Impact of Saturated Hydraulic Conductivity on the Prediction of Tile Flow
B. P. Mohanty,* T. H. Skaggs, and M. Th. van Genuchten
ABSTRACT
Preferential flow through macropores and other structural voids
in field soils most often occurs at or near saturation. Our earlier
research revealed significant differences in the value of the saturated
hydraulic conductivity (A's) of a glacial till soil in central Iowa whenobtained with five different measurement techniques. The five techniques included one laboratory constant-head penneameter method
and four in situ methods: disc penneameter, Guelph penneameter,velocity permeameter, and double-tube permeameter. Differences inmeasured K, values were attributed to differences in sample size, theexistence or absence of open-ended macropores, and measurement
principles. In this study, we used the different A", estimates in a twodimensional numerical model, CHAIN_2D, to predict water flow into
a subsurface tile drain in the same field. Comparisons between predicted and observed tile flows were made during four crop growing
seasons. Preferential flow observed in the tile drain during large storm
events was predicted best by the model when using A", values measured
with the disc permeameter method, which least disturbed the boundary conditions of the flow field and better accounted for the macropore
structures of the field soil. Quantitative and qualitative findings suggest that the disc permeameter was best suited for the field site.

T

HE SATURATED hydraulic conductivity (Ks) is a key
parameter needed for analyzing or modeling waterflow and chemical transport in the subsurface soil. Several laboratory and in situ techniques have been developed during the past several decades to measure thisparameter. The different techniques often show significant differences in ,KS that reflect inherent experimental
or mathematical limitations (Lee et al., 1985; Kanwar
et al., 1989; Logsdon et al., 1990; Mohanty et al., 1991,
1994; Paige and Hillel, 1993; Gupta et al., 1993). One
logical question that arises is how the different Ks measurements may impact predictions of flow and transport
when used in a computer model. This question seems
especially important when water flow or solute transport
at or near saturation is considered in a macroporous
field soil. In other words, Ks probably plays an especially
important role in the vadose zone (between the soil
surface and shallow groundwater table) during periods
following heavy rainfall or irrigation. The objective of
our study was to determine the impact of Ks values
measured with four different techniques on the abilityof the two-dimensional variably-saturated flow andtransport numerical model (CHAIN_2D) to predict tile
drain outflow from a macroporous, no-till, tile-drained
agricultural field. For this study, the Ks measurement
techniques included: (i) an in situ Guelph permeameter,(ii) an in situ velocity permeameter, (iii) an in situ discpermeameter, and (iv) a constant-head permeameter inthe laboratory using detached soil cores.

MATERIALS AND METHODS
Field Site
A 115 by 36 m field was selected at the Agronomy andAgricultural Engineering Research Center near Ames, IA.
The experimental plot was part of the Field no. 5 that lies on
a Wisconsin-age glacial-till soil of the Des Moines lobe witha maximum slope of 2%. The soils are Nicollet (fine-loamy,
mixed, mesic Aquic Hapludoll) and Clarion (fine-loamy,
mixed, mesic Typic Hapludoll) loams in the Clarion-NicolletWebster association. The experimental plot had been underno-tillage practice and continuous corn production since 1984.
Singh et al. (1991) reported that 2 to 12% of the planar surface
area of the field site was occupied by biological and structural
macropores (>l-mm diameter), and that the macropore area
decreased with increasing soil depth. Everts and Kanwar(1989) and Singh and Kanwar (1991) also showed the significance of macropore flow at the field site. A 20-m-thick layerof aquitard material with relatively low saturated hydraulicconductivity (0.001 m d"1) at a depth of 3.9 m separated thelower confined aquifer from the upper unconfined aquifer at
the field site. Heavy storm events at the site often cause theshallow groundwater table in this field to rise close to thesoil surface. Parallel 1.22-m-deep subsurface drains at 36.6-m
spacings drain excess water from different experimental plotsat the field site. The 10.2-cm-diameter clay tile drains were
installed in 1960. The drain system was assumed to be completely stabilized by the time of our investigation (1984-1990).
Tile flow rates were measured daily throughout the crop growing season (April-October) using an automated flow measurement system at the downgradient end of the tile.
A series of Ks measurements was made in this experimental
field at five sites at four different depths (15, 30, 60, and 90cm) using five Ks measuring techniques. As no definite rule
exists for knowing the actual measurement (soil) volume forthe in situ techniques, we determined the depth intervals on
the basis of visual examination of the profile stratigraphy
and the suitability of different methods at different depths.
Although some Ks measurements could be made near the soil
surface (0-15 cm depth) using detached soil cores and discpermeameter technique, no measurement was possible using
the Guelph permeameter. Thus, for this comparison study,
we did not use the near-surface Ks measurements. The measurements were made in June and July 1990 during the corn
growing season. Detailed descriptions of the Ks measurements
and a comparison of the different techniques can be foundelsewhere (Mohanty et al., 1994). Here we only briefly summarize the salient features of these techniques. The double-tube
permeameter measurements in Mohanty et al. (1994) were
not used in this study since experimental difficulties led tovery few data points.
Guelph Permeameter Method

