imposed at the bottom
boundary (L).
Several representations of retention curves are available,
and we expect that any reasonable representation of retention
can be applied to this laboratory method. However, we usedthe van Genuchten (1980) relationship, because it is commonlyused in the soils community, and because it seems to representthe ␪(␺) and K (␪) functions for most soils. The relationship is:[6]

where Ks is the saturated hydraulic conductivity (L T⫺1 ), and0 ⬍ m ⬍ 1 (van Genuchten, 1980).
Using Eq. [5] and [6], as many as five parameters can be
fitted: ␪r, ␪s, ␣, n, and Ks. HYDRUS-1D simulates water flowusing assigned initial and boundary conditions, compares themodeling results with observed data, and reinitializes the simulation using updated parameters. After each simulation, theprogram calculates an objective function (called SSQ in HYDRUS) that compares the simulated versus observed spacetime (auxiliary) variables (see Šimůnek et al. (1998b) for acomplete description). For this work, the auxiliary variables
were soil water pressure head at two locations and cumulativeflux. Weighting coefficients were calculated as the inverse
product of the measurement variance and the number of observations for each auxiliary variable (Clausnitzer and Hopmans, 1995), which causes the objective function to become
the average weighted squared deviation normalized by themeasurement variances. Parameter estimation uses the Marquardt (1963) nonlinear optimization routine.
To reduce the number of fitting parameters, we chose to
assign ␪r as the air-dry water content. Other parameters werebased on educated guesses as starting points for the optimization. The parameters were then sequentially fitted, beginningwith those found to be most sensitive, and finishing with thosefound to be least sensitive. The optimized parameter values
were used as starting points in subsequent simulations, so thatfour parameters were fitted in the final simulation.
An average retention curve for each soil type was obtainedby simultaneously fitting paired ␺-␪ values, obtained using the
optimized van Genuchten parameters for the three replicates,with the RETC code (v1.0, van Genuchten et al., 1991). The
resulting global set of parameters represent the average soil
hydraulic properties. To evaluate whether these parameters
are representative of the soil, they were used in HYDRUS1D in forward mode to predict water flow behavior in thelayered soil column experiments. These predictions were evaluated against observed experimental data. Thus, if the numerical simulation accurately predicts water flow behavior in asoil column when the boundary conditions and soil layering
are known, this would be evidence that the global parameter
set, when used in the van Genuchten relationship, can be used
for predictive purposes.
Table 2. Experimental results for soil columns containing no layering.
Soil
VintonVintonVintonAppling
Appling
Appling

Replicate

Initial/final
bottom tension

Cumulative
infiltration

Arrival timetop tensiometer

1
2
3
1
2
3

cm
⫺8.55/⫺3.56⫺8.50/⫺3.65⫺8.60/⫺3.70⫺8.20/⫺2.70⫺8.40/⫺2.55⫺8.60/⫺3.20

cm (mL)3.99 (80.87)
4.74 (96.07)
4.06 (82.29)
3.60 (72.97)
3.47 (70.33)
3.39 (68.71)

min
34.83
29.50
40.17
361.50216.50203.5060

SOIL SCI. SOC. AM. J., VOL. 66, JANUARY–FEBRUARY 2002

Results and Discussion
Uniform Soil and Inverse Modeling

Table 2 summarizes the results of the experiments
for uniformily-packed soil columns. Slight differencesin the bottom pressure and soil bulk density led to somedifferences in the cumulative infiltration and the wetting
front arrival. The largest difference in cumulative flux
between replicates (e.g., Vinton Rep 1 versus Rep 2)

was 15.20 mL of water (18.8%), but differences werenormally close to 2 to 4 mL. Average front velocities
for Vinton and Appling soils were 0.287 cm min⫺1 and
0.038 cm min⫺1. Front velocities were significantly
slower in the clayey-textured, Appling soil, because of
the decrease in soil water diffusivity. Wetting front arrival at the lower tensiometer varied between 1.17 and3.33 min for the Vinton soil and between 3.00 and 16.00
min for the Appling soil. Though the range in arrival

Fig. 2. Observed pressure head (A) and cumulative flux (B) for uniformily-packed Vinton (Rep 1) and Appling (Rep 2) soils. Observed (symbols)
and predicted (line) pressure head (C and D) and cumulative fluxes (E and F) are shown for same replicates for Vinton and Appling soils,
respectively. Predicted line may be hidden by symbols. Note different scales for abscissa for A and B.

