Journal of Hydrology 389 (2010) 325–335

Contents lists available at ScienceDirect

Journal of Hydrology

journal homepage: www.elsevier .com/ locate / jhydrol

Seasonal groundwater contribution to crop-water use assessed with lysimeterobservations and model simulations

Yi Luo a,b,c,*, Marios Sophocleous d

a The Key Lab of Ecological Network Observation and Simulation, Chinese Academy of Sciences (CAS), Beijing 100101, Chinab Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, Chinac Xinjiang Institute of Ecology and Geography, CAS, Urumqi 830011, Chinad Kansas Geological Survey, University of Kansas, Lawrence, KS 66047, USA

a r t i c l e i n f o

Article history:Received 22 September 2009Received in revised form 4 June 2010Accepted 6 June 2010

This manuscript was handled byGeoff Syme, Editor-in-Chief

Keywords:LysimeterGroundwater evaporationWinter wheatHYDRUS model

0022-1694/$ - see front matter � 2010 Elsevier B.V. Adoi:10.1016/j.jhydrol.2010.06.011

* Corresponding author at: Institute of GeograResources Research, CAS, Beijing 100101, China. Tel.:

E-mail address: luoyi.cas@gmail.com (Y. Luo).

s u m m a r y

Groundwater evaporation can play an important role in crop-water use where the water table is shallow.Lysimeters are often used to quantify the groundwater evaporation contribution influenced by a broadrange of environmental factors. However, it is difficult for such field facilities, which are operated underlimited conditions within limited time, to capture the whole spectrum of capillary upflow with regard tothe inter-seasonal variability of climate, especially rainfall. Therefore, in this work, the method of com-bining lysimeter and numerical experiments was implemented to investigate seasonal groundwater con-tribution to crop-water use. Groundwater evaporation experiments were conducted through a weighinglysimeter at an agricultural experiment station located within an irrigation district in the lower YellowRiver Basin for two winter wheat growth seasons. A HYDRUS-1D model was first calibrated and validatedwith weighing lysimeter data, and then was employed to perform scenario simulations of groundwaterevaporation under different depths to water table (DTW) and water input (rainfall plus irrigation) drivenby long term meteorological data. The scenario simulations revealed that the seasonally averagedgroundwater evaporation amount was linearly correlated to water input for different values of DTW.The linear regression could explain more than 70% of the variability. The seasonally averaged ratio ofthe groundwater contribution to crop-water use varied with the seasonal water input and DTW. The ratioreached as high as 75% in the case of DTW = 1.0 m and no irrigation, and as low as 3% in the case ofDTW = 3.0 m and three irrigation applications. The results also revealed that the ratio of seasonal ground-water evaporation to potential evapotranspiration could be fitted to an exponential function of the DTWthat may be applied to estimate seasonal groundwater evaporation. In this case study of multilayered soilprofile, the depth at which groundwater may evaporate at potential rate was 0.60–0.65 m, and the extinc-tion depth of groundwater evaporation was approximately 3.8 m.

� 2010 Elsevier B.V. All rights reserved.

1. Introduction

There are approximately 7.33 million hectares (mha) of irri-gated cropland in the Yellow River Basin (Li, 2003), China,3.66 mha of which spread out along the lower Yellow River, wherewinter wheat is one of the most important crops (Fig. 1). Winterwheat grows from early October to early June of the following year.During the winter wheat growth season, the mean seasonal precip-itation of 150 mm in the area cannot meet the winter wheat crop-water requirements, especially during the months of March, Apriland early May. Therefore, irrigation is commonly practiced to sup-plement the water requirements of winter wheat. The water tablein the irrigated cropland fluctuates between 0.5 m and 2.5 m below

ll rights reserved.

phic Sciences and Natural+86 10 6488 8920.

ground surface due to irrigation recharge. However, in the recenttwo decades, because of decreasing runoff in the lower reach ofthe Yellow River and ever increasing competition with other waterusers, crop irrigation has been confronted with insufficient watersupply from the Yellow River. Therefore, investigating the potentialof groundwater evaporation contribution to crop-water use may behelpful in reducing water demand from the river.

Groundwater upflow can play an important role in contributingto crop-water use. Crop-water use from the water table may beimportant in arid and semi-arid regions (Sepaskhah et al., 2003).Shallow groundwater should be viewed as a potential water re-source for crop use provided that upflow does not contribute toprocesses of soil deterioration, such as salinization or acidification,nor limits crop growth through water logging (Benz et al., 1984).One option to reduce the amount of irrigation water is to includeshallow groundwater use as a source of water for crop productionwhen scheduling irrigation (Soppe and Ayars, 2003). Crop-water

Fig. 1. Distribution of the irrigation districts in the Yellow River Basin, China, and the lysimeter location of this study.

326 Y. Luo, M. Sophocleous / Journal of Hydrology 389 (2010) 325–335

use of shallow groundwater depends on many factors, includingwater-table depth, soil hydraulic properties, such as water holdingcapacity and hydraulic conductivity, evaporative demand, croproot growth and distribution, and toxicity levels in both soil–waterand groundwater. The irrigation method and management also af-fect shallow groundwater use (Thorburn, 1997; Soppe and Ayars,2003). Capillary rise from groundwater and salt accumulation inthe soils are difficult parameters to measure in the field. Lysimetersoffer an option for simultaneously measuring these and other com-ponents of the water budget in a field-like situation (Zhang et al.,1999; Hermsmeyer et al., 2002; Soppe and Ayars, 2003; Kellenerset al., 2005; Durner et al., 2008). However, it is still difficult forthe limited observations that are usually undertaken within lim-ited time to quantify the whole spectrum of groundwater contribu-tion to crop-water use under the influences of inter-seasonalrainfall variability.

Based on field observations, empirical formulas were proposedto estimate capillary upflow under bare or vegetation-coveredground conditions (Doorenbos and Pruitt, 1977; Yang et al.,2007; Luo et al., 2008). Soil moisture is usually a factor in thoseparametric approaches to account for the impacts of soil–waterstatus on capillary upflow. However, soil moisture is usually notavailable, and, hence, limits the application of the parametric ap-proaches in practice.

Modeling and scenario analyses can complement the field workby exploring alternative irrigation management strategies under amuch wider range of conditions (Hurst et al., 2004). A model sim-ulation driven by a long meteorological data series can account forthe impacts of seasonally variable rainfall. HYDRUS-1D (Simuneket al., 1998b), a software package that simulates water movementin one-dimensional variably saturated soils with water table pres-ent and root uptake, has been widely used under a variety of con-ditions (Sommer et al., 2003; Skaggs et al., 2006).

Therefore, the objective of this paper is to employ a combinedmethod of lysimeter observations and numerical experiments toassess the seasonal groundwater contribution to crop evapotrans-piration under the joint impacts of water table, variable rainfall,and irrigation schedules.

