Reprinted fromGEODERMAAN INTERNATIONAL JOURNAL OF SOIL SCIENCE

Geoderma 64 ( 1994) 9 3 - 110

Analysing problems in describing field andlaboratory measured soil hydraulic properties’
G. Wessolek”, R. Plagge”, F.J. Leijb, M.Th. van Genuchted’“lnstitutjii’r iikologie, FG Rodenkunde, Technische Universitiit Berlin, D-10587 Berlin, Germany
hU.S. Salinity Laboratory, USDA, ARS, Riverside, CA 92501, USA(Received January 27, 1993; accepted after revision December 22, 1993)

ELSEVIER

GEODERMA
Geoderma 64 ( 1994) 93-1 IO

Analysing problems in describing field andlaboratory measured soil hydraulic properties’
G. Wessolek”, R. Plagge”, F.J. Leijb, M.Th. van Genuchtenb“Institutfiir iikologie,

FG Bodenkunde, Technische Universitiit Berlin, D-10587 Berlin, Germany
bU.S. Salinity Laboratory, USDA, ARS, Riverside, CA 92501, USA(Received January 27, 1993; accepted after revision December 22, 1993)

Abstract
Accurate in situ determination of unsaturated soil hydraulic properties is often not feasible becauseof natural variability of most field soils, and because of instrumental limitations. Therefore the soil
hydraulic properties are often measured in the laboratory, or derived by computer models using simple
standard laboratory methods.
This paper analyses problems in describing field hydraulic properties of a Ap horizon of a silty
loam, basing on data from different laboratory methods: (i) A standard pressure plate apparatus and(ii) a constant-head permeameter were used to measure the static retention characteristics and thesaturated hydraulic conductivity independently. (iii) An instantaneous profile method was applied tomeasure water retention and conductivity simultanously. Relatively new technics involving “undisturbed” soil samples instrumented with mini tensiometers and Time Domain Reflectometry (TDR)
mini probes characterise the experiment. The models by Mualem and van Genuchten (MvG) wereused to describe the soil hydraulic functions. The different laboratory results were then comparedwith the hydraulic field properties measured in instantaneous profile manner.
The laboratory method allows a high spatial and temporal resolution; this facilitates an investigationof some of the assumptions made, when fitting the MvG models to hydraulic data. A reasonably good
description of the hydraulic data was obtained when setting the residual water content, 0,, to 0 andthe pore connectivity factor, I, to 0.5 because 0, and 1 were not sensitive. However, a poor fit resulted
when the saturated water content, 8,, was equated to the porosity, and the saturated hydraulic conductivity, k,, to its independently measured value. Values for 0, and k, derived from field measurements
were somewhat higher than those obtained from laboratory samples.
To demonstrate the influence of the different input data on a water balance, the cumulative drainage
from an initially saturated soil column was simulated with different sets of hydraulic parameters
estimated from field and laboratory data. Parameters derived from the laboratory results consistently
yielded lower predictions of cumulative drainage compared to hydraulic parameters derived fromfield measurements. The differences were relatively small when an initial water content corresponding
to 60 cm suction (field capacity) was used.

‘Dedicated to Prof. Dr. K. Bohne on the occasion of his 60th birthday
0016-7061/94/$07.00 0 1994 Elsevier Science B.V. All rights reserved
SSDI0016-7061(93)E0121-B

