Original Research

C. Stumpp*
W. S chler
M. Kandolf
J. Šimůnek

We inves gated diﬀerences in waterflow and solute transport due todiﬀerent land use in five long-term
lysimeter studies. Stable water isotopes were key parameters providing
insight into mean transit mes andeﬀec ve water contents. Both land
use and nonuniform infiltra on patterns during snow melt impacted
water flow and solute transport.
C. Stumpp and J. Šimůnek, Dep. of Environmental Sciences, Univ. of California,
Riverside, CA 92521, USA; C. Stumpp and W.
S chler, Helmholtz Zentrum München–German Research Center for Environmental
Health–Ins tute of Groundwater Ecology,
Ingolstädter Landstr. 1, D-85764 Neuherberg,
Germany; M. Kandolf, Höhere Bundeslehrund Forschungsanstalt für Landwirtscha(HBLFA) Raumberg-Gumpenstein, Abteilung
für Umweltökologie, Raumberg 38, A-8952
Irdning, Austria. *Corresponding author(chris ne.stumpp@helmholtz-muenchen.de).
Vadose Zone J.
doi:10.2136/vzj2011.0075
Received 27 June 2011.

© Soil Science Society of America
5585 Guilford Rd., Madison, WI 53711 USA.

All rights reserved. No part of this periodical may
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from the publisher.

Eﬀects of Land Cover andFer liza on Method on Water
Flow and Solute Transport in Five
Lysimeters: A Long-Term Study
Using Stable Water Isotopes
Land cover and agricultural management prac ces can significantly influence soil structure.
However, li le is known about how fer lizer applica ons and land cover aﬀect soil hydrology
and groundwater recharge over long me periods. The objec ve of this study was to use
stable water isotopes as environmental tracers to provide addi onal informa on requiredfor be er understanding of water flow and solute transport processes in the unsaturated
zone influenced by land cover and type of fer lizer applica ons. Five lysimeters containing
undisturbed soil monoliths from the same agricultural field site were inves gated over a periodof 5 yr. Liquid ca le slurry and solid animal manure were applied to the maize (Zea mays L.) andwinter rye (Secale cereale L.) lysimeters. The grass–clover lysimeter was treated with mineral
fer lizer. Quan ta ve influence of land cover and type of fer lizer applica on on water flow
and solute transport was evaluated for all lysimeters using a modified version of HYDRUS-1D.
The highest drainage was observed in the maize lysimeter treated with ca le slurry, andthe lowest in the grass lysimeter treated with mineral fer lizer. Pronounced diﬀerences inwater contents and es mated saturated hydraulic conduc vi es between the lysimeters
were restricted to the upper 25 to 30 cm of the soil. In par cular, the lysimeters treated withanimal manure had higher porosi es, indica ng a higher content of organic ma er. Maindiﬀerences in discharge between the lysimeters were observed in spring and during the plant
growth periods, indica ng the importance of nonuniform, patchy infiltra on pa erns duringsnow melt and of root water uptake, respec vely. Mean water flow veloci es, transit mes,and eﬀec ve water contents were es mated from the stable water isotope data, providing
evidence on the impact of land cover and type of fer lizer applica on. We found smaller
mean transit mes in the maize lysimeters and for soils with liquid ca le slurry applica ons.
Simula ons indicate that numerical modeling can reproduce the general trend of waterflow and isotope transport. Despite diﬀerences in mean transit mes, fi ed dispersivi es
were all in the same range, sugges ng similar soil structures in the five lysimeters. However,more data for calibra on and more informa on about heterogeneous infiltra on would be
required to improve the model accuracy. In general, stable water isotopes clearly contributed
an added value, elucida ng diﬀerences in mean flow parameters between the lysimeters.
Thus, they provided evidence of the impacts of land cover and fer lizer applica ons, which
are not obvious from water balance and mean discharge rates alone.
Abbrevia ons: BCs, boundary condi ons; LAI, leaf area index; LMWL, local meteoric water line; Lys,lysimeter; NSE, Nash–Sutcliﬀe eﬃciency; TDR, me domain reflectometry; V-SMOW, Vienna-Standard
Mean Ocean Water.

Soil hydraulic proper es control infiltration and movement of water and solutes
through the unsaturated zone. Particularly in agricultural soils, knowledge of these properties is essential to predict water fluxes affecting the crop yield and the fate of agrochemicals
affecting the groundwater quality. Numerous studies have shown that soil hydraulicproperties, bulk density, and soil structure are affected by land use and agricultural management practices (Coutadeur et al., 2002; Miller et al., 2002; Bormann and Klaassen,
2008; Fares et al., 2008; Bachmair et al., 2009; Hu et al., 2009; Chakraborty et al., 2010).
Cultivated soils are exposed to continual changes and ongoing soil structural development due to plowing, addition of fertilizers, and plant and root growth. These changes
can result in the development of different pore-size distributions in the same initial soilmaterial and can induce spatial variations in water flow and solute transport. A summaryof various effects on soil properties, focusing mainly on wheel track compaction, tillage,
and macropore systems, was given by Green et al. (2003). Schwartz et al. (2003) showedthat land use (native grassland, recently tilled cropland, and re-established grassland)

