Journal of Contaminant Hydrology 104 (2009) 36–60

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Journal of Contaminant Hydrology
j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / j c o n h yd

A review of model applications for structured soils: b) Pesticide transport
John Maximilian Köhne a,⁎, Sigrid Köhne b,2, Jirka Šimůnek c,1abcHelmholtz Centre for Environmental Research—UFZ, Theodor-Lieser-Straße 4, D-06120 Halle (Saale), Germany
University Rostock, Institute of Land Use, Soil Physics, Justus-von-Liebig Weg 6, 18059 Rostock, Germany
Department of Environmental Sciences, University of California Riverside, Riverside, CA 92521-0424, USAa r t i c l e

i n f oArticle history:
Received 10 October 2007
Received in revised form 20 August 2008
Accepted 3 October 2008
Available online 17 October 2008
Keywords:
Modelling
Pesticide
Preferential ﬂow
Macropores
Structured soil
Solute transport
Non-equilibriuma b s t r a c t
The past decade has seen considerable progress in the development of models simulating
pesticide transport in structured soils subject to preferential ﬂow (PF). Most PF pesticidetransport models are based on the two-region concept and usually assume one (vertical)
dimensional ﬂow and transport. Stochastic parameter sets are sometimes used to account for
the effects of spatial variability at the ﬁeld scale. In the past decade, PF pesticide models werealso coupled with Geographical Information Systems (GIS) and groundwater ﬂow models forapplication at the catchment and larger regional scales. A review of PF pesticide model
applications reveals that the principal difﬁculty of their application is still the appropriate
parameterization of PF and pesticide processes. Experimental solution strategies involve
improving measurement techniques and experimental designs. Model strategies aim atenhancing process descriptions, studying parameter sensitivity, uncertainty, inverse parameter
identiﬁcation, model calibration, and effects of spatial variability, as well as generating model
emulators and databases. Model comparison studies demonstrated that, after calibration, PF
pesticide models clearly outperform chromatographic models for structured soils. Considering
nonlinear and kinetic sorption reactions further enhanced the pesticide transport description.
However, inverse techniques combined with typically available experimental data are often
limited in their ability to simultaneously identify parameters for describing PF, sorption,
degradation and other processes. On the other hand, the predictive capacity of uncalibrated PF
pesticide models currently allows at best an approximate (order-of-magnitude) estimation ofconcentrations. Moreover, models should target the entire soil–plant–atmosphere system,
including often neglected above-ground processes such as pesticide volatilization, interception,
sorption to plant residues, root uptake, and losses by runoff. The conclusions compile progress,
problems, and future research choices for modelling pesticide displacement in structured soils.
© 2008 Elsevier B.V. All rights reserved.

Abbreviations: 1D, One-Dimensional; 2D, Two-Dimensional; 3D, ThreeDimensional; BC, Boundary Conditions; BTC, Break Through Curve; CDE,
Convection-Dispersion Equation; CNE, Chemical Non-Equilibrium; DFM,
Discrete Fracture Model; DPM, Dual-Permeability Model; ET, evapotranspiration; GIS, Graphical Geographic System; IC, Initial Conditions; KD, Sorption
distribution coefﬁcient; KOC, Sorption distribution coefﬁcient normalized toorganic carbon; KW, Kinematic Wave; m, soil matrix; MIM, Mobile-Immobile
Water Content Model; MRM, multireaction model; PF, Preferential Flow;
PNE, Physical Non-Equilibrium; SOM, soil organic matter; SPM, SinglePorosity Model; T, temperature; θ, soil moisture content.
⁎ Corresponding author. Tel.: +49 345 5585 5406.
E-mail addresses: max.koehne@ufz.de (J.M. Köhne), jiri.simunek@ucr.edu(J. Šimůnek).
1
Tel.: +1 951 827 7854.
2
Now at: Martin-Luther-University Halle-Wittenberg Dept. 6 Research EU
Representative 06099 Halle (Saale) Germany.
0169-7722/$ – see front matter © 2008 Elsevier B.V. All rights reserved.
doi:10.1016/j.jconhyd.2008.10.003

1. Introduction
As a result of agricultural practices, pesticides have been
detected in many aquifers and surface waters. In structured
soils, macropore ﬂow often causes rapid nonuniform leaching
via preferential ﬂow paths, where a fraction of the contaminant percolates into ground water before it can degrade
or be adsorbed by the soil (e.g., Stagnitti et al., 1994). With
regard to pesticides, moderately sorbed compounds with relatively short half-lives are particularly affected (Larsson andJarvis, 2000). Travel times for pesticides preferentiallyleached below the root zone are comparable to those for conservative solutes, with losses of typically less than 1% of theJ.M. Köhne et al. / Journal of Contaminant Hydrology 104 (2009) 36–60

applied dose, but reaching up to 5% of the applied mass (Flury,
1996; Kladivko et al., 2001; FOCUS, 2001). These apparently
small numbers can be put into perspective by considering the
EU drinking water standard, which states that concentrationsof a single pesticide may not exceed 0.1 μg l− 1. For a dose of
0.2 kg ha− 1 and an annual recharge of 200 mm, this implies amaximum allowed leaching loss of only 0.1% of the appliedamount (Jarvis, 2007). Hence, macropore ﬂow should be considered in risk assessment of ground water contamination
with pesticides (FOCUS, 2000).
Pesticide leaching through the vadose zone to groundwater is a complex process controlled by a range of soil andenvironmental conditions. Accordingly, pesticide fate models
account for a variety of processes including soil water ﬂow,
solute transport, heat transport, pesticide sorption, transformation and degradation, volatilization, crop uptake, and surface runoff. A particular modelling challenge is to predictpesticide transport at very low leaching levels important forpesticide registration. These low leaching percentages may be
associated with PF, where only a small fraction of the chemical
percolates downward at a fast pace, while the remaining bulk

37of the substance leaches more slowly than would be expectedfrom the chromatographic theory (Boesten, 2000). On theother hand, it has been argued that for very low concentrations, approaching the level of quantiﬁcation, the criteria for
accuracy need not be as rigorous, particularly when theanalysis takes into account the uncertainty of data and modeloutcome (Carbone et al., 2002). An evaluation of model
predictive accuracy is often made using the factor-of-f
approach, where the agreement between model estimatesversus measured values is considered satisfactory within two-,
ﬁve-, and 10-fold differences (Parrish and Smith, 1990). Thisapproach allows the nature of the measured data to serve as an
input to set the bounds that deﬁne the precision of the model(Carbone et al., 2002).
This contribution reviews recent model applications forevaluating pesticide transport in structured soils, at scales
ranging from the soil column to the catchment. We attempt toidentify the progress made during the past 10 years, as well assome of the developments still required. This paper complements
the overview of model applications of preferential water ﬂow andtracer transport (Köhne et al., 2008-this issue). Since pesticides

Fig. 1. Principal processes governing pesticide transport and fate in agricultural structured soil systems. The central frame is explained in Fig. 2.

38

J.M. Köhne et al. / Journal of Contaminant Hydrology 104 (2009) 36–60

encompass a variety of organic chemicals with a wide range ofphysico-chemical properties, the conclusions presented in thismanuscript are not limited necessarily to pesticides, but may also
be relevant to other reactive organic solutes with similar leaching
behavior. The paper starts with an overview of reviewed models(Section 2). Model applications are then reported in sections
related to particular scales, ranging from the soil column up to theﬁeld (Sections 3.1–3.2). Selected applications at larger scales arethen reported in Section 4. Strengths and potential weaknesses ofmodels most commonly applied for simulating preferential
transport of pesticides at the ﬁeld scale are discussed in Section
4. Finally, in Section 5 conclusions are drawn about the current
state of modelling of preferential pesticide transport and future
research needs are proposed.

2. Overview of models for simulating pesticide transport
in preferential ﬂow systems
The principal processes governing pesticide transport andfate in agricultural structured soil systems are illustrated inFig. 1. Soil matrix and macropore characteristics invoking
different transport patterns are highlighted in Fig. 2.
Descriptions of models for simulating transport of pesticides (and other chemicals) can be found in several reviews
and model comparison studies (e.g., Jarvis, 1998; Armstrong
et al., 2000a; Boesten, 2000; Vanclooster et al., 2000a; Beulke
et al., 2001a; Garratt et al., 2003; Malone et al., 2004a;
Gärdenäs et al., 2006; Ma et al., 2007; Šimůnek and van
Genuchten, 2008). In this section, a brief overview of thoseFig. 2. Fractures and microtopography are triggers for preferential inﬁltration (top), Diverse structure/matrix interfaces stained by dye tracer visualize different
preferential transport paths; these interfaces may affect lateral diffusion, sorption and degradation (middle). Soil matrix and macropore characteristics andresulting transport patterns; actual patterns also depend on the characteristics of rainfall and of overlaying soil horizons (simpliﬁed after Weiler and Flühler, 2004)(bottom).

J.M. Köhne et al. / Journal of Contaminant Hydrology 104 (2009) 36–60

models that account for preferential pesticide transport isgiven. For the description of models simulating water ﬂow andnon-reactive transport, the reader is referred to our companion
paper (Köhne et al., 2008-this issue). Main model characteristics additionally relevant for simulating preferential pesticidetransport are listed below. Besides physical nonequilibrium(PNE) caused by rate-limited solute transfer between transport
regions of different mobility, several models also consider
chemical nonequilibrium (CNE) caused by rate-limited sorption. As an example, a model concept of two mobile regionswith equilibrium and kinetic sorption is illustrated in Fig. 3.
Unless stated otherwise, all models are one-dimensional (1D)and use a numerical solution. Features of some more common
models are additionally shown in Table 1.
2.1. Root Zone Water Quality Model (RZWQM)
The RZWQM (Ahuja et al., 2000a,b) is an agricultural
systems model that can simulate water ﬂow and agrochemical movement in macroporous soils. The pesticide component supports various reaction processes, such as ﬁrst-orderdegradation adjusted for soil water content, temperature, anddepth, and sorption in the matrix (but not in the macropores)
to equilibrium, kinetic, and irreversibly binding sites. Chemical transfer between macropores and matrix is simulated
assuming instant mixing between water in the macropores
and in an adjacent thin (e.g., 0.6 cm; Malone et al., 2001)
boundary layer in the matrix. Transfer between mobile andimmobile subregions of the matrix is calculated as a diffusionlike process. RZWQM supplies default values for essential parameters for the active ingredient of most commercial pesticides. Management options include crop rotations schemes,
tillage, irrigation, and applications of fertilizer, pesticide andmanure. Additional features are listed in Table 1. Further descriptions of RZWQM components and recent applications
can be found in Malone et al. (2004a), Wauchope et al. (2004),
and Ma et al. (2007).

