Infiltration and drainage processes in multi-layered coarse soils

Infiltration and drainage processes in multi-layeredcoarse soils

Mingbin Huang1,3, S. Lee Barbour1, Amin Elshorbagy1, Julie D. Zettl1, and Bing Cheng Si2,4

1Department of Civil and Geological Engineering, University of Saskatchewan, Saskatoon, Saskatchewan, CanadaS7N 5A9; 2Department of Soil Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada S7N 5A9;

and 3State Key Laboratory of Soil Erosion and Dryland Farming on Loess Plateau, Northwest A&F University,China. Received 11 December 2009, accepted 16 January 2011.

Huang, M., Barbour, S. L., Elshorbagy, A., Zettl, J. D. and Si, B. C. 2011. Infiltration and drainage processes in multi-layered coarse soils. Can. J. Soil Sci. 91: 169�183. Infiltration and drainage processes in multi-layered soils are complicatedby contrasting hydraulic properties. The objective of this study was to evaluate the performances of the hysteretic and non-hysteretic models to simulate the infiltration and drainage processes from three different natural soil profiles containing asmany as 20 texturally different layers. Hydraulic properties were estimated from soil textures using pedotransfer functionsand were calibrated and validated using measured water contents during infiltration and drainage phases, respectively. Theresults supported the use of the Arya-Paris pedotransfer function to estimate the wetting curve when contact angles areincorporated. The unique Kozeny-Carmen equation parameter was evaluated by optimizing the estimated saturatedhydraulic conductivity. The calibrated numerical model (Hydrus-1D) accurately simulated soil water content profiles andwater volumes during the infiltration and drainage phases. The mean error of prediction (MEP) between the measured andestimated soil water contents varied from �0.030 to 0.010 cm3 cm�3, and the standard deviation of prediction (SDP) from0.003 to 0.057 cm3 cm�3. The simulation was improved for more heterogeneous soil profiles when hysteresis was takeninto account. The measured and simulated results indicated that the soil profile with vertical heterogeneity in soil texturecan store more water than the similar textured vertically homogeneous soils under drained conditions.

Key words: Infiltration, drainage, pedotransfer function, hysteresis, layered soils

Huang, M., Barbour, S. L., Elshorbagy, A., Zettl, J. D. et Si, B. C. 2011. Infiltration et drainage dans les sols grossiers acouches multiples. Can. J. Soil Sci. 91: 169�183. Des proprietes hydrauliques contrastantes compliquent l’infiltration et ledrainage dans les sols a couches multiples. La presente etude devait evaluer l’utilite des modeles d’hysterese et de non-hysterese pour simuler l’infiltration et le drainage dans trois profils naturels de sol incluant jusqu’a 20 couches de texturedifferente. Les auteurs ont estime les proprietes hydrauliques des sols a partir de leur texture grace a des fonctions depedotransfert, puis les ont etalonnees et validees en mesurant leur teneur en eau pendant les phases d’infiltration et dedrainage, respectivement. Les resultats appuient l’usage de la fonction de pedotransfert d’Arya-Paris pour estimer lacourbe d’humectation quand on tient compte de l’angle de contact. Le parametre unique de l’equation de Kozeny-Carmena ete evalue par optimisation de la conductivite hydraulique estimee au point de saturation. Le modele numerique etalonne(Hydrus-1D) simule avec precision le profil de la teneur en eau du sol et le volume d’eau pendant l’infiltration et ledrainage. L’erreur moyenne de prevision entre la teneur en eau mesuree et celle estimee varie de �0,030 a 0,010 cm3 parcm3, et l’erreur-type de prevision, de 0,003 a 0,057 cm3 par cm3. Les auteurs sont parvenus a ameliorer la simulation pourles sols plus heterogenes en prenant en compte l’hysterese. Les resultats obtenus par quantification et par simulationindiquent que les sols a texture verticalement heterogene peuvent stocker plus d’eau que les sols homogenes de textureverticale similaire quand il y a drainage.

Mots cles: Infiltration, drainage, fonction de pedotransfert, hysterese, sol a couches multiples

Soil water is a key variable in agriculture (crop produc-tion), forestry, hydrology (flooding, runoff) and envir-onmental issues (greenhouse gas emission, fate andtransport of agrochemicals or wastes). Infiltration parti-tions rainfall into runoff and soil water, while soil waterpercolates into deep soil or groundwater and becomesunavailable to plants. Therefore, modeling of waterinfiltration and drainage processes in soils is requiredfor effective management of soils.

In nature, the textures associated with the soil profileare commonly layered rather than uniform. The dy-namics of soil water movement in layered profiles havereceived considerable interest in recent years as thesesystems have become part of engineered cover systemsassociated with waste containment and mine wasteclosure. These systems are designed to promote theformation of flow barriers or capillary breaks in order toincrease soil storage capacity and reduce percolation,

4Corresponding author (e-mail: [email protected]).

Abbreviations: MCP, multisensory capacitance probe; MEP,mean error of prediction; PTF, pedotransfer function; SDP,standard deviation of prediction; SWRC, soil water retention curve

Can. J. Soil Sci. (2011) 91: 169�183 doi:10.4141/CJSS09118 169

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which are very important to waste disposal (Khire et al.2000; Alfnes et al. 2004) and mine land reclamation(Fenske et al. 2006; Elshorbagy and Barbour 2007) inarid and semi-arid regions.

There have been some studies describing infiltrationand drainage experiments in layered soils used to studythe mechanisms of water flow and solute transport inlayered soils (Kung 1990a,b; Khire et al. 2000; Alfneset al. 2004). Other studies have focused primarily ondeveloping numerical algorithms to estimate the inter-layer hydraulic conductivity between two neighboringnodes positioned in different soil layers for improvingsimulation accuracy (Stauffer and Dracos 1986;Romano et al. 1998). Most of these studies have beencarried out with artificially constructed profiles com-posed of only two or three materials. In a natural soil,the profile may be more heterogeneous and has morevariation in materials than in artificial profiles. Inaddition, the effects of hysteresis on water movementin infiltration and drainage processes were often ne-glected in these previous studies. Many studies haveshown the importance of hysteresis effects on unsatu-rated soil water movement and solute transport (Daneand Wierenga 1975; Russo et al. 1989; Heinen and Raats1999). Neglecting hysteresis in numerical simulationscan lead to significant discrepancies between simulatedand measured results (Kool and Parker 1987; Mitchelland Mayer 1998; Si and Kachanoski 2000).