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influenced the pore structure and bulk density in the upper 20
cm of the soil profi le. In the study, differences in soil hydraulicproperties were impacted more by the type of vegetation than by
the type of soil. Although topsoil properties, such as bulk density
and organic matter content, were influenced by different land use
in four soil series, different times when hydraulic conductivities
were measured (either in spring or autumn) produced more variable results than the soil type and land use (Zhou et al., 2008).
However, whether these temporal variations could be explained byhysteresis effects remains unknown. Ndiaye et al. (2007) compared
the influence of two agricultural practices on soil hydraulic properties. They used mineral fertilizer for maize plots and pig slurry fora grass–maize rotation and found a one order of magnitude difference in hydraulic conductivities in the upper 35 cm. Bachmair et al.
(2009) qualitatively investigated water flow at five sites with similar
texture, different land cover (grass, farmland, forest), and land use(tilled and untilled). Preferential flow, which was observed at allsites, differed according to soil structure, microtopography, texture,and soil cover. An increase in the infi ltration rate was not always
accompanied by an increase in preferential flow. Flow conditions
were highly complex and variable, highlighting the importance ofstructural heterogeneities and spatial variability.
As indicated above, most studies have evaluated how various
factors affected soil hydraulic properties near the soil surface orleaching of nutrients (Knappe et al., 2002; van Es et al., 2004,
2006). However, little is known about their indirect influenceon water flow, groundwater recharge, and solute transport. The
question remains whether these changes near the soil surface,resulting from different fertilizer applications or land cover, haveany net effect on groundwater recharge over long time periods
or whether they merely impact soil structure. Particularly forhighly vulnerable agricultural soils, we need to understand theinfluence of fertilization methods and the growth of different
plants, not only on the development of soil structure and soil
hydraulic properties, but also on water flow and solute transport.
Knowledge of spatial heterogeneities is required for the adequate
use of mathematical models (Herbst et al., 2005; Stumpp et al.,2009b), the design of effective irrigation systems (Green et al.,2006), and predictions on whether nutrients (Kosmas et al., 1991),
pesticides (Pot et al., 2005; Stone and Wilson, 2006), or water may
bypass the root zone as a result of preferential flow (Gärdenäs etal., 2006). Heterogeneities in real-world soils and their effects on
soil–plant–atmosphere interactions have to be investigated so thatthe upscaling of crucial processes can finally be fully understoodfor more than just bare soils (Vereecken et al., 2007).
As mentioned above, structural heterogeneities, such as changesof soil due to vegetation and treatment, can result in water
flux heterogeneities and/or preferential flow. Other water flux
heterogeneities are induced by patchy infi ltration patterns andwater content distributions within the soil profile due to vegetation(e.g., Oswald et al., 2008), snow melt/frozen soil (Bayard et al.,2005), or differences in surface slopes (Bergkamp, 1998).
Lysimeters, combined with the use of stable isotopes andmathematical modeling, are adequate experimental tools for
obtaining valuable information about the impact of fertilization
method and land cover on water flow at the scale of agricultural
fields. Lysimeters, containing undisturbed, cultivated soils, areexposed to natural environmental conditions, which can bemeasured in situ over time. Using several lysimeters, containing
initially the same soil, can enable us to investigate soil hydrology
as it is affected by fertilization method and land cover.
Lysimeters have been used successfully together with stable water
isotopes as environmental tracers to identify flow heterogeneities indifferent sandy soils and to improve the applicability of both lumpedparameter and transient mechanistic flow and transport models
in the unsaturated zone (Maciejewski et al., 2006; Maloszewski
et al., 2006; Stumpp et al., 2009a,c). These studies showed thatthe ratios of water isotopes (δ18O and δ 2H) are ideal tracers forinvestigating flow processes in the unsaturated zone in both bareand planted sandy soils, under humid climatic conditions. On thecontrary, single, artificial, one-time tracer injections (e.g., dye tracer
experiments) can only reveal information for a specific point of time,
and thus for specific initial and boundary conditions. Information
about stable water isotopes in precipitation and lysimeter discharge
provides an integral description about water flow and solutetransport processes over time. Uniform flow, as well as preferential
flow, can be quantified quite well using water isotopes (Stumpp et al.,2007; Stumpp and Maloszewski, 2010). For example, quantitative
differences in preferential flow and in distributions of transit times,
both due to land use, were found (Stumpp and Maloszewski, 2010).
The greatest mean transit times (52 wk) were observed for lysimeterswith maize, compared to those with mustard (Brassica L.) or grains(42 wk). On average, 3% of total precipitation contributed topreferential flow in the lysimeter with grains, which was more thanin lysimeters with mustard (2.2%) or maize (1%). Water isotopes
combined with a dual-continuum modeling approach were also
successfully used as tracers to determine preferential flow paths on
the hill slope scale (Vogel et al., 2010).
The objective of this study is to provide a comprehensive
understanding of water flow in cultivated soils under natural
conditions and whether fertilization methods and land coveraffect soil hydrology and groundwater recharge over long time
periods. Moreover, we investigate if stable water isotopes usedas environmental tracers can provide additional information
required for better understanding of soil hydrological processes.
For that purpose, five lysimeters containing undisturbed soilmonoliths from the same field site were investigated. The lysimeters
were embedded in agricultural fields, which were planted withdifferent crops and treated with different fertilization methods.
Soil hydraulic properties, drainage water fluxes, and water isotope

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concentrations were measured to understand differences between
three different land covers (maize, rye, and grass–clover) andthree different fertilization treatments (liquid cattle slurry, solid
animal manure, and commercial phosphorus–potassium fertilizer).
The quantitative influence of land use on water flow and solutetransport was evaluated for all lysimeters using a modified versionof HYDRUS-1D (Šimůnek et al., 2008).

6 Experimental Setup
The experimental site is located at the research area of the HBLFA
Raumberg-Gumpenstein (47°30′ N, 14°6′ E, 700 m elevation)
in Gumpenstein, 190 km southwest of Vienna (Austria). It is ahumid region, with a mean annual temperature of 6.9°C and amean annual precipitation of 1035 mm.
Since 1999, five lysimeters (Lys1–Lys5) have been embedded inagricultural fields (8 by 2.5 m) that differed in their crop andfertilization methods (Stichler et al., 2005). Liquid cattle slurry(Lys1, Lys3) and solid animal manure (Lys2, Lys4) were applied
to the maize (Lys1, Lys2) and winter rye fields (Lys3, Lys4).
The fertilizers were applied on the soil surface and manually
incorporated into the upper 20 cm of the soil during sowing times.
The fertilizers were additionally sprayed on the maize lysimetersin June. Maize was sown annually at the beginning of May andharvested in September or October. Winter rye was sown eachyear in October and harvested between the end of August andthe beginning of October. Additionally, a grass–clover mixture
was planted at the fi ft h field (Lys5), which was fertilized withphosphorous–potassium mineral fertilizer in the spring. The grass–clover mixture was maintained using a three-cut system.
Cylindrical lysimeters contained undisturbed soil monolithsof Dystric Cambisol with three soil horizons (Ap: 0–25 cm,
Bv: 26–100 cm, Cv: >100 cm). However, thicknesses of the soil
horizons varied slightly between the lysimeters and were notindividually identified during the installation. Lysimeter vessels
were pushed into the soil without any disturbance and cut atthe bottom (Eder and Stenitzer, 2003). A layer of fluvioglacial
sediments (5 cm), acting as a capillary barrier and enabling seepage
face boundary conditions for modeling, and a plate with tubes forfree drainage were installed at the bottom. The lysimeters wereplaced in the agricultural fields at the same spots as originallycollected. They had a depth of 1.5 m and a surface area of 1 m2 .
The land surface had a slope of 5°. To allow manual cultivation, theupper 30 cm of the stainless-steel lysimeter vessels were removable.
Time domain reflectometry (TDR) probes (Trase, UMS GmbH,
Munich, Germany) measuring water contents were installed atseven depths (10, 15, 25, 45, 70, 100, and 130 cm), and the data
were logged starting in 2004. At the end of the observation period
in 2007, tensiometers measuring soil water pressure heads (T4 type,
UMS GmbH, Munich, Germany) were installed in three depths(20, 40, and 100 cm). Apart from these in situ measured variables,

texture was analyzed (sieving, sedimentation) and soil hydraulicproperties θ(h) and K(h) were determined in laboratory drainage
experiments on 100-cm3 soil samples, collected from four different
depths close to the lysimeters during their installation in 1999.
Between May 2002 and February 2007, precipitation and seepage
water from the lysimeters were collected on an event basis and inweekly intervals, respectively. Samples were analyzed for δ18O
and δ 2H contents using dual-inlet mass spectrometry (e.g., at
the IAEA, Vienna, or at the HMGU, Institute of Groundwater
Ecology, Neuherberg), having a precision of ±1‰ for 2H and±0.15‰ for 18O. The content of isotopes (Rsample) is given in thedelta notation as the δ-content (‰), which is a relative deviationfrom the standard (Rstandard):δ [‰] =