2. Materials and methods

2.1. Weighing lysimeter

Experiments were conducted in Shandong Province, China(Fig. 1), using a weighing lysimeter (WLYS; Fig. 2) installed at theYucheng Comprehensive Experimental Station (YCES) of the Chi-nese Academy of Sciences, which is located in Yucheng City. Thestation is located also within the Panzhuang Irrigation District(PID), which ranks as the second largest in command area alongthe lower reach of the Yellow River (Fig. 1).

The weighing lysimeter has been in operation since 1991. It isplaced in the middle of a 10-hectare cultivated field, where thesame crops (winter wheat and summer maize) are planted, andwater management is performed similarly. It has a steel soil cylin-der with a diameter of 2.0 m and a height of 5.0 m (Fig. 2). The soilsurface in the column is 0.05 m below the top of the lysimeter so asto minimize microclimatic changes. The bottom 0.5 m is a filterlayer made of gravel and sand. The lysimeter was backfilled withdisturbed horizons of the natural soil from the field nearby. Thesoil was excavated in 0.1 m increments, backfilled in the same se-quence and settled using water. The soil column rests on a sensi-tive weighing system capable of measuring the total mass ofapproximately 35 Mg to the nearest 60 g. A Marriotte system isconnected to the soil column to control and record the water tableinside, and to measure the amount of water that is supplied to the

Dep

thto

aw

ter

tabl

e

Drainage collector

Water inlet Air inlet

Filter layer

Basement

Marriotte bottle

Mar

riot

tebo

ttle

sup

port

er

Soil

colu

mn

Neu

tron

prob

eac

cess

tube

Weighing system

Ground surface Exit

Flex

ible

pip

e

2.0 m

4.95

m

0.05 m

Fig. 2. A schematic description of the weighing lysimeter.

Y. Luo, M. Sophocleous / Journal of Hydrology 389 (2010) 325–335 327

soil column and/or leaks out of it (Fig. 2). Gravity drainage is re-corded by a drainage collector. By recording the weight changeof the soil column, water leakage from or water supply to the soilcolumn, the irrigation and/or rainfall amount, and the total evapo-transpiration from the lysimeter can be obtained through a massbalance approach (Yang et al., 2007).

2.2. Soil and water measurements

The percentages of sand, silt, and clay of the soils were analyzedin the laboratory using the hydrometer approach, their textureswere identified using the textural triangle approach, and their bulkdensity was measured using the weighing approach.

During the periods from early March to early June in 2007 and2008, the depth to water table within the soil column of the weigh-ing lysimeter was maintained at 1.5 m through the connected Mar-riotte bottle in order to measure groundwater evaporationprecisely at a fixed water table. The following items were mea-sured during the experiment. Water supply through the Marriottebottle to the soil column to maintain the water table constant;drainage from the soil column; and change of soil column weight,recorded twice a day at 8:00 am and 8:00 pm. Irrigation-wateramount to the lysimeter was recorded each time. Rainfall was re-corded by a tipping bucket unit located at the station. Thus, dailyevapotranspiration from the soil column can then be obtainedthrough the mass balance approach (Yang et al., 2007), and the dai-ly lower boundary fluxes at the water table can be obtained by thewater supply and drainage records. Additionally, soil water contentwithin the soil column was measured every five days at 0.1-mintervals down to a depth of 1.5 m through the neutron probe ac-cess tube. Additional soil water measurements were made afterrainfall events and before and after irrigation events. The depthto water table in the field around the lysimeter was regularly mea-sured during the experiment. Leaf area index (LAI) for winterwheat in the field around the lysimeter was measured with theLI-3100C area meter (LI-COR Biosciences, Lincoln, Nebraska, USA).

2.3. Model simulation with HYDRUS-1D

The simulations were performed using the HYDRUS-1D model.The water flow part of the model can deal with prescribed headand flux boundaries, boundaries controlled by atmospheric condi-tions, as well as free drainage boundary conditions. The HYDRUS-1D also includes a Marquardt–Levenberg type parameter optimiza-tion algorithm (Simunek et al., 1998b) for inverse estimation of soilhydraulic parameters from measured transient or steady-stateflow data.

The HYDRUS-1D was calibrated and validated with observeddata from the weighing lysimeter in 2007 and 2008, respectively.The parameterized HYDRUS-1D model was then used for scenariosimulations driven by a meteorological data series of 26 years(1981–2006) in an attempt to cover a broad range of rainfallvariability.

2.3.1. Boundary and initial conditions2.3.1.1. Upper boundary conditions. Evaporation from the soil sur-face and transpiration by plants were simulated using the HY-DRUS-1D model. In calculating evaporation and transpiration,potential evapotranspiration is split into two components, potentialevaporation and potential transpiration. Potential evapotranspira-tion rate, ETp is computed by the Penman–Monteith FAO-56 ap-proach (Allen et al., 1998). Potential evaporation rate, Ep, iscomputed as:

Ep ¼ expð�aLAIÞETp ð1Þ

where LAI is the leaf area index, and a is an extinction coefficient ofradiation (Feddes et al., 1974, 1978; Belmans et al., 1983; Wu et al.,1999; van Dam, 2000; Babajimopoulos et al., 2007). The potentialtranspiration rate is then computed as

Tp ¼ ½1� expð�aLAIÞ�ETp ð2ÞFor the extinction coefficient a, Al-Khafaf et al. (1978) and

Babajimopoulos et al. (2007) used the value of 0.623. Ritchie

328 Y. Luo, M. Sophocleous / Journal of Hydrology 389 (2010) 325–335

(1972) and Feddes et al. (1978) used 0.39 for common crops. Fol-lowing Ritchie and Feddes, 0.39 was used in this paper as well.

2.3.1.2. Lower boundary conditions. During the experiments, thedepth to water table within the soil column of the weighing lysim-eter was maintained at 1.5 m through the connected Marriotte bot-tle. Therefore, the lower boundary was set as a constant pressure-head type in the HYDRUS-1D model that assumed the pressurehead at the water table of 1.5 m as 0.0 m.

2.3.1.3. Initial conditions. For calibration and validation simulations,use of the measured soil–water profiles was made as the initialconditions. For scenario simulations, the initial soil–water contentprofile was assumed at field capacity.

2.3.2. Root uptake modeling and root-distribution observationsThe Feddes model (Feddes et al., 1978) was selected to simulate

root water uptake. HYDRUS-1D assumes that actual root depth isthe product of the maximum rooting depth and a root growth func-tion. The Verhulst-Pearl logistic growth function was used to de-scribe the root growth (Simunek and Suarez, 1993). In theVerhulst-Pearl logistic growth function, the initial root growthtime was set 5 days after planting (DAP) and harvest time wasset at 213 DAP. Initial rooting depth was assumed 0.04 m.