Simulation of infiltration and drainage processesrequires soil hydraulic properties, which include thehydraulic conductivity function and the soil waterretention curve (SWRC). Because field determinationof the hydraulic conductivity function and the SWRC isoften laborious and costly (van Genuchten and Leiji1992), there has been widespread development and useof estimation methods, which are classified as pedo-transfer functions (PTF). Pedotransfer functions can becategorized into four groups: statistical regressionequations (Hutson and Cass 1987), fractal geometricalmethods (Tyler and Wheatcraft 1990; Huang and Zhang2005), physicoempirical equations (Arya and Paris 1981;Campbell and Sho 1990; Aubertin et al. 1998; Arya et al.1999), and the neural network methods (Schaap andBouten 1996; Schaap and Leij 1998). Many authorshave evaluated the performance and suitability ofdifferent PTFs for estimating hydraulic parameters(Espino et al. 1995; Sobieraj et al. 2001; Wosten et al.2001; Al Majou et al. 2007). In general, they found thatthe performance of PTFs was largely dependent on thedata used for their calibration (Schaap and Leij 1998),and inaccurate predictions often result due to extrapola-tion of derived PTFs (Cornelis et al. 2001; Hodnett andTomasella 2002). But, except for the physicoempiricalequations, most pedotransfer functions cannot take intoaccount the hysteresis in soil water retention function. Inthe physicoempirical equations, the Arya-Paris PTF(Arya and Paris 1981; Arya et al. 1999) has been widelytestified to correctly predict the main drying curve for

coarse-textured soils (Zhuang 2001; Hwang and Powers2003; Nimmo et al. 2007). However, little is knownabout whether the Arya-Paris PTF can be used toestimate the main wetting curve for simulating infiltra-tion and drainage processes with hysteresis.

The objectives of this study were as follows: (1) toexamine if the Arya-Paris PTF (Arya and Paris 1981;Arya et al. 1999) can be used to estimate the wettingcurve; and (2) to evaluate the performance of a finiteelement simulation model with hysteresis in simulatingdrainage processes in coarse soils with a high degree oflayered heterogeneity. All simulations were carried outwith the program HYDRUS-1D (Simunek et al. 2006),and all experiments were conducted 50 to 120 km northof Fort McMurray, Alberta, Canada.

MATERIALS AND METHODS

Field and Laboratory ExperimentsAs discussed in Zettl et al. (2011), infiltration anddrainage experiments were conducted at seven sites northof Fort McMurray, Alberta, Canada. These sites arerespectively located at ‘‘a’’, ‘‘b’’ and ‘‘d’’ ecosites in theBoreal Mixedwood Ecoregion of Alberta (Beckinghamand Archibald 1996). The soil moisture and nutrientregimes increase from ‘‘a’’ to ‘‘d’’ ecosites. The SV10 andSV27 sites were located at ‘‘a’’ ecosite, the SV59, SV62,and NLFH2 at ‘‘b’’ ecosite, and the SV60 and NLFH1 at‘‘d’’ ecosite. Soils at these study sites are natural coarsetextured, with parent materials glaciofluvial outwash orice contact deposits, some of which were modified byeolian activity (Turchenek and Lindsay 1982). TheCanadian System of Soil Classification (Soil Classifica-tion Working Group 1998) indicated the soils in thisstudy are Eluviated Dystric and Eluviated Eutric Bruni-solic soils. The following is a very brief summary of thefield and laboratory experiments conducted at each site.

Details of the infiltration testing, soil samplingprocedures and laboratory testing methods are de-scribed in detail by Zettl et al. (2011). A PVC accesspipe was installed to a depth of 160 cm at each site.A double-ring infiltrometer was centered over the PVCpipe and seated to a minimum depth of 15 cm. A stringof multisensory capacitance probes (MCP) were placedwithin the PVC pipe in order to measure the soil watercontent every 10 cm. A constant ponded depth of waterbetween 5 and 10 cm was maintained in the double-ringinfiltrometer. The wetting front advance was monitoredat 4-min intervals until the wetting front advanced to100 cm, after which no further water was added to therings and the rings were allowed to drain. The drainagethrough the soil profile was monitored continuouslywith a data-logger until field capacity conditions werereached.

Following drainage, a soil pit was excavated to adepth of approximately 110 cm. Disturbed samples werecollected in 2- to 10-cm intervals for laboratory analysis.The disturbed samples were analyzed for moisture

170 CANADIAN JOURNAL OF SOIL SCIENCE

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content, dry bulk density, and grain size analysis. Bulkdensity was calculated from the dry weight and theknown volume of the sampling device. The grain sizeanalysis was conducted using laser diffraction (LaserScattering Particle Size Distribution Analyzer ModelLA-950, Horiba Instruments Inc. 2008) for 93 particlediameters between 3.0 mm and 1.1e-05 mm. Undis-turbed samples were collected in 10-cm intervals foreach site. Selected undisturbed samples were used tomeasure the drying SWRC. The drying SWRC wasmeasured using Tempe pressure cells (Soil MeasurementSystems, USA) for the suctions from 0.1 to 30 kPa andmultiple specimen pressure plate apparatus (Soilmois-ture Equipment Corp., USA) for the suctions from 30 to1500 kPa. Seven drying curves were used to evaluate theArya-Paris PTF, two from SV10 and SV27, one fromSV60 and two from SV62.

In this study, the infiltration and drainage processeswere only simulated at three sites: SV10, SV62, andSV60.

Model Description

Governing EquationThe governing equation used in this study was Richards’equation for one-dimensional isothermal Darcian flowin a variably saturated, rigid porous medium:

@u

@t�

@

@z

�K(h)

�@h

@z�1

��(1)

where t is time [T], u is volumetric water content [L3

L�3], h is the soil water pressure head [L], z is thevertical spatial coordinate [L] taken as positive upward,K is the unsaturated hydraulic conductivity [L T�1].Equation 1 was solved numerically by HYDRUS-1Dversion 4.0 (Simunek et al. 2006).

Soil Hydraulic ParametersThe unsaturated hydraulic properties were described bythe following equations (van Genuchten 1980):

u(h)�ur�us � ur

[1 � jahjn]mhB0 (2)

and

u(h)�us h]0 (3)

K(h)�KsS1=2e [1�(1�S1=m

e )m]2 hB0 (4)

and

K(h)�Ks h]0 (5)

Se�u� ur

us � ur

(6)

where Se is the effective water content; u is thevolumetric water content [L3 L�3]; subscripts r and srefer to residual and saturated volumetric water con-

tents, respectively; a [L�1], n, and m are van Genuchtenmodel parameters; and m�1�1/n; and Ks is the satu-rated hydraulic conductivity [L T�1].