Fig. 3. Model concept of physical non-equilibrium (lateral solute transfer) andchemical non-equilibrium (kinetic sorption) in a dual-permeability medium(modiﬁed after Šimůnek and van Genuchten, 2008); in the plot, θm and θf arewater contents of the matrix and macropore (fracture) regions, respectively; c
are concentrations of the corresponding regions, se are sorbed concentrations inequilibrium with the liquid concentrations of the corresponding regions, and sk
are kinetically sorbed solute concentrations of the corresponding regions. Less
general model concepts neglect some processes (e.g., matrix region isconsidered to be immobile, or only equilibrium sorption is considered, etc.).

39

2.2. MACROThe MACRO model (Jarvis, 1994; Jarvis et al., 2003; Larsbo
and Jarvis, 2003, 2005) also is an agricultural systems modelwith particular focus on macropore ﬂow processes. The dualpermeability model (DPM) can describe sorption as an instantaneous equilibrium or a two-site equilibrium-kinetic process(kinetic sorption in the matrix only). The same Freundlich
adsorption isotherm is assumed for both micro- and macropores,
with the total sorption partitioned into two fractions for the twodomains. The sorption characteristics can be adjusted at anumber of dates during the simulation to mimic sorption aging.
Exponential pesticide degradation can be calculated separately
for the solid and liquid phases of both domains, while
degradation parameters can be adjusted for temperature andmoisture effects. Lateral solute transfer between the macropore
domain and matrix is calculated as a ﬁrst-order rate approximation to the advection-diffusion equation. Jarvis et al. (1997)
developed the pesticide modelling tool MACRO_DB by linking
databases of soils, pesticide properties, climate and crop data to
MACRO, and by including sets of pedotransfer functions andother parameter determination rules. The purpose of
MACRO_DB is to facilitate the predictive application of MACRO.
For additional features see Table 1.
2.3. HYDRUS-1D/-2D/(2D/3D)
The HYDRUS-1D model package (Šimůnek et al., 1998,
2003, 2005; Šimůnek and van Genuchten, 2008) is a processbased model simulating water, heat, and solute movement in
the vadose zone. In the latest version, the mobile-immobile
water content model (MIM) and different DPM approaches(Gerke and van Genuchten, 1993) were included and extended
to calculate preferential transport of a parent pesticide and its
metabolites formed by ﬁrst-order degradation. Actual decay
rates can be calculated as functions of temperature and soil
moisture, for dissolved and solid phases, in the PF region andthe matrix (or the immobile region). Sorption can be describedusing linear, Freundlich, or Langmuir isotherms for equilibrium, one- or two-site kinetic sorption, assumed to be either
similar or different in the different soil regions (Fig. 2). Firstorder terms for bi-directional exchange of water and solute areused (for additional features see also Table 1).
Multi-site sorption concepts that can consider kinetic,
irreversible, and attachment-detachment approaches werealso implemented (e.g., Wehrhan et al., 2007), as well as particle (e.g., Bradford et al., 2003) and colloid-facilitated solutetransport (Šimůnek et al., 2006a,b) options, but so far only forthe equilibrium Richards and CDE based models in HYDRUS1D. The HYDRUS-2D model package (Šimůnek et al., 1999),
and its latest update HYDRUS (2D/3D) (Šimůnek et al., 2006b),
are the two- and two/three-dimensional equivalents of HYDRUS-1D, respectively. Additional features in HYDRUS-2D
concern boundary conditions (e.g., tile drainage) and tools forcalculating spatially distributed model parameters.
2.4. SIMULAT
The model SIMULAT (Diekkrüger, 1996) is a 1D DPM thatconsiders sorption in the matrix using equilibrium or kinetic
forms of Freundlich or Langmuir isotherms. The model can

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J.M. Köhne et al. / Journal of Contaminant Hydrology 104 (2009) 36–60

Table 1
Characteristics of common 1D models for preferential transport of pesticides
RZWQM

SIMULAT

PLM

Diekkrüger(1996)Hall (1994), Nicholls
and Hall (1995)

SPM, MIM, DPM,
DP-MIM

DPM

DP-MIM

Convection–dispersion Immobile

Convection–dispersion

Immobile

Convection–dispersion Convection

Convection

Convection

First-order advection–diffusion
Advection (routing),ﬁrst-order diffusion

First-order advection–diffusion
First-order advection–diffusion

Advection (Darcy)–

First-order diffusion

Documentation Ahuja et al. (2000a,b),
Larsbo and Jarvis (2003,
Malone et al. (2004a),
2005)
Wauchope et al. (2004),
Ma et al. (2007)
DPM, DP-MIM
SPM, DPM
Modelconcept (s)Solute transporta) Matrix
Convection, partial
Convection–dispersion
mixingb) PF domain Convection, mixing
Convection(expon. depth decrease)
Solute transfera) PFdomainmatrixb) MatrixPfdomainc) DP-MIM:
mo-im
Sorptiona) Matrixb) PFdomain

Degradationa) Matrix

Instant mixing with aboundary matrix layer
Instant mixing with aboundary
matrix layer
First-order diffusion

HYDRUS-1DaŠimůnek et al. (2005), Armstrong et al.
Šimůnek and van(2000b)Genuchten (2008)

MACRO

Linear, Freundlich, three- Linear, Freundlich,site (equilibrium, kinetic, two-site (equilibrium,bound or aged residues, kinetic), aging
approximation
pH-dependence)No
Linear, FreundlichFirst-order, function of T, First-order, functionθ (aerobic or anaerobic) of T, θb) PFdomainNoﬁrst-order, functionof T, θHeat ﬂux
Root growth
Root uptake
Volatilization

Yes
Yes
Water, Solute
Yes

Yes
Yes
Water, Solute(Yes)—lumped
dissipation
rate
(Yes)
No
Tillage

Surface runoff Yes
Solute in runoff Yes
Management
Crop rotations, tillage,
options
fertilizer, manure, pestic.
application mode
Tile drainage
Yes
Yes
Inverse method No
SUFI
GUI
Yes
Yes
Selected further Pesticide degradation on Swell-shrink dynamics,
featurescrop foliage and residue, compaction, surface
sealing, pesticide
crop growth and yield,
intersection canopy
organic matter anddissipation and washoff,nutrient cycle, ion
uncertainty estimation,
dissociation, chemical
metabolites
transfer to surface
runoff, erosion

CRACK-NP

MIM

Advection (Philip)–diffusion (Fick)
Diffusion-likec,
Peds-peds/pedsPFdomain–

Linear, Freundlich,Linear, Freundlich,
Langmuir, two-site(equilibrium)b(equilibrium, kinetic)

Advection(?)–

c

Advection–

Diffusion-likec

Linear, (equilibrium),
Linear, Freundlich,
Langmuir, (equilibrium, aging approximation
in top 5 cm
three sites)

Linear, Freundlich,
Langmuir, two-site(equil.-kin.)

No

No

No/Yes (only in slow
mobile water)

First-order, functionof T, θ; Degradation
on solidFirst-order, functionof T, θ; Degradation
on solid
Yes
Yes
Water, Solute
YesFirst-order, functionof T, θNo

First-order or Michaelis First-order, function
Menten or (co)metabolic, of T, θfunction of T, θ
No
No

No
Yes
Water
No

Yes
Yes
No
No

No
Yes
No
No

(Yes)
No
No

(Yes)
No
No

No
No
No

No
No
No

Yes
Levenberg–Marquardt
Yes
Particle transport, CO2transport, chemistryof carbonate system,
major ion chemistry,metabolites

(Yes)
No
No
Swelling-shrinking,canopy interception

Yes
No
No

(Yes)
No
No
Time step is 1 day,space increment is
5 cm, nogroundwater table(ET—evapotranspiration, T—temperature, mo—mobile region, im—immobile region, PF—preferential ﬂow, KD—Sorption distribution coefﬁcient, θ—soil moisture
content, (Yes) means: is considered, but in a simpliﬁed way, SPM—single porosity model, MIM—mobile-immobile water model, DMP—dual-permeability model,
DP-MIM—dual-permeability model with immobile water), GUI—Graphical User Interface.
aSimilar features are included in HYDRUS-2D and HYDRUS (2D/3D).
b
Multi-site sorption concepts such as kinetic, irreversible, and attachment-detachment based approaches are included in a special HYDRUS-1D versions(e.g., Wehrhan et al., 2007).
c
In publications, the equations used for process description were not indicated.

J.M. Köhne et al. / Journal of Contaminant Hydrology 104 (2009) 36–60

consider up to three different sorption sites. Pesticide degradation can be of exponential, Michaelis–Menten, metabolic or cometabolic type, and can depend on soil moisture and temperature. Water and solute exchange between macropores andmatrix is represented as a ﬁrst-order rate process. Plant growth,heat transport and tile drainage can also be simulated (Table 1).
2.5. CRACK-NP
CRACK-NP (Armstrong et al., 2000b) describes water ﬂow
and the transport of tracers, nitrate or pesticides in clay soils
with shrinkage cracks. Sorption can be described as a linear or
Freundlich equilibrium process, while the ﬁrst-order degradation is calculated according to Walker and Barnes (1981)and depends on soil temperature (Arrhenius equation) andmoisture. Crop height and root depth increase at a constantrate after crop emergence until they reach a maximum value(Armstrong et al., 2000b).
2.6. PLM
The Pesticide Leaching Model (PLM, version 3) developed byHall (1994) and Nicholls and Hall (1995) is a relatively simple
capacity model, which assumes that water moves downward in
the soil proﬁle in the ‘tipping-bucket’ fashion. PLM uses
discretization layers of 5-cm thickness and a calculation timeinterval of one day. The soil is divided into fast mobile, slow
mobile, and immobile regions. The mobile and immobile
domains are divided by a pressure set at −5 kPa (ﬁeld capacity).
The percentage of the fast mobile phase (air capacity) is anempirical input parameter, and is constant for all horizons,irrespective of their different characteristics. In the top
numerical layer, solute is at equilibrium between liquid andsolid phases in all regions. In lower layers, lateral equilibration is governed by water and advective solute exchange
between the regions. Linear equilibrium sorption is assumed.
Sorption in the top layer increases gradually with time tosimulate aging or diffusion effects. First-order degradation is
calculated as a function of the soil depth, temperature andsoil water content. One soil layer can be speciﬁed to containdrains and a speciﬁed percentage of the water reaching thislayer can ﬂow into the drains, while the remainder continues
to seep downwards.
2.7. Other approaches
The DPM of Gerke and van Genuchten (1993) based on the
Richards and convection–dispersion equations for both the matrix and fracture pore regions was extended by Ray et al. (1997,2004) to simulate pesticide leaching. The resulting S1D DUAL
model allows for biodegradation, and for linear equilibriumand kinetic pesticide sorption. Both of these processes canvary with soil depth and between ﬂow domains. A sensitivity
analysis was conducted to evaluate the effects of various
parameters characterizing the preferential pesticide transport (Ray et al., 2004).
The ADAPT (Agricultural Drainage And Pesticide Transport)
model of Chung et al. (1991a,b) is a capacity model that combinesparts of the GLEAMS daily simulation model (Leonard et al.,1987) with the capability of accounting for subsurface drainage
and subirrigation using DRAINMOD (Skaggs, 1980). Pesticides