94

G. Wessolek et al. /Geoderma 64 (1994) 93-l IO

1. Introduction
Our ability to mathematically model water and solute movement in the subsurface seems
to be well ahead of our ability to accurately quantify the flow and transport properties ofsoils. This is particularly true for the unsaturated hydraulic properties involving the soil
water retention, 19(h), and hydraulic conductivity, k(h), functions, where 0 is the volumetricwater content ( cm3 cmp3), h is the soil water pressure head (hPa), and k is the hydraulicconductivity (cm day- ‘). In situ field measurements of the soil hydraulic properties aretime consuming and costly. Moreover, the results are often unreliable because of experimental shortcomings and high spatial and temporal variability. Because of these problems,
the unsaturated hydraulic properties are frequently determined in the laboratory, or estimatedindirectly from other soil properties which can be measured more easily and accurately. A
drawback of such alternative methods is, that they may yield values which are not representative for field conditions. Still, for many purposes, laboratory-measured data are helpful
as a complement or substitute for field data since in situ measurements are usually notavailable at relatively low water contents, and ordinarily do not allow for the same spatial
and temporal resolution as laboratory measurements.
The primary objective of this paper is to analyse problems in describing field and laboratory measured soil hydraulic properties using the RETC program. The laboratory data
were obtained with an improved laboratory method for determining the unsaturated soil
hydraulic properties. The 0(h) and k(h) curves were obtained on “undisturbed” samples
from the Ap horizon of a silty loam instrumented with mini tensiometers and Time Domain
Reflectometry (TDR) mini probes. The laboratory set-up is well-suited for accurately
determining a large number of hydraulic data. Additionally, the water retention characteristic
and the saturated hydraulic conductivity were obtained with pressure plate extractors and aconstant-headpermeameter. The field hydraulic properties were determined in instantaneous
profile manner, using neutron scattering and tensiometer with transducer.
Some of the advantages of closed-form analytical expressions for the hydraulic properties
were summarized by Van Genuchten and Nielsen ( 1985) : (i) the ability to predict k(h)from (h) measurements; (ii) more convenient numerical simulations of flow and transport
in the vadose zone; and (iii) comparisons, substitutions, or scaling of the hydraulic properties for different soils. The observed hydraulic properties in this paper are modeled withthe expressions of Van Genuchten ( 1980). The program RETC (Van Genuchten et al.,1991) was used to obtain the hydraulic parameters by fitting the model parameters to
observed field and laboratory data using different fitting conditions.
A secondary objective of this paper is to investigate the performance of the fitted hydraulicparameters in a numerical model for variably-saturated flow. The approach is somewhat
similar to that used by W&ten et al. ( 1986) who evaluated the relative accuracy of hydraulicfunctions in terms of numerical predictions of such functional criteria as travel time, depthof water table, and downward water flux. In this paper we shall study the sensitivity of thepredicted cumulative drainage to different parameter sets.
2. Materials and methods
2. 1.. Measurement of hydraulic properties
A schematic of the experimental set-up for measuring the soil hydraulic properties in thelaboratory is shown in Fig. 1. More detailed descriptions of the method are given by Malicki

95

G. Wessolek et al. / Geoderma 64 (1994) 93-1 I 0

and Skierucha ( 1989) and Plagge ( 199 1) The laboratory procedure is based on a similar
concept as the evaporation method by Wind ( 1966), modified by Boels et al. ( 1978) andTamari et al. ( 1993). The improved technology provides a detailed description of 8 and hin time and space. The measuring cell consists of five pairs of sensors to measure h and 8
in 5 positions and time during the evaporation experiment. Each measuring cell consist of
a 250 cm3 core sampler, 10 cm high and 5.5 cm in diameter, to take undisturbed soil coresin the field. Table 1 provides some specifications of the tensiometers and the TDR equipmentused as sensors for h and 8, respectively. The mini tensiometers are composed of a 2.8 mm
diameter ceramic cell with an inner boring permitting quick response times, a brass tube,
and a pressure transducer with a resolution of 0.2 hPa. Transducers are monitored by a realtime multitasking computer to control the measurement and collect data dependent to defined
events. The used TDR system (EASY TEST Ltd.) is operating with a 2.0 X lo-‘” s risetime needle pulse, supported by an Atari personal computer. The TDR mini probes consistof two 54 mm long stainless steel needles with a diameter of 0.8 mm, separated 5 mm fromone another. The probes are connected by a 50 W cable to the TDR meter. For the experiments the standard deviation of the observed water content was about k 0.006 g cme3. The
TDR probe and the tensiometer, aligned horizontally at an angle of 90”, are installed througha pair of trapped holes in standard sampling steel cylinders. Five pairs of sensors, verticalfanlids with holes

minitensiometers

Fig. 1 Schematic diagram of the experimental apparatus for simultaneously measuring the hydraulic conductivity
and soil water retention curves
Table 1
Properties of the tensiometers and TDR equipment