Hysteresis in Soil Hydraulic PropertiesThe HYDRUS code incorporated hysteresis by using theempirical model introduced by Scott et al. (1983). Thismodel-simulated hysteresis in the SWRC requires boththe main drying and main wetting curves. Two curveswere described with Eq. 2 using the parameter vectors(urd, usd, ad, nd) and ( urw, usw, aw, nw), respectively, wherethe subscripts d and w indicate drying and wetting.The following restrictions are expected to hold in theapplications of HYDRUS (Simunek et al. 2006):

udr �uw

r ; nd �nw; and ad 5aw (7)

So that the parameters usd, ad, usw, and aw are the onlyindependent parameters to describe the hysteresis in theSWRC. If the main hysteresis loop was not closed atsaturation, the water content at saturation for a parti-cular wetting scanning curve could be estimated usingthe empirical equation given by Aziz and Settari (1979).Studies suggested that there is little hysteresis in hydrau-lic conductivity or it is so small that it can be easilymasked by the error of the measurements, and thus canbe ignored (Si and Kachanoski 2000; Jaynes 2003). Inthis study, we assume that Ks

d�Ksw�Ks, so that the

hysteretic retention model is characterized with sevenparameters (ur, usd, ad, n, usw, aw, and Ks)

Initial and Boundary ConditionsBoundary conditions for the infiltration phase of theexperiment consisted of the following:

h(z; t)�hi(z); t�0 (8)

h(z; t)�h0; z�L (9)

@h(z; t)

@z�0; z�0 (10)

where hi is the initial pressure head (cm) in soil profile;h0 is the pressure head at soil surface, equal to 10 cm inthis study; L is the depth coordinate of the soil surface,and is equal to 110 cm based on the maximum depththat soil dry bulk density and grain-size distributionwere measured and analyzed.

The initial conditions for the drainage component ofthe test were taken from the water distribution of the lastsimulated time from the infiltration phase of the test. Theinitial and boundary conditions can be described asfollows:

h(z; t)�hi(z); t�0 (11)

�K

�@h

@z�1

��0; z�L (12)

HUANG ET AL. * INFILTRATION AND DRAINAGE PROCESSES IN SOILS 171

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@h(z; t)

@z�0; z�0 (13)

Estimation of Hydraulic Parameters

Soil Water Retention CurvesThe Arya-Paris equation uses capillary theory to con-vert the pore radius, ri (cm), to equivalent pressure head,hi (cm):

hi�2scos(b)

rgri

(14)

where s is the surface tension at the air-water interface(g s�2), r is the density of water (g cm�3), g is theacceleration due to gravity (cm s�2), and b is the contactangle (degree). The pore radius is related to the particleradius, Ri (cm), by

ri�0:816Ri

ffiffiffiffiffiffiffiffiffiffiffiffiffiffien

(1�oi)i

q(15)

where o is the scaling parameter, n is the number ofspherical particles, and e is the void ratio. The watercontent ui is obtained from the summation of water-filled pore volumes according to

ui� (fSw)Xj�i

j�1

wj; i�1; 2 . . . . . . n (16)

where is the total porosity, Sw is the ratio of measuredsaturated water content to theoretical porosity, and wi isthe fraction of solid mass corresponding to particleradius ri,. The total porosity was estimated for eachmaterial from the measured bulk density and averageparticle density. Meanwhile, we assumed that the upper100 cm of the profile was saturated following the in-filtration phase of the field experiment, thus, themeasured maximum water content for each materialwas assumed to represent the saturated value. This valueand the calculated total porosity were used to estimateSw. The effect of entrapped air on saturated watercontent was ignored, and the value of Sw obtainedfrom the infiltration phase for each material is assumedunchanged for the drainage phase; that is, ud

s �uws :

A similar assumption was used by Simunek et al.(1998) for estimating hysteresis in the SWRC fromcone permeameter experiments. Maqsoud et al. (2006)obtained the same results (ud

s �uws ) with the modified

MK model.With the restrictions of ud

r �uwr and nd �nw and the

assumption of uds �uw

s the following relationship can beeasily obtained from Eqs. 2 and 14:

ad �cos(bw)

cos(bd)aw (17)

where bd and bw are contact angles for drying andwetting processes, respectively. For the drying process,

the contact angle is typically assumed to be 08 (Marshallet al. 1996; Aubertin et al. 1998; Fredlund et al. 2002),but for the wetting process, the contact angle is larger.Letey et al. (1962) and Kumar and Malik (1990) foundthat bw could be as high as 608 to 808 in sand based oncapillary rise and horizontal infiltration testing, andMaqsoud et al. (2006) considered that it was reasonableto take bw values between 578 and 618 for calculating themain wetting curve.

In this study, the value of bw was optimized using themeasured main wetting curves for the same texturedsoils by Yang et al. (2004). Yang et al. (2004) used theTempe pressure cells to determine the main dryingcurves for two sand soils, while the main wetting curveswere obtained using a capillary rise tube. From litera-tures (Letey et al. 1962; Kumar and Malik 1990; andMaqsoud et al. 2006), the bw value varies from 578 to808. We adjusted its value and used Eqs. 14�16 toestimate the main wetting curves. The optimal bw wasobtained when the squared error between the measuredand estimated values was minimized. Therefore, Eq. 17is simplified as:

aw�ad=cos(bw) (18)

Using Eq. 18, the number of parameters describingthe hysteresis model were reduced to five (ur, us, ad oraw, n, and Ks) from seven parameters.

The seven samples with the measured main dryingcurves from the study sites were first used to test theArya-Paris PTF. The main drying curves for allmaterials were estimated using the Arya-Paris PTF,and then Eq. 2 was fitted to the data to obtain theparameters (ur, us, ad, and n) using the least-squareoptimization program RETC (van Genuchten et al.1991).

Saturated Hydraulic ConductivityThe saturated hydraulic conductivity (Ks) for eachmaterial was calculated using the Kozeny-Carmanequation. The Kozeny-Carman equation is one of themost widely accepted and used methods for estimatingKs based on grain size (Mathan et al. 1995; Mbonimpaet al. 2002). This equation was originally proposed byKozeny (1927) and was then modified by Carman (1938,1956) to become the Kozeny-Carman equation. TheKozeny-Carman equation is:

Ks�D�g

v�

�f3

(1 � f)2

�d2

10 (19)

where D is an empirical parameter, g is accelerationdue to gravity (cm s�2), v is the kinematic viscosity ofwater (cm2 s�1), is the porosity, and d10 is the grain sizeat which 10% of the particles are smaller than thisdiameter (cm).

This combination of estimation methods allows thehydraulic parameters required for numerical simulation(Ks, ur, us, ad, aw, and n) to be estimated from a limited


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number of assumptions and the measured soil texturedata. These properties were then used in the HYDRUS-1D program to simulate soil water dynamics during theinfiltration and drainage phases.