USDA ARS, U.S. Salinity Lab., 450 W. Big Springs Road, Riverside,
CA 92507. Received 14 Nov. 1997. *Corresponding author (bmohanty®
ussl.ars.usda.gov).
Published in Soil Sci. Soc. Am. J. 62:1522-1529 (1998).
1522

A Guelph permeameter (Reynolds and Elrick, 1986) is aconstant-head permeameter that measures a composite of thevertical and horizontal Ks in the field. A 5-cm-diameter and 15cm-deep vertical borehole was augured. Proper preparation
Abbreviations: Ks, saturated hydraulic conductivity; RMSE, root
mean square error.

MOHANTY ET AL.: IMPACT OF SATURATED HYDRAULIC CONDUCTIVITY ON TILE FLOW PREDICTIONof the borehole is critical to accurate measurement of Ks;
commercially available augers and a brush (designed for the
Guelph permeameter) were used to make a clean borehole
and to minimize wall smearing. Two sets of steady-state measurements were made at two different constant heads, and Kt
was calculated using the calibrated relationship. Stable readings took from 1 to 3 h depending on the antecedent soil
moisture condition. Obtaining stable readings was somewhat
of a problem at sites having low conductivity owing to slowly
declining readings, even after 4 to 6 h of infiltration, as poresbecame plugged by sediments. Other possible reasons for thisbehavior may include vertical-horizontal anisotropy in K, anda water front moving through different soil layers.

Velocity Permeameter Method
A velocity (or falling-head) permeameter was adapted forfield use (Merva, 1987). An 8.4-cm-diameter cylinder was
pushed =7 cm into the soil. We experienced some soil compaction and macropore blockage when the sample cup was hammered into the ground. The top of the cylinder was closed
and connected to two hoses, one of which was connected toa reservoir providing water for infiltration. The second hose
was used to vent air from the cylinder and connect it to an
observation tube. Saturated hydraulic conductivity for the soil
inside the cylinder was calculated from cylinder geometry (i.e.,soil length and core diameter) and the rate at which the waterlevel in the observation tube decreased. For every soil core,
several estimates were made as the wetting front moved
through the soil. When the wetting front moved below the insitu soil core, Ks estimates approached a pseudoconstant valuethat was taken as the hydraulic conductivity of the sample.
Depending on depth and permeability, one complete saturated-conductivity reading took from 15 to 45 min.

Disc Permeameter Method
A disc permeameter is a constant-head infiltrometer thatcan operate at either a positive or a negative head (Perroux
and White, 1988). A precursor of this method is the suction
crust infiltrometer (Hillel and Gardner, 1969, 1970; Bouma,
1982; Booltink et al., 1991). Suction crust infiltrometers, however, have never been popular since making a crust is rather
cumbersome. On the contrary, disc permeameters are being
widely used because of their ease of operation. Infiltration
takes place through a 2-cm layer of between 0.25- and 0.42mm-diameter (40-60 mesh) sand inside a 25.4-cm-diameterring. Sand placed on the surface was estimated by the KozenyCarman equation to have a minimum hydraulic conductivityof 4881.6 cm d"1 (Carman, 1937). The Ks were estimated basedon infiltration measurements made by maintaining 0 to 10 mmof positive head in the permeameter. Procedural details on
the hydraulic conductivity estimation and the supporting evidence of using a small positive head were described earlier
in a parallel study (Everts and Kanwar, 1993). A single infiltration reading required =50 min on average. Because the instrument sits on top of the soil without causing much soil disturbance, readings could be obtained for all four depths unlesshampered by a high water table. When the supply conditions
changed from tension to ponding, the sorptivity value doubled
or tripled in our study. Similar trends were noted by Perroux
and White (1988) in their original study, indicating the relatively larger contribution of macropores under ponded conditions. One limitation of the disc permeameter is that for very
permeable soils the value of Ks during a short time interval
is often limited by the conductance of the contact material(sand) and the porous membrane of the permeameter (Reynolds and Zebchuk, 1996a).