Winter wheat root depth can be influenced by soil, fertilization,and irrigation. Liu et al. (2008) investigated winter wheat root dis-tribution patterns through intensive sampling under different irri-gation treatments in Zhengzhou, which is located within theirrigation districts along the lower reach of the Yellow River, andfound that winter wheat root depth reached 2.5 m below ground.However, 80% of the roots concentrated within the 0–1.0 m depthinterval, and more than 90% within the 0–1.5 m depth interval. Luoet al. (2003) investigated the winter wheat root distribution in thefield 5.0 m away from the lysimeter and found that the winterwheat root depth reached 1.3 m, and the root distribution in soillayers 0.3 m, 0.6 m and 1.0 m below ground comprised 81%, 94%,and 99% of the total profile, respectively. Root density decreasedexponentially with soil depth (Luo et al., 2003; Liu et al., 2008).Therefore, in this study, the maximum rooting depth was assumed0.2 m above the water table when the depth to water table (DTW)is less than 1.5 m. When the DTW is more than 1.5 m, the maxi-mum rooting depth was assumed as 1.3 m. Root depth distributionwas assumed to be exponentially decreasing with root depth withan exponent coefficient of 0.105 cm�1.

2.3.3. Soil hydraulic parameter calibration and validationExperimental determination of physical and chemical proper-

ties of soil in the field or laboratory is tedious, time-consumingand involves considerable uncertainty for most practical applica-tions. Recently, inverse modeling has been introduced to estimateeffective properties by deducing them from, e.g., a measured timeseries of soil water content (Ritter et al., 2003). Different inversesolution algorithms have been tried, and relatively efficient proce-dures for estimating soil hydraulic properties have been proposedbased on measured soil–water contents (Ritter et al., 2003), thecumulative infiltration curve and final soil–water content (Simu-nek et al., 1998a), or time series of soil water content, pressurehead, and resident-solute concentration data (Jacques et al.,2002) as the objective functions for parameter optimization. Asmentioned previously, the HYDRUS-1D model provides a Marqu-ardt–Levenberg type of parameter optimization algorithm for in-verse solution of soil hydraulic properties.

In this paper, the van Genuchten model (1980) was selected forthe unsaturated soil hydraulic conductivity. The parameters to becalibrated in the van Genuchten model (1980) include the satu-rated soil–water content, hs, the residual soil–water content, hr,

the constants a and n, and the saturated hydraulic conductivity,Ks. As the saturated soil–water content, hs, was determined fromthe observed soil–water contents, only the remaining parametersfor each soil group were calibrated. The observation data of 2007were used for estimating the soil hydraulic parameters. The mini-mization of the time series difference between measured and sim-ulated soil–water content at different depths of the soil profile wasused as the objective function for inverse estimation of the param-eters. The calibration was done in a combined way using bothautomatic optimization and trial-and-error procedures. We opti-mized one parameter at a time, e.g., Ks, for each of the six soilgroups, with the remaining parameters being kept fixed. Thiswas repeated for each parameter. On the basis of the calibratedparameter set, we further attempted a trial-and-error procedureto further optimize the parameters until the simulated soil–watercontent profiles were acceptable. Meanwhile, the program outputof the actual evapotranspiration and lower boundary flux werecompared to observed values to assess the calibration process.The observation data of 2008 were used for validating the cali-brated soil hydraulic parameters. The simulated and measuredevapotranspiration, lower boundary flux, and soil–water contentwere compared during the validation stage. Agreement betweenthe simulated and measured values was quantitatively evaluatedusing the Nash–Sutcliffe efficiency (NSE), the root mean square er-ror to observations standard deviation ratio (RSR), and the standardregression, and rated ‘Very Good’, ‘Good’, ‘Satisfactory’, or ‘Unsatis-factory’ according to suggested criteria by Moriasi et al. (2007).

Details of the indices NSE and RSR and the standard regressioncan be found in Moriasi et al. (2007). NSE ranges between �1and 1.0, with NSE = 1.0 being the optimal value. Values between0.0 and 1.0 are generally viewed as acceptable levels of perfor-mance, whereas a value <0.0 shows that the mean observed valueis a better predictor than the simulated value, which indicatesunacceptable performance. RSR incorporates the benefits of errorindex statistics and a scaling/normalization factor, so that theresulting statistic and reported values can apply to various constit-uents. RSR varies from an optimal value of 0, which indicates zeroroot mean square error (RMSE) or residual variation and thereforeperfect model simulation, to a large positive value. The lower RSR,the lower the RMSE is, and the better the model simulation perfor-mance is. The slope and y-intercept of the best-fit regression linecan indicate how well simulated results match measured data.The slope indicates the relative relationship between simulatedand measured values. The y-intercept indicates the presence of alag between model predictions and measured data, or that the datasets are not perfectly aligned. A slope of 1 and y-intercept of 0 indi-cate that the model perfectly reproduces the magnitudes of mea-sured data. The slope and y-intercept are commonly examinedunder the assumption that measured and simulated values arelinearly related, which implies that all of the error variance iscontained in simulated values and that measured data are errorfree.

2.3.4. Simulation scenariosSimulation scenarios were designed to investigate influences of

rainfall, irrigation, and depth to water table on groundwater contri-bution to crop-water use. The scenarios are combinations of irriga-tion schedules and depths to water table.

The lysimeter observations indicated that the averaged seasonalevapotranspiration of the winter wheat is approximately 466 mm,which corresponds to a range of atmospheric demand, irrigationapplication, and water-table conditions. Rainfall during the winterwheat season from early October to early June of the following yearis 150 mm, which is far less than the wheat water requirements,especially during the months of March, April, and May. For thosethree months, rainfall can only meet crop demand by approxi-

0

25

50

75

100

125

150

175

200

Plough layer

Loam

Clay

Mixture of sand, loam, and clay

Clay

Silty clay loam

Clay

Loamy sand

Silty clay loam

Loamy sand

Soil stratification In situ description

Dep

th b

elow

sur

face

(cm

)

1

2

3

4

63

6

5

35

Fig. 3. Soil stratification and in situ description in field around the weighinglysimeter. Circled numbers represent the soil group each soil was classified under.

Y. Luo, M. Sophocleous / Journal of Hydrology 389 (2010) 325–335 329

mately 30%. Therefore, irrigation is essential to crop production inthis region.

Traditionally, winter wheat planters in this region apply irriga-tion after the harvest of summer maize in late September or earlyOctober so as to prepare soil wetness for sowing wheat. Other irri-gation applications take place in March, April, and May to supple-ment winter wheat water requirements. Irrigation in March isusually practiced in the middle of the month when soil tempera-ture recovers and winter wheat turns green with air temperaturegoing up. Irrigation in April is usually implemented in the middleof the month. After that irrigation, winter wheat goes into the flow-ering stage, which is sensitive to water stress. Winter wheat maybe irrigated around 10 May, when grain filling starts, and thisapplication depends upon rainfall occurrences. Maintaining a suffi-cient water supply for winter wheat during the grain filling periodis a guarantee to crop yield production.