In this study, the Ks value for each material wasfirst estimated using Eq. 19 based on value of D equalto 0.498 as suggested by Odong (2007). Without anyadjustments or calibration, the simulated soil watercontent profiles were not in good agreement withmeasured values for the infiltration phase. It wasassumed that the greatest potential source of erroramong the five hydraulic parameters (Ks, ur, us, aw,and n) was in the estimate of Ks. The us values weredetermined based on measurements of soil watercontent during the infiltration process as well asbulk density measurements. The values of ur, aw, and nwere estimated from the measured particle size distribu-tion and bulk density using the Arya et al. (1999)method.

The uncertainty in Ks is assumed to be much greaterthan that of parameters ur, us, aw, and n. Consequently,Ks was selected as the primary variable to be adjustedthrough a calibration scheme. The differences betweenmeasured and simulated water contents during theinfiltration phase were minimized by adjusting the Dvalue used in the Kozeny-Carman equation at eachstudy site. This was facilitated using the software PEST-ASP (Doherty 2002) coupled with the Hydrus-1D(Simunek et al. 1998). The objective function used byPEST-ASP is the least square error (LSE) betweenmeasured and simulated soil water contents during theinfiltration phase.

LSE�XN

i�1

(um�ue)2 (20)

where um and ue are measured and estimated soil watercontents during infiltration, respectively; N is the total

number of measurements, and is equal to 288, 165, and176 for sites SV10, SV62, and SV60, respectively.

Data AnalysesTwo HYDRUS output variables are compared with: (1)the measured soil water content, u (cm3 cm�3), atdifferent depths, and (2) the calculated average soilwater content,u (cm3 cm�3) in the profile as a functionof time during infiltration. The mean error of pre-diction (MEP) and the standard deviation of prediction(SDP) are computed to evaluate the precision of themodel:

MEP�

XN

i�1

(Ei � Mi)

N(21)

SDP��

1

N

XN

i�1

[(Ei�Mi)�MEP]2

1=2

(22)

where Ei and Mi are simulated and measured soil watercontents for the ith observation, and N is the totalnumber of observations. The MEP corresponds to thebias and indicates whether the prediction overestimated(positive) or underestimated (negative) the measuredwater content, whereas SDP measures the precision ofthe prediction (Al Majou et al. 2008).

RESULTS AND DISCUSSION

Particle Size Distribution and its Variation inProfileFigure 1 summarizes the particle size distributionprofiles at each site. Generally, clay and silt contentswere very low, with total silt and clay sizes less than 6%.Site SV10 had the greatest amount of clay and silt(5.9%), while site SV62 had the least (1.8%). Sandcontent was very high in all the soil profiles, whilethe amount of fine, medium, and coarse sand varied

Cumulative fraction solid mass (%)

d<0.06mmd 3mm d 2mm d 0.5mmd 0.25mm d 0.002mm

0

10

20

30

40

100

SV62

Coarse sand

Fine sand

50

60

70

80

90

Dep

th (

cm)

SV10

Mediumsand

Coarse sand

SV60

Medium sand

0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100

Fig. 1. Vertical variation in soil texture for the three sites (particle size fractions based on FAO soil classification, clay d50.002 mm,silt 0.002 mmBd50.06 mm, fine sand 0.06 mmBd50.25 mm, medium sand 0.25 mmBd50.5 mm, coarse sand 0.5Bd52 mm,coarse gravel d53 mm).


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considerably among the three sites. For site SV10,average fine sand content was only 6.7% in the profile,while medium sand content was 48.6%, and coarse sandcontent was 38.7%; for site SV62, the correspondingfine, medium, and coarse sand contents were 25, 18, and53%, respectively, and for site SV60, the correspondingvalues were 42, 27, and 24%, respectively. Therefore,site SV60 had the finest texture among the three sites.

Figure 1 also shows that site SV10 had a relativelyhomogeneous soil texture, while sites SV62 and SV60were much more heterogeneous. There was a 40-cmfine sand layer overlying a medium sand layer at siteSV62, while site SV60 had a fine sand layer at a depthof 45 to 84 cm in the profile with overlying andunderlying medium sand layers. It was anticipated thatdifferences in soil texture within the profiles at the threesites would result in differences of water movement andstorage.

Soil Water Retention CurvesBecause the main soil textures at the three natural siteswere fine sand and medium sand, the Arya-Paris PTFwith bd�08 was first assessed by comparing estimatedmain drying curves with the measured curves from Yanget al. (2004) for fine and medium sand. The comparisonsbetween the measured and estimated values for the twosoils are presented in Fig. 2. It is clear that with hecontact angle of 08 for a drying process, the SWRCpredicted from the Arya-Paris PTF was in close agree-ment with measurements, except for small discrepanciesat values of suction of 100 to 1000 kPa for fine sand soil,and at the suctions of 10 to 30 kPa for medium sand soils.

The measured main wetting curves for two soils wereused to optimize the value of bw in the Arya-Paris PTF.Figure 2 shows that the main wetting curves predictedfrom the Arya-Paris PTF with bw�608 accuratelymatched the measured values, and led to the smallesterror between the measured and estimated values.Therefore, Eq. 18 is simplified as

aw�2ad (23)

The relationship between aw and ad has also beenreported in the literature. Bouwer (1966) suggested thatthe value of aw was typically twice that of ad, based onfield measurements. A similar ratio was reported byHaverkamp et al. (2002) for the particular case when nd

and nw all equal 1. Kool and Parker (1987) reported aaw/ad ratio of 2.08, where ad and aw were calculatedindependently using the best fit Eq. 2. One of the lowestratios of aw/ad found in the literature was 1.84 asestimated by Gupta and Larson (1979).

In order to further assess the suitability of the Arya-Paris PTF for the site soils, another seven samples wereselected from four of the study sites (SV10, SV62, SV60and SV27) and the measured and estimated main dryingcurves were compared. The measured and estimated soilwater contents at the same suction are compared in

Fig. 3. The measured soil water contents varied from0.01 to 0.32 cm3 cm�3, and the related suction rangefrom 0.1 to 1500 kPa. The good linear relationshipwith a slope of 1.02, and an intercept close to 0.0 (i.e.,�0.015), suggests that the Arya-Paris PTF reasonablyestimated the main drying curves for the sands presentat the study sites. The points far from the line 1:1 werefrom two samples, and the discrepancy might be fromthe measured errors in the drying process. In general, theArya-Paris PTF was used to estimate the main dryingand wetting curves for all studied soils.