61

NOTES

times is wider for the Appling soil, the coefficient ofvariation (CV ⫽ Std. Dev./Mean) was virtually identicalbetween the two soil types (53.9% for Vinton versus
54.5% for Appling).
Figures 2A and 2B show the pressure head at theupper tensiometer and cumulative fluxes for the uniformily-packed Vinton (Rep 1) and Appling (Rep 2)
experiments (results from all experiments are not graphically shown). Though the pressure heads recorded prior
to wetting front arrival were near the upper end offunctionality of the tensiometer method itself and therefore may not be accurate, the wetting front arrival was
detected unambiguously in each case, and provided accurate data for the inversion modeling. The final pressure heads at the bottom tensiometer were almost identical to the pressure head applied at the bottom
boundary, indicating insignificant head loss through thesponge and nylon materials. Final pressure heads at
the top tensiometer were typically 苲10 cm lower (morenegative) than heads at the bottom tensiometer, reflecting the increased height above the water source.
Fluid applied with the Mariotte system exhibited somenoise because of growth of air bubbles at the base of
the bubbling tube, and possibly because of changes in
air temperatures in the laboratory, but generally thesignals were quite strong.
Results of the numerical simulations are presented inTable 3, and in Fig. 2C to 2F. Results in Fig. 2A and2B for pressure head and cumulative flux are simulated
in Fig. 2C and 2D, and in Fig. 2E and 2F, respectively.
Pressure head data for the lower tensiometer is somewhat obscured by the y-axis, but the simulated pressure
head at the upper tensiometer was closely simulated by
HYDRUS-1D, indicating that the VG (van Genuchten)
parameter set represented the behavior of the soil. Tensiometer responses tended, in most cases, to show morediffuse arrivals of wetting fronts, due mainly to the spa-

Table 3. Final parameter values for Upward infiltration method
UIM experiments.
Soil

Replicate ␪rVintonVintonVintonVinton-global
Appling
Appling
Appling
Appling global

1
2
3
1
2
3

␪s

m 3 m ⫺3
0.007 0.359
0.007 0.395
0.007 0.360
0.007 0.372
0.0285 0.410‡
0.0285 0.398
0.0285 0.414
0.0285 0.409

␣

n

Ks

r2

SSQ†

cm⫺1
cm/min0.062 2.507 0.339 0.983 0.103
0.053 2.223 0.353 0.909 0.352
0.060 2.319 0.279 0.990 0.196
0.059 2.324 0.324 0.990–
0.171 1.387 0.561 0.985 0.049
0.093 1.315 0.243 0.972 0.059
0.165 1.370 0.945 0.995 0.030
0.162 1.328 0.583 0.980–† Sum of squares.
‡ Held constant for this experiment only.

tial averaging in the tensiometers. Using the final ␪(␺)
conditions in the objective function, as recommendedby Šimůnek et al. (1998c) did not improve overall fit to
the tensiometer and flux data.
Correlation existed between some fitted parameter
pairs. Specifically, ␪s and Ks showed the highest positively correlation, and ␪s and n showed the highest negative correlation of parameter pairs considered. Thoughhigh correlation among parameters does not inhibit convergence of the fitting procedure (Durner et al., 1999),
it does slow down the convergence rate and lead toquestions about the independence of the parameters.
Also, some discrepancy was noted between observed
and predicted pressure heads corresponding to the initial water content (e.g., observed ␪(␺) ⫽ 0.027 m3 m⫺3(⫺500 cm) versus fitted ␪(␺) ⫽ 0.027 m3 m⫺3 (⫺196 cm)
for the Vinton soils). Experience indicated that the airdry water content was consistently close to 0.007 m3
m⫺3, so this was chosen as the fixed value of ␪r. Given
that large changes in pressure head translate to smallchanges in water content on the dry end of the retention
curve, the discrepancy could be caused either by error

Fig. 3. Fitted (symbols) and global (line) soil water retention (A and C) and hydraulic conductivity functions (B and D) for Vinton and Applingsoils, respectively. Squares, diamonds, and triangles correspond to Replicates 1 to 3, respectively.