RSample − RStandard
RStandard

1000

[1]

The Vienna-Standard Mean Ocean Water (V-SMOW) is the meanisotope content of seawater, and is used as a reference standard.
Positive δ contents indicate enrichment relative to the standard;
negative contents indicate depletion. More details about principles
of isotope hydrology and measurement techniques are given by the
IAEA (1983) and Leibundgut et al. (2009).
Meteorological parameters were measured at the research site, andused to calculate daily potential evapotranspiration (ETo) using
the Penman–Monteith equation (Penman, 1948; Monteith, 1965):

ET0 =

1
λ

Δ(Rn −G ) +ρ c p(es − ea )⎛ r⎞
Δ+ γ⎜⎜1 + c ⎟⎟⎟⎜⎝ ra ⎠⎟

ra[2]

where ET0 is the potential evapotranspiration [L T−1], λ is thelatent heat of vaporization [L2 T−2], Δ is the slope of the vapor
pressure curve [M L−1 T−2 K−1], Rn is net radiation [M T−3], G
is the soil heat flux density [M T−3], ρ is the atmospheric density[M L−3], cp is the specific heat of the air [L2 T−2 K−1], es is thesaturation vapor pressure [M L−1 T−2], ea is the actual vapor
pressure [M L−1 T−2], rc is the crop canopy resistance [T L−1], rais the aerodynamic resistance [T L−1], and γ is the psychrometricconstant [M L−1 T−2 K−1]. The parameters ra and rc were calculated
individually for each plant from the crop heights, leaf area index(LAI) and wind velocity measurements (Allen et al., 1998).
Information about the leaf area index and plant growth weretaken from Knisel and Davis (2000) and the FAO-56 guidelines(Allen, 2000). Unknown parameters about crops (maximum root
length, maximum crop height, LAI) were estimated according topersonal communications with onsite operators and the literature(Allen, 2000). Thus, the maximum root depth was set to 100 and
60 cm for crops and grass (Bohner et al., 2007), respectively, andthe maximum crop height (maximum LAI) of 170 cm (2.85), 130

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cm (2.85), and 50 cm (5.0) was assumed for maize, rye, and grass(also considering the three-cut system), respectively.

Es ma on of Mean Transit Timesand Flow Parameters
Although the amplitude of the seasonal signal of the isotope
contents in precipitation is attenuated in the lysimeters due todispersion, it is still observable in the discharge of the lysimeters.
This enabled us to estimate mean transit times of water by looking
at the convective shift in the isotope peaks between the input andoutput and considering dispersion effects. The most pronounced
and distinct correspondence was found in all lysimeters between
the depleted (i.e., more negative δ contents) precipitation peak of
the winter period of 2005–2006 and its corresponding lysimeter
discharge. During this time period (18 Nov. 2005 to 14 Apr. 2006),
the isotope content in precipitation was smaller than its mean
weighted value. Likewise, the time period containing this wintersignal had smaller isotope contents in the discharge than its mean
weighted value. The difference between the centers of these twotime periods, tq [T] and tp [T], defines the estimated mean peakconcentration time t*m [T]:t  t  t  t 
tm*   q1 q2    p1 p2   tq  tp
 2   2 [3]where tp1 and tp2 are times [T] when the period with depleted
isotopes from winter precipitation starts and ends (isotope contents
are smaller than the weighted mean value), respectively, and tq1
and tq2 are corresponding times for isotopes in the discharge.
Accounting for dispersion effects, the mean estimated transit time
t*0 [T] can be determined according to (Leibundgut et al., 2009):
t0* =

t m*
⎛ λ ⎞2 ⎛ λ ⎞1 + ⎜⎜⎜3 L ⎟⎟⎟ −⎜⎜⎜3 L ⎟⎟⎟⎝ L⎠ ⎝ L⎠

[4]

[5]
t

q0* =

q2
t0*
V (t i )∑tq2 − t p1 t p1q*
θ 0* = 0*
v0

t0*

=

Q 0*
t0*

Numerical Modeling
The processes in the unsaturated zone of the lysimeters were
simulated using the HYDRUS-1D soft ware package (Šimůnek
et al., 2008). Th is numerical model considers variably saturated
water flow, root growth, root water uptake, heat transport, andsolute transport. The solute transport module was modified toaccount for isotope transport. We assumed that we can neglect
fractionation processes and that the relative concentration of
isotopes (δ content) does not accumulate at the upper boundary
due to evaporation. Th is is in contrast to the standard treatmentof solutes during evaporation in HYDRUS-1D, where solutes staybehind in the soil while water is removed.

Water Flow with Snow Hydrology
Variably saturated water flow was calculated using the Richards
equation:⎤∂q ∂ ⎡∂θ∂h=− = ⎢K (h) + K (h)⎥ − S⎢⎥∂t∂z ∂z ⎣
∂z
⎦

[6]

[8]

where z is the depth below soil surface (positive upward) [L], q is
the water flux [L T−1], t is time [T], S is a sink term representing
root water uptake [T−1], and K(h) is the soil hydraulic conductivity[L T−1], which is a function of pressure head h [L] and volumetricwater content θ. The water retention characteristic θ(h) and thehydraulic conductivity function K(h) were described as suggested
by van Genuchten (1980) and Mualem (1976):⎧⎪
θ s −θ r⎪⎪θ r +n m
θ(h) = ⎪⎨
1+ αh
⎪⎪
⎪⎪θ
⎪⎩ s

(

where λ L is the longitudinal dispersivity [L], and L is the length ofthe lysimeter [L]. Finally, the estimated mean water flow velocity
v*0 [L T−1], the estimated mean water flux q*0 [L T−1], and theestimated mean water content θ*0 are given byL
v0* = *
t0

where V [L] is the discharge of the lysimeter (based on the surface
area of 1 m2) during the specific observation period ti [T], and Q*0[L] is the mean measured discharge from the lysimeter (based on
the surface area of 1 m2) during the specific time period (from tp1
to tq2) relative to the mean transit time.

K (h) = K s

)

h<0
[9]
h≥0

[1−(α h)n−1[1 + (α h)n ]−m ]2
m[1 + (α h)n ] 2[10]where θ s and θ r are the saturated and residual water contents,
respectively; α [L−1], n, and m (= 1 − 1/n) are empirical parameters
defining the shape of the retention curves, and Ks is the saturated
hydraulic conductivity [L T−1]. Scaling factors (α θ) were used
to adjust different saturated water contents at different depths
of individual soil horizons based on measured water contents(Vogel et al., 1991). The depths to which these scaling factors wereassigned were chosen in the middle between measurement points.