41

move upward in the ﬂow region due to evaporation and downward due to macropore ﬂow and inﬁltration. Linear pesticide
sorption and degradation are calculated on a daily basis. Kalita
et al. (1998) successfully tested ADAPT using ﬁeld data collectedin shallow groundwater under three water table management
practices near Aimes, Iowa, USA. However, the effect of macropore ﬂow was not discussed.
The WAVE model (Vanclooster et al., 1994) uses a simpliﬁed description of preferential transport. The Richards
equation is used for calculating equilibrium water ﬂow, andthe MIM for describing solute transport. Heat transport ismodelled based on Fourier's law. Root water uptake, linear
equilibrium sorption, and ﬁrst-order degradation adjusted forsoil moisture and temperature are also considered.
LEACHM (Hutson and Wagenet, 1992) describes water ﬂow
and chromatographic solute transport using the Richards andconvection–dispersion equations, respectively. LEACHM wasused by Elliott et al. (1998) to consecutively simulate independent matrix and PF region transports. The results of the twosimulations were then combined to yield an approximate DPM(without mass transfer between regions) simulation of preferential solute displacement (Elliott et al., 1998).
Discrete fracture models (DFM) were primarily designed forsaturated ﬂow and transport in the fractured rock. FRACTRAN(Sudicky and McLaren, 1992) is a 2D model that assumes
DARCY's ﬂow and convection–dispersion transport in thematrix, while the cubic law and CDE are used to model ﬂow
and transport in the fractures. Linear sorption and ﬁrst-orderdegradation can also be considered. FRACTRAN was applied
to simulate pesticide transport in clayey till (Jørgensen et al.,1998).
Several speciﬁc laboratory scale research models havebeen designed to analyse and better understand selected
processes. For instance, to understand the nonideal solutetransport, relative contributions of nonequilibrium processes
related to physical heterogeneity versus those related to ratelimited sorption need to be determined (Johnson et al., 2003).
Combined PNE and CNE descriptions were also implemented, in addition to models discussed above (e.g., MACRO andHYDRUS-1D), into steady-state transport models developed bySelim and his collaborators (e.g., Selim and Ma, 1997; Selim et al.,1999, 2002; Zhou and Selim, 2001; Ma and Selim, 2005). The
second-order two-site kinetic sorption model (SOTS) assumes
that sorption rates depend on both solute concentrations in thesolution and the availability of adsorption sites in the matrix.
Different SOTS models (CNE) were coupled with the MIM (PNE)
to form the SOTS-MIM models (Selim et al., 1999). Additionally,
the multireaction model (MRM) considers Freundlich equilibrium sorption, kinetic sorption with different ﬁrst-order rates
for ad- and desorption, and irreversible sorption. The MRM wasalso coupled with the MIM to form the MRM-MIM model (Selimand Ma, 1997).
The Lattice Boltzmann Model (LBM) was extended tosimulate transport of reactive solutes such as pesticides by
Zhang and Ren (2003). The LBM simulates the local-scaletransport process by numerical particle tracking in space andtime according to physically based collision rules that
preserve mass and momentum. Tracking directions can be
upward, downward, and (internally) horizontal to allow for
instantaneous (‘inner’ wall) and kinetic (‘outer’ wall) sorption
reactions (Zhang and Ren, 2003).

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3. Model application
3.1. Scale I (Soil column, lysimeter)
Repacked soil columns with artiﬁcial PF paths were
utilized as well-deﬁned systems to facilitate the analysis of
the complex pesticide transport behavior. Steady-state ﬂow
conditions were often chosen to further simplify the problem(Section 3.1.1). On the other hand, intact (undisturbed) soilcolumns preserve the natural soil structure, and thus better
represent the ﬁeld soil conditions. Lysimeters, because theirdimensions are larger than laboratory soil columns and areoften constructed in ﬁeld settings, best represent the local
ﬁeld conditions (Section 3.1.2). Therefore, lysimeters are usedin European countries as a higher tier for assessing the
groundwater contamination potential of pesticides (Boesten,2007). Pesticide transport in undisturbed soil columns andlysimeters was studied under both steady-state and transient
ﬂow conditions.
Solute transport models were often evaluated using theanalysis of collected breakthrough curves (BTC). A difﬁcultywith this approach is that PNE and CNE may sometimes cause
similar or even identical irregularities in the BTC shape(Nkedi-Kizza et al., 1984; Selim et al., 1999). Classical methodsof distinguishing between PNE and CNE in BTC analysis
involve applying a tracer together with the pesticide, measuring the sorption behavior in separate batch and kinetic
tests, and evaluating degradation in incubation experiments.
Physical transport processes at this scale are most often
described using two-domain 1D approaches, which are
combined with different submodels accounting for chemical
processes, such as equilibrium or kinetic sorption and otherpesticide related reactions. Degradation is described mostly
as a ﬁrst-order rate process (Beulke et al., 2000). An overview
of model applications at scale I is given in Table 2.
3.1.1. Constructed soil columns
Transport experiments with artiﬁcial and repacked soilcolumns were conducted to identify model oversimpliﬁcations
and potential misinterpretations of model results. For example,
the BTCs of a tracer and hydrophobic organic compounds thatleached through a cylindrical macropore ﬁlled with quartz sandembedded in the silty soil matrix were well matched using a
MIM with assumed equilibrium sorption (Rahman et al., 2004).
However, the ﬁtted sorption coefﬁcients (KD) were up to 80%
lower than those for batch experiments, which was explained

Table 2
Applications of models simulating preferential pesticide (or reactive solute) transport at scale I
Scale Model type; name
AC

MIM; n.a.

Model dimension, model description (as applied)a

1D, steady-state, advection in the mobile region, diffusive transfer,linear equilibrium sorption (2×)
AC
SOTS, MRM, SOTS-MIM, MRM-MIM,
1D, steady-state, SOTS-MIM with two-site sorption kinetics (2×),
SPM
or MRM-MIM with multiple sorption reaction rates
ACb
MIM; CXTFIT
1D, steady-state, MIM, transfer: ﬁrst-order or radial diffusion,
spherical or cylindrical geometry, linear equilibrium sorption (2×)
AC
Lattice Boltzmann Model (LBM)
1D, microscopic numerical particle tracking in space and time,equilibrium and kinetic sorption
DPM; S-1D-DUAL
1D, Richards (2×), CDE (2×), ﬁrst-order transfer of water andUCbsolute, linear equilibrium and kinetic sorption (2×), ﬁrst-orderdecay (2×)
UC
MIM with two-site sorption; n.a.
1D, mobile–immobile water ﬂow, MIM with sorption kinetics(2×), ﬁrst-order decay (2×)
UC
DPM; Root Zone Water Quality Model
1D, Poiseulle, convection (macropore), Green-Ampt, convection(RZWQM)(matrix), nonlinear and kinetic adsorption (matrix), decay(matrix), plant uptake (matrix)
UC
SPM, MIM, DPM, DP-MIM; HYDRUS-1D 1D, Richards (1–2×), CDE (2×) or MIM, ﬁrst-order transfer ofwater and solute, nonlinear equilibrium and kinetic sorption(2×), degradation (2×)
UC, L DPM; MACRO
1D, KW, convection (macropore), Richards, CDE (matrix), linear
equilibrium sorption (2×), ﬁrst-order decay (2×); inverse tools:
SUFI, GLUEL
MIM (Solute); WAVE
1D, Richards (1×), MIM, diffusive transfer
L
DFM; FRACTRAN and FRAC3Dvs version 2D (FRACTRAN) and 3D (FRAC3Dvs), steady-state, Darcy, CDE,
4.0
ﬁrst-order decay, linear sorption (matrix), cubic-law ﬂow
between parallel plates, advection (fracture), diffusive transfer
L
DPM; SIMULAT
1D, Richards, CDE (matrix), gravity ﬁlm ﬂow, convection(macropores), sorption (matrix): Freundlich, decay (matrix):ﬁrst-order or metabolic
various 1D nonequilibrium transport models, topics: model
L
Model reviews and comparison: MIM,
performance, inverse parameter estimation, uncertainty, user
DPM; e.g. SWAT, MACRO, MACRO_DB,
subjectivity, transferability of lab measured data (e.g., pesticide
CRACK-NP, SIMULAT, PLM
degradation rates) to the ﬁeld

Reference (Scale)
Rahman et al. (2004)
Selim and Ma (1997), Selim et al. (1999),
Selim et al. (2002)c, Ma and Selim (2005)
Young and Ball (1997, 2000)
Zhang and Ren (2003)
Ray et al. (2004)

Gaston and Locke (2000)
Malone et al. (2001, 2003, 2004a,b,c)

Pot et al. (2005), Köhne et al. (2006a,b),
Javaux et al. (2006)
Beulke et al. (2004) (UC); Roulier
and Jarvis (2003a,b) (UC); Brown et al.
(1999) (L); Dubus and Brown (2002) (L)Vereecken and Dust (1998) (L)Jørgensen et al. (1998)

Stange et al. (1998), Aden and Diekkrüger(2000)
Francaviglia et al. (2000), Vanclooster et al.
(2000a,b), Dubus et al. (2003a,b),
Gottesbüren et al. (2000)

Artiﬁcial or Repacked Soil Column (AC), Undisturbed Column (UC), Lysimeter (L), CDE—convection–dispersion equation, SPM—single porosity model, MIM—mobile
immobile model, DPM—dual permeability model, TPM—triple permeability model, KW—kinematic wave, PF—preferential ﬂow, SOTS—second-order two-site
model, MRM—multireaction model, (2×)—double; refers to the matrix and PF path regions.
aModel applications may differ from full model capabilities; only ﬂow, transport, fate (plant–atmosphere interactions are not mentioned).
b
Synthetic model parameters.
cTransient ﬂow.