1523

Constant-Head Permeameter Method
Measurements of Ks in the laboratory (Klute, 1965) werebased on direct application of Darcy's law to a saturated soilcolumn of uniform cross-sectional area. A hydraulic-head difference was imposed on the soil column, and the resulting
flux of water was measured. Five replicated detached soilcores, 7.6 cm long and 7.6 cm in diameter, were collected fromthe different depths using a Uhland core sampler. All cores
were carefully inspected for cracks resulting from core recovery; only intact cores were used further. The cores were saturated in the laboratory by wetting from the bottom, and Ks
was measured under a constant head. This method measured
the vertical conductivity. Limitations experienced with thismethod included some soil compaction during core extraction,wall leakage in loose samples, and piping due to the presenceof worm holes or root channels open at both ends of the soilcore. Because high moisture contents caused compaction ofmost soil samples at the deeper depths, no good samples wereleft for analysis below the depth of 90 cm. The average timerequired to obtain steady-state readings for the soil cores was=45 min.

Numerical Methods
The CHAIN_2D computer model (Simunek and van Genuchten, 1994), modified to apply piecewise continuous hydraulic functions (Mohanty et al., 1997), was used to simulate twodimensional isothermal saturated-unsaturated flow throughthe rigid soil in a cross section perpendicular to the tile drain(Fig. 1). The use of piecewise-continuous hydraulic functions
is a simple and yet practical approach for handling preferential
flow in field soils (Mohanty et al., 1997); however, note thatthe definition of field-scale preferential flow (Mohanty et al.,1997) differs operationally from bypass flow or macropore
flow (Booltink and Bouma, 1991). Recently, the modified
CHAIN_2D model was field tested in a semiarid condition
and found to be performing reasonably well to simulate waterand nitrate transport to a tile drain following the flood irrigation events in Las Nutrias, NM (Mohanty et al., 1997,1998).
Considering two-dimensional isothermal Darcian flow, thegoverning flow equation in CHAIN_2D is given by the Richards' equation

36
dt

Tf\ KA\* ,aXj,*7[1]

where 6 is volumetric water content (L3 L~ 3 ), h is the soil
water pressure head (L) (for notational convenience assumed
to be positive downward), S is a sink term (T~l), Xj (i = 1,2)
are the spatial coordinates (L), t is time (T), K^ and K& arecomponents of a dimensionless anisotropy tensor KA, and K
is the unsaturated hydraulic conductivity function (L T~l).
As suggested by Mohanty et al. (1997), piecewise-continuous
hydraulic functions Q(h) and K(h) were used to keep theunsaturated hydraulic parameters the same and independentof the different saturated hydraulic conductivity values. Fora multimodal pore-size distribution, the soil water retention
and hydraulic conductivity functions can be written as (Mohanty et al., 1997,1998):[2]e = ej(i=1) h>h%K(h) = 2, W) =

[3]

1524

SOIL SCI. SOC. AM. J., VOL. 62, NOVEMBER-DECEMBER 1998,dh

A. i h < h.

Atmospheric Boundary

Ocm

Tile Drain= 0/or t = 0 e Q

h(x,z,t) = /.0(x,z)

390cm-«•»/£*«• ')]«, -3660cmOcm

Fig. 1. Finite element flow domain where a (-) are prescribed (rainfall water flux) functions of x, z, and I; and n, are the components of theoutward unit vector normal to the boundary TAB; E is the maximum potential rate of infiltration or evaporation under the current atmosphericconditions; h is the pressure head at the soil surface, and h\ and h, are, respectively, minimum and maximum pressure heads allowed under
the prevailing soil conditions. /Cd[ain is the adjusted hydraulic conductivity (L T '), and Cd is a correction factor (-).