[7]
Snow normally occurs during the winter season in the observation
area. Snow cover was modeled using a simplified approach (e.g.,

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Jarvis, 1994) that assumed that precipitation during periods withbelow zero temperatures was in the form of snow and did not
immediately infi ltrate the soil. Snow melt was then simulated,assuming that it is proportional to the air temperature above
freezing, using a degree-day snow melting constant of 0.43 cm
d−1 K−1 (e.g., Jarvis, 1994). This melting constant indicates whatamount of snow melts during 1 d for each degree above freezing.

Root Water Uptake, Root Growth,and Plant Development
Root water uptake was simulated using the Feddes model withdifferent stress response functions depending on the vegetation
type, which are available in the HYDRUS-1D code (Šimůnek etal., 2008). Root depths were observed down to 60 to 100 cm for
all three kinds of plants (Bohner et al., 2007). For the lysimeterwith grass, the root depth was assumed to be constant (60 cm). For
the other crops, a linear increase in root depth was assumed. Root
growth started in spring, based either on the sowing date (maize)or the spring temperature increase (rye). A maximum rooting
depth of 100 cm was reached in the mid season (approximately theend of July) when the plants were fully developed. After that, theroot length was assumed to be constant until the harvest. Similarly,
plant growth and LAI were assumed to increase linearly during thegrowing phase and be constant thereafter. The estimated LAI wasused to separate the evaporation and transpiration rates from ETp
using an extinction coefficient of 0.6 (Ritchie, 1972).

Isotope Transport
Assuming a single porous medium (all water considered tobe mobile), solute transport is described using the advectiondispersion equation:∂(θ C ) ∂ ⎛ ∂ C ⎞⎟ ∂( qC )= ⎜⎜⎜θ D− SC⎟−∂t∂ z ⎝ ∂ z ⎠⎟
∂z

[11]

where C [M L−3] is the tracer concentration, D is the dispersion
coefficient [L2 T−1], and S is the root water uptake [T−1] from Eq.
[8]. Note that the last term accounts for the passive root solute
uptake. For one-dimensional transport, D is defined according to
Bear (1972):
D=

λL q+ Dw τ wθ[12]

where λ L is the longitudinal dispersivity [L], D w is the moleculardiff usion coefficient in free water [L2 T−1] (10−9 m2 s−1), and τw
is the tortuosity factor.
However, when the delta notation is used (see Eq. [1]), water
isotopes cannot be treated in the same way as other solutes ortracers. For example, water isotope contents do not increase in
the infi ltrating water due to evaporation, as other solutes do. As
we can neglect any fractionation effect due to evaporation (as will

be discussed later), the relative isotope content in the infi ltratingwater remains constant and is independent of the evaporation
rate. The code for solute transport was therefore modified at theupper boundary; that is, evaporation had no effect on the soluteconcentration (on the isotope delta content). While water leaves thesoil profi le (evaporates) and solutes stay behind (and concentrateat the upper boundary) in the standard treatment of evaporation,
both water and isotopes can leave at the same rate in the modified
code. As with the transpiration process, the isotope content taken
up by roots is equal to the solute concentration, without having
a fractionation effect (Zimmermann et al., 1967; Allison et al.,1984). This option was already available in HYDRUS-1D to allow
for passive root solute uptake.

Inverse Modeling and Parameter Es ma on“Atmospheric boundary condition with a surface layer” and “seepageface” were used as upper and lower boundary conditions (BCs)for water flow, respectively. Both boundary conditions involve
both Neumann and Dirichlet BCs, which are used alternatively
depending on conditions in the soil. For the upper boundary, the
Neumann boundary condition is used as long as the surface pressure
head is within a prescribed pressure head interval, and the Dirichletboundary condition is used once the limits of this interval arereached. The “seepage face” lower boundary condition implies thatthe bottom water flux (a Neumann BC) is equal to zero as long as
the bottom of the soil profi le is unsaturated (h < 0) and that the
Dirichlet BC (h = 0) is used when the bottom becomes saturated
and water flux is calculated. Cauchy and Neumann boundary
conditions were used for solute transport at the top and bottom of
the soil profi le, respectively. The Neumann BC implies that only
convective solute flux occurs at the outflow boundary.
Water retention characteristics were determined in the laboratory
using soil core samples taken in the field. However, water contents
measured in the laboratory were higher at the same pressure
heads than those measured in situ in the lysimeters, indicatingthat lab measurements were not representative of field conditions.
Therefore, soil hydraulic parameters were determined using theinverse option (Marquardt–Levenberg optimization algorithm)of HYDRUS-1D to parameterize the model. Initial estimatesof soil hydraulic parameters (Eq. [9] and [10]) were determinedfor each horizon from about 190 in situ measured water content–pressure head points covering a wide range of pressure heads (froma maximum of −10 cm to a minimum of −1000 cm) in 2007. The
residual water content was set to zero to reduce the number offitting parameters. To avoid compensating effects of parametersfrom different horizons during the optimization process, theparameter estimation was accomplished in a stepwise procedure.
During the first three steps, soil hydraulic properties weresuccessively determined for the three soil horizons, starting withthe top Ap horizon, while considering in the objective function
only measured variables for a particular horizon. The objectivefunction included water contents in relevant depths during the last

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2 yr of the experiment (except for the winter period when the soil
was frozen), water retention characteristics measured in situ 1 yr
after the observation period, and saturated hydraulic conductivities
investigated in the laboratory using column experiments. During
the fourth step, the parameters of all soil horizons were refi ned,considering additionally also the cumulative bottom discharge
in the objective function. During the fifth step, dispersivities
were inversely determined using the measured isotope contents
in the discharge in the objective function. As δ18O contents were
measured only in the discharge, data were not sufficient to inversely
fit dispersivities for each soil horizon. Therefore, only an apparent
longitudinal dispersivity for the entire lysimeters was estimated.

Modeling Eﬃciency
The Nash–Sutcliffe efficiency (NSE) (Nash and Sutcliffe, 1970)
was used to determine the goodness of the modeling:T∑[ξo (t )−ξm (t )]2

NSE = 1− t=1T[13]