Two irrigation schedules were designed for scenario simulationon the basis of the crop demand and local irrigation custom. Oneschedule designates two irrigation applications, the first on 15March, and the second on 15 April. Another schedule designatesthree applications, the first two of which follow the first scheduleabove, and the last irrigation is applied on 10 May. For comparison,a non-irrigation scenario was also set up. Flooding irrigation ismost popularly used in this region. A 75-mm water depth was as-sumed to be applied at each irrigation application in the scenarios.

Five depths to water table were assumed, 1.0 m, 1.5 m, 2.0 m,2.5 m, and 3.0 m, respectively. Consequently, 15 scenarios weregenerated and simulated based on combinations of the above-mentioned five depths to water table and 3 irrigation schedules.Scenario simulations were driven by meteorological data seriesfrom 1981 to 2006. Statistical analysis was performed to investi-gate groundwater evaporation influenced by water-table depth,rainfall, and irrigation.

3. Results

3.1. Soil properties and water-table statistics

In situ observations indicated that the soil profile in the imme-diate vicinity of the weighing lysimeter consists of 10 layers thatcan be grouped into six soils (Fig. 3). The measured soil physicalproperties are shown in Table 1. The initial soil hydraulic parame-ters (residual and saturated soil–water contents, saturated hydrau-lic conductivity, and constants a and n in the van Genuchten model(1980)), which were derived from the measured soil-profile prop-erties through the Rosetta Lite v. 1.1 program embedded in the HY-DRUS-1D software, and the calibrated soil hydraulic parametersusing 2007-measured soil–water content data are also shown inTable 1.

During the 2007–2008 study period, the depth to water table inthe field around the lysimeter was between 1.1 m and 2.7 m, witha mean value of 2.0 m and standard deviation of 0.4 m during 2007,and between 1.2 m and 2.9 m, with a mean value of 2.2 m andstandard deviation of 0.4 m during 2008.

3.2. Lysimeter water balance

The incoming water in the unsaturated soil profile of the lysim-eter vessel included rainfall, irrigation, and groundwater upflow,while the outgoing included crop evapotranspiration and percola-tion to the water table. There is no surface runoff for the lysimeter,as is the case of local croplands generally, except during very heavyrains.

From October 10, 2006 to June 10, 2007, total evapotranspira-tion was 434 mm. Rainfall was 86 mm and irrigation 117 mm. Soil

water was depleted by 87 mm. Percolation to the water table was17 mm. Groundwater upflow was 162 mm. From March 7 to June 2of 2008, total evapotranspiration was 318 mm. Rainfall was 85 mmand irrigation 46 mm. Soil water was depleted by 30 mm. No per-colation occurred. Groundwater upflow was 94 mm.

The observed lower boundary flux of the soil is depicted in Fig. 4.Generally, the observed lower boundary water flux followed thetemporal pattern for the winter wheat and summer maize rotationdescribed by Yang et al. (2007), which was recognized as three dis-tinctly different phases. Phase 1 was identified as the water down-ward period, corresponding to the seedling stage through theripening stage of maize. Phase 2 was the period of small or no waterflux at the water table, covering the early growth stages of the win-ter wheat from sowing (usually around October 10) to re-greening(usually at the middle of February). Because of low evapotranspira-tion rates and impeded wheat growth in winter, soil–water move-ment in the unsaturated zone was rather limited. Only a fraction ofgroundwater ascended into the upper soil throughout this period,e.g., 33 mm for the case of 2007 and contributed 35% of the evapo-transpiration during that period. Phase 3 was the upward flux per-iod, lasting from the re-greening stage to wheat harvest (usuallyaround June 10). The redistribution of water in the unsaturatedzone is governed by the gradient in soil–water potential. Rainfallor irrigation events may reduce or even reverse the flux direction(Fig. 4). Groundwater contributed water to the unsaturated zonefor most of the time of phase 3 because of high water requirementsof wheat and insufficient rainfall in this region. Groundwater evap-oration reached 128 mm for the growth season 2007 and contrib-uted 38% of the evapotranspiration during the same period, andreached 93 mm for the growth season 2008, contributing 29% ofthe evapotranspiration during the same period.

Table 1Soil-profile physical properties, and model-predicted and calibrated hydraulic parameters.

Soil groups (see Fig. 3) Bulk density (g/cm3) Clay (%) Silt (%) Sand (%) Soil texture

Measured soil physical properties1 1.34 22 65 13 Silt loam2 1.40 20 47 33 Loam3 1.29 29 60 10 Silt clay loam4 1.37 21 56 23 Silt loam5 1.45 11 74 16 Silt loam6 1.38 17 72 11 Silt loam

Model-predicted and calibrated hydraulic parametershr hs a n Ks (cm/d)

Predicted using rosetta in HYDRUS-1D1 0.08 0.45 0.005 1.62 13.352 0.06 0.40 0.007 1.58 13.963 0.09 0.47 0.007 1.55 18.964 0.07 0.42 0.006 1.64 16.875 0.06 0.40 0.006 1.68 27.826 0.07 0.43 0.005 1.67 23.26

Calibrated through HYDRUS-1D using lysimeter observations1 0.04 0.45 0.023 1.44 20.002 0.04 0.45 0.023 1.44 15.003 0.01 0.41 0.013 1.36 2.004 0.03 0.41 0.016 1.37 6.005 0.07 0.41 0.020 1.41 10.806 0.03 0.41 0.016 1.37 6.00

hr is residual soil–water content; hs is saturated soil–water content; a and n are constants; and Ks is saturated hydraulic conductivity of soils.

0

20

40

60

80

100-2

-1

0

1

2

3

10/10/2006 12/09/2006 02/07/2007 04/08/2007 06/07/2007

Rai

nfal

l plu

s ir

riga

tion

(mm

)

Low

er b

ound

ary

flux

(mm

/d)

Date

2007-Rainfall plus irrigation

2007-Boundary Flux

0

20

40

60

80

100-2

-1

0

1

2

3

10/10/2007 12/09/2007 02/07/2008 04/07/2008 06/06/2008

Rai

nfal

l plu

s ir

riga

tion

(mm

)

Low

er b

ound

ary

flux

(mm

/d)

Date

2008-Rainfall plus irrigation

2008-Boundary Flux

Fig. 4. Time series of rainfall plus irrigation depth and of the observed lower boundary flux during the growth season of winter wheat. Positive values indicate groundwaterevaporation and negative values indicate percolation from the soil profile to the water table.