Parameter
Measuring range
Resolution
Spacial resolution
Contact surface or measured volume
Total volume for equipment”“Soil core volume 250 cm3.

Tensiometry

TDR

h (hPa)O-850 hPa
0.2 hPa
2.8 mm X 15 mm
1.38 cm’0.13 cm’

0 (%)
0-100%
0.6%
7mm X 54mm
2.1 cm’0.03 cm’

G. Wessolek et al. /Geoderma 64 (1994) 93-110

96

arranged, leads to five 2 cm thick soil layers. More technical details of the tensiometer and
TDR system are given by Plagge ( 199 I), Malicki and Skierucha ( 1989) and Malicki et al.
( 1992). The volume occupied by all sensors was < 0.1% of the whole soil core volume.
Fig. 2 shows a cross section of the measuring cell. The used TDR technique is applicablefor soils with a salt content < 1 mS.
Undisturbed soil samples were taken at a mean depth of 1.5 cm from the Ap horizon of asilty loam from Ohlendorf, Germany. Some basic soil properties are listed in Table 2. The
soil cores were water-saturated from the bottom during 3 weeks and then subsequently
sealed at both ends before the sensors were carefully inserted from the sides. After thetensiometer and TDR readings indicated that the cores were in hydraulic equilibrium, themeasuring cells were placed on a balance ( f 0.01 g) to monitor the evaporation rate. The

standardsoil core sampler
25ocm3

guide nut

TDR miniprobe

buJ

BNC-connector

Fig. 2. Cross section of a measurement cell showing a TDR probe and a minitensiometer at a particular depth.
Table 2
Chemical and physical properties of the Ap horizon of a silt loam
Parameter

Value

Depth
Particle size
<2pm
2-6.3 pm
>63 Frn
Bulk density
Porosityk,
Organic matter
pH (CaC12)
CEC

O-30 cm
9.2%
81.2%
3.6%
1.37gcm0.487 cm? cm-?
35.0 cm day-’
1 .O% c6.3
11.2meq/l00g

G. Wessolek et al. / Geoderma 64 (1994) 93-110

97

upper seal was subsequently removed and the soil subjected to controlled evaporation fromthe top using small ventilators. The experiment was terminated when the uppermost tensiometer in the soil core reached a suction of approximately 850 hPa. The measurements wereconducted in a room with a controlled air temperature of 20°C. Unsaturated hydraulicconductivity values, k(h), were obtained from the transient head profiles and calculated
water fluxes using Bezier fitting procedures for data smoothing (Sobczuk et al., 1992). The
saturated hydraulic conductivity, k, was obtained from the geometric average of 20 replications measured with a constant-head permeameter (Hartge and Horn, 1989). The hydraulic properties in the field were determined previously by Duynisveld and Strebel ( 1983)
according to the instantaneous profile method, using neutron scattering and tensiometersconnected to pressure transducers.
2.2. Mathematical description of hydraulic properties
The water retention and hydraulic conductivity curves were described using the respectivemodels (Van Genuchten, 1980) :k(h)=ks~{1-(~h)"-'[l+(~h)nl'-m}2[l+(ah)“]”(2)where 19, and 0, are the residual and saturated water contents ( cm3 cm-‘), respectively; ais an empirical parameter (cm- ‘) whose inverse is sometimes referred to as the air entry
value, n is a fitting constant reflecting the steepness of the retention curves, m = 1 - l/n, and
1 is an empirical pore connectivity factor frequently set to 0.5 following Mualem ( 1976).
For notational convenience, h and (Y are assumed to be positive in this study (i.e., h denotes
suction).
Eqs. ( 1) and (2) contain up to six independent coefficients, represented by the parameter
vector b = { 0,) 0,) a, n, 1, k, } T. We adopt the view by Van Genuchten and Nielsen ( 1985)
and Luckner et al. ( 1989) that the different parameters are essentially empirical coefficients
without much physical significance. Their values were estimated by fitting the retention andconductivity models to the observed data using the parameter optimisation program RETC
of Van Genuchten et al. ( 199 1) . This program uses Marquardt’s maximum neighborhood
method (Marquardt, 1963; Daniel and Wood, 1971) to minimize the objective function,
O(b):
(3)
where 0, and ii are the observed and fitted water contents, respectively, k and l, are theobserved and fitted conductivity data, N is the number of retention data, and M is the total
number of observed data points. The parameter W, is set by the optimization program toaccount for differences in the number of measurements and the adopted units of 0 and K.
The input parameter W, may be used to change the weight of the conductivity data in their
entirety with respect to the retention data.