2
∑ ⎡⎣⎢ξo (t )−ξo ⎤⎦⎥Impact of Fer liza on Methodt=1

where ξo and ξm are the observed and simulated values at agiven time t, and ξ o is the mean observed value over the entire
observation period. Efficiencies close to 1 indicate a good
simulation result. Efficiencies of zero or smaller indicate that themodel is only as good as or even worse than the mean observed
value, respectively. The goodness of the simulation was determinedby comparing observed and simulated water contents, discharge,and δ18O.
Additionally, the coefficient of determination was computed toevaluate if there is a linear relationship (R2 = 1) between calculated
and measured values.
⎧⎪
⎫⎪2
T
⎪⎪
⎪
⎡
⎤
⎡
⎤
∑ ⎣⎢ξo (t )−ξo ⎦⎥ ⎣⎢ξm (t )−ξm ⎦⎥ ⎪⎪⎪
⎪⎪
t=1
⎪⎬
R = ⎪⎨⎪⎪ T
2 T
2⎪⎪⎪ ∑ ⎡⎢ξ o (t ) −ξ o ⎤⎥ ⋅ ∑ ⎡⎢ξ m (t ) −ξ m ⎤⎥ ⎪⎪⎪⎦⎣⎦ ⎪⎪⎪ t=1 ⎣t=1⎩⎭⎪

by the length of the vegetation period for different crops andresulting differences in root water uptake. However, the difference
in discharge between two maize lysimeters was also quite large,
with lower discharge from the lysimeter treated with animal
manure. The application of fertilizers or differences in the actual
infiltration rates may thus cause differences in drainage, which will
be discussed later. When comparing lysimeters with the same crops(Lys1 and Lys2, or Lys3 and Lys4), and thus with the same durationof the vegetation period, differences in observed discharge weremore distinct for the same crops in 2004 (Fig. 1) than in otheryears. Here, the total discharge was higher in Lys1 and Lys4 thanin Lys2 and Lys3, which was caused by higher discharge in spring
during snow melt (Lys1 and Lys4) and higher discharge in lateautumn (Lys1). The same effect was found for the rye lysimetersin 2005 (Fig. 1b). Differences in the spring during snow melt canbe caused either by different contributions of preferential flow andinfi ltration due to partially frozen soils (Stähli et al., 1996) or bydifferent infiltration into lysimeters due to runon–runoff processes
during snow melt (Bayard et al., 2005).

[14]

Less total discharge was observed in the soils treated with animal
manure (Fig. 1a,b). However, this only holds true if the total
observation period is considered. When comparing only the data
till 2006, there is no apparent trend between different fertilization
methods, as higher discharge was measured both from maizelysimeters with cattle slurry and from rye lysimeters with animal
manure. Animal manure generally has a higher organic matter
content than cattle slurry (Antil et al., 2005) and can thus bindmore water near the soil surface, making it readily available forevapotranspiration. Also, organic matter has lower density than thesoil material itself, proportionally decreasing the bulk density andincreasing aggregate stability (MacRae and Mehuys, 1985), thereby

Table 1. Water balance, mean isotope contents in precipitation and discharge, andestimated flow parameters: precipitation P, discharge Q, mean water flux q, andevapotranspiration ET. All values are for the duration of the experiments (i.e., a5-yr period).
Crop

Fertilizer

Precipitation

where ξ m is the mean simulated value over the entire
observation period.

6 Results and Discussion
Water Balance

Impact of Land CoverThe lowest discharge was observed in the grass–clover
lysimeter and the highest in the maize lysimeter treatedwith cattle slurry (Table 1). On average, the discharge
decreased from maize (2537 ± 247 mm) to rye (2466± 42 mm) to grass (2264 mm). Th is was likely caused

P or Q q

ETref† ETa‡

δ18O

δ 2H

mm

mm d−1 ——mm —————‰ ———

5182

2.99

−9.86

−72.8

3170

Discharge
Lys1

maize

liquid cattle 2711
slurry1.56

2376 −10.29

−78.8

Lys2

maize

solid animal 2362
manure1.362788 −10.27

−79.4

Lys3

winter ryeliquid cattle 2496
slurry1.442576 −10.09

−77.1

Lys4

winter ryesolid animal 2436
manure1.40

2654 −10.37

−79.3

Lys5

grass–clover mineral PK

1.30

2816

−73.1

2264

† Potential reference evapotranspiration according to FAO-56 (Allen, 2000).
‡ Actual evapotranspiration calculated with HYDRUS.

www.VadoseZoneJournal.org−9.64

Fig. 1. Measured (symbols) and simulated (line) cumulative discharge for (a) the maize lysimeters, (b) the rye lysimeters, either treated with cattle slurry(black) or animal manure (gray), and (c) the grass–clover lysimeter.

producing differences in soil hydraulic properties. Increased organic
matter contents not only affect water flow, but also impact sorption
processes (e.g., Celis et al., 2006), which additionally need to beconsidered when investigating reactive transport.
To summarize the water balance analysis, differences in discharge
rates may be caused by (i) local structural heterogeneities ordifferences in soil hydraulic properties, resulting in preferential
flow during snow melt; (ii) different infi ltration amounts due torunon–runoff processes; (iii) different duration of plant growth
periods; or (iv) different fertilization methods.

Soil Hydraulic Proper es
Impact of Land Cover
Although the lysimeters contained undisturbed material, all watercontents measured in situ in the lysimeters were lower (Δθ = 0.14–0.26) than water contents measured at similar pressure heads indrainage experiments in the lab (Table 2). This indicates a possible
soil compaction during the installation of the lysimeters. Field
measured water contents can also be lower than lab measurements
due to entrapped air. It was found previously that field-measured
water contents were about 20 to 30% lower than lab measurements
due to entrapped air (Coquet et al., 2005). From the measured

water contents and pressure heads, three different soil horizons
were identified, which varied in depths within the five lysimeters(see modeling results). Water retention functions for soils in the Ap
horizon of the two rye lysimeters were almost parallel (Fig. 2b) andsimilar to those in the grass lysimeter (Fig. 2c). However, soils in
the maize lysimeters had retention curves of two different shapes(Fig. 2a). In the deeper Bv soil horizon, the in situ measured water
retention curves were almost parallel for all five lysimeters, with little
variability in observed water contents (Fig. 3). In the Cv horizon,
only a narrow range of pressure heads (from −30 to −80 cm) andsmall differences in water contents (±0.01–0.03) were observed(Fig. 4). Th is was mainly due to the imposed bottom boundary
condition (i.e., seepage face), which keeps moisture conditions close
to full saturation. Besides, the effects of root water uptake in upperhorizons significantly smoothed variations in flux related variables
at greater depths. Generally, the smallest water contents were
measured in the maize lysimeters (Fig. 2a–4a) and the largest in therye lysimeter treated with animal manure (Fig. 2b–4b).

Impact of Fer liza on Method
The higher amount of the organic matter in soils treated withanimal manure also resulted in a relative increase of porosity (andwater contents) in the upper soil layer in Lys2 and Lys4 (Fig. 2).