J.M. Köhne et al. / Journal of Contaminant Hydrology 104 (2009) 36–60

as a model artifact revealing rate-limited sorption (CNE) during
PNE-PF (Rahman et al., 2004). Moreover, under unfavorable
conditions the solute retardation factor and the ﬁrst-ordertransfer coefﬁcient in MIM may be interdependent (Young andBall, 1997). Temporal moments calculated for reactive solute
BTCs from a macropore column further suggested data
truncation as yet another reason for column-determined
retardation factors being lower than batch-estimated values(Young and Ball, 2000).
The predictive capabilities of various models (MIM, SOTSMIM, MRM, MRM-MIM) were tested to describe atrazine BTCs
for different pore water velocities, aggregate sizes, ﬂow
interruptions, and lengths of repacked clay soil columns. On
average, SOTS-MIM with four parameters and a time-dependent mass transfer coefﬁcient best described atrazine transport
in these clay soil columns (Selim and Ma, 1997). Selim et al.
(1999) further compared the performance of SOTS and SOTSMIM for simulating the transport of metolachlor in clay soilcolumns. To trigger nonequilibrium conditions, the column
experiments were conducted for different aggregate sizes andwith ﬂow interruptions. Sorption model parameters wereindependently derived from kinetic ad- and desorption batch
experiments. Model predictions of metolachlor BTCs improved
after ad-/desorption hysteresis was included in the model.
Again, the best predictions were obtained using SOTS-MIM(Selim et al., 1999). Additionally, these and modiﬁed model
approaches were applied to experimental data obtained oncolumns packed with other soils, in layered soil systems (Zhouand Selim, 2001), to atrazine transport in soil–mulch systems(Ma and Selim, 2005), and to alachlor transport in variablysaturated soil columns subject to transient ﬂow (Selim et al.,2002).
Zhang and Ren (2003) compared simulation results obtainedusing their 1D Lattice Boltzmann Model (LBM) with measured
Br− and atrazine BTCs, and against the two-site sorption model.
There was close agreement between the collected data and bothmodels. However, tremendous effort would be required to
expand the LBM to simulate pesticide transport in real agricultural ﬁeld settings (Zhang and Ren, 2003).
3.1.2. Undisturbed soil columns and lysimeters
A large number of studies involving modelling of pesticidetransport in intact soil cores or lysimeters utilized RZWQM,
HYDRUS-1D, or MACRO. Therefore applications of these models are described ﬁrst. Applications of various other models
are summarized thereafter.
3.1.2.1. Root Zone Water Quality Model (RZWQM).
Although
the RZWQM (Ahuja et al., 2000a,b) was intended mostly forﬁeld scale applications, some of its individual model components were evaluated at the column scale. For example, Malone
et al. (2001) assessed the performance of the macropore component of RZWQM in simulating atrazine and alachlor transport
in no-till soil blocks for dry, intermediate, and wet initialmoisture states. Even though simulated chemical concentrations in percolate were within a factor of 2 of observed values,
their observed and simulated patterns were quite different. It
was suggested that the macropore component of RZWQM
could be improved by considering sorption kinetics in macropores and a dynamic effective macroporosity that would
increase with greater rainfall (Malone et al., 2001).

43

Using RZWQM, Malone et al. (2003) studied the effects oftillage (moldboard plowed and no-till) on the macroporosity ofundisturbed silt loam soil blocks, and on alachlor and atrazine
transport in percolate. Although the number of macropores was
similar in both soil blocks, the initial percolate breakthroughwas faster with higher herbicide concentrations in the no-till
soil than in the moldboard plowed soil. Modelling results
suggested that lower values of the saturated hydraulic conductivity of the soil matrix, which reduced lateral transfer
between macropores and the matrix, caused rapid waterpercolation and breakthrough in the no-till soil. However, thisstatement cannot be generalized. Evidence from many otherstudies points to the importance of large vertically continuous
macropores formed by anecic earthworms under no-till as thereason for this (see discussion in Jarvis, 2007). For the same soil,
Malone et al. (2004b) used RZWQM to analyze the effects ofvariable rainfall intensity on pesticide transport throughmacropores. The variable-intensity storm quadrupled simulatedalachlor losses and doubled atrazine losses when compared tothe constant-intensity storm of the same total depth (Malone
et al., 2004b). The signiﬁcant effect of within-storm variability
on simulated macropore ﬂow was conﬁrmed by Struthers et al.
(2007), based on detailed DPM scenario simulations.
Malone et al. (2004c) simulated the effect of the RZWQM
parameter ‘nmacro’, which deﬁnes the number of percolateproducing macropores, on breakthrough time and concentrations for the ﬁrst storm after application. They used observations of alachlor and atrazine leaching in 39 intact soil blocks
sampled from ﬁve no-till and moldboard plowed ﬁelds.
Increasing nmacro decreased the simulated leached herbicideconcentrations and increased breakthrough time (Malone
et al., 2004c). This somewhat counterintuitive result showsthat proper application of a model requires detailed understanding of the underlying concepts. The explanation here isthat when nmacro is increased, the RZWQM-simulated macropore ﬂow is distributed among a greater number of macropores, which in turn increases the total macropore wall area
available for equilibration with the matrix.
3.1.2.2. MACRO.
Brown et al. (1999) applied MACRO 4.0 tosimulate isoproturon leaching as observed in a lysimeter
experiment. A radioscanning technique and dye tracer wereused to assess the movement of radiolabeled isoproturon in amacroporous heavy clay soil with different tilth systems. At an
8-cm depth, only 0.5% of the soil area showed increased
activity. Thus, preferential movement was already initiatedwithin the top 10 cm. Rhodamine-B red dye then spreadlaterally across the topsoil–subsoil layer boundary, andappeared in cracks and ﬁssures in the subsoil. The dye wasfound only in a small part of the total crack surface area, andthus was adsorbed only along that part of the macroporosity.
The applied MACRO version 4.0 accounts for equilibrium
sorption in matrix and macropores. The simulation was
successful only after calibrating the fraction of sorption sites
in the macropores to a value of 0.01 (which may reﬂect thesorption in only part of the macroporosity or kinetic effects),
and varying the half-width (aggregate size) parameter for ﬁne
and standard tilth (Brown et al., 1999).
Strömqvist and Jarvis (2005) applied MACRO to analyze
leaching of the fungicide iprodione in a golf green. MACRO
matched the measured drainﬂow and iprodione concentrations

44

J.M. Köhne et al. / Journal of Contaminant Hydrology 104 (2009) 36–60

in the soil and drainﬂow reasonably well. Underestimated peakconcentrations were attributed to ﬁnger ﬂow caused by water
repellency (Strömqvist and Jarvis, 2005).
3.1.2.3. HYDRUS-1D.
Pot et al. (2005) used HYDRUS-1D toanalyze the impact of different constant rainfall rates on Br−,
isoproturon, and metribuzin leaching in undisturbed soil cores
collected from the grassed ﬁlter strip. Various approaches,
including DPM, MIM, DP-MIM, and DPM with kinetic sorption,
were compared to simulate the observed BTCs. Observations
showed a strong impact of rainfall intensity on Br− and herbicideleaching. At the highest rainfall intensity, macropore ﬂow
caused rapid chemical breakthrough. The DP-MIM simulation
results best approximated the observed Br− breakthrough,suggesting that three porosity domains contributed to transport
at the high rainfall intensity. At lower rainfall rates, the less rapid
preferential transport could be described well using two-region(MIM or DPM) approaches. Herbicide transport was affected bykinetic sorption at all ﬂow velocities. Signiﬁcantly higher
estimated values for degradation rate parameters, as compared to batch data, were correlated with the extent of nonequilibrium sorption (Pot et al., 2005).
Similarly, Köhne et al. (2006a) applied HYDRUS-1D, further
modiﬁed for transient ﬂow conditions, to simulate transport ofisoproturon, terbuthylazine and Br− observed in an aggregatedloamy soil column subject to several irrigation-redistribution
cycles. First, forward simulations of herbicide transport wereconducted using model parameters derived from the Br−transport experiment, literature data for herbicide degradation,and equilibrium batch or kinetic sorption experiments. The
early isoproturon breakthrough in the soil could be qualitatively
predicted when using the DPM with two-site kinetic sorption.
The subsequent inverse simulation suggested fewer equilibrium sites and slower sorption in the PF paths than wasmeasured for the bulk soil in batch tests. This suggests thatbatch tests with disturbed soil cannot be used to derive accurate
sorption parameters for intact structured soils. Moreover,degradation and sorption rate coefﬁcients were highly correlated, had similar effects on the BTC, and thus could not be
estimated simultaneously when using only the leaching BTC. As
a result, the small ﬁtted isoproturon half-lives of only a few days
may have been a model artifact reﬂecting irreversible sorption(Köhne et al., 2006a). Pot et al. (2005) report several studies that
estimated higher degradation rates for soil columns thanobtained in incubation tests, which could be uncertain unlessresident ﬁnal resident concentrations were additionally measured. Malone et al. (2004d, see below) analyzed the effects of
having equilibrium or kinetic sorption models on the simulateddegradation in RZWQM. The uncertainty in corresponding
parameters in MACRO was analyzed in detail providing
additional insights (see Section 3.2.3), which will be discussed
together with the above ﬁndings.
3.1.2.4. Other models.
A sensitivity analysis was conductedusing S-1D-DUAL to evaluate leaching of strongly sorbed triﬂuralin and weakly sorbed atrazine through a hypothetical 1-m
deep soil proﬁle. The results suggested that displacement ofpesticides is strongly affected by the sorption rate in the PF
paths and the decrease of sorption with depth (Ray et al., 2004).
Gaston and Locke (2000) examined the sorption, degradation, and mobility of the post-emergence herbicide aciﬂuorfen