330 Y. Luo, M. Sophocleous / Journal of Hydrology 389 (2010) 325–335

3.3. Calibration and validation of the HYDRUS-1D model

Simulated and observed soil water content profiles at the early,middle, and final calibration (2007) and validation (2008) stagesare shown in Fig. 5, and simulated and observed soil–water storagefor various depths to water table for both the calibration and vali-dation stages are shown in Fig. 6. Simulated and observed actualevapotranspiration and lower boundary fluxes for the calibrationand validation stages are shown in Fig. 7. The above-mentioned fig-ures give a visual comparison of overall model performance duringthe calibration and validation stages. To assess the model perfor-mance quantitatively, agreement between simulated and observedsoil–water content profiles, groundwater evaporation, and evapo-transpiration was evaluated using the statistical indices NSE, RSR,

briefly described in Section 2.3.3, and regression analysis for thestages of calibration and validation. In our case, the performanceof the calibration and validation were rated as ‘Very good’ for mostcases. For the soil water storage within the top 20 cm layer at thecalibration stage and the lower boundary flux at the validationstage, the model performances were rated as ‘Good’. It was foundthat the simulated lower boundary fluxes during both the calibra-tion and validation stages attenuated the daily fluctuation of themeasured values. The relatively high sensitivity of the weighinglysimeter (see Section 2.1 in Materials and Methods) might be animportant reason for the significant daily variation of the lowerboundary fluxes. The statistical indices indicated that the cali-brated parameters can be used to perform the scenario simulations(Table 2).

0

20

40

60

80

100

120

140

160

0.0 0.1 0.2 0.3 0.4 0.5

Dep

th (c

m)

SWC

2006/10/17

2006/10/17

2007/03/19

2007/03/19

2007/05/24

2007/05/24

0

20

40

60

80

100

120

140

160

0.0 0.1 0.2 0.3 0.4 0.5

Dep

th (c

m)

SWC

2008/03/14

2008/03/14

2008/04/13

2008/04/13

2008/05/26

2008/05/26

Fig. 5. Comparison of simulated to measured soil water content profiles for the calibration (left figure) and validation stages (right figure). SWC, soil water content; thesymbols indicate the measured points; the lines indicate the simulated values.

0

50

100

150

200

250

300

350

400

09/30/06 11/29/06 01/28/07 03/29/07 05/28/07

SWS

(mm

)

Date

SWS100cm(m) SWS100cm(s)

SWS50cm(m) SWS50cm(s)

SWS20cm(m) SWS20cm(s)

0

50

100

150

200

250

300

350

400

09/30/07 11/29/07 01/28/08 03/28/08 05/27/08

SWS

(mm

)

Date

SWS100cm(m) SWS100cm(s)

SWS50cm(m) SWS50cm(s)

SWS20cm(m) SWS20cm(s)

Fig. 6. Comparison of simulated to measured soil water content for the calibration and validation stages. SWS, soil water storage in mm; (m), measured; (s), simulated; thenumbers 20, 50, and 100 indicate the thickness of the soil layers in cm.

Y. Luo, M. Sophocleous / Journal of Hydrology 389 (2010) 325–335 331

3.4. Model simulation results

The scenario simulations were driven by daily meteorologicaldata from 1981 to 2006. The mean value of the seasonal rainfallwas 153 mm with maximum 308 mm, minimum 70 mm, standarddeviation 57 mm, and coefficient of variation 0.37. The seasonalrainfall followed a normal distribution with statistical significance0.05 (Statistical testing was performed using Statistica 6.0 {Stat-soft, Inc., 1984–2001}). It was, therefore, believed that the meteo-rological data set covered the wet, normal, and dry seasons, andwas adequate for interpreting the seasonal variability of rainfallof the study region.

Analysis of the daily output of fluxes from the HYDRUS-1D sim-ulation was performed with the aid of Microsoft Excel 2007. Table 3presents the simulated mean values of seasonal rainfall and irriga-tion, evapotranspiration, groundwater evaporation, and percola-tion to the water table with regard to water table and irrigationschedules. Table 3 shows that the seasonal amount of groundwaterupflow decreased with increasing irrigation plus rainfall andincreasing DTW. The ratio of water table contribution to evapo-

transpiration decreases with increasing DTW and water input. Itmay reach as high as 0.75 when DTW is shallow and no irrigationwater is applied. When the water table drops as low as 3.0 mand three irrigations were applied during the season, that ratio be-comes a minor value of 0.03 only. Percolation increased with ap-plied irrigation-water amount and decreased with droppingwater table. Crop evapotranspiration decreased with deeper watertable, while increased with increasing irrigation. However, in thecase of the shallowest DTW of 1.0 m, evapotranspiration showedonly minor difference among the different irrigation schedulessince sufficient groundwater supply was available.

4. Discussion

4.1. Evapotranspiration

Evapotranspiration increases with increasing seasonal water in-put, which includes rainfall and irrigation. Compared to no irriga-tion, two irrigations added 150 mm water to the soil profile.

0

2

4

6

8

10

09/30/06 11/19/06 01/08/07 02/27/07 04/18/07 06/07/07

Observed ETa

Simulated ETaE

vapo

tran

spir

atio

n (m

m/d

)

Date

0

2

4

6

8

10

02/22/08 03/13/08 04/02/08 04/22/08 05/12/08 06/01/08

Observed ETa

Simulated ETa

Date

Eva

potr

ansp

irat

ion

(mm

/d)

-4

-2

0

2

4

09/30/06 11/19/06 01/08/07 02/27/07 04/18/07 06/07/07

Observed BFLX

Simulated BFLX

Bou

nd

ary

flu

x (m

m/d

)

Date

0

1

2

3

02/22/08 03/13/08 04/02/08 04/22/08 05/12/08 06/01/08

Observed BFLX

Simulated BFLX

Bou

nd

ary

flu

x (m

m/d

)

Date

Fig. 7. Comparison of simulated and observed actual evapotranspiration (upper panel) and lower boundary fluxes (lower panel) for the calibration (left figures) andvalidation stages (right figures). ETa is the actual evapotranspiration, in mm/d; BFLX is the lower boundary fluxes, in mm/d.

Table 2Evaluation of the calibration and validation results by comparing simulated soil water content, evapotranspiration, and lower boundary flux to measured values.

Stages Items Soil layers (cm) a b R2 NSE RSR Performance rating

Calibration2006.10.10–2007.06.10 SWS 0–20 0.74 15.66 0.88 0.66 0.56 Good

0–50 0.88 15.55 0.96 0.94 0.24 Very good0–100 0.94 8.03 0.95 0.88 0.34 Very good

ETa 1.08 �0.10 0.92 0.78 0.47 Very goodBFX 1.11 �0.14 0.75 0.91 0.29 Very good

Validation2008.03.07–2008.06.02 SWS 0–20 0.86 10.28 0.58 0.96 0.21 Very good

0–50 1.01 �5.38 0.82 0.98 0.13 Very good0–100 1.27 �93.40 0.89 0.99 0.08 Very good

ETa 0.99 �0.47 0.83 0.79 0.46 Very goodBFX 0.50 0.23 0.53 0.59 0.64 Good

Note: SWS is soil water storage; ETa is actual evapotranspiration; BFX is the lower boundary flux; a is the slope of the linear regression; b is the intercept of the linearregression; R2 is the coefficient of determination; NSE is the Nash – Sutcliffe efficiency (NSE); and RSR is the root mean square error to observation standard deviation ratio.