The 110-cm profiles were divided into 14 layers at siteSV10, 16 layers at site SV62, and 18 layers at site SV60,based on soil texture and bulk density. For eachmaterial, the values of ur, ad, and n estimated byRETC (van Genuchten et al. 1991) using the measuredus and the estimated main drying curve from the Arya-Paris PTF are presented in Table 1. The value of us

Soi

l wat

er c

onte

nt (

cm3

cm–3

)

0

0.1

0.2

0.3

0.4

0.5

Suction (kPa)

Estimated drying curveEstimated wetting curveMeasured drying curveMeasured wetting curve

Medium sand soil

0.1 1 10 100 1000 10000

Soi

l wat

er c

onte

nt (

cm3

cm–3

)

0

0.1

0.2

0.3

0.4

0.5

Suction (kPa)

Estimated drying curveEstimated wetting curveMeasured drying curveMeasured wetting curve

Fine sand soil

0.1 1 10 100 1000 10000

Fig. 2. Comparison between the measured and estimated waterretention curves for two sandy soils [measured curves fromYang et al. (2004)].


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varied from 0.37 to 0.50 cm3 cm�3 with a mean of 0.43cm3 cm�3 and a standard deviation (SD) of 0.05 cm3

cm�3 for site SV10. Site SV62 had us from 0.29 to 0.47cm3 cm�3 with a mean of 0.35 cm3 cm�3 and an SD of0.05 cm3 cm�3. Site SV60 had the widest range of us,from 0.26 to 0.54 cm3 cm�3, with a mean of 0.39 cm3

cm�3 and a SD of 0.08 cm3 cm�3. The largest meanvalue of us occurred in site SV10, whereas the largest SDvalue of us occurred in site SV60, which was in goodagreement with the soil texture and the heterogeneityprofiles at the three sites. The smallest us values, 0.26cm3 cm�3 at site SV60 and 0.29 cm3 cm�3 at site SV62,mainly resulted from the relatively high bulk density, inwhich the measured bulk density was 1710 kg m�3 forSV60 and 1690 kg m�3 for SV62.

The estimated values of ad ranged from 0.027 to 0.176cm�1 among 48 materials, with mean values of 0.058,

0.111, and 0.067 cm�1 for sites SV10, SV62, and SV60,respectively, whereas the estimated values of n rangedfrom 1.488 to 4.215, with mean values of 3.599, 1.965,and 2.257 for sites SV10, SV62, and SV60, respectively.These values of ad and n were all reasonable for sandysoils (van Genutchen et al. 1991). These estimatedparameters (ur, us, aw, ad, and n,) were then used inthe simulation to optimize Ks by fitting measured soilwater contents in the profile during infiltration.

Saturated Hydraulic ConductivityThe optimizedD value with the estimated parameters (ur,us, aw, ad, and n) for each site is presented in Table 2.The optimized value of D varies from 0.148 at site SV60to 0.249 at site SV62, which are less than the suggestedvalue of 0.498 (Odong 2007). The optimized D valuesreflected the effects of soil texture and porosity on Ks.The biggest D value was obtained for site SV62, whichhad the coarsest soils and the biggest average porosity.The optimized values of D resulted in a substantialimprovement in the agreement between predicted valuesand field measurements, compared with the initial valuesof D. The LSE values were reduced from 13.39 to 0.88cm6 cm�6 for site SV10, from 6.27 to 0.45 cm6 cm�6 forsite SV62, and from 7.32 to 0.44 cm6 cm�6 for site SV60.

The Ks value for each material was calculated usingEq. 19 with the optimized D value at each site andparameters d10 and p (Table 1). Among the three sites,site SV10 was the most homogeneous and had thesmallest variability in Ks from 1.54 to 2.38 cm min�1,with a SD value of 0.240 cm min�1. Site SV62 was themost heterogeneous and had the largest variability in Ks,from 0.19 to 1.17 cm min�1, with a SD value of 0.701cm min�1.

In this study, the Kozeny-Carman equation was usedto estimate Ks for all materials, in which organic matterwas not included as a predictive variable. Some research-ers have shown that when organic matter increases, theKs value decreases. Nemes et al. (2005) investigated the

y = 0.9395x - 0.0019

R2 = 0.917

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Measured water content (cm3cm–3)

Est

imat

ed w

ater

con

tent

(cm

3 cm

–3)

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35

Fig. 3. The relationship between the measured and estimatedsoil water contents at the same suction for seven samples.

Table 1a. The estimated and optimized hydraulic parameters for site SV10

Layer Depth (cm) ur (cm3 cm�3) us (cm3 cm�3) ad (1/2aw) (cm�1) n Ks (cm min�1)

1 0�8 0.017 0.500 0.114 3.235 2.3782 8�14 0.016 0.500 0.086 3.195 1.9853 14�18 0.019 0.500 0.071 3.511 2.0934 18�24 0.025 0.492 0.062 2.993 1.7105 24�30 0.028 0.420 0.067 2.684 1.6936 30�40 0.007 0.417 0.064 2.630 1.6637 40�46 0.001 0.427 0.049 3.602 1.7618 46�60 0.000 0.392 0.048 3.605 1.6169 60�70 0.000 0.418 0.053 3.653 1.85510 70�80 0.000 0.374 0.042 3.608 1.54611 80�86 0.000 0.393 0.038 3.641 1.67012 86�94 0.000 0.393 0.042 3.638 1.56613 94�100 0.000 0.411 0.043 3.644 1.63014 100�110 0.000 0.430 0.039 3.551 1.535Mean 0.008 0.433 0.058 3.360 1.764Standard deviation 0.011 0.045 0.021 0.377 0.240


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influence of organic matter on the estimation ofKs. Theirresults showed that there is a negative relationshipbetween organic matter and Ks. The reasons include:(1) organic matter retains soil water well and does notallow water to flow freely; (2) organic matter affects thepore size distribution of the soil through soil structuredevelopment, which also affects Ks (Lado et al. 2004).Therefore, the estimated Ks values by the Kozeny-

Carman equation might be larger than the actual valuesfor the top materials.