measured in silty clay loam soil columns for steady-state
unsaturated ﬂow. The early Br− BTC was matched well using theMIM, which suggested PNE. Using the ﬁtted Br− transportparameters and measured sorption isotherms and kinetics, theaciﬂuorfen efﬂuent BTC and the residual 14C in the column were
better predicted with the MIM and assuming two-site kinetics
than with the Freundlich equilibrium sorption. Neither model,
however, captured the long tailing in the BTC. Compared to thebatch and incubation studies, it became apparent that more
aciﬂuorfen was residually bound and its degradation was fasterin the soil columns (Gaston and Locke, 2000).
Vereecken and Dust (1998) applied WAVE (Vanclooster
et al., 1994) with a Monte Carlo method to assess the effect ofsoil heterogeneity on leaching of the herbicide [14C-] methabenzthiazuron (MBT) as observed in ﬁve replicate lysimeters
and in the ﬁeld. Considering the spatial variability of soil
hydraulic properties and of degradation parameters obtainedin laboratory experiments resulted in extremely large
variability of predicted MBT residues in the soil (Vereecken
and Dust, 1998).
SIMULAT (Diekkrüger, 1996) was evaluated in two different lysimeter studies. Stange et al. (1998) carried out lysimeter experiments to study pesticide transport through ﬁve
undisturbed macroporous silt loam monoliths (1 m height,
0.30 m diam.). The herbicides chlorotoluron and MBT wereapplied, together with Br−, to these soil monoliths. While
SIMULAT's soil hydraulic parameters were derived from measurements and sorption parameters taken from the literature,
macropore parameters were ﬁtted manually. SIMULAT reproduced soil water suctions and depth distributions of Br− andboth herbicides well. It was concluded that the macropore
continuity between the plough horizon and the subsoil controls bypass ﬂow (Stange et al., 1998).
Aden and Diekkrüger (2000) evaluated SIMULAT using
three ﬁeld datasets and one lysimeter dataset for a macroporous loam soil at Tor Mancina, Italy. For the lysimeterdataset, soil hydraulic parameters were manually calibrated
and the longitudinal dispersivity was set at 10 cm. Sorption
and degradation parameters were taken from the literatureand lab experiments. Neither tracer and metolachlor concentrations in the leachate, nor their concentrations in the soil
proﬁle, were matched well. It was argued that the results do
not necessarily invalidate SIMULAT for this soil, but that it isoften not possible to distinguish between the model quality
and user experience (Aden and Diekkrüger, 2000).
Jørgensen et al. (1998) applied the 2D discrete fracture
model (DFM) FRACTRAN (Sudicky and McLaren, 1992) tostudy the migration of the herbicides mecoprop and simazine,
together with Cl− and ﬂuorescent dye, in three undisturbed,
water saturated, fractured clayey till columns (0.5 m high,
0.5 m diam.). Transport experiments were conducted at
different ﬂow velocities. Dye was observed in nearly all thevisible, weathered fractures, as well as in the root channels.
The saturated hydraulic conductivity of the columns decreased with increasing depth and fracture spacing. Using
measured fracture spacing and calculated hydraulic aperture
values (27–84 μm), assuming no degradation, and ﬁtting
retardation factors (assumed to be similar in the fractures andmatrix), FRACTRAN predicted the observed ﬂow and pesticidetransport in all columns. The retardation of mecoprop andsimazine decreased with increasing ﬂow rate. At high ﬂow

J.M. Köhne et al. / Journal of Contaminant Hydrology 104 (2009) 36–60

rates, pesticides arrived simultaneously with Cl−, suggesting
PF and rate-limited sorption (Jørgensen et al., 1998).
Furthermore, particle-facilitated displacement of pesticides
in the PF pathways may be quantitatively important in someﬁeld soils, such as Colluvisols with deep penetrating earthworm
burrows (Zehe and Flühler, 2001), but it was hardly consideredin modelling studies (e.g., Villholth et al., 2000).
3.1.3. Sensitivity, calibration, uncertainty at scale I
As was seen above (Pot et al., 2005; Köhne et al., 2006a),
inverse identiﬁcation of pesticide parameters, which moreovercan differ between ﬂow regions, can become a very difﬁcult
problem in modelling preferential pesticide transport. Studying
the parameter sensitivity and uncertainty can suggest reasons
behind the identiﬁcation problems, and indicate the type ofexperimental information additionally needed for parameter
identiﬁcation. For this purpose, MACRO was used (Sohrabi et al.,
2002; Dubus and Brown, 2002; Roulier and Jarvis, 2003a,b).
Roulier and Jarvis (2003a) assessed the performance of the
optimization technique SUFI (Abbaspour et al., 1997) for
estimating values and the associated uncertainty of 9 key
MACRO parameters controlling pesticide transport. Undisturbed soil columns (20 cm height, 20 cm diam.) were sampled from the hilltop, slope, and hollow positions of a ﬁeld in aloamy moraine catchment in South Sweden. Leachingexperiments with Cl− and the mobile herbicide MCPA were
performed under transient ﬂow conditions during a 4-month
period. Rapid MCPA leaching through the ﬁne-textured
hilltop soil showed evidence of PF, while MCPA leaching
was minimal in the organic-rich hollow soil. The MACRO-SUFI
results closely followed the observed patterns of percolation
and breakthrough of Cl− and MCPA. Interestingly, MACRO
parameter estimates varied signiﬁcantly between different
landscape positions and were clearly related to the soil
texture and organic matter content. Such relations showed
potential for use in pedotransfer functions (Jarvis et al., 2007).
Sorption and degradation parameters could be identiﬁed. The
short MCPA degradation half-lives of only 1–2 days obtainedin the latter study were subsequently conﬁrmed in incubation
studies (Lindahl et al., 2005). However, signiﬁcant uncertainties remained for some key parameters thought to beimportant for solute transfer and preferential leaching, including the saturated matrix conductivity and the masstransfer coefﬁcient. To reduce these uncertainties, threechanges were suggested. These were (i) to increase thenumber of strata during the last few optimization iterations tomaintain the sensitivity of the objective function to the
optimized parameters, (ii) to use posterior uncertainty domains to deﬁne initial values in a subsequent gradient-type
optimization procedure, and (iii) to improve experimental
designs (Roulier and Jarvis, 2003a).
In a follow-up study using MACRO-SUFI, Roulier and Jarvis(2003b) used numerically generated data representing similar experiments to quantify, among others, the effect ofexperimental errors on inverse model simulations. Althoughexperimental errors increased the uncertainty of estimatedcoefﬁcients, optimized parameters were still properly identiﬁed. However, MACRO results were reliable only when strong
macropore ﬂow was present and when both resident and ﬂux
concentrations were utilized in the inverse procedure. Onceagain, the mass transfer coefﬁcient was insensitive to model

45

predictions and highly uncertain. Adsorption and degradation
parameters could not be estimated accurately due to theirlarge mutual correlation (Roulier and Jarvis, 2003b). Similar
problems were also experienced (e.g., Pot et al., 2005; Köhne
et al., 2006a) when using the DPM (Gerke and van Genuchten,1993) in HYDRUS-1D.
Moreover, the MACRO-SUFI performance depended onwhether water and solute parameters were estimated simultaneously or sequentially. The best results were achieved for asimultaneous parameter estimation procedure when the goal
function included water percolation, the leaching rate, andresident concentrations for both tracer and pesticide (Roulier
and Jarvis, 2003b). Using HYDRUS-1D, similar results were
found for tracer transport (Köhne et al., 2006b).
It can be considered as a modelling progress that inverse
estimation pitfalls and uncertainty issues could be identiﬁed,
since this can help in the development of improved experimental designs. Inverse DPM applications require bothresident and ﬂux pesticide concentrations, as well as waterﬂow and tracer data, to describe degradation and sorption in
structured soils. But even then, inverse identiﬁcation of theseprocesses for individual ﬂow regions is often subject to large
uncertainty. In the case of tracer transport, computer
tomography and dye tracing could provide the additionallyrequired information (e.g., Vanderborght et al., 2002—seecompanion paper (this issue)), although at a substantial
experimental effort. It could be tested if independent sorption
and degradation measurements in macropores, or withmaterial from macropore walls, could provide independent
estimates of the corresponding DPM parameters.
Experimental studies of the sorption properties of crack
walls, earthworm burrows, and root channels were completed by e.g. Bundt et al. (2001a,b,c), Turner and Steele(1988), Stehouwer et al. (1994), and Mallawatantri et al.
(1996). Bundt et al. (2001a,b,c) found for different soils thatthe cation exchange capacity, the base saturation, and theSOM content were all signiﬁcantly larger in the dye stained PF
paths than in the soil matrix, while sorption was enhanced for
Cu but not for Sr (Bundt et al., 2001a,b,c). Dye stained PF paths
had higher microbial biomass carbon contents, lower organiccarbon, and lower afﬁnity for dye (erioglaucine) sorption thanthe unstained soil (Gaston and Locke, 2002). Total and watersoluble organic carbon contents, alkaline phosphatase activity, and atrazine ad- and desorption constants were found tobe elevated in earthworm burrow linings compared with bulk
soil, and pH and clay content of lining material were lessvariable than of the bulk soil (Stehouwer et al., 1993). The
microbial degradation of isoproturon in the subsoil earthworm burrows was characterized by a half-life of 15 days andwas almost as fast as in the topsoil. In the surrounding soil
matrix, no degradation at all was observed within 30 days
(Bolduan and Zehe, 2006). These results conﬁrmed earlier
ﬁndings of faster degradation of pesticides in macropore walls
compared with the respective bulk soil or matrix materials(Stehouwer et al., 1994 (earthworm burrows); Pivetz andSteenhuis, 1995 (artiﬁcial macropores); Mallawatantri et al.,1996 (Bt macropore linings); Vinther et al., 2001 (red-ox
structures)). Results about sorption in PF paths are lessconclusive (see also discussion in Jarvis, 2007). When organiccarbon content was higher, sorption was elevated. Macropores constitute a small volume fraction of structured soils.