[1 +

{1-

i

[1 +
[4]

(7, = 1 -Kni(h) = K* + K*[exp(h - A*)8 /zf < /z < 0/C./W = #* + £*[exp(-/z*)8 - 1]

h >0

[5]
[6]

where,
Kj

is the hydraulic conductivity for capillary-dominated flow domain i (L T~'),Kni
is the hydraulic conductivity for noncapillarydominated flow domain ni (L T'1),
6S,
is the saturated water content for capillarydominated flow domain / (L3 L~ 3 ),6r ,is the residual water content for capillary-dominated flow domain / (L3 L~3),his the equilibrium soil water pressure head ofthe "bulk" soil (across all flow domains) (L),h% = h$ = h* is the critical or break-point soil water pressure
head where flow changes from capillary-dominated to noncapillary-dominated flow, or viceversa (L),K*
is the hydraulic conductivity corresponding toh* (LT'1),
8
is a fitting parameter representing effective
macroporosity or other structural features contributing to noncapillary dominated flow(L-1),
a,, 3;
are the hydraulic shape parameters (van Genuchten, 1980) for the capillary-dominated flow

ni
w,
hi

domain / (L ', -),is the number of capillary-dominated flow domains. For i = 1, the sum type multimodal van
Genuchten-Mualem hydraulic functions (i.e.,
Eq. [2] and [4], reduce to the original unimodal
van Genuchten-Mualem functions),is the noncapillary-dominated flow domain,is the weighting factor for capillary-dominatedflow domain i (-); subjected to Sw, = 1, and
0 < wt < 1, andis the saturated hydraulic conductivity for capillary-dominated flow domain i (L T~l); subjected to Efc, = K*.

Besides Ks, unsaturated hydraulic properties [K(h) and 6(/z)
functions] were measured between -5 and -15 000 cm of soil
water pressure head using the detached soil cores and adopting
the multistep outflow procedure (Gardner, 1956; Richards,
1965). These hydraulic functions are essential for the numerical flow modeling. Using a nonlinear curve fitting procedure,
we found that the soil hydraulic properties were best describedusing a bimodal pore-size distribution with one capillary and
one noncapillary flow domain matched at h* = —5 cm. In a
few instances, at 30- and 60-cm depths, ^s values measured
with the Guelph permeameter were lower than the unsaturated K at -5 cm pressure head measured using the soil cores.
For these cases, we used unimodal hydraulic functions with theunsaturated hydraulic conductivity connected to Ks measured
with the Guelph permeameter. Table 1 presents the hydraulicparameters of the soil at different depths in the experimental plot.
For the numerical simulations, we discretized the flow domain (cross section) into 9185 nodes and 8964 quadrilateralelements. Based on the hydraulic conductivity measurements,

MOHANTY ET AL.: IMPACT OF SATURATED HYDRAULIC CONDUCTIVITY ON TILE FLOW PREDICTION

1525

Table 1. Parameters of bimodal piecewise-continuous hydraulic functions for different soil horizons.
K*

Depth

Guelph

Velocity

Disc

Soil core

0.84

0.03

0.72
0.54
0.40

0.65
0.22
-

— -1

0-15
15-30
30-60
60-390t

0.467
0.462
0.435
0.430

0.011
0.020
0.090
0.080

0.018
0.015

1.63
1.60

0.020
0.023

1.50
1.47

0.555
2.40
4.55

11.20

-5
-5
-5
_c

0.52
—
—
0.17

0.62
0.21
0.36
0.31

t As soil cores at the 90-cm depth in our study plot were compacted, unsaturated hydraulic conductivity measurements from an adjacent plot were used
for our modeling study.