Simulation of Infiltration ProcessThe MCP measured and simulated soil water contentprofiles and soil water storage in the infiltration phasefor each site are presented in Fig. 4 and Fig. 5,respectively. The simulations were all undertaken with

Table 1b. The estimated and optimized hydraulic parameters for site SV62


1 0�6 0.000 0.467 0.129 1.986 0.6202 6�15 0.000 0.300 0.108 1.875 0.2163 15�27 0.000 0.379 0.051 2.296 0.4704 27�32 0.000 0.388 0.114 1.736 0.7195 32�37 0.000 0.389 0.093 2.070 0.5766 37�42 0.000 0.405 0.095 1.994 0.5547 42�46 0.000 0.400 0.152 2.044 0.5058 46�54 0.000 0.324 0.176 1.711 1.3119 54�60 0.000 0.311 0.119 1.955 0.75010 60�67 0.000 0.305 0.101 1.982 0.78911 67�71 0.005 0.302 0.099 1.931 1.05912 71�78 0.005 0.305 0.105 1.970 1.86513 78�81 0.005 0.302 0.105 1.851 2.10914 81�88 0.003 0.289 0.106 1.997 2.63615 88�97 0.004 0.316 0.107 2.019 1.90116 97�100 0.004 0.332 0.109 2.024 1.268Mean 0.002 0.345 0.111 1.965 1.084Standard deviation 0.002 0.052 0.027 0.135 0.701

Table 1c. The estimated and optimized hydraulic parameters for site SV60


1 0�7 0.000 0.483 0.070 1.717 0.9442 7�10 0.007 0.508 0.057 1.968 0.7103 10�17 0.007 0.536 0.102 2.517 0.7034 17�20 0.011 0.504 0.093 2.736 0.6245 20�23 0.000 0.452 0.068 2.388 0.5216 23�30 0.000 0.401 0.034 2.302 0.3417 30�35 0.001 0.355 0.058 2.136 0.3518 35�45 0.000 0.356 0.057 2.169 0.4569 45�48 0.000 0.360 0.057 2.095 0.42310 48�53 0.000 0.356 0.056 2.396 0.26011 53�59 0.000 0.352 0.060 2.568 0.38212 59�68 0.000 0.405 0.049 2.455 0.27013 68�74 0.000 0.385 0.027 2.711 0.18714 74�78 0.000 0.378 0.132 1.488 0.29715 78�84 0.000 0.264 0.083 2.109 0.27216 84�97 0.013 0.303 0.092 2.206 0.72517 97�102 0.000 0.287 0.046 2.362 1.16918 102�110 0.000 0.340 0.064 2.306 0.457Mean 0.002 0.390 0.067 2.257 0.505Standard deviation 0.004 0.078 0.025 0.319 0.262

Table 2. Optimized value of D and least square error (LSE) for each site

SV10 SV62 SV60

Site D LSE (cm6 cm�6) D LSE (cm6 cm�6) D LSE (cm6 cm�6)

Initial value 0.498 13.390 0.498 6.273 0.498 7.316Final value 0.185 0.881 0.249 0.449 0.148 0.435


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the optimal D value developed for each site. Figures 4and 5 clearly show that the changes in soil water contentand storage in the profiles with time were successfullycaptured for all sites, and that small discrepancies onlyexisted at the beginning of the infiltration process. Thesediscrepancies may be attributed to the presence of a highorganic content and low bulk density layer at the surface,which resulted in a high near surface Ks layer and theinstability of the ponded water depth near the beginningof the infiltration test. The bulk density and Ks of thenear-surface soils at the three sites were as follows: 1.18 gcm�3 and 2.378 cm min�1 at site SV10, 1.22 g cm�3

and 0.620 cm min�1 at site SV62, and 1.05 g cm�3 and0.944 cm min�1 at site SV60. The high Ks resulted inhigher infiltrated water volumes and water contents inthe simulations than observed in measurements duringearly times. In addition, the initial infiltration rates atthe study sites were very high and it often took 3 to 5 minto develop a stable pressure head within the infiltrationrings.

Themodel also underestimates the change of soil watercontent near the wetting front with slightly more ‘‘smear-ing’’ of the wetting front in the field measurements.This may be a result of preferential flow. When prefer-

ential flow occurs in a soil profile, the single-porositymodel of Eq. 1 often inaccurately simulates soil waterchanges in the wetting front zone (Gerke and vanGenuchten 1993, 1996). In addition, the interface atdifferent-textured soils in the natural soil profile was notas transilient as that in the numerical model, which mighthave resulted in soil water underestimation at the wettingfront zone. In layered soils, the hydraulic barrier gen-erating at the interface between fine and coarse sand soilslimits the wetting front advance. Due to differenthydraulic properties in different strata, the water move-ment in these profiles is highly controlled by thesecontrasting hydraulic properties, resulting in non-uni-form water infiltration and drainage in soils. Forexample, in a simply stratified profile of a coarser-grainedlayer overlying a finer-grained layer, a hydraulic barriermay be formed by the lower effective hydraulic con-ductivity of the finer-grained layer. This situation occurswhen the flow rate is relatively high. However, if the finer-grained layer is above the coarser-grained layer, acapillary ‘‘break’’ develops when the matric potential ofthe coarser-textured layer at the end of drainage is muchlower than that expected for the finer-textured layer. Thisresults in elevated water contents being stored within the

SV62

0.0 0.1 0.2 0.3 0.4 0.5 0.6Soil water content (cm3cm–3)

Initial SWC8min16min24min32min40min60minSimulated

8min

16min

24min

32min

40min

60min

0.0 0.1 0.2 0.3 0.4 0.5 0.6

SV60


8min

16min

24min

32min

40min

64min

SV10

0

10

20

30

40

50

60

70

80

90

100

110

Dep

th (

cm)


3min

5min

7min

9min

13min

35min

0.0 0.1 0.2 0.3 0.4 0.5 0.6

Fig. 4. Comparison between the simulated and MCP measured soil water content profiles for the infiltration phase at three sites

Soi

l wat

er s

tora

ge (

mm

/1.1

m)

SV60

Measured SimulatedS

oil w

ater

sto

rage

(m

m/1

.1m

)

Infiltration time (min)

SV62

Measured Simulated

0

50

100

150

200

250

300

350

400

450

500

0 5 10 15 20 25 30 35 40 45 50 55 60 65 0 5 10 15 20 25 30 35 40 45 50 55 60 65 0 5 10 15 20 25 30 35 40 45 50 55 60 65

Soi

l wat

er s

tora

ge (

mm

/1.1

m)

SV10

Measured Simulated

Fig. 5. Comparisons between MCP measured and simulated soil water storage over 110 cm (1.1 m) profile during the infiltrationphase at three sites.


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finer-textured layer beyond that which might be expectedfor a fully drained finer-textured profile (Miyazaki 1988;Walter et al. 2000).

The MEP and SDP of individual soil water content(u) and average soil water content (u) calculated by Eqs.21 and 22 are presented in Table 3. The value of MEPfor u varied from �0.004 to 0.010 cm3 cm�3, and thevalue of SDP from 0.034 to 0.057 cm3 cm�3; The MEPand SDP value for , varied from �0.003 to 0.010 cm3

cm�3, and from 0.007 to 0.024 cm3 cm�3, respectively.The small errors for u and demonstrated that theinfiltration phase was simulated well using the optimizedKs values and the respective parameters in the multi-layered profiles.