Table 2. Soil properties measured in laboratory experiments on samples collected near the location were the undisturbed lysimeters were taken,
including bulk density ρ b, porosity n, gravels, sand, silt, clay, saturated hydraulic conductivity Ks, water content θ, and pressure head h.
SoilhorizonρbnGravels

Sand

Silt

Clay

Ks

θ(h)
−10 cm −30 cm −60 cm −100 cm −300 cm −800 cm −3000 cm −15000 cm

cm

g cm−3%%%%cm d−1

0–25

1.17

0.56

16.4

36.8

36.8

10

946

0.46

0.42

0.39

0.36

0.33

0.29

0.21

0.11

25–80

1.32

0.51

16.2

37.7

35.2

10.9

188

0.41

0.38

0.35

0.33

0.30

0.27

0.19

0.10

80–110

1.25

0.54

20.7

38.9

32.5

7.9

175

0.44

0.41

0.38

0.36

0.32

0.29

0.20

0.12

110–150

1.53

0.44

34.0

48.2

15.8

2

673

0.30

0.25

0.22

0.20

0.15

0.13

0.10

0.06

www.VadoseZoneJournal.org

Fig. 2. Measured (symbols) and optimized (lines) water retention functions in the Ap horizon of (a) the maize lysimeters, (b) the rye lysimeters, either
treated with cattle slurry (black) or animal manure (gray), and (c) the grass–clover lysimeter.

Fig. 3. Measured (symbols) and optimized (lines) water retention functions in the Bv horizon of (a) the maize lysimeters, (b) the rye lysimeters, either
treated with cattle slurry (black) or animal manure (gray), and (c) the grass–clover lysimeter.

Fig. 4. Measured (symbols) and optimized (lines) water retention functions in the Cv horizon of (a) the maize lysimeters, (b) the rye lysimeters, either
treated with cattle slurry (black) or animal manure (gray), and (c) the grass–clover lysimeter.

As mentioned above, it is known that bulk densities decrease andporosities increase correspondingly, depending on the rates ofmanure amendment (Miller et al., 2002; Fares et al., 2008). The

maize lysimeter with animal manure not only displayed larger
water contents and storage capacities, but as already mentioned
above, also a different shape of the retention function. As thiswww.VadoseZoneJournal.org

was not observed for the rye lysimeter, thismay have been an effect of local structural
heterogeneities rather than an impact ofdifferent treatments.
These in situ measured water contents
indicate that differences in physicalparameters due to fertilizer applicationand land cover, such as water contents andporosities, are restricted to the upper 30 cm
of the soil profi le, which is in agreement withother studies (Schwartz et al., 2003; Ndiaye
et al., 2007). At greater depths, temporalvariations of water contents are caused
mainly by structural heterogeneities rather
than soil treatment or plant growth.

Stable Isotopes of Water

Fig. 5. Isotopic compositions in precipitation (expressed as the local meteoric water line, LMWL)
and in the lysimeter discharge.

Both δ 18 O and δ 2 H were measured inwater samples of the lysimeter discharge.
Note that the isotopic compositions of thedischarge, plotted with the local meteoric water line
(LMWL, Fig. 5), indicated a negligible fractionation
effect due to evaporation. Therefore, we only consideredδ 18 O as a tracer in the analysis since more data wereavailable for this isotope. Nevertheless, using δ 2 H
would have resulted in the same results and conclusions.
Water isotope contents in precipitation showed seasonalvariation, with daily inputs between 0 and −30‰ forδ18O (Fig. 6), with mean weighted contents for the entire
observation period of 5 yr of −9.86 and −72.8‰ for δ18O
and δ 2H, respectively (Table 1). Mean weighted summer
and winter isotope contents in precipitation were −8.31
and −60.5‰ for δ18O and δ 2H and −12.85 and −96.4‰
for δ18O and δ 2H, respectively.

Impact of Land CoverFig. 6. Precipitation, calculated grass reference evapotranspiration, and δ18O
The mean weighted isotope contents from the discharge
contents in precipitation.
are given in Table 1. The mean isotope contents of thecrop lysimeters (Lys1–4) were generally smaller than in the
precipitation. This indicates a higher contribution of winterprecipitations, which have smaller isotope contents than summer
to −16‰ for δ18O), was also observed in the water isotopes ofprecipitations, to the discharge in the crop lysimeters compared to
the lysimeters discharge (Fig. 7); it was similar in all lysimeters.
the grass lysimeter. Lys1–4 remained bare in autumn and winter, andHowever, water fluxes seemed to be highly variable throughout thetherefore less water was lost by evapo(transpi)ration. For the grass–whole observation period, as a clear periodic signal in precipitation
clover lysimeter (Lys5), the weighted mean isotope contents were
was echoed in the irregular and erratic signal in the discharge. In
close to the precipitation contents, indicating a closer resemblance
the lysimeters planted with rye (Lys3, Lys4), the seasonal isotopeof the annual course of precipitation and ET than for the othersignal was less pronounced than in the lysimeters planted withlysimeters. Thus, the mean isotope content in the discharge reflectsmaize, indicating higher dispersivities. The isotope distribution in
the seasonal origin of recharging water, depending on the vegetation.
the discharge was also less smooth than in other lysimeter studies(Maloszewski et al., 2006; Stumpp et al., 2009a,c). In one of these
The seasonal distribution, albeit with smaller amplitudes than instudies (Stumpp et al., 2009a), isotope contents in the discharge
precipitation due to weekly sampling and dispersion (from −8of two lysimeters, containing the same soil and either planted

www.VadoseZoneJournal.org

with maize or crop rotation, was much smoother. However, theselysimeters also revealed differences in seasonal peaks of isotope
contents and in water fluxes with respect to the vegetation (Stumpp
et al., 2009a). In this previous study, the seasonality was more
pronounced in the maize lysimeter, which had greater fluxes andthus correspondingly higher pore water velocities. Although such
obvious differences between vegetation covers were not found in
the present study, more complex differences in water flow velocities(transit times) and transport parameters were observed. These may
have been caused by a combination of several factors, including
structural heterogeneities, water flow variability, and/or different
plant growth.

Impact of Fer liza on Method
A clear trend in the mean values and the temporal distributionof isotope contents is absent when comparing different
fertilizer applications.

Es ma on of Mean Flow Parameters
Impact of Land CoverMean transit times determined from the peak shift of the depleted
winter precipitation of 2005–2006 were shorter in the maizelysimeters than in the rye lysimeters (Table 3), although thedischarge during this time period was greater in the rye lysimeters(Table 3). Consequently, mean water flow velocities were fasterin the maize lysimeters than in the rye lysimeters. In the grass
lysimeter, mean transit times were between those calculated for themaize and rye lysimeters (Table 3). By analyzing the water balancealone, one would conclude that transit times in the rye lysimeterare faster than in the maize lysimeters. Only the environmental
tracers provide evidence of actual transit times. This is due to small
differences in the effective mean water contents, emphasizing theimportance of this variable. The estimated mean water contents
were similar, ranging from 0.265 to 0.280, with smaller values
calculated for the maize lysimeters compared to the rye lysimeters(Table 3). These small differences in water contents still produced
significant differences in mean transit times. The estimated watercontents corresponded well with measured water contents at all
depths (Fig. 2, 3, 4).