332 Y. Luo, M. Sophocleous / Journal of Hydrology 389 (2010) 325–335

Correspondingly, evapotranspiration increased. For DTW = 1.0 m,the increase was found minor. However, for the other four DTWvalues (Table 3), an increase of 30–35 mm was found. ForDTW = 2.5 m, evapotranspiration increased by 31 mm, which wasmuch less than the irrigation-water amount. For the cases of threeirrigations, 75 mm of additional water was added to the soil profilecompared to the 2 irrigation scenarios. Evapotranspiration in-creased only by approximately 13 mm. For DTW = 1.0 m, irrigationcaused minor increase of crop evapotranspiration. On average, twoirrigations increased crop evapotranspiration by 31.5 mm, and the

third irrigation by 13 mm for all the depths to water except forDTW = 1.0 m.

Evapotranspiration decreases with increasing depth to watertable. The scenarios of DTW = 1.0 m have the highest evapotranspi-ration, and the scenarios DTW = 3.0 m the lowest. Meanwhile, thedecrease slows down as the depth to water table increases. The ra-tio of actual evapotranspiration to potential evapotranspiration foreach season was plotted against the depth to water table for differ-ent irrigations (Fig. 8). The decline of the ratio with DTW was fittedwith the following formula (Shah et al., 2007):

Table 3Seasonally averaged groundwater evaporation, recharge to groundwater, and evapotranspiration for different scenarios of the simulation years 1981–2006.

Scenarios P + I ETa E_gw Perc E_gw/ETa BFV

DTW Irrigations Mean STDEV CV

1.0 No 153 522 393 49 0.13 �31 0.75 3622 303 523 325 41 0.13 �108 0.62 2173 378 526 297 39 0.13 �151 0.57 146

1.5 No 153 419 229 24 0.10 �13 0.55 2162 303 449 158 27 0.17 �34 0.35 1243 378 462 131 25 0.19 �53 0.28 78

2.0 No 153 365 112 14 0.13 �10 0.31 1012 303 400 71 16 0.28 �17 0.18 543 378 413 58 17 0.34 �25 0.14 33

2.5 No 153 349 68 8 0.11 �9 0.19 592 303 380 33 9 0.28 �11 0.09 223 378 393 30 10 0.34 �15 0.08 14

3.0 No 153 338 28 4 0.13 �7 0.08 212 303 369 13 4 0.31 �8 0.04 63 378 382 13 4 0.34 �8 0.03 4

Note: DTW is the depth to water table, in m; ETa is actual evapotranspiration, in mm; E_gw is groundwater evaporation, in mm; Perc is percolation from the unsaturated soilprofile, in mm; P + I is precipitation plus irrigation, in mm; BFV is lower boundary flow volume, BFV = E_gw + Perc, in mm.

0.4

0.6

0.8

1.0

0 1 2 3 4

ET

a / P

ET

Depth to water table (m)

No irrigation

2 irrigations

3 irrigations

No irrigation

2 irrigations

3 irrigations

Fig. 8. Ratio of actual evapotranspiration (ETa) to potential evapotranspiration (PET)with depth to water table (DTW) under different irrigation scenarios. For clarifi-cation, points for no irrigation were shifted to right by 0.05 m and for threeirrigations to left by 0.05 m.

Table 4Fitted parameters and derived variables from the decay equations of evapotranspi-ration or groundwater evaporation with depth to water table.

Parameters ETa/PET E_gw/PET

Irrigations Irrigations

0 2 3 0 2 3

y0 0.571 0.608 0.623 �0.034 �0.012 �0.006a (1/m) 1.59 1.19 1.08 1.07 1.42 1.68b (m) 0.31 �0.03 �0.16 0.69 0.61 0.59R2 0.98 0.98 0.98 0.99 0.99 0.99d0 (m) 0.80 0.70 0.70 0.65 0.60 0.60Unaffected depth (m) 3.00 3.30 3.40Extinction depth (m) 3.83 3.73 3.63

Note: ETa is actual evapotranspiration; PET is potential evapotranspiration; E_gw isgroundwater evaporation; y0, a, b are the parameters in Eqs. (3) and (4) in the text;R2 is the coefficient of determination; d0 is the water-table depth above whichgroundwater evaporates at the potential rate; ‘‘unaffected depth” is the water-tabledepth below which evapotranspiration is no longer affected; ‘‘extinction depth” isthe water-table depth below which groundwater evaporation ceases.

Y. Luo, M. Sophocleous / Journal of Hydrology 389 (2010) 325–335 333

ETa

PET¼ 1;DTW 6 d0

y0 þ e�aðDTW�bÞ;DTW > d0

(ð3Þ

where ETa is actual evapotranspiration; PET is potential evapotrans-piration; y0, a, b, and d0 are constants.

Shah et al. (2007) set y0 as zero, and took b and d0 as the samevalue, which represented a depth above which evapotranspirationis atmosphere-controlled. However, in this paper, it was found thata non-zero y0 formula did a much better fitting performance withthe coefficient of determination exceeding 98%. The fitted parame-ters are given in Table 4 and corresponding curves depicted inFig. 8. A significant offset of the curves for irrigation from the curveof no-irrigation (rainfall only) exists, which indicates that irrigationcaused changes in soil–water status over the soil profile and henceaffected crop evapotranspiration. Values of d0, defined in Shah et al.(2007) as a threshold value above which evapotranspiration isatmosphere-controlled, were derived by setting ETa/PET = 1.0. Asa result, the values of d0 were 0.8 m, 0.7 m, and 0.7 m for 0, 2,and 3 irrigations, respectively (Table 4).

The depths to water table at which the derivative d(ETa/PET)/d(DTW) = �2% were set as threshold values, named ‘‘unaffecteddepths,” below which evapotranspiration was unaffected by depthto water table. The results showed that the DTW values (unaffecteddepths) were 3.0 m, 3.3 m, and 3.4 m for 0, 2, and 3 irrigations,respectively (Table 4).

4.2. Groundwater evaporation

Groundwater evaporation decreased with increasing depth towater table and irrigation-water amount. In the case of depth towater table of 1.0 m and no irrigation, averaged seasonal ground-water evaporation during the simulation years 1981–2006 reachedas high as 393 mm. However, in the case of depth to water table of3 m and three irrigations, it was reduced to as low as 13 mm. Theimpact of irrigation on groundwater evaporation is weakened byincreasing depth to water table. In the case of depth to water tableof 2.5 m, two irrigations caused a decrease of groundwater evapo-ration of 35 mm, however, one more additional irrigation causedonly minor reduction. In the case of depth to water table of3.0 m, two irrigations caused a decrease of groundwater evapora-tion of 15 mm, and the third irrigation caused almost no morereduction. Percolation increased with increasing irrigation water

0.0

0.2

0.4

0.6

0.8

1.0

0 0.5 1 1.5 2 2.5 3 3.5 4

E_g

w /

PE

T

Depth to water table (m)

No irrigation

2 irrigations

3 irrigations

No irrigation

2 irrigations

3 irrigations

Fig. 10. Decay of ratio of groundwater evaporation (E_gw) to potential evapotrans-piration (PET) with depth to water table (DTW) under different irrigation scenarios.For clarification, points for no irrigation were shifted to right by 0.05 m and forthree irrigations to left by 0.05 m.