G. Wessolek et al. / Geoderma 64 (I 994) 93-110

98

The parameters can be fixed at their initial estimates, or adjusted for a “best” fit during
program execution. Better results are generally obtained when the number of unknown
parameters can be limited by using independently determined parameters, or by carryingout a sequential fit by fixing those parameters which are found to be highly correlated withother parameters. The maximum number of iterations was set to 30, the input parameter W,
to 1 .O. With these considerations in mind, Eqs. (1) and (2) were fitted to the observed 8
and calculated k, assuming the following fitting scenarios:(i) 1 is fitted (Case 1) or set equal to 0.5 (Case 2))(ii) 0, is maximal and set equal to the mean porosity, E, of 0.487 cm3 cmP3 (Case 3),(iii) k, is fixed at 35.0 cm day-’ measured independently by the constant-head permeameter in the laboratory (Case 4),(iv) 0, is fixed at 0.487 cm3 cm-j and k, at 35.0 cm day- ’ (Case 5),(v) 0(h) is fitted and K(h) is predicted assuming k, = 35.0 cm day ~ ’ and 1= 0.5 (Case6).
2.3. Modeling cumulative drainage
A simple flow problem was simulated numerically to assess the implications of thedifferent fitting scenarios. We selected the example of cumulative drainage from an initially

0.325

0.275

0.250 T D i s t a n c e from
0.225 -

evaporating surface
A 9cm
0 7cm

0.200 -

W 5cm
0 3cm

0.175 -

0

0

lcm

1

3

2

4

Time [d]Fig. 3. Water content, measured with TDR at five positions in the soil column, versus time of evaporation.

G. Wessolek et al. / Geoderma 64 (1994) 93-110

99

Distance from_ evaporating surface
A 9cm

600

0 7cm
I 5cm
0 3cm
0

lcm

400

300

200

100

ot”““““““““’
1
2
3
4
0
Time [d]Fig. 4. Soil water pressure head, measured with tensiometers at five positions in the soil column, as a function ofsaturated soil column (silt loam). Drainage was simulated numerically using the linear
finite element code HYDRUS described by Kool and Van Genuchten ( 199 1) Two different
initial soil water contents were used: (i) the water content at saturation, I!?= 0,, and (ii) thewater content at a suction of 60 hPa, which we designate as field capacity ( 8= 0.33 cmcm-‘). The simulation involved cumulative drainage from a 30 cm long column over a
100 day period assuming a zero flux condition (no evaporation or infiltration) at the soil
surface, and a zero pressure head condition (gravity drainage) at the bottom of the finite
column. Cumulative drainage was predicted with parameter sets { Or, OS, a, n, 1, k, } obtainedby fitting the field or laboratory data using RETC assuming the aforementioned five fitting
scenarios.