46

J.M. Köhne et al. / Journal of Contaminant Hydrology 104 (2009) 36–60

However, when pesticide leaching is entirely constrained tomacropores, then their local sorption and degradation
properties control the leached fractions, together with lateraltransfer and sorption in the matrix. As a complication, higher
sorption and degradation capacities of macropore linings may
not be fully exploited during PF conditions, due to limited
contact time (rate limitation) or area (e.g., rivulet ﬂow). The
above experimental and modelling studies so far show that PF
paths have distinct characteristics for retaining adsorbed anddegradable pesticides, which cannot be characterized by thestandard batch and incubation tests conducted with the bulk
soil material.
Some of the studies below show that parameter sensitivity
depends on context, such as length and time scales, boundary
conditions, and soil type, which together control the transport
pattern.
Stenemo and Jarvis (2007) conducted a Monte Carloanalysis of the uncertainty in pedotransfer functions used in
MACRO simulations of pesticide leaching into groundwater.
The saturated matrix conductivity and the mass transfer
coefﬁcient were the most sensitive parameters, together withthe van Genuchten's (1980) parameters n and α. This
divergence from the ﬁndings of Roulier and Jarvis (2003a,b)shows that the mass transfer coefﬁcient is more sensitive forlong-term simulations and deeper soil proﬁles, than it is for
short term experiments carried out on short columns.
Pedotransfer functions need to be improved to reduce uncertainties in model predictions (Stenemo and Jarvis, 2007).
For example, for describing BTCs using MACRO, over half ofthe observed variation in the mass transfer coefﬁcient could
be explained by two basic soil properties related to soil
texture and organic matter content (Jarvis et al., 2007).
Dubus and Brown (2002) studied the sensitivity anduncertainty of MACRO simulation results at the lysimeterscale as related to the variation of 43 (i.e., almost all) MACRO
parameters in an exhaustive sampling. Two different
approaches were compared for the sensitivity analysis: ﬁrst,
varying one parameter at a time (a “one-at-a-time” procedure), and second, a simultaneous variation of multiple
parameters using the Monte Carlo analysis. Four base-casescenarios were generated by simulating the leaching of two
hypothetical pesticides in a sandy loam and a structured clay
loam soil during a four-year period. The accumulated waterpercolation and the accumulated pesticide mass leached to a
1-m depth were the model outputs used for the assessment ofmodel sensitivity. Model sensitivities depended on the soil–
pesticide combination. For the sandy loam, the Freundlich
distribution coefﬁcient, Freundlich exponent and degradation
rate constant were most sensitive, and sorption and degradation were the main processes limiting pesticide leaching. For
the clay loam, soil structure related parameters were evenmore sensitive, and controlled pesticide leaching. Due to PF,
simulated pesticide losses were higher in the clay loam thanin the sandy loam. The latter ﬁnding was corroborated bysimilar experimental observations in lysimeter studies conducted with these two soils (Beulke et al., 2001b). Four conclusions can be drawn from this study: (i) the choice ofsensitivity investigation method may affect sensitivity results(the Monte Carlo analysis and the one-at-a-time procedure
produced similar results for only two out of the four
scenarios), (ii) the ranking of model parameter sensitivities

is not universal, but depends to some extent on the soil andthe substance, (iii) PF may cause higher pesticide losses fromﬁne textured than coarser textured soils, and (iv) different
processes control pesticide leaching in different soils. For thesubsequent site-speciﬁc sensitivity analysis, the selection of
the 10 to 15 most sensitive parameters from the results of thisstudy was recommended (Dubus and Brown, 2002).
Uncertainty analyses can additionally indicate the effect of
spatial variability on pesticide breakthrough. For example,
consideration of uncertainty in MACRO input parameters
resulted in a 20% higher simulated mean ﬂow rate, along with
two-to-three times larger atrazine leaching rates and concentrations, than when using mean input parameters (Sohrabi et al., 2002). While the uncertainty in predicted ﬂow rates
was relatively small, the uncertainty associated with theatrazine concentrations was an order of magnitude larger
than that of the corresponding input parameters. Macropore
ﬂow related parameters contributed the most to the variability of atrazine transport results (Sohrabi et al., 2002).
Beulke et al. (2004) evaluated a probabilistic application of
MACRO 4.1 to simulate leaching of isoproturon as observed
during one winter in large columns (50 cm length, 25 cm
diam.) containing sandy loam and moderately structured clay
loam. Soil properties, and isoproturon sorption and degradation, were independently determined from soil samples. The
uncertainty in model output distributions was compared withthe experimental variability in seven replicates. For the sandy
loam soil, varying only pesticide degradation and sorption
properties allowed for matching of the observed variation of
cumulative isoproturon leaching. By contrast, for the clay
loam, the variability of selected soil hydraulic properties had
to be considered additionally (Beulke et al., 2004). The latter
result corroborated the ﬁndings of Dubus and Brown (2002)
as described above.
Dubus et al. (2002b,a) discussed sources of uncertainty inpesticide fate modelling, such as (i) uncertainty in the data due
to measurement errors or the spatial and temporal variabilityof environmental variables, (ii) uncertainty in the derivation ofmodel parameters, such as the use of a wrong model (e.g., theapplication of ﬁrst-order decay kinetics to data which do notfollow this pattern), procedures to derive input parameters
using limited information (e.g., pedotransfer functions),
selection of a representative variable (e.g., arithmetic or geometric mean), inverse parameter estimation procedures andparameter non-uniqueness, and (iii) uncertainties related to amodel user, including modeller's subjectivity in the choice ofthe pesticide fate model, the use of model concepts (e.g., KOC
is only established for non-ionic compounds, but often used
for all molecules), or the parameterization of the model. Even
techniques accounting for uncertainties are themselves
subject to uncertainty. For instance, the overall results fromMonte Carlo based probabilistic assessments may be inﬂuenced by the selection of input parameters, the probabilistic
functions attributed to input parameters, correlations
between parameters, and the sampling scheme used. It wassuggested that those signiﬁcant sources of uncertainty thatare not currently considered, such as the model error andmodeller's subjectivity, be integrated into probabilistic modelling exercises (Dubus et al., 2002b, 2003a). However, even if
these suggestions could be realized, they seem likely to resultin huge prediction uncertainty of little practical value.

J.M. Köhne et al. / Journal of Contaminant Hydrology 104 (2009) 36–60

Dubus et al. (2002a) provided a review on calibration ofpesticide leaching models. The paper discusses data requirements, data quality assessment, the selection of a model andparameters for calibration, inverse techniques, how tocompare simulated and measured data (e.g., visual andstatistical assessments), calibration procedures, the assessment of calibrated parameter values, model validation, anduncertainty assessment (e.g., response surface analysis).
Guidelines for calibration within the scope of pesticide
registration were also proposed (Table 2 in Dubus et al.,2002a).
3.1.4. Model comparisons at column and lysimeter scales
Because of their increasing use in pesticide registration
procedures worldwide, it is important to compare existingpesticide leaching models and to assess their ability to simulate pesticide transport, especially when subject to preferential ﬂow processes (Scorza Junior and Boesten, 2005).
Several studies compared different pesticide leaching models
at scale I (e.g., Francaviglia et al., 2000; Gottesbüren et al.,
2000; Vanclooster et al., 2000b).
Francaviglia et al. (2000) evaluated the ﬁve pesticideleaching models GLEAMS (Leonard et al., 1987), PELMO (Klein,
1995), SIMULAT (Diekkrüger, 1996), the Pesticide Root Zone
Model PRZM-2 (Mullins et al., 1993), and VARLEACH (Walker
and Hollis, 1994) against data collected on four lysimeters
installed in a calcareous clayey loam soil in Rome, Italy. The
soil water drainage and leaching of Br− and metolachlor weremonitored over a three-year period. None of the models
compared could adequately describe monitored water, tracer
and pesticide dynamics. Only SIMULAT, the single model thatconsidered PF, reproduced the trace amounts of metolachlor
observed during the ﬁrst 2 years in the lysimeter leachates.
However, the blind validation test for the third year failed
even for SIMULAT, which predicted metolachlor leachingwhen none was observed (Francaviglia et al., 2000).
Herbst et al. (2005a) compared four other models,
MARTHE (Thiéry et al., 2004, Thiéry, 1995), TRACE (Vereecken
et al., 1994) coupled with 3dLEWASTE (Yeh et al., 1992),
ANSWERS (Bouraoui et al., 1997), and MACRO 5.0 (Jarvis et al.,2003, Larsbo and Jarvis, 2003, 2005), by simulating verticaldrainage and methabenzthiazuron (MBT) transport in a
cropped free-draining lysimeter over the course of 627 days.
After calibration, the models based on the Richards equation(MARTHE, TRACE, MACRO) predicted water ﬂow better thanthe capacity model ANSWERS. Only the PF model MACRO
could be calibrated to describe the MBT concentrations in theoutﬂow. However, the difﬁculty in estimating parameterscharacterizing macropore transport remains an obstacle tocommon model applications (Herbst et al., 2005a).
Dubus et al. (2003b) conducted sensitivity analyses forthree chromatographic models (PELMO, PRZM, PESTLA) and
one PF model (MACRO) by simulating the leaching of two
hypothetical pesticides in 1-m deep sandy loam and clay loam
soils, respectively. The results depended greatly on the modeltype. In the chromatographic models, water ﬂow predictions
were controlled by atmospheric boundary conditions and thesimulated pesticide loss was sensitive to sorption and degradation parameters. In MACRO, soil hydrological and PF related
properties controlled pesticide leaching in the clay loam soil
(Dubus et al., 2003b).

47

3.2. Scale II (plot, ﬁeld)
3.2.1. Model concepts
While plots are usually considered homogeneous in model
applications (apart from layering), cultivated soils at the ﬁeld
scale are often highly heterogeneous in terms of their physicaland chemical properties. This heterogeneity results inspatially variable transport velocities and conditions forpesticide degradation and sorption. A common modelling
concept describes the average ﬁeld behavior deterministicallywithout considering the internal spatial heterogeneity (e.g.,‘integrated leaching response of a tile drained ﬁeld’). This
deterministic approach uses input data derived from laboratory measurements of pesticide and soil properties, in
combination with weather, soil and crop information collected in the experimental ﬁeld. However, since for nonlinear
processes effective model parameters do not correspond withsimple averages of measurements in the heterogeneous ﬁeld
(e.g., Vereecken et al., 2007; Vanderborght et al., 2006;
Sohrabi et al., 2002; Lindahl et al., 2005), this approach
theoretically requires model calibration. By contrast, aprobabilistic approach termed here the ‘nonlocalized stochastic’ approach (e.g. Monte Carlo), does not use a single
parameter set as an input for the deterministic model, butuses multiple parameter sets representing different realizations of random variables with corresponding probability
distributions. This allows for uncertainty estimation in theensemble of model simulation results, or for indirect characterization of spatial variability. On the other hand, stochastic distributed approaches describe the spatial variability in
2D or 3D transport models using geostatistical or spatial
correlation functions. Table 3 provides an overview of model
applications at scale II.
3.2.2. Deterministic model applications
3.2.2.1. Deterministic applications of the RZWQM. The
RZWQM (Ahuja et al., 2000a,b) is frequently applied for ﬁeld
scale modelling of pesticide leaching. Malone et al. (2004a)
reviewed many RZWQM applications for pesticide transportmodelling. The review revealed the importance of (i) an
accurate parameterization of low permeability soil horizons;(ii) considering pesticide sorption kinetics, and (iii) calibrating
the pesticide half-life. If key input parameters were calibrated,RZWQM was found to match ﬁeld observations of evapotranspiration, water percolation and runoff, soil water content, plant
growth and pesticide fate (Malone et al., 2004a).
Ghidey et al. (1999) evaluated the performance of RZWQM
3.2 in predicting atrazine and alachlor loss to surface runoff in
claypan soils with corn and soybean under conventional andno-till systems at a Management System Evaluation Area
(MSEA) in Missouri, USA. Data from a 35-ha ﬁeld, two 0.35-haﬁeld plots and 40 smaller runoff plots (3 m by 27 m) were
included in the study. Using the macropore option of RZWQM
strongly improved the prediction of pesticide losses to seepage
from lysimeters in the ﬁeld plots. The measured soil watercontents at several depths and annual runoff were adequately
simulated, but runoff events in the drying soil with cracks were
overestimated. For events where surface runoff was described
well, atrazine and alachlor concentrations in surface runoff
could be also simulated. It was suggested that RZWQM should

48

J.M. Köhne et al. / Journal of Contaminant Hydrology 104 (2009) 36–60

Table 3
Applications of models simulating preferential pesticide (or reactive solute) transport at scale II
Scale

Model type; name

Model dimension, Model description (as applied)

Authors (Scale)S

DP-MIM; Pesticide Leaching Model vs.3
(PLM)

Nicholls et al. (2000) (S)

L, P
Pa,b

DPM; Agricultural Drainage And Pesticide
Transport (ADAPT)dMIM, DPM; n.a.