The cumulative infiltration volumes in the inner andouter rings were measured using flow meters during theinfiltration period. These volumes (adjusted for therespective areas of the rings) are compared withthe simulated infiltration volumes in Fig. 6 for eachsite. The simulated and measured infiltration volumespresented different features among the three sites. Forsite SV10, the measured infiltration volumes in the innerand outer rings were in good agreement, suggestinglimited lateral flow out from the outer ring and homo-geneous conditions horizontally as well as vertically. Forsites SV62 and SV60, however, the measured infiltrationvolumes were always less in the inner ring than in theouter ring. This is likely due to greater amounts oflateral flow from the outer ring. The model over-estimated the infiltration volumes in the first 20 min atsite SV10, but then underestimated the total infiltrationvolume in the later 20 min. For site SV62, the simulatedvolume matched well with the measured infiltrationvolume from the inner ring and was less than thatmeasured in the outer ring. At site SV60, the simulatedinfiltration volume was in good agreement with themeasured value for the inner ring in the first30 min, and then was less than the measured value inlater time. The increased complexity in the infiltrationbehavior at the heterogeneous sites is not surprising

given the increased variability and complexity of the soilprofiles.

Simulation of Drainage ProcessThe calibrated model was also used to simulate thedrainage process with the initial and boundary condi-tions of Eqs. 11�13. Drainage with hysteresis as wellas without hysteresis was considered. For the non-hysteresis model, the main wetting curve was also usedto simulate the drainage phase. Comparisons of theMCP measured and estimated soil water contents in soilprofiles for different elapsed times are shown in Fig. 7.For sites SV62 and SV60, the vertically varying soiltexture resulted in the abrupt changes of soil waterprofiles at the layered interfaces. Clearly, the hysteresisand non-hysteresis models generally produced similarsoil water dynamics in the profile with time for the threesites and with reasonable agreement with the measure-ments. However, the abrupt changes in the soil watercontent profiles were missed by the non-hysteresismodels. For example, for site SV60, the water contentsat a depth of 62 cm were underestimated during thedrainage period. The hysteresis model provided a muchbetter simulation of the soil water content profilesduring the drainage phase at site SV60, and was slightlybetter for sites SV10 and SV62 than the non-hysteresismodel. For example, at site SV60, the soil watercontents at a depth of 30�60 cm were underestimatedby the non-hysteresis model at the beginning of drai-nage. When the hysteresis was considered, the simulatedsoil water contents were closer to the measured values.Compared with the simulation by the non-hysteresismodel, the hysteresis model resulted in an increase insoil water content at the interface between the fine andcoarse materials, such as at the depths of 41 and 52 cmat site SV62. A similar result was obtained by Dane andWierenga (1975) in a profile of clay loam overlying asand layer. In general, for site SV60, the soil watercontent profiles simulated by the hysteresis model werehigher than those by the non-hysteresis model, because

Table 3. Comparison between measured and simulated soil water contents for Infiltration phase

SV10 SV62 SV60

Site uz (cm3cm�3) uy (cm3 cm�3) u (cm3cm�3) u (cm3 cm�3) u (cm3cm�3) u (cm3 cm�3)

No. 288 36 165 15 176 16

MeasuredMean 0.39 0.36 0.23 0.20 0.30 0.28SD 0.13 0.09 0.16 0.07 0.19 0.07

SimulatedMean 0.36 0.36 0.23 0.21 0.30 0.27SD 0.15 0.11 0.17 0.06 0.120 0.08MEP �0.030 0.005 0.001 0.010 �0.003 �0.004SDP 0.057 0.007 0.052 0.024 0.034 0.011

zSoil water content at different depths.yThe average soil water content in the profile.


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the hysteresis function relationships restrict downwardwater movement.

The MCP measured and simulated soil water storagevolumes during the drainage phase by the hysteresis andnon-hysteresis models are presented in Fig. 8. Thehysteresis model resulted in a better simulation of soilwater storage for two sites (SV60 and SV62) than thenon-hysteresis model. Further, the hysteresis modelprovided a better simulation of the soil water storagevolume for sites SV60 and SV62, although not muchimprovement for site SV10. The main reasons for thiswere that: (1) soil materials at site SV10 were coarserthan at sites SV60 and SV62 and (2) soil texture in thesoil profile was more uniform at site SV10 than those ofsites SV60 and SV62. Yang et al. (2004) measured themain drying and wetting curves for five sandy soils, andfound that a coarse-grained soil had less total hysteresis(the area between the drying and wetting curves) than afine-grained soil.

Both the hysteresis and non-hysteresis models pro-vided a reasonable simulation of the effect of layering onwater movement. At site SV60, the presence of texturalbreaks between the finer- and coarser-sand layers

created a capillary ‘‘break’’, reducing water percolationand creating increased water storage within the studyprofile. After 1200 min of drainage, the more uniformsite, SV10, stored only 83 mm of water in the upper 110cm of the profile as compared with the 165 mm of waterstored within the coarser, but more heterogeneous site,SV60.

The MEP and SDP values for individual soil watercontent (u) and average soil water content (u ) calculatedby Eqs. 21 and 22 are presented in Table 4 for thehysteresis and non-hysteresis models. With the hyster-esis model, the MEP value for u , varied from �0.014to 0.001 cm3 cm�3, and the SDP value from 0.003 to0.012 cm3 cm�3; for u, the MEP and SDP values variedfrom �0.016 to �0.001 cm3 cm�3 and from 0.019 to0.030 cm3 cm�3, respectively. Compared with thesimulation of the infiltration phase, the errors of u andu for all three sites decreased in the simulation of thedrainage phase, even without additional calibration. Themain reason for this appears to be the longer time framefor drainage relative to infiltration and, consequently,the greater number of measured values of soil watercontent. When hysteresis was neglected, the errors

SV60

Measured in inner ring

Measured in outer ring

Simulated

Infiltration time (min)

SV62

Measured in inner ring


Simulated

0

100

200

300

400

500

600

700

800

900

1000

0 5 10 15 20 25 30 35 40 45 50 55 60 65 0 5 10 15 20 25 30 35 40 45 50 55 60 65 0 5 10 15 20 25 30 35 40 45 50 55 60 65

Cum

ulat

ive

infil

trat

ing

wat

er(m

m/u

nit a

rea)

SV10Measured in inner ring


Simulated

Fig. 6. Comparisons between the measured and simulated cumulative infiltrated water volume at three sites.