3. Results and discussion
3.1. Measurement of hydraulic properties
As an example of the type of results obtained with the TDR mini probes, Fig. 3 shows aplot of the measured water content versus time for all five TDR positions in the 10 cm long

100

G. Wessolek et al. /Geoderma 64 (1994) 93-110
10 ’ 10 core 1- 0

l core 2v core 3+ core 4
m core 5

l

-

10 -2
10

0 v
00

Ll
lo-

lo3

Pressure Head, h [hPa]
Fig. 5. Laboratory-measured hydraulic conductivity data as a function of pressure head for five samples

soil core. As expected, the largest decrease in water content occurred closest to the evaporating surface. Differences in pressure head at the various locations in the core become
noticeable 2.5 days after the experiment started (Fig. 4).
The hydraulic conductivity was computed according to the instantaneous profile orunsteady drainage-flux method as outlined by Green et al. (1986). The evaporative flux
was estimated from the overall column weight. Fig. 5 shows the measured K( h) determinedon five different soil samples. The symbols in Fig. 5 represent results for five different cores
over time and depth using averaging. The K(h) values appear to follow an approximately
linear K-log h relationship for all samples. The considerable variability among the samples,
especially in the wet range correspond to bulk density differences between the single
samples.
3.2. Hydraulic data analysis
Figs. 6 and 7 show the observed and fitted water retention and hydraulic conductivity
curves, respectively, for both the field and laboratory data. Because of the large number ofdata points measured in the laboratory, only average values of the variables h and K for any
particular value of 8 and h, respectively, were plotted. Figs. 6 and 7 indicate a fairly close
match between the observed and fitted data. The fitted curves were obtained by allowing

G. Wessolek et al. /Geoderma 64 (1994) 93-110

101

all coefficients, except 1, to vary in the parameter optimization process. Their values can befound under Case 1 in Table 3, which lists the fitted parameter values along with thecorrelation coefficient, r*, and the objective function O(b) for all fitting scenarios. The field
retention data exhibited more scatter than the laboratory data. The increased scatter may be
attributed to the increased variability at the field scale, and to differences in accuracy andresolution in the observed 8 and h data. Because two different techniques were used todetermine the laboratory retention data, the (h) curve in Fig. 6 exhibits a slight discontinuity
between 800 and 1000 hPa. the hydraulic conductivity data, as well as the fitted curves,
were fairly similar for the field and laboratory experiments (Fig. 7). Higher values for thesaturated water content, l3,, and saturated hydraulic conductivity, k,, were found for the fielddata compared to the laboratory data. Initial optimizations of the data with RETC indicatedthat no “residual” water was present in both the field and laboratory determined retention
curves. In accordance with the studies by Greminger et al. ( 1985), W&ten and Van
Genuchten ( 1988), and Nimmo ( 199 1) , we therefore decided to fix 13,, to zero in all
subsequent runs.
To assess the importance of 1 as a fitting parameter, the parameter sets
{ 0,) a, n, 1, k, ] (Case 1) and { O,, a, n, k, } (Case 2) were fitted to the data. Table 3 indicatesthat the fitting was relatively insensitive to the value for 1. A wide range of values has beenreported for 1. For example, Schuh and Cline ( 1990) found values ranging from - 8.73 tolo4- Laboratory 0

0.0

0.1

0.2

0.3

0.4

Water Content, e [cm’lcm’lFig. 6. Measured and fitted curves for the field and laboratory retention (Case 1; 0,= 0)

G. Wessolek et al. / Geoderma 64 (1994) 93-110

102- Laboratory 0

1o-4 ’
100

;
I*

0

- - Field

m ’ ’ **...’ ’ ’ ’ mmmam’
10'

lo2

c ’ ’ “”103

Pressure Head, h [hPa]
Fig. 7. Measured and fitted curves for the field and laboratory conductivity (Case 1; 0, = 0).