the soil profile was divided into four soil horizons: 0 to 15,15
to 30, 30 to 60, and 60 to 390 cm. These four horizons were
further subdivided into a total of 167 layers for numericalsimulation purposes. Relatively fine (0.1-1 cm) discretizations
were used near the soil surface, across horizon interfaces, andaround the subsurface tile drain to enable accurate descriptionof abrupt changes in local fluxes and hence pressure gradients.
For the simulations, we used a time-variant evapotranspiration
rate assuming a normalized constant water uptake distribution
across the top 60 cm of the soil profile in accordance withthe approach of Feddes et al. (1978) and Simunek and van
Genuchten (1994). Daily potential evapotranspiration rates
were estimated using atmospheric data from a nearby weather
station 2 km away. Daily precipitation data were also usedfrom the same weather station. Water table elevations estimated from the groundwater monitoring wells along the plot
boundary were used to define the initial conditions in thetwo-dimensional flow domain (Fig. 1), ft. Time-dependent
precipitation rates at atmospheric boundary nodes, and noflow conditions along impermeable bottom boundary nodes,
were invoked. We simplified the two hydrologic boundaries(FAD and FBC, Fig. 1) by assuming them to be impermeable,
thus neglecting regional flow contributions. The tile drain at
the center of the plot was treated as a boundary node surrounded by four regular square elements with adjusted hydraulic conductivities according to the electric analog approach of Vimoke et al. (1963) and Fipps et al. (1986). Flow
initial and boundary conditions for our initial value problem
are shown in Fig. 1.

RESULTS AND DISCUSSION
Measurement of Saturated
Hydraulic Conductivity
Values of Ks obtained with the above methods at
different depths of our experimental field site, comprising several plots and tile drains, were previously given
in Tables 2 through 4 of Mohanty et al. (1994). In thisstudy, we will focus our modeling effort on a single plot
for which comprehensive tile flow data are available
Table 2. Saturated hydraulic conductivity (A's) measured by different methods at Site 5 (Tile 4) (after Mohanty et al., 1994).
Method
Depth

15

30
60
90

Guelphf

Velocityt

Disct

Soil core§

7.40
0.09
1.63

12.18
6.76

36.89
88.99
66.78
83.72

60.13
14.01

26.78

26.44
53.05

0.62
csfl

t No replications (i.e., one measurement for each depth at Site 5).
§ Geometric mean of five replicates.
11 cs is compacted soil sample.

(representing Tile 4 and Site 5 of Mohanty et al., 1994).
Values of Ks for this plot are presented in Table 2.
Important inferences from the Ks comparisons (Mohanty et al., 1994) were that (i) the Guelph permeameteryielded the lowest average ^s values, possibly becauseof small sample size, vertical-horizontal anisotropy, wallsmearing, and air entrapment; (ii) the laboratory
method produced the greatest variability at shallow
depths (15 and 30 cm), presumably because of smaller
sample size, the presence or absence of open-ended
macropores, and variable soil compaction during coreextraction; (iii) the velocity permeameter led to Ks values that were close to values estimated from detachedsoil cores measured in the laboratory, probably becauseof forced vertical flow in both cases; and (iv) the discpermeameter predicted higher Ks values in comparison
with other in situ methods, probably because the larger
sample sizes contained more intact macropores. This isin agreement with Anderson and Bouma (1973), Lauren
et al. (1988), and Bouma (1991), who claimed saturated
hydraulic conductivity increases with the sample volume. Because of their comparable values (Mohanty etal., 1994), velocity permeameter data were used in lieu
of unavailable laboratory soil core data at the 90-cm
depth for modeling purposes. We note also that, by
principle, different in situ methods measure different
combinations (ratios) of vertical and horizontal Ks, andsince no pure vertical or horizontal Ks measurements
could be made in situ, Ks in this study is assumed to
be isotropic.

Simulation Results
Figures 2 through 5 compare measured daily tile flow
over four crop growing seasons (April-October) during
1984 to 1990 with flow predicted by CHAIN_2D using
different Ks values (or 8 in the bimodal hydraulic functions) obtained from the four Ks measurement methods.
Simulations for 1985, 1988, and 1989 were excluded
since no or very little flow in the tile drain was observedin these drier years. The comparisons of simulated andmeasured tile flow indicates that tile flow in this fieldis very sensitive to /Cs (or 8 in the bimodal hydraulicfunctions). In addition to Ks, precipitation intensity andevapotranspiration rate influenced the tile flow pattern
across each crop growing season. In 1984 (Fig. 2), during
the early stages of corn growth, a lower evapotranspiration rate and heavy precipitation resulted in high tile
flow. During the late crop growth stage (mature corn),however, the moderate amount of precipitation was