334 Y. Luo, M. Sophocleous / Journal of Hydrology 389 (2010) 325–335

input. However, for depths to water table 1.5 m, 2.0 m, 2.5 m, and3.0 m, the irrigation impact on percolation became increasinglyinsignificant. The third irrigation basically enriched the soil waterprofiles at the late season.

The simulated seasonal groundwater evaporation under differ-ent depths to water table, rainfall, and irrigation supply are de-picted in Fig. 9 as scattered points, which were fitted with linearregression lines of groundwater evaporation versus water inputfor each depth to water table (Table 5). Generally, the regressionline accounted for more than 70% of the variation. For depths towater table of 1.5–2.0 m, the regression line accounted for 82% ofthe variation. The shallower the water table is, the stronger thevariation. The unsaturated soil profile acts as a buffer zone be-tween the atmospheric demand, root uptake, and groundwater up-ward flow. The thicker the buffer zone is, the less the impact of theatmospheric demand and root uptake on groundwater evapora-tion. Consequently, the less the variation of groundwater evapora-tion is.

4.3. Extinction depth of groundwater evaporation

Ratios of seasonal groundwater evaporation to seasonal poten-tial evapotranspiration are plotted against the depth to water tablein Fig. 10. The following equation is used to fit the points (Shahet al., 2007).

0

100

200

300

400

500

0 100 200 300 400

Seasonal water input (mm)

E_g

w (

mm

)

DTW=1.0 m

DTW=1.5 m

DTW=2.0 m

DTW=2.5 m

DTW=3.0 m

Fig. 9. Simulated seasonal groundwater evaporation under conditions of differentdepths to water table (DTW).

Table 5Correlation coefficients for groundwater evaporation data E_gw

versus seasonal water input shown in Fig. 7 under different depthsto water table (DTW).

DTW (m) a b R2

1.0 �0.69 485 0.801.5 �0.58 294 0.822.0 �0.33 149 0.822.5 �0.22 90 0.743.0 �0.09 37 0.70

Note: a is the slope of the regression line; b is the intercept on theE_gw axis; R2 is the coefficient of determination of the linearregression.

E gw

PET¼ 1;DTW 6 d0

y0 þ e�aðDTW�bÞ;DTW > d0

(ð4Þ

where E_gw is groundwater evapotranspiration; d0 is a threshold va-lue above which groundwater evaporates at potential evapotranspi-ration rates. The remaining parameters are the same as in Eq. (3).

The fitted equations for different irrigations are shown in Fig. 10.The coefficients of determination exceed 99% for all cases (Table 4),suggesting the model captured the relationship between ground-water evaporation and depth to water-table well. The values of d0

for 0, 2, and 3 irrigations are 0.65 m, 0.60 m, and 0.60 m, respec-tively (Table 4). They are different from, but very close to the valuesof b in Eq. (4) because of very small intercept values y0.

Extinction depth was defined as a depth to water table wheregroundwater evaporation decayed to a value of zero (McDonaldand Harbaugh, 1988; Shah et al., 2007). The extinction depth canvary considerably as a function of the presence of phreatophytesand seasonal and long-term climatic conditions, among other fac-tors (Anderson and Woessner, 1992; Shah et al., 2007).

The extinction depths were derived from Eq. (4) by settingE_gw = 0. The extinction depth is approximately 3.8 m with slightdifferences among different irrigations (Table 4). Irrigation causeda decrease of the extinction depth. Shah et al. (2007) derived theextinction depths for combinations of 12 soils ranging from sandto clay and three land-cover types of bare soil, grass, and forestthrough model simulation. Their results indicated that the extinc-tion depth varied significantly from 0.50 m for the sand and baresoil combination to 8.20 m for the clay and forest combination.The extinction depth derived in this paper was very close to thecombination of the shallow rooted grass and loam soil in Shah’set al. (2007) soil-land cover combinations.

5. Conclusions

Groundwater evaporation is influenced by the depth to watertable (DTW), water input to the soil profile including rainfall andirrigation, root uptake, and inter-seasonal climatic variability. Inthis work, we were able to quantify the contribution of groundwa-ter to crop-water use over quite a wide spectrum of rainfall/cli-matic variability, and derived practical regression equations toestimate seasonal capillary upflow/groundwater evaporation un-der different depths to water table and different levels of seasonalirrigation.

Y. Luo, M. Sophocleous / Journal of Hydrology 389 (2010) 325–335 335

It was found that the seasonally averaged groundwater evapo-ration amount was linearly correlated to water input for differentDTW values. The linear regression could explain more than 70%of the variability. The seasonally averaged ratio of the water-tablecontribution to crop-water use varied with the seasonal water in-put and depth to water table. The ratio reached as high as 75% inthe case of DTW = 1.0 m and no irrigation, and as low as 3% inthe case of DTW = 3.0 m and 3 irrigation applications.

The results also revealed that the ratio of seasonal groundwaterevaporation to potential evapotranspiration could be fitted to anexponential function of the depth to water table, which is relevantto estimating seasonal groundwater evaporation while consideringthe impacts of water table.

In this case study of multilayered soil profile, the depth at whichgroundwater may evaporate at potential evapotranspiration ratewas 0.60–0.65 m and that depth was slightly influenced by thenumber of irrigation applications. The extinction depth of ground-water evaporation was approximately 3.8 m with slight differencesamong the considered irrigation scenarios.

Acknowledgements

This research was partially financed by the Natural SciencesFoundation of China (No. 90502005), the National High-tech Pro-gram of China (No. 2007AA10Z223), and the Knowledge InnovationProject of Chinese Academy of Sciences (No. KZXC2-YW-BR-12).

References

Al-Khafaf, S., Wierenga, P.J., Williams, B.C., 1978. Evaporative flux from irrigatedcotton as related to leaf area index, soil water and evaporative demand.Agronomy Journal 70, 912–917.

Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. CROP Evapotranspiration-guidelines for Computing Crop-water Requirements. Irrigation and Drain, PaperNo. 56. FAO, Rome, Italy.

Anderson, M.P., Woessner, W.W., 1992. Applied Groundwater Modeling: Simulationof Flow and Advective Transport. Academic Press, New York.

Babajimopoulos, C., Panoras, A., Georgoussis, H., Arampatzis, G., Hatzigiannakis, E.,Papamichail, D., 2007. Contribution to irrigation from shallow water tableunder field conditions. Agricultural Water Management 92, 205–210.