62

SOIL SCI. SOC. AM. J., VOL. 66, JANUARY–FEBRUARY 2002

Fig. 4. Observed pressure head (A) and cumulative flux (B) for Appling/Vinton (Rep 3) and Vinton/Appling (Rep 1). Observed (symbols) andpredicted (line) pressure head (C and D) and cumulative fluxes (E and F) are shown for same replicates.

in the pressure head measurement or model prediction,
the use of the VG equation to represent soil water
retention, or a combination of these.
Global hydraulic properties, obtained by simultaneously fitting individual paired ␺-␪ data using RETC, arelisted on Table 3. The measured parameters are in close
agreement to the arithmetic average of those values
individually obtained using the HYDRUS-1D model.
Figure 3 shows the retention and conductivity functions(replicates and global) for Vinton and Appling soils.
Retention curves were highly reproducible. Differencesin the conductivity function were mainly from uncertainties in the fitted value of Ks, which was affected by theneed to extrapolate beyond the conditions setup in theexperiment. An independently measured Ks could reduce this uncertainty.
Layered Soil and Forward Modeling
Figure 4 shows the observed and predicted pressure
head and cumulative flux for two of the six layered soilcolumn experiments (replicate numbers are listed in
the figure caption). Overall, the parameter estimatesobtained from the uniform column experiments described the hydraulic behavior of the soil material, suchthat forward predictions were quite accurate. However,

63

NOTES

Table 4. Experimental and numerical results obtained for layeredsoil columns.
Soil

Appling/Vinton†
Appling/Vinton
Appling/Vinton
Vinton/Appling
Vinton/Appling
Vinton/Appling

Replicate

1
2
3
1
2
3

Cumulative flux

Wetting front arrivalcm
min
Observed Predicted Observed Predicted
3.75
3.45
87.67
81.47
3.70
3.50
98.00
82.97
4.00
3.94
98.83
84.27
3.99
3.79
231.67229.104.05
3.76
228.70233.104.01
3.76
321.00237.40† First soil type listed indicates upper layer, and second soil type indicatesbottom layer.

some differences existed between observed and simulated responses. For example, simulated cumulative
fluxes were underestimated by 苲0.2 to 0.3 cm (Table4), a relatively small amount (苲5–7%) considering thatthe predictions used only the globally-fitted parameters
and the known boundary conditions. Average differences in the wetting front arrival times between thereplicates were 15 and 13% for Appling/Vinton and
Vinton/Appling, respectively. In one case (Vinton/Appling Rep 3), the observed wetting front traveled slowly
and was not modeled very well (difference ⫽ 83.6 min
or 35.2%).
Toward the drier range, uncertainty increases becauseof a need to extrapolate beyond the tensiometer range.
For example, in the cases presented here, ␪r was assumed
equal to air-dry water content, and pressure head measurements were limited values of greater than 苲 ⫺700
cm. Neither situation supports the estimate of hydraulicproperties in the very dry range. Independent verification of the soil hydraulic properties in the wetting direction were not available. However, a recent study by Zou
et al. (2001) using the Vinton soil during a controlled
infiltration experiment, produced a set of hydraulic parameters (␣ ⫽ 0.048–0.061 cm⫺1, n ⫽ 2.47–2.66, Ks ⫽0.106–0.113 cm min⫺1 ), the range of values determinedat different depths. Their results are very similar to
those listed in Table 3, with the exception of Ks, which
was found to be smaller.

Conclusions
A modified UIM procedure was used to characterizesoil hydraulic properties in the wetting direction duringtransient water flow, rather than in the drying direction,
as obtained through outflow methods. The procedure
relies on a constant head bottom boundary condition,rather than a constant flux boundary condition, allowing
the cumulative flux data to be included as an auxiliary
variable in the objective function. The simplicity andrapidness of the modified UIM experimental procedure,
its reproducibility, and its ability to capture the soil
parameters in the wetting direction makes the method
attractive for characterizing soil hydraulic properties.
The method is generally applicable to both disturbed
and undisturbed soil.
The utility of the method was demonstrated throughthe repeatability of experimental results and the numerical output. The globally-fitted hydraulic parameters derived from uniform soil columns were then indepen-dently used to predict soil water flow in layered soilcolumns. A comparison of observed and predicted responses to water addition showed an average deviationin cumulative flux of 5.91%. The method will be mostuseful in the wetter range of the retention and conductivity functions, where the bulk of data are typically collected.
Acknowledgments
The authors acknowledge the helpful comments by the
anonymous reveiwers. We appreciate the support provided
by the Education Research and Development Associationof Georgia Universitites (ERDA), Atlanta, GA (Contract#DOE-004), Dr. Ratib Karam, Project Manager. The information contained herein has not been reviewed by ERDA anddoes not necessarily represent their views.