1526

SOIL SCI. SOC. AM. J., VOL. 62, NOVEMBER-DECEMBER 1998
•g-

6.0

~

4.5

o

| 3.0
1
°-

.till Illlh

15

o.o

2000

1500
CM

1000

500

80

100

120

140

160

180

200

220

240

260

280

300

Time (Day of the Year)
Fig. 2. Comparison of observed and predicted tile flow during the crop growing season of 1984 at the field site using different Ks. Also shown
are precipitation dats at a nearby weather station during the same period.

mostly consumed by evapotranspiration and did not
produce substantial amounts of measured or predicted
tile flow. In 1986 (Fig. 3), several flow peaks could not be
captured by any of the simulations, perhaps indicating
inaccurate evapotranspiration estimates, unrepresented
runoff, base flow, regional flow, or some other process
not accounted for in the model. In early 1987 (Fig. 4),
the model often underpredicted tile flow for different
precipitation events, while the opposite was the case in
the later part of 1990 (Fig. 5). These findings suggest thatthe model could be further improved by accommodatingsome of the above-mentioned field-scale flow processes.
Other possible reasons for the discrepancies between
observation and prediction include spatial-temporalvariability of the soil hydraulic properties, precipitationand groundwater table variability across the relatively
large field, and insufficient frequency (i.e., daily values)of tile flow measurements to capture the sharp peaks
that often occurred following heavy precipitation. Also
the frequency, intensity, and amount of precipitation•? 6.0

I3.0
Q.

LlL

1.5

0.0

LLLUiL

1800

1300
o

300

80

100

120

140

160

180

200

220

240

260

280

300

Time (Day of the Year)
Fig. 3. Comparison of observed and predicted tile flow during the crop growing season of 1986 at the field site using different Ks. Also shown
are precipitation data at a nearby weather station during the same period.

MOHANTY ET AL.: IMPACT OF SATURATED HYDRAULIC CONDUCTIVITY ON TILE FLOW PREDICTION
E

1527

8.0

o

c
o

6.0

m

4.0

*=

'a.
S

0.

.a. I

2.0
0.0

5000
._._._.- Guelph_ 4000———— Velocity•
Observeds-5

I 3000u
3i£ 2000 -

\

JB

•
1000

\

J»

\f

*/\l

0

yiiL^

• •

i

i

i

i

i

i

i

i

80

100

120

140

160

180

200

220

240

i

i

i

260

280

300

Time (Day of the Year)
Fig. 4. Comparison of observed and predicted tile flow during the crop growing season of 1987 at the field site using different A"s. Also shown
are precipitation data at a nearby weather station during the same period.

across the rolling landscape near the field site was highlyvariable. Hence, precipitation measured at the weather
station may sometimes be inaccurate for our simulationof the experimental plot. Still, with the exception of theabove-mentioned periods, the model seemed to perform
well, certainly in a qualitative sense. Besides these qualitative comparisons, goodness-of-fit of the model predictions were objectively evaluated for different crop years
and Ks measurement techniques using the root meansquare error, RMSE (Table 3). This is defined as(Loague and Green, 1991)

1 nor il°-5

RMSE = ^Sf=i (Mi - 5,.)2j[7]

where M, is the measured tile flow, 5, is the corresponding tile flow simulated J>y the model, N is the numberof measurements, and M is the mean of the measured
tile flow. A smaller RMSE (percentage) indicates amore accurate simulation.
Given the same unsaturated hydraulic and retention

6.0
40

2.0

LUu

11

0.0

3000

2000

1000

80

100

120

140

160

180

200

220

240

260

280

300

Time (Day of the Year)
Fig. 5. Comparison of observed and predicted tile flow during the crop growing season of 1990 at the field site using different A",. Also shown
are precipitation data at a nearby weather station during the same period.

1528

SOIL SCI. SOC. AM. J., VOL. 62, NOVEMBER-DECEMBER 1998

Table 3. Root mean square error (RMSE) for the tile flow predictions.f
Numberof

MethodYear

Giielph

Velocity

Disc

Soil coreobservations

71.5
80.7
128.0
250.4

94.2
96.4
100.8
210.4

91
180
43
41

o/

1984
1986
1987
1990

122.1
132.4
127.4
174.8

87.7
92.7
110.5
209.8

t Zero flow(s) or missing observation(s) because of any other reason were
not used in the root mean square error (RMSE) computation.