Table 3
Estimated hydraulic parameters for the Ohlendorf silt loam used in this study
Case

Field
Lab
Field
Lab
Field
Lab
Field
Lab
Field
Lab
Field
Lab

0,
(cm’cm-‘)(Y(cm-‘)nk(cm day-‘)

1

rz

O(b)
x lo1

0.386
0.353
0.422
0.362
0.487”
0.487”
0.446
0.364
0.487”
0.487”
0.446
0.382

0.010
0.007
0.014
0.008
0.017
0.014
0.018
0.012
0.017
0.014
0.018
0.013

1.42
1.31
1.40
1.31
1.45
1.36
1.38
1.25
1.45
1.36
1.38
1.29

18.0
12.2
22.9
13.7
33.8
36.2
35.0
35.0”
35.0”
35.0”
35.0”
35.0”

1.87
1.07
0.50"
0.50"
0.50"
0.50
0.50"
0.50
0.50"
0.50"
0.50"
0.50"

0.980
0.981
0.978
0.981
0.976
0.972
0.858
0.978
0.976
0.972
0.954
0.993

4794
5810
4955
5867
5825
15620
1778
6746
5827
15621
4615
353

“Fixed parameters.
bBest fit for conductivity data only.

G. Wessolek et al. / Geoderma 64 (1994) 93-l 1010314.80, while W&ten and Van Genuchten ( 1988) reported values between - 16 and 2.2 for
medium- and fine-textured soils. From these and other studies (e.g., Yates et al., 1992), it
appears that the value of 1 has only a relatively small effect on the objective function, O(b).
Therefore, as suggested by Mualem ( 1976), 1 was fixed at 0.5 for the remaining cases.
For Case 3, the parameter 19, was either fitted or fixed at a value equal to the porosity, E,
as measured on the same soil core for which the hydraulic properties were reported. When0, was set equal to E, the fit resulted in a poor description of the retention curve. Fig. 8
shows that the retention curve is severely overpredicted in the wet range when 0, was fixed
to the measured porosity of 0.487 cm3 cm- 3. Fig. 9 demonstrates that the fitted K(h) curve,
assuming 13, to be equal to 6, also overpredicts the conductivity in the wet range, albeit lessin comparison with the retention curve in Fig. 8. These results are consistent with thepresumed presence of a “satiated” water content (e.g., Hillel, 1980) somewhat less thanfull saturation (or porosity, E) because of entrapped or dissolved air. The relationshipbetween E and 0, depends on both soil type and the experimental procedure used formeasuring 0, For example, Ghosh ( 1976) used a value of 0.9~. It is our experience,
however, that the hydraulic data are best described with a variable 0, when a complete data
set is available, rather than fixing 0, at some arbitrary “field-satiated” value less than E orusing a reduction factor.

0 observed, lab. data
- &s fitted- - 0s = 0.407 cm’lcm’

Water Content, 8 [cm3/cm31
Fig. 8. Measured and fitted curves with 0, fitted or fixed to the porosity for the laboratory retention (Case 3).

104

G. Wessolek et al. / Geoderma 64 (I 994) 93-1 IO

Statistical pore-size distribution or other theoretical models for predicting the hydraulicconductivity generally contain the saturated hydraulic conductivity, k,, as a “matching”
point (e.g., Mualem, 1986). Such an approach has become relatively standard, in part
because k, is more easily measured than unsaturated K(h) values. Hence, the value for k,
in the conductivity model is usually fixed during the optimization procedure. Case 4 involves
the optimization of retention and conductivity data, where k, is either fitted or fixed to thevalue determined independently using the constant-head permeameter method. The resultsin Figs. 10 and 11 indicate a reasonably good fit for the laboratory data. However, the fielddata are relatively poorly described, as indicated by the low r* values in Table 3 (Case 4).
This is in agreement with previous studies showing that k, should not be used as a matching
point for the unsaturated conductivity curve (Van Genuchten and Nielsen, 1985; Vogel andCislerova, 1988). Instead, and if available, a measured conductivity value at a water content
somewhat less than saturation should be used as a matching point for the predicted curve.
Such “matrix-K sat values” work much better than k, at, which is more an expression of
structural porosity (Bouma, 1992).
In Case 5 we attempted to describe the hydraulic data with Eqs. ( 1) and (2) while fixing
both 0, and k, in the optimization process, i.e., fitting only LY and II. The values of 0, and k,
were now both fixed to their independently measured values. Table 3 indicates that thisCase resulted in a much poorer description of the data than if both were kept as unknowns