Impact of Fer liza on Method
Calculated mean transit times were shorter for the lysimeters withthe application of cattle slurry (Lys1, 3) than in the lysimeters withthe animal manure application (Lys2, 4), which is in agreementwith the higher discharge rates in Lys1 and Lys3 compared to lower
rates in Lys2 and Lys4. This again supports the notion that higherorganic contents from solid animal manure remain in the upper
soil and increase the water storage capacity easily available forevapotranspiration, resulting in smaller mean water flow velocities.
Fig. 7. Measured (symbols) and calculated (lines) δ18O contents inthe discharge of (a) the maize lysimeters, (b) the rye lysimeters, either
treated with cattle slurry (black) or animal manure (gray), and (c) the
grass/clover lysimeter.

In general, mean flow parameters estimated from the stable
isotopes of water clearly elucidate differences between thelysimeters, providing evidence of the impact of land cover andwww.VadoseZoneJournal.org

Table 3. Estimated mean flow parameters: mean concentration peakshift time tm*, mean transit time t 0*, mean water volume Q 0*, meanwater flow velocity v 0*, mean water flux q0*, and mean water content
θ 0* determined from the peak shift of the depleted winter precipitation
peak of 2005–2006.
Lysimeter tm*

Q 0*

v0*

————d ————

t 0*

mm

——— mm d−1 ———

q0*

Lys1

196

212

405

7.1

1.9

0.271

Lys2

214

229

398

6.6

1.7

0.265

Lys3

228

250

410

6.0

1.6

0.274

Lys4

249

272

417

5.5

1.56

0.278

Lys5

224

244

420

6.1

1.7

0.280

Table 4. Optimized soil hydraulic and transport parameters for thefive lysimeters; note θ r was set zero to reduce the number of optimized parameters.
z

θr

θs

cm

θ 0*

Lys1

Lys2

Lys3

Lys4

fertilization method. This is not obvious from water balance andmean discharge rates alone.

Modeling Water Flow and Isotope Transport

Lys5

Inversely determined soil hydraulic and solute transportparameters are summarized in Table 4. The optimized water
retention characteristics are presented in Fig. 2 through 4. The
optimized retention function for the Cv horizon of Lys5 indicatedmuch higher water contents close to saturation than actually
measured (Fig. 4c). However, using a saturated water content of
about 0.20 cm3 cm−3 was too small, and thus, model accuracy
decreased compared to the simulations using the fitted retentionfunction with a saturated water content of 0.34 cm3 cm−3.

α

n

Ks

cm−1

–

cm d−1 cm

λL

Ap

0–25

0

0.30

0.112

1.186

639

3.9

Bv

26–97

0

0.31

0.057

1.076

434

3.9

Cv

98–150

0

0.30

0.235

1.294

1034

3.9

Ap

0–30

0

0.32

0.008

1.607

Bv

31–97

0

0.29

0.019

1.070

1179

Cv

98–150

0

0.31

0.060

1.678

236

3.4

Ap

0–30

0

0.30

0.023

1.140

110

4.7

6.2 3.4
3.4

Bv

31–90

0

0.32

0.076

1.070

6000

4.7

Cv

91–150

0

0.32

0.016

1.900

110

4.7

Ap

0–30

0

0.35

0.067

1.080

1790

4.6

Bv

31–85

0

0.30

0.092

1.100

4150

4.6

Cv

86–150

0

0.30

0.038

1.320

276

4.6

Ap

0–25

0

0.33

0.146

1.098

935

4.3

Bv

86–97

0

0.37

0.307

1.070

550

4.3

Cv

98–150

0

0.34

0.063

1.369

694

4.3

was not particularly good and varied with depths, with generally
higher NSE in the upper 25 cm (Table 5). While we report inTable 5 NSE for all lysimeters and all depths, we believe that the
NSE is not a particularly useful measure when measured watercontents vary in a very narrow range (often within the precisionof measurements), with measured values most of the time close
to their mean values. The denominator in Eq. [13] is then verysmall, resulting in low NSE, particularly in deeper soil layers wherewater contents stayed more or less constant over time. Although

Estimated saturated hydraulic conductivities (Table 4) varied
three orders of magnitude (about 101–103 cm d−1) in the Ap
horizon. The range of Ks was independent of land use and did
not increase with increasing water contents due to the manure
application as shown in Fares et al. (2008).
However, the lysimeters with the smallest Ks Table 5. Nash–Sutcliff efficiencies (NSE) and coefficient of determination (R2) determined byin the Ap horizon (Lys2 and Lys3) also had the comparing measured and simulated weekly discharge rates, isotope contents in the discharge,
and water contents.
lowest α (Table 4), likely indicating a strong
Water content
correlation between these two parameters.
18
Q
δ O
10 cm
15 cm
25 cm
45 cm
70 cm
100 cm130 cm
Ks values for the Bv and Cv horizons also did
not show any trend with respect to fertilizer
NSE
application or land cover and exhibited less Lys1
0.25
0.37
0.17
0.29
0.26
0.16
−0.11
−1.58
−0.17
variation than for the Ap horizon. Note that Lys2
0.52
0.21
0.28
0.10
0.13
0.18
0.23
0.02
0.12
full saturation was never reached in these soils, Lys3
0.60
0.34
0.22
0.26
0.46
0.18
0.23
−0.40
0.06
and thus the saturated hydraulic conductivity Lys4
0.69
0.14
−0.04
0.38
0.36
−0.25
nd†
0.40
−1.93
is far beyond the range of inversely determined Lys5
0.67
0.31
−0.26
−0.27
0.02
−0.71
−9.51
0.05
−2.69
values. Consequently, differences between the
R2lysimeters in the soil hydraulic conductivity
Lys1
0.41
0.44
0.25
0.37
0.49
0.42
0.29
0.13
0.04
function in the interval of actually measured
Lys2
0.57
0.28
0.27
0.32
0.29
0.59
0.65
0.57
0.57
water contents were much less pronounced.
While simulated discharge and water isotope
contents corresponded well with measured
values, the goodness of fit for water contents

Lys3

0.62

0.41

0.20

0.25

0.51

0.61

Lys4

0.74

0.24

0.21

0.39

0.46

Lys5

0.68

0.47

0.34

0.39

0.52

† nd, water content was not determined.