functions, initial soil water conditions, and flow boundary conditions, tile flow predictions with the lowest Ks
values (the Guelph permeameter) generated the lowest
peaks and longest tails in comparison with predictions
using higher ^s estimates of the other methods. The
high KS estimates of the disc permeameter produced
flow with the highest peaks and shortest tails. Predictions using Ks obtained with the laboratory constandhead permeameter and the velocity permeameter produced intermediate tile flow rates during heavy storm
events. Using quantitative evaluation procedure, discpermeameter ^s produced the lowest (best) RMSE for1984 and 1986 (Table 3). In 1987 and 1990, however, all
Ks methods produced relatively high (>100%) RMSE.
Note that RMSE calculated using the Guelph permeameter estimated Ks in 1990 was lowest among all four
methods. Careful examination of Fig. 5, however, indicated the zero (predicted) flow by using the Guelph
permeameter Ks for the entire crop growing season.
Thus, for a more unbiased evaluation of these techniques, a prudent combination of qualitative (e.g.,graphical) and quantitative (e.g., RMSE) evaluation
methods should be used. In general, Ks measured witha disc permeameter gave a better qualitative representation of the soil's matrix and macropore contributions to
the hydraulic conductivity than the other three methods.
As pointed out by several early users and found in thisstudy, limitations of the Guelph permeameter include
smearing of the well wall during preparation, clogging
of the soil pore structure by sedimentation, and artifacts
involved in the original two-head analysis. These problems all lead to lower Ks estimates (e.g., Kanwar et al.,
1989; Mohanty et al., 1991, 1994; Gupta et al., 1993;
Paige and Hillel, 1993). In recent years, several researchers have tried to alleviate some of these problems to
make the Guelph permeameter more representativeand robust for in situ Ks measurements. Possible modifications and improvements include chipping of the well
wall to open smeared pores under dry conditions, using
wire screens and filter material to prevent sinking of
the reservoir tip and limit sedimentation, and using an
improved one-head mathematical analysis to avoid negative numbers (Elrick and Reynolds, 1992; Reynolds etal., 1992; Reynolds, 1993; Reynolds and Zebchuk,
1996b; W.D. Reynolds, 1997, personal communication).
Reynolds and Zebchuk (1996b) reported improvementin Ks estimation by adopting some of these modifications
in experimental procedure and data analysis. More recently, Bosch (1997) rebutted the earlier claims of im-provement of Ks estimation using single-head analysis
over two-head analysis (as used in this study) for any
fine textured soil. Once these differences are resolved
completely and an improved Guelph permeameter
methodology is commercially available, similar comparative studies for different methods should be made toevaluate its performance under different soil and hydrologic conditions.
Our results also indicated that the K^ estimates obtained with the velocity permeameter and laboratory
constand-head permeameter methods led to underpredicted tile flow rates. These two methods yield strictly
vertical Ks measurements. In reality, hydraulic properties of the glacial till soil at the field site are probably
not completely isotropic as assumed in our model. Also,these two methods use relatively small soil cross-sectional areas during measurements, and the bottom
boundary conditions assumed by these methods may
not be representative of flow processes in the field. By
comparison, the disc permeameter method is based on
three-dimensional flow pattern, and appeared to leadto a better model simulations of the tile flow behavior
at our field site.

SUMMARY AND CONCLUSIONS
Differences in Ks resulting from the different measurement techniques should reflect differences in sampling volumes, the degree to which the pore-size distribution of the soil is altered by the techniques, differencesin imposed flow boundary conditions, and differencesin the one- or multidimensional flow pattern forcedby the techniques. This study was designed to test theapplicability of several Ks measurement techniques
when used for numerical simulation of tile flow in amacroporous agricultural soil. Field-scale measurements and modeling of flow indicated the importanceof Ks for predicting tile flow during high and low precipitation events with different evapotranspiration rates
during the crop growing season. Observed and simulated tile flow matched better during the crop growing
seasons when using disc permeameter Ks estimates thanwhen using Ks measured with any of the other threemethods. It is likely that this result reflects the fact thata disc permeameter better represents the natural flow
conditions. Moreover, its ease of operation and minimal
pore structure disturbance during the hydraulic conductivity measurements seem to give this technique additional advantages over other methods.

MOHANTY ET AL.: IMPACT OF SATURATED HYDRAULIC CONDUCTIVITY ON TILE FLOW PREDICTION

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