‘OZs

0

observed, lab.data- ks and 8s are fitted--- ks is fitted, 8s = 0.487 cm’lcm’

1o-4

’

’ B ’ m .**.’’ ’ ’ m *Ban’

loo

10’

102

’ ’ ’ ‘on-J
103

Pressure Head, h [hPa]
Fig. 9. Measured and fitted curves with 0, fitted or fixed to the porosity for the laboratory conductivity (Case 3).

G. Wessolek et al. /Geoderma 64 (1994) 93-l 10

105

0 observed, lab. dataIO’

- k s fixed lo 35 cm/d--

ks filled

0.2

0.3

0.4

Waler Content, 0 [cm’lcm’lFig. 10. Measured and fitted curves with k, fitted or fixed to the value measured with the constant-head permeameterfor the laboratory retention (Case 4).

in the optimization (Case 2). This holds especially true for the laboratory data where thesum of squares given by Eq. (3) increases from 0.05867 to 0.15621. Relatively poor results
should be expected when k, and especially (3, are used as known parameters in the fitting
process.
Finally, Case 6 in Table 3 involves the prediction of the K(h) function from measured hand k, data, again assuming 1= 0.5. This is a common approach since conductivity data are
not widely available because they are more cumbersome to measure than retention data.
The results in Table 3 indicate that the predicted K(h) describes the observed curve remarkably well, especially the laboratory data. In view of the previous results, the slight overprediction of the hydraulic conductivity using the independently measured k, value was tobe expected.
3.3. Drainage calculations
The second objective of this study was to investigate the sensitivity of flow simulations
to variations in the hydraulic parameter sets as obtained with the different optimization
scenarios. Fig. 12 shows the simulated cumulative drainage from an initially saturated 30
cm long soil column using parameters from Cases I, 3, and 4. The numerical results for

106

G. Wessolek et al. / Geoderma 64 (1994) 93-110

hydraulic parameters estimated from the field data (denoted by closed symbols in Fig. 12),yielded higher drainage rates than predictions made with parameters derived from thelaboratory data. This difference is caused by the higher k, and 19, values estimated from thefield data. For the laboratory data the predicted cumulative outflow increased by 63 % when
13, was set equal to the porosity (Case 3), and by 13% when k, was fixed at the measured
value (Case 4), compared to the total drainage predicted using Case 1 parameters.
The drainage simulations assumed an initially completely saturated soil profile. Such
conditions rarely occur in the field. For example, measurements by Duynisveld and Strebel( 1983) for a similar soil as used in this study indicate that complete saturated conditions
did not occur during a three-year period. Hence, the cumulative drainage simulations wererepeated assuming an initial water content equivalent to an initial pressure head of - 60
hPa, or pF = 1.8, which roughly corresponds to field capacity. Using the hydraulic parameters for Case 1, the simulated cumulative drainage amounts decreased significantly fromthose obtained when an initially saturated soil was assumed (Fig. 12). The differences
between he laboratory and field-based predictions are also much smaller. This is to be
expected since the water contents at - 60 cm for the field and laboratory cases are now very
similar (Fig. 6). The results in Fig. 12, as well as those in Figs. 7-10, raise doubts about
the usefulness of the parameters k,, and especially E, to serve as matching or endpoints in

I-

10’

10‘

IO3

Pressure Head, h [hPa]

Fig. 1 1. Measured and fitted curves with k, fitted or fixed to the value measured with the constant-head permeameterfor the laboratory conductivity (Case 4).