P

DFM; FRACTRAN and FRAC3Dvs version 4.0

P

SPM, DPM; HYDRUS-1D

P

MIM, DPM, SPM; HYDRUS-2DPa,b

DPM with database; MACRO_DB

F (S,P)

DPM; MACRO

1D capacitance model, 5-cm layers, daily water balance,
linear sorption (daily increasing in top 5 cm), ﬁrst-orderdegradation = f(T,θ),
1D, capacity-type macropore ﬂow, convection, ﬁrst-orderdecay, linear sorption, drainage
1D, steady-state, MIM-CDE or 2 ×CDE with spatial randomvariables Ks, KD, degradation rate, diffusion transfer rate
2D (FRACTRAN) and 3D (FRAC3Dvs), steady-state ﬂow:
Darcy, CDE (matrix), cubic-law ﬂow between parallel plates,
advection (PF region), diffusive transfer, linear sorption (2×),
ﬁrst-order decay
1D, Richards (1–2×), CDE (1–2×), ﬁrst-order transfer of waterand solute, Freundlich sorption, degradation (2× in liquid andsolid phase), root water uptake
2D, Richards (1–2×), CDE (1–2×), ﬁrst-order transfer of waterand solute, linear sorption (1–2×), ﬁrst-order degradation (1–2×,
liquid and solid phase), root water uptake, crop effects, drainage
1D, MACRO with database for soils, pesticides, climates andcrops; parameter determination routines
1D, KW and convection (macropore), Richards and CDE(matrix), linear or Freundlich sorption (2×), ﬁrst-order decay;
Inverse tools: SUFI, GLUEFDPM-DFM; ‘MACRO-FRAC3Dvs’

F

DPM; Root Zone Water Quality Model(RZWQM)F

SPM with bimodal ﬂow scenario; LEACHMF

DPM; SIMULATF

MIM, 1D-distributed; MICMACF

SPM; Pesticide Root Zone Model 3.1(PRZM)
MIM; CRACK-NP

F

F (L, P) Model comparison or application
reviews: MIM, DPM; e.g. SWAT,
MACRO, MACRO_DB, CRACK-NP,
SIMULAT, PLMd

1D—distributed MACRO linked with 3D discrete fracture
model FRAC3Dvs
1D, Poiseulle, Convection (macropore), Green-Ampt,
convection, decay, nonlinear and kinetic adsorption, root
uptake (matrix). Hooghoudt drainage, chemical washofffrom soil and plant foliage to overland ﬂow.
1D, two independent regions (Richards, CDE) with decay,
linear sorption.
1D, Richards, CDE with sorption, decay (matrix); KW
gravity ﬁlm ﬂow and convection (macropores)
1D distributed conceptual model of multiple soil columns
with cylindrical macropore located in the center. Monte
Carlo simulation.
1D, no PF, Monte Carlo stochastic analysis
1D, Philip inﬁltration, Poiseulle ﬂow and convection(cracks), Fickian diffusive transfer (into matrix),
Freundlich sorption, ﬁrst-order decay = f(T,θ)
Various 1D nonequilibrium transport models, questionsof inverse parameter estimation and uncertainty

Kalita et al. (1998)
Huang and Hu (2001), Hu and Huang (2002)
Jørgensen et al. (2002, 2004)

Kodešová et al. (2005)

Gärdenäs et al. (2006)

Jarvis et al. (1997)
Jarvis et al. (2000) (S); Villholth et al.
(2000) (P); Larsson and Jarvis (1999, 2000)(P)a; Brown et al. (2004), Larsbo and Jarvis(2005), Roulier et al. (2006)
Stenemo et al. (2005)
Kumar et al. (1998), Ghidey et al. (1999),
Jaynes and Miller (1999), Wauchope et al.
(2004) (reviewb); Malone et al. (2004c,d),
Fox et al. (2004, 2006)
Elliott et al. (1998)
Aden and Diekkrüger (2000) (F)Bruggeman et al. (1999)

Carbone et al. (2002)
Armstrong et al. (2000b)

Gottesbüren et al. (2000), Armstrong
et al. (2000a), Vanclooster and Boesten(2000) (P); Vanclooster et al. (2000a,b)(L-F); Boesten (2000) (review); Beulke et al.
(2001a,b), Dubus et al. (2002a,b) (review),(2003a) (P-F); Garratt et al. (2003), Herbst
et al. (2005a,b), Alavi et al. (2007) (P)

Soil Proﬁle (S), Plot (P), Hillslope (H), Field (F); ET—evapotranspiration, T—temperature, PF—preferential ﬂow, KD—Sorption distribution coefﬁcient, Ks—saturated
hydraulic conductivity, θ—soil moisture content, (Yes) means: is considered, but in a simpliﬁed wayaSynthetic model parameters.
b
No data.
c
Combines GLEAMS and DRAINMOD with algorithm for macropore ﬂow.
d
The following models without PF component are also included: MARTHE, TRACE, ANSWERS, PRZM, LEACHP, WAVE, GLEAMS and PELMO.

consider variable soil cracking as a function of the soil moisture(Ghidey et al., 1999).
Kumar et al. (1998) evaluated the performance of the PFcomponent of RZWQM 3.25 in simulating the effects of theﬁeld measured macroporosity on atrazine concentrations insubsurface drain ﬂow from corn ﬁelds with no-till andmoldboard plow systems. Atrazine concentrations measured
in 1990 were used for calibration, and those from 1991 and1992 were used for model validation. Compared to the
RZWQM simulation without macropore ﬂow, using the PFcomponent gave similar results for tile ﬂows, but a strongly

improved agreement between simulated and observed
atrazine concentrations in both calibration and validation
time periods. Simulated atrazine losses were sensitive tolateral mass transfer from the macropores to the matrix, thesaturated hydraulic conductivity of the surface layer, and themacroporosity (Kumar et al., 1998).
Malone et al. (2004d) used RZWQM to investigate theeffects of hydrology, sorption, degradation and management
options (tilled vs. no-till) on observed metribuzin concentrations in percolate, runoff and in the silt loam soil proﬁle of twobare soil ﬁeld plots (22 m × 7 m; 10% slope). The results

J.M. Köhne et al. / Journal of Contaminant Hydrology 104 (2009) 36–60

indicated that (i) the accuracy of the simulated metribuzin
degradation rate in the matrix depended on the sorption
model used (equilibrium, equilibrium/kinetic, or equilibrium/bound), (ii) the simulated metribuzin degradation rate wasapparently slowing down with time due to sorption, and (iii)equilibrium sorption in the matrix was an adequate model
despite PF at this site.
Fox et al. (2004) applied RZWQM to study the effects ofmacropores and subsurface drainage on transport of Br− andthe herbicide isoxaﬂutole at a 30.4 ha Indiana corn ﬁeld. Aftermodifying RZWQM to include an ‘express fraction’ of 2% of themacropores that are in direct hydraulic connection withdrains, the model captured the ﬁrst measured peak inbromide and isoxaﬂutole concentrations.
3.2.2.2. Deterministic applications of MACRO, a) tracer calibration—pesticide prediction. Larsson and Jarvis (1999) evaluated
MACRO 4.1 for simulating the transport behavior of weakly
sorbed herbicide bentazone in a silty clay soil plot (0.4 ha) at theartiﬁcially drained ﬁeld site Lanna (SW Sweden). After no-till
management for a decade, the soil had numerous cracks andbiopores. On-site hourly precipitation measurements were usedas the model boundary conditions. Flow and tracer transportparameters in MACRO were manually calibrated using observed
water contents, drain water ﬂow, and Br− concentrations indrain discharge and in the soil proﬁle. The calibration provided
acceptable results. Bentazone transport was then predictedusing KOC to derive KD values, assigning a 2% fraction of thesorption sites to the macropore region, and a laboratory
measured half life of 12.5 d in the topsoil and essentially inﬁnite
in the subsoil. MACRO could predict the bentazone distribution
in the soil proﬁle (model efﬁciency 0.87), while 66% and 89% of
simulated concentrations in tile drainage water were within afactor 2 and 5 of the measured values, respectively. Concentration ﬂuctuations in tile outﬂow were, however, poorly captured.
Macropore ﬂow reduced bentazone leaching in this clay soil,
because bentazone residing in the matrix was ‘protected’against bypass ﬂow in macropores (Larsson and Jarvis, 1999).
Brown et al. (2004) used MACRO 4.1 to model the
exposure of agricultural drainage ditches to the herbicide
sulfosulfuron in a 7.7-ha ﬁeld with clay soil in central England.
Sulfosulfuron concentrations were monitored in a single
drain outlet, and in the receiving ditch 1 km downstream. The
total loading to drains was 0.5% of applied pesticide, 99% of
which was lost in the ﬁrst 12.5 mm of drain ﬂow. In thereceiving ditch, most concentrations were diluted to below
the detection limit. In the application of MACRO, mostparameters were derived from measured data mostly using
empirical relations given in MACRO_DB (Jarvis et al., 1997).
The Freundlich sorption coefﬁcient was derived from a linear
relation to pH, independent of the soil organic matter content.
Water tension at the boundary between the matrix andmacropores was set to values between −2.5 and −3.5 kPa,
based on previous applications of MACRO to similar soils(nowadays this value is usually set to −1 kPa, e.g., Jarvis et al.,2007). The amount of pesticide spray intercepted by the
canopy crop was set to 79% to match the measured initial soil
residues. The MACRO simulation overestimated macropore
ﬂow. The peak sulfosulfuron concentrations in drainﬂow andthe total loading to drains were overestimated by factors of
2.4 and 5, respectively (Brown et al., 2004).