Soil water content (cm3cm–3)

SV62

Initial values52min200min400min1000minSimulated-HSimulated-NH

52min

200min

400min

1000min

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

SV60

Initial SWC52min200min400min1000minSimulated-HSimulated-NH

52min

200min400min

1000min

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

SV10

0

10

20

30

40

50

60

70

80

90

100

110

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Dep

th (

cm)

Initial SWC12min52min100min1000minSimulated-HSimulated-NH

12min

52min

100min

1000min

Fig. 7. Comparisons between the simulated and MCP measured soil water content for the hysteresis (H) and non-hysteresis (NH)models during the drainage phase at three sites.


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increased for site SV60 and SV62, and no significantchanges were found for site SV10 (Table 4).

In general, the optimized Ks and the estimatedparameters (ur, us, ad, aw, and n) can be used tosuccessfully simulate soil water dynamics in the drainageprocess, and the hysteresis model had greater accuracythan the non-hysteresis model. Some errors were in-herent with the estimated hydraulic parameters in thisstudy. Sobieraj et al. (2001) evaluated the performanceof nine published PTFs for estimating Ks in modelingthe stormflow generated in a rainforest catchment. Theirresults showed that uncalibrated PTFs result in either anunderestimation or overestimation of runoff. Schaapand Leij (1998) found that the performance of PTFs waslargely dependent on the data used for their calibration.In this study, the Arya-Paris equation for estimatingSWRC was evaluated using the measured data, whilethe parameter in the Kozeny-Carman equation used toestimate Ks was optimized against measured soil watercontent profiles. Therefore, these hydraulic parametersestimated from the calibrated PTFs can be used tosuccessfully simulate soil water content profiles for thedrainage phase in multi-layered soil profiles.

Soil water content is the limiting factor for forestproductivity and biodiversity in arid and semi-aridenvironments, particularly in sandy materials. There-fore, maximizing water storage is critical for successfulreclamation of sandy materials. This study indicatedthat numerical models such as HYDRUS-1D can beused to simulate soil water dynamics in these verticallyheterogeneous soils with hydraulic parameters estimatedfrom a PTF. This is significant, because the combinationof a numerical water flow model with the Arya-ParisPTF can be used as a tool for designing and evaluating areclamation prescription with multiple layers of differ-ent soil texture. This study further demonstrated thatlayering of sands with different particle size distributionscould substantially increase soil water storage. This isimportant for understanding productivity and biodiver-sity of ecosystems in sandy soils.

CONCLUSIONSThe performance of the one-dimensional simulationmodel (HYDRUS-1D) with hysteresis and non-hysteresis was evaluated for simulating soil waterdynamics in multi-layered sandy soils. All hydraulic

Table 4. Comparison between measured and simulated soil water contents for drainage phase

SV10 SV62 SV60

Site uz (cm3cm�3) u y (cm3 cm�3) u (cm3cm�3) u (cm3 cm�3) u (cm3cm�3) u (cm3 cm�3)

No. 3223 229 3366 306 3069 279

MeasuredMean 0.10 0.10 0.15 0.14 0.19 0.19SD 0.04 0.04 0.06 0.03 0.09 0.04

Simulated with hysteresisMean 0.08 0.08 0.15 0.14 0.18 0.18SD 0.05 0.05 0.07 0.04 0.08 0.05MEP �0.016 �0.014 �0.001 0.001 �0.004 �0.011SDP 0.019 0.012 0.028 0.003 0.03 0.004

Simulated without hysteresisMean 0.08 0.08 0.14 0.14 0.18 0.18SD 0.05 0.04 0.07 0.04 0.08 0.05MEP �0.016 �0.017 �0.002 0.003 �0.006 0.016SDP 0.020 0.012 0.029 0.009 0.040 0.012

zSoil water content at different depths.yThe average soil water content in the profile.

0

50

100

150

200

250

300

350

400

450

500

Soi

l wat

er s

tora

ge (

mm

/1.1

m) SV10

MeasuredSimulated HSimulated NH

0 200 400 600 800 1000 1200

Soi

l wat

er s

tora

ge (

mm

/1.1

m)

Drainage time (min)

SV62MeasuredSimulated HSimulated NH

0 200 400 600 800 1000 1200

Soi

l wat

er s

tora

ge (

mm

/1.1

m) SV60

MeasuredSimulated HSimulated NH

0 200 400 600 800 1000 1200

Fig. 8. Comparisons between the MCP measured and simulated soil water storage in the profile for the hysteresis (H) and non-


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properties required for simulation, including the maindrying and wetting curves, were estimated using particlesize distribution and dry bulk density of each layerthrough PTFs: the Arya-Paris PTF for SWRC, and theKozeny-Carman equation for Ks. The following conclu-sions can be drawn from this study.

(1) Although the Arya-Paris PTF only provides anestimate of the primary drying curve, it can also be usedto estimate the main wetting curve by incorporating achange in contact angle to estimate the wetting curve.A comparison between the measured and estimated soilwater contents under the same suction resulted in a goodlinear relationship, with a slope of 1.021 (close to 1.0)and an intercept of �0.015 (close to 0.0).

(2) The single parameter, D, in the Kozeny-Carmanequation could be optimized at each site by comparingsimulated and measured water contents during infiltra-tion. The optimized parameter value reflected the effectof soil texture on Ks and resulted in a very small LSEbetween the measured and simulated soil water contentsfor the infiltration phase for each site.

(3) Using the optimized Ks and respective parametersfrom infiltration, the Hydrus-1D model accurately simu-lated soil water content profiles and soil water storagevolumes during the infiltration phase in multilayeredcoarse-grained soils with small MEP and SDP. For thethree sites, the MEP varied from �0.030 to 0.010 cm3

cm�3, and the SDP from 0.007 to 0.057 cm3 cm�3.(4) The calibrated and optimized soil hydraulic

parameters from the infiltration phase provided accu-rate simulations of the soil water dynamics and soilwater storage volumes during the drainage phase at allthree sites with the MEP from �0.017 to 0.016 cm3

cm�3, and the SDP from 0.009 to 0.040 cm3 cm�3.(5) Including hysteresis in the relationship between

soil moisture and suction had some effect on soil waterdynamics during drainage. The simulation was im-proved marginally when hysteresis was taken intoaccount.

(6) The measured and simulated results demonstratedthat vertical variations in soil texture can result inenhanced water storage relative to a similarly texturedhomogeneous soil profile.

ACKNOWLEDGEMENTSThe authors acknowledge funding provided from theCumulative Environmental Management Association.

Al Majou, H., Bruand, A., Duval, O., Le Bas, C. and Vautier,

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Infiltration and drainage processes in multi-layered coarse soils

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