G. Wessolek et al. / Geoderma 64 (I 994) 93-1 IO

10

0

20

30

40

50

60

70

107

80

Time [d]CaseField Laboratory

1.1 Complete fit

AA

2.1 6s = 0.467 [cm’lcm’]

00

3.1 ks = 35 [cm/d]

T

V

Complete fit with initialwaler content at h = 60 hPan0Fig. 12. Numerically predicted cumulative drainage assuming an initial water content at saturation or at field
capacity, using parameter sets (O,, a. n, k, } obtained by fitting Eqs. ( 1) and (2) to laboratory data for various
optimization scenarios.

the unsaturated hydraulic functions. Moreover, the great disparity of the simulated drainage
curves in Fig. 12 shows that proper selection of the “saturated” conductivity and soil water
retention values is not just a theoretical exercise, but can have important practical implications for simulating variably-saturated flow in the field.

4. Summary and conclusions
Soil water retention and hydraulic conductivity curves were determined by monitoringwater contents and pressure heads with TDR and mini tensiometers, respectively, duringforced evaporation from an “undisturbed” soil column. The method is relatively quick andprovides hydraulic data with a high spatial and temporal resolution. Previously determined

108

G. Wessolek et al. / Geoderma 64 (1994) 93-110

in situ field measurements of the hydraulic properties were included for comparison, The
field data showed greater variability in the 13(h) and K( h) curves compared to the laboratory
data. Still, the field- and laboratory-measured curves were reasonably similar over the rangeof measurements.
The program RETC was used to fit the model given by Eqs. ( 1) and (2) to the observedlaboratory and field retention and conductivity data. Several fitting options were used to
examine whether or not the number of fitting parameters could be reduced. Results were
found to be relatively insensitive to the value for 1, whereas good results were obtained
when 0, was set equal to zero. The best optimization resulted when 0, and k, were fitted
rather than fixed at independently measured laboratory values. A relatively good match withthe conductivity data was also established when K(h) was predicted from the measured
B(h), in conjunction with the measured k, value. However, for good results with RETC,
the use of f3 and k near saturation (perhaps at h = 10 hPa) is the best way to avoid structuraleffects. However, for characterizing the flow regime, the flow through macropores should
be separated from the one through the soil matrix (Bouma, 1982). Thus, additional measurements with other methods are needed.
The crust test by Boolting et al. ( 1991) may be one way to get these informations. Other
methods to measure the matrix conductivity near saturation might be the use of a tension
infiltrometer in the field. Nevertheless, our field experiences with the tension permeametershow that surface sealing and the use of a geometry parameter causes errors and in case oflayered soils with stagnic horizons the interpretation of field data becomes difficult. In thelaboratory, the use of the steady state evaporation method according to Plagge ( 1993) may
be a better alternative to yield a k( ii) close saturation.
The fitted hydraulic properties were subsequently used as input in a numerical model
simulating drainage from an initially saturated soil column. The simulated cumulativedrainage from the column was higher for the field- than for the laboratory-derived hydraulicproperties, primarily because of a higher estimated 0, value for the field data. Using theporosity for Bs significantly increased the predicted outflow. Similar drainage simulations
with an initial water content roughly equivalent to field capacity resulted in much smallerdifferences in the predicted drainage for the laboratory and field hydraulic properties. The
simulated drainage amounts in this case were also much less than those for the other caseswith an initially saturated column.
The parameter estimation and drainage simulation results demonstrate again the necessityof new technics and methods to measure the soil hydraulic parameters near saturation in
the field and laboratory.

Acknowledgements

Financial support by the German Research Foundation (DFG) is gratefully acknowledged. We would like to thank Dr. 0. Strebel, Bundesanstalt fur Geowissenschaften undRohstoffe/Hannover, for providing the in situ data.

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