www.VadoseZoneJournal.org

0.62

0.18

0.73

0.57

nd

0.61

0.53

0.50

0.33

0.55

0.57

water contents were generally well represented by the model, thiscannot be concluded in this case from the NSE. For example,
differences between measured and simulated water contents inthe depth of 130 cm in Lys5 were small, varying between 0 and
0.015 cm3 cm−3 throughout the entire observation period. Here,the average measured water content (0.279 cm3 cm−3) was only
about 0.01 cm 3 cm−3 higher than the simulated average value(0.267 cm3 cm−3). Such low differences in water contents between
measured and simulated values are in the range of precision for
TDR measurements (0.01 cm3 cm−3) (Evett et al., 2006). Thus,
the NSE does not provide a full picture about the modeling
results when simulating variables that vary within a narrow rangeof values. Note that the NSE measure (Nash and Sutcliffe, 1970)
was specifically designed for evaluating dynamic rather thanstatic variables (i.e., fluxes/discharge rather than water contents).
Therefore, we additionally calculated coefficients of determination(R2) indicating that simulated water contents generally are in good
agreement with measured ones (Table 5), particularly in the depths
below 25 cm.
The results of the isotope transport modeling are presented in Fig. 7.
The best modeling efficiencies were obtained for the simulations in
the lysimeters with cattle slurry and in the grass lysimeter (Table 5).
Particularly, at the end of the observation period (after April 2006),
simulated and measured isotopes in the discharge were in good
agreement. We found that the apparent dispersivity was smallest
for the maize lysimeters and greatest for the rye lysimeter (Table 4).
In a similar study (Stumpp et al., 2009a), higher dispersivities were
found in the 150 cm deep lysimeter with crop rotation (8.1 cm)
than with maize monoculture (6.8 cm); however, they were about
two times higher than in the present study (3.4–4.7 cm), whichmight be caused by the fi ner texture of the soil in the previous
study (10% more clay). Nevertheless, the dispersivities were all inthe same range, emphasizing the intrinsic nature of this soil. The
optimized values, although low, are comparable to values found forsimilar experimental setups (Vanderborght and Vereecken, 2007),
indicating more homogeneous transport behavior (less spreading).
Vanderborght and Vereecken (2007) reported dispersivities
ranging from 2 to 20 cm for field and column experiments with
80- to 200-cm transport distance and water fluxes smaller than
10 mm d−1. On average, dispersivities of about 5 cm were foundin experiments with similar transport distance (80–200 cm) incoarse sediments compared to about 9 cm in finer sediments(Vanderborght and Vereecken, 2007).
Main discrepancies between simulated and measured values canbe generally attributed to uncertainties related to infiltration
during snow melt. The actual quantity and timing of infi ltratingwater often remain unknown in soils where soil frost, snow cover,and snow melt may play a crucial role. Snow drift and irregularthawing of soil may produce differences in the infi ltration pattern.
Furthermore, surface runon and runoff processes in agricultural
fields may cause patchy and nonuniform infi ltration patterns as

the soil close to the surface can be partly frozen during snow melt
events, none of which is considered by the model or can be actually
measured. For example, the isotope content of infi ltrating waterfrom the melting snow layer remains unknown and differs fromthe contents of fresh snow. It was shown that diff usion processes inthe vapor phase resulted in the homogenization of isotope contents
in the snow layer (Herrmann et al., 1981; Stichler and Schotterer,
2000). Besides, the fi rst melt water is always more depleted in
isotopes than the initial content due to fractionation (Herrmann
et al., 1981). In the same study it was shown that isotopic exchange
between the rainwater and the snow layer is on the order of
10%. Thus, the isotope content is different for rainwater when
it infi ltrates into the soil through a snow layer. Snow lysimeters
would be required to determine the isotope content of the actual
infi ltrating water to account for isotope effects in snow layers andimprove the modeling results.
To conclude, simulations indicate that numerical modeling canreproduce the general trend of water flow and isotope transport,
but more data, such as pressure heads in the same depths aswater content measurements and isotope contents of snow melt
infiltration, would be required to improve its accuracy. Furthermore,soil frost and surface runon–runoff processes during snow melt
may have been crucial processes that were not considered by ourmodel. Our results indicate that patchy infi ltration patterns mayvary over time and play a crucial role in water flow and transport
processes on the plot scale. Also, structural heterogeneities canresult in water flow heterogeneities impacted by land cover andfertilizer application. Both heterogeneous infiltration patterns andflow heterogeneities not only occur in our study, but are general
phenomena that need to be implemented in transport models tobetter predict atmosphere–plant–soil–groundwater interactions
under variable flow conditions. Even under steady-state irrigation
conditions, highly heterogeneous flow patterns were observed
for tracer experiments in a sandy loam that was formerly used
for agricultural purposes (Öhrstrom et al., 2004). Similar results
were observed by Williams et al. (2003), who found spatial flow
variability, including preferential flow patterns, in a grass field
during steady-state irrigation experiments.

6 Summary and Conclusions
Soil hydraulic parameters, lysimeter discharge rates, water isotopes,
and mean transit times were used to determine the influence ofland cover and fertilizer application on water flow and solutetransport. Five lysimeters containing undisturbed soil monoliths
were taken from and placed at the same agricultural field site.
Maize, winter rye, and grass–clover were planted over a period of
5 yr. Lysimeters with maize and rye were each treated with liquid
cattle slurry or solid animal manure, while a PK fertilizer was used
for the grass lysimeter.

www.VadoseZoneJournal.org

The largest discharge was measured in the maize lysimeter withthe cattle slurry application, and the smallest in the grass lysimeter.
The main differences in discharge between the five lysimeters
were observed in spring and during the plant growth periods,
indicating the importance of runon–runoff processes duringsnow melt and root water uptake, respectively. The crucial roleof patchy infi ltration patterns during snow melt was also obvious
from water isotope contents in the discharge and from inaccurate
model simulations during this time period.
In situ water content measurements indicated an impact of thedifferent fertilization treatments on the soil structure in the upper
25 to 30 cm. In particular, water retention functions were different
for the maize lysimeters, indicating higher porosities and water
storage capacities for soils treated with manure amendments
in comparison to those treated with cattle slurry. Furthermore,
fitted Ks of the upper soil horizons varied by about three orders ofmagnitude between the lysimeters. However, an obvious impact
due to plants or treatments causing these great differences in Ks
cannot be clearly demonstrated. Below a depth of 30 cm, the shapeof the water retention functions was similar in all lysimeters andonly differences in porosities were found.
The seasonal signal of infi ltration was attenuated, but much lesssmooth compared to other studies. Still, it was possible to determinemean flow parameters (water velocity, transit times, and watercontents), which clearly helped to identify and quantify the impactof land cover and fertilization method, emphasizing the added
value of using water isotopes as an environmental tracer in longterm studies. Smaller mean transit times were found in the maizelysimeters and for soils with the liquid cattle slurry application. The
estimated mean water contents were in agreement with measured
values. Although numerical calculations yielded good simulations,abrupt erratic changes in isotope contents in the discharge could not
be adequately simulated. Nevertheless, the general trend in waterflow and isotope transport was well simulated with HYDRUS-1D,
which was modified to adjust the upper solute boundary conditions
for isotope transport. Additionally, the simulations yielded similardispersivities in all lysimeters, suggesting similar soil structure andless pronounced impact of land cover and fertilization method ontransport compared to water flow.
Differences between the lysimeters suggest that impacts due tofertilizer application and land cover were restricted to only theupper soil horizon. However, even more important than thesefactors were the differences in infi ltration patterns and the effectof snow melt, which were independent of fertilization method andland cover, and which had a significant influence on water flow andtransport processes.

Acknowledgments

This work was supported by a fellowship within the Postdoc-Programme of theGerman Academic Exchange Service (DAAD). Partial support of this work throughEU Genesis Project no. 226536 (FP7-ENV-2008-1) is kindly acknowledged.

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