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IMPROVED DESIGN FOR AN
AUTOMATED TENSIONINFILTROMETER

that minimized bubble-induced variability and improved
measurement precision. A tension infiltrometer (Fig. 1)
is a device that measures unsaturated infiltration rates,
and the improvement by Ankeny et al. (1988) increased
the reliability of soil hydraulic properties such as sorptivity, unsaturated hydraulic conductivity, and macroporosity. The Ankeny et al. (1988) improvement uses two
gage transducers, one at the top and the other at thebottom of the infiltrometer reservoir.
The use of two transducers improves infiltration measurement precision; however, there is still some error
in the precision of the measurements due to bubblinginduced variability, synchronization of the two gagetransducer measurements, and accuracy of the gage transducer calibrations. Ankeny et al. (1988) suggests the useof a differential transducer to improve the measurement
precision. The use of a differential transducer would also
eliminate the need for extensive calibrations required by
the two gage transducer method. The purpose of thisresearch was to automate a tension infiltrometer using asingle differential transducer and provide a description,
evaluation, and application of this set-up.

Francis X. M. Casey* and Nathan E. Derby
Abstract
Automated measurements of water infiltration rates are commonly
done using two gage transducers to measure water level changes in
the reservoir of an infiltrometer. Previous studies have evaluated anddescribed in detail infiltrometers automated with two gage-transducers and have shown that measurement precision and accuracy ofsoil hydraulic properties are improved. A previous study has alsosuggested the use of a single differential transducer to automate an
infiltrometer to eliminate measurement error associated with air bubbles in the infiltrometer reservoir. In this study, the automation of atension infiltrometer using a differential transducer was developed,
evaluated, and applied. A single differential transducer was installedat the bottom of an infiltrometer reservoir and the other end wasconnected by tubing to the head-space in the reservoir. Calibrationof the reservoir height measurements vs. transducer voltage output
was simplified over previous methods and was even demonstrated insitu. Measurement precision was also improved by two orders ofmagnitude over previous methods. Measurements were also done todemonstrated the use of the single differential transducer set-up to
obtain field measurements of unsaturated hydraulic conductivity andsorptivity. Unlike previous methods though, this method does notallow for the determination of the imposed potential at the soil surface
unless adaptations are made.

A

utomated measurements of marriotte reservoir water levels were first introduced by Constanz andMurphy (1987), who used a single gage transducer. This
improvement lead to faster measurements of a large
range of fluxes and higher accuracy in outflow measurements from the reservoir; however, the single gage transducer measurements of water height were not precise
because of bubbling-induced variability. Ankeny et al.
(1988) developed a method to automate water heightmeasurements in the reservoir of a tension infiltrometer
Dep. of Soil Science, North Dakota State Univ., Fargo, ND 58105.
Received 1 Sept. 2000. *Corresponding author (Francis.Casey@NDSU.
NoDak.edu).
Published in Soil Sci. Soc. Am. J. 66:64–67 (2002).

Materials and Methods
The infiltrometer that was used in this study was a Soil
Measurement System1 model SW-080B, which has a 20-cm
diam. baseplate that was separate from the water tower. The
water tower was comprised of a reservoir (inside diam. ⫽ 5.1
cm, length ⫽ 81 cm) and bubbling tower (inside diam. ⫽ 2.54
cm). The bubbling pressure of the membrane covering thebaseplate was 2.9 kPa. The air entry ports of the bubbling
tower could be changed to supply infiltration tensions ranging
from 2 to 50 cm of water.
The manufacturer automates the infiltrometer as Ankeny
et al. (1988) describes, using two Series PX-136 four-wire fullbridge gage transducers (Omega Engineering, Stanford, CT).
Rather, in this study, a single Series PX26-001DV differential
transducer (Omega Engineering, Stanford, CT) was used toautomate the infiltrometer. A schematic of the differential
transducer installation is shown in Fig. 1, where one port was
installed at the bottom position on the reservoir and the otherport was connected, using tubing, to the head-space of thereservoir. To automate the water height measurements, thefour pins of the differential transducer were connected to a1
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