Belmans, C., Wesseling, J.G., Feddes, R.A., 1983. Simulation of the water balance of acropped soil: SWATRE. Journal of Hydrology 63, 271–286.

Benz, L.C., Doering, E.J., Recihman, G.A., 1984. Water-table contribution to alfalfaevapotranspiration and yields in sandy soils. Transactions of ASAE 27, 1307–1312.

Doorenbos J., Pruitt W.O., 1977. Guidelines for Predicting Crop Water Requirements,FAO Irrigation and Drainage Paper No. 24, Rome.

Durner, W., Jansen, U., Iden, S.C., 2008. Effective hydraulic properties of layered soilsat the lysimeter scale determined by inverse modeling. European Journal of SoilScience 59, 114–124.

Feddes, R.A., Bresler, A.E., Neuman, S.P., 1974. Field test of a modified numericalmodel for water uptake by root system. Water Resources Research 10, 1199–1206.

Feddes, R.A., Kowalik, P.J., Zaradny, H., 1978. Simulation of Field Water Use and CropYield. John Wiley & Sons, New York, NY.

Hermsmeyer, D., Hsemann, J., Bachmann, J., van der Ploeg, R., Horton, R., 2002.Model calculations of water dynamics in lysimeters filled with granularindustrial wastes. Journal of Plant Nutrition and Soil Science 165, 339–346.

Hurst, C.A., Thorburn, P.J., Lockington, D., Bristow, K.L., 2004. Sugarcane water usefrom shallow water tables: implications for improving irrigation water useefficiency. Agricultural Water Management 65, 1–19.

Jacques, D., Simunek, J., Timmerman, A., Feyen, J., 2002. Calibration of Richard’s andconvection–dispersion equations to field-scale water flow and solute transportunder rainfall conditions. Journal of Hydrology 259, 15–31.

Kelleners, T.J., Soppe, R.W.O., Ayars, J.E., Simunek, J., Skaggs, T.H., 2005. Inverseanalysis of upward water flow in a groundwater table lysimeter. Vadose ZoneJournal 4, 558–572.

Li, G.Y., 2003. Management of the Yellow River: Philosophy and Practices. ChinaWaterPower Press in Beijing and Yellow River Conservation Press in Zhengzhou,China (in Chinese).

Liu, R.H., Zhu, Z.X., Fang, W.S., Deng, T.H., Zhao, G.Q., 2008. Distribution pattern ofwinter wheat root system. Chinese Journal of Ecology 27 (11), 2024–2027 (inChinese).

Luo, Y., He, C., Sophocleous, M., Yin, Z., Ren, H., Ouyang, Z., 2008. Assessment of cropgrowth and soil water modules in SWAT2000 using extensive field experimentdata in an irrigation district of the Yellow River Basin. Journal of Hydrology 352,139–156.

Luo, Y., Ouyang, Z., Yuan, G., Tang, D., Xie, X., 2003. Evaluation of macroscopic rootwater uptake models using lysimeter data. Transactions of the ASAE 46 (3),625–634.

McDonald, M.G., Harbaugh, A.W., 1988. A Modular Three-dimensional Finite-difference Ground-water Flow Model. USGS Techniques of Water-ResourcesInvestigations, book 6, chap. A1. Reston, Virginia.

Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel, R.D., Veith, T.L.,2007. Model evaluation guidelines for systematic quantification of accuracy inwatershed simulations. Transactions of the American Society of Agriculturaland Biological Engineers 50 (3), 885–900.

Ritchie, J.T., 1972. Model for predicting evaporation from a row crop withincomplete cover. Water Resources Research 8, 1204–1213.

Ritter, A., Hupet, F., Munoz-Carpena, R., Lambot, S., Vanclooster, M., 2003. Usinginverse methods for estimating soil hydraulic properties from field data as analternative to direct methods. Agricultural Water Management 59, 77–96.

Sepaskhah, A.R., Kanooni, A., Ghasemi, M.M., 2003. Estimating water tablecontributions to corn and sorghum water use. Agricultural WaterManagement 58, 67–79.

Shah, N., Nachabe, M., Ross, M., 2007. Extinction depth and evapotranspiration fromgroundwater under selected land covers. Ground Water 45 (3), 329–338.

Simunek, J., Suarez, D.L., 1993. Modeling of carbon dioxide transport and productionin soil: 1. Model development. Water Resources Research 29 (2), 487–497.

Simunek, J., Angulo-Jaramillo, R., Schaap, M.G., Vandervaere, J.-P., van Genuchten,M., 1998a. Using an inverse method to estimate the hydraulic properties ofcrusted soils from tension-disc infiltrometer data. Geoderma 86, 61–81.

Simunek, J., Sejna M., Van Genuchten, M.Th., 1998. The HYDRUS-1D SoftwarePackage for Simulating the One-dimensional Movement of Water, Heat, andMultiple Solutes in Variably-saturated Media. US Salinity Laboratory,Agricultural Research Service, US Department of Agriculture, Riverside,California, US.

Skaggs, T.H., Shouse, P.J., Poss, J.A., 2006. Irrigating forage crops with saline waters:2. Modeling root uptake and drainage. Vadose Zone Journal 5, 824–837.

Sommer, R., Folster, H., Vielhauer, K., Carvalho, E.J.M., Vlek, P.L.G., 2003. Deep soilwater dynamics and depletion by secondary vegetation in the Eastern Amazon.Soil Science Society of American Journal 67, 1672–1686.

Soppe, R.W.O., Ayars, J.E., 2003. Characterizing ground water use by safflower usingweighing lysimeters. Agricultural Water Management 60, 59–71.

Thorburn, P.J., 1997. Land management impacts on evaporation from shallow salinewater tables. In: Taniguchi, M. (Ed.), Subsurface Hydrological Responses to LandCover and Land Use Changes. Kluwer Academic Publishers, Boston, pp. 6–71.

van Dam, J., 2000. Field-scale Water Flow and Solute Transport. SWAP ModelConcepts, Parameter Estimation and Case Studies. Doctoral Thesis, WageningenUniversity. ISBN 90-5808-256-3.

van Genuchten, M.Th., 1980. A closed-form equation for predicting the hydraulicconductivity of unsaturated soils. Soil Science Society of American Journal 44,892–898.

Wu, J., Zhang, R., Gui, S., 1999. Modeling soil water movement with water uptake byroots. Plant and Soil 215, 7–17.

Yang, J.F., Wan, S., Deng, W., Zhang, G., 2007. Water fluxes at a fluctuating watertable and groundwater contributions to wheat water use in the lower YellowRiver flood plain, China. Hydrological Processes 21 (6), 717–724.

Zhang, L., Dawes, W.R., Slavich, P.G., Meyer, W.S., Thorburn, P.J., Smith, D.J., Walker,G.R., 1999. Growth and ground water uptake responses of Lucerne to changes ingroundwater levels and salinity: lysimeter, isotope and modeling studies.Agricultural Water Management 39, 265–282.