49

Scorza Júnior et al. (2007) tested MACRO 5.1 based on acomprehensive data set obtained at the tile-drained Andelstﬁeld site (the Netherlands) with cracking clay soil. In line with
EU (FOCUS) procedures, the model was ﬁrst calibrated against
the measured moisture proﬁles and bromide concentrations
in soil and in drain water. The dynamics of the bentazone
breakthrough in drain water could then be predicted rather
well based on laboratory measurements of sorption anddegradation. Predictions of both bentazone and imidacloprid
concentrations were within a factor of 3 at worst. This appears
to be one of the most accurate predictions, compared to otherstudies using MACRO (see above), or different models, where
uncalibrated prediction often resulted in complete failure (seebelow, Section 3.2.5). What made this difference, except for
using a fairly comprehensive data-set? While further reasonsare discussed in section 3.2.5, one aspect of this study was tocombine inverse estimation with expert knowledge: the masstransfer coefﬁcient and the kinematic exponent were manually ﬁne-tuned to match those parts of the bromide BTC(initial peak timing and height) thought to be of particularsigniﬁcance for pesticide leaching (Scorza Júnior et al. 2007).
3.2.2.3. Deterministic applications of MACRO, b) inverse analysis
and process studies. Larsbo and Jarvis (2005) analyzed datarequirements for an inverse identiﬁcation of input parametersfor MACRO 5.0 (Larsbo and Jarvis, 2003). MACRO was applied tomodel concentrations of Br− and bentazone measured during
one year in a macroporous tile-drained ﬁeld clay soil (95 by
42 m) at the Lanna site, SW Sweden. Two different calibration
and uncertainty estimation methodologies were used: (i)sequential uncertainty ﬁtting (SUFI) (Abbaspour et al., 1997)
and (ii) generalized likelihood uncertainty estimation (GLUE)(Beven and Binley, 1992). Six parameters controlling macropore
ﬂow, sorption and degradation were determined using inverse
modelling and measured Br− and bentazone concentrations intile drainage water and in the soil. GLUE required both resident
and ﬂux concentrations to obtain highly conditioned andunbiased parameters characterizing pesticide transport. Tracer
observations signiﬁcantly improved the conditioning of macropore ﬂow parameters. Overall, SUFI was assessed to be an
efﬁcient parameter estimation tool, whereas GLUE seemed
better suited for estimation of uncertainty in predictions(Larsbo and Jarvis, 2005).
Using the simulation for the Lanna site as a base case,
Larsson and Jarvis (2000) used MACRO to investigate the effectsof compound properties and macropore ﬂow on pesticideleaching for 60 hypothetical compounds with widely differing
sorption and degradation characteristics. Sorption and degradation reactions had a similar effect on the vulnerability ofpesticide to macropore ﬂow. Macropore ﬂow decreased
leaching for mobile and persistent pesticides, while the effects
of macropore ﬂow were of little environmental signiﬁcance forstrongly sorbing compounds. Thus, the most pronounced effectof macropore ﬂow was predicted for moderately mobile
compounds (50 b Koc b 5000 cm3g− 1, half-lifeb 10 days). Reductions in the application dose were suggested to reduce theleaching of such compounds in structured soils. A simple
power-law function was proposed to forecast the effect ofmacropore ﬂow on leaching (Larsson and Jarvis, 2000).
Villholth et al. (2000) modiﬁed MACRO to consider
particle transport, and to simulate particle-facilitated50

J.M. Köhne et al. / Journal of Contaminant Hydrology 104 (2009) 36–60

pesticide leaching. The modiﬁed MACRO was calibrated todescribe prochloraz transport in a 5-m by 5-m subsurfacedrained ﬁeld plot in Denmark. The plot, which had amacroporous sandy loam soil, was irrigated 3 times during
an 8-day period. The drainage ﬂow rate, particulate matter
content, and prochloraz concentrations (dissolved andattached to the particulate matter) in the drainage water
were monitored. About 0.2% of the applied pesticide mass, 6%of which was associated with the particulate phase, appearedin the drain outﬂow. MACRO could be calibrated to simulatethese observations. Pesticide degradation was less importantduring this short study time, while sorption was critical. Any
discrepancies were attributed to the model's inability to
account for kinetic sorption mechanisms to macropore linings
and particles (Villholth et al., 2000).
3.2.2.4. Other models. Nicholls et al. (2000) used thePesticide Leaching Model (PLM 3), as described by Hall
(1994) and Nicholls and Hall (1995), to simulate the
distributions of Br−, bentazone and ethoprophos in a sandy
soil proﬁle (Vredepeel, Netherlands) and in a cracking clay soil(the Brimstone farm, UK). After an extensive calibration PLM
could approximate the drain ﬂow and associated pesticideconcentrations at Brimstone, and pesticide distribution
proﬁles at Vredepeel. However, due to the high sensitivityof input parameters that govern macropore ﬂow, thepredictive ability of PLM appeared limited (Nicholls et al.,2000).
Jørgensen et al. (2002) used the 3D numerical discrete
fracture model FRAC3Dvs 4.0 (Therrien and Sudicky, 1996) to
describe vertical pesticide transport through fractured clay-richwater saturated till. The concentrations of Br−, the mobile
compounds mecoprop and metsulfuron and the strongly
sorbed prochloraz were monitored in wells of two ﬁeld plotsof 40 m2 each. Most (96 to 98%) of the vertical water ﬂow took
place along root channels within the fractures, while theremaining ﬂow occurred in the clay matrix. Using observed
effective fracture spacings and mean fracture apertures as input
parameters, FRAC3Dvs approximated measured pesticide concentrations reasonably well (Jørgensen et al., 2002). Jørgensen
et al. (2004) further applied FRAC3Dvs to predict the downward
migration of mecoprop through a fractured, saturated, clayey
till aquitard. The model was ﬁrst calibrated against laboratory
experiments conducted with undisturbed fractured till columns (0.5 m diam., 0.5 m high) and ﬁeld data of hydraulicheads, fracture spacings, and water budget. Subsequent model
predictions suggested that the mecoprop mass ﬂux into theunderlying aquifer is largely controlled by pesticide degradation and ground water recharge (Jørgensen et al., 2004).
Kodešová et al. (2005) used HYDRUS-1D with DPM(Šimůnek et al., 1998, 2003) to simulate water movement andchlorotoluron concentrations in the soil proﬁle beneath a 4-m2
plot. The soil hydraulic properties, sorption distribution coefﬁcient and degradation rate were determined in the laboratory.
Simulated chlorotoluron concentrations were overestimatedand tended to be higher when using the Freundlich instead of
the Langmuir sorption isotherm, even if both isotherms could
be ﬁtted equally well to the measured batch sorption data.
Assuming degradation in both solid and liquid phases improved
the simulation of resident chlorotoluron concentrations (Kodešová et al., 2005). Simulations using a chromatographic single

porosity model (SPM) in HYDRUS-1D failed to calculate thechlorotoluron distribution in the soil proﬁle. The DPM signiﬁcantly improved the correspondence between calculated
and observed herbicide concentrations (Kodešová et al., 2005).
Alavi et al. (2007) compared the performance of S1D DUAL
(Vogel et al., 2000; Ray et al., 2004) and MACRO 4.3 (Jarvis,
2001) using resident concentration proﬁles measured forbromide and 5 pesticides in 3 plots with tilled, ﬁne-textured,aggregated tropical soils. Both uncalibrated models gavesatisfactory order-of-magnitude predictions of concentration
depth proﬁles. However, there was no evidence of preferential
transport (Alavi et al., 2007), which might have been partly
attributed to the lack of water and solute ﬂux information.
Elliott et al. (1998) modiﬁed LEACHM to simulate preferential displacement of the moderately mobile herbicide
clopyralid in an irrigated 4.6-ha tile-drained ﬁeld site withbarley crops under conventional tillage in Saskatchewan,
Canada. An estimated 1.6% of the applied clopyralid was lostin the tile drain efﬂuent. LEACHM was used as a pseudo-DPM
with independent matrix and preferential ﬂow domains.
Surprisingly good agreement between observed and simulated
clopyralid concentrations was obtained in both the tile efﬂuent
and the soil when 40% of the water was assumed to movethrough the PF paths characterized using hydraulic conductivities two orders of magnitude larger than for the bulk soil, andthe remaining 60% of the water moved through the matrix
according to measured hydraulic characteristics. The good
performance of the model with independent transport regions
led to the hypothesis that lateral transfer may have no net effecton vertical solute ﬂuxes at the ﬁeld scale (Elliott et al., 1998).
Armstrong et al. (2000b) applied CRACK-NP to model
pesticide leaching at the Brimstone farm site (Oxfordshire,UK), characterized by a cracking clay soil. Field observations of
the water table, tile drain outﬂow and isoproturon concentrations in tile efﬂuent of a winter season (approx. daily
measurements), and for one selected week (approx. hourly
data), were utilized in simulations. Ped sizes, ped sorptivity,
macroporosity, and macropore hydraulic conductivity were
measured independently. Selected transport parameters were
calibrated using measured concentrations of nitrate (assumed
to be a conservative tracer) in the tile outﬂow. The sorption
coefﬁcient (KD) and the half-life of isoproturon were apparently not calibrated, although it was not explicitly stated how
they were obtained. The model could predict the drainhydrographs, the timing of the isoproturon main peak, andpeak concentrations in the drain occurring with discharge
peaks moderately well (Armstrong et al., 2000b).
Aden and Diekkrüger (2000) applied SIMULAT 2.3 (Diekkrüger,1996) for the modelling of herbicide dynamics at four ﬁeld
sites, including a cracking clay soil (Brimstone, UK) and amacroporous loam soil (Tor Mancina, Italy). Only the soil
hydraulic parameters were manually calibrated, while transportand reaction parameters were independently estimated frommeasurements and literature. For the two sites with the PF
paths, SIMULAT could not describe the temporal pattern ofdrainﬂow. The isoproturon concentration pattern in theleachate of the Tor Mancina site was not reproduced, althoughabsolute concentrations mostly varied within the measured
range. For the Brimstone site, isoproturon concentrations indrainﬂow were severely underestimated. This was explained as
being the result of the lack of coupling between the submodels

J.M. Köhne et al. / Journal of Contaminant Hydrology 104 (2009) 36–60

for macropore and tile drain ﬂow, which forced water into thesoil matrix before it could enter the drainage system. Furthermore, using degradation parameters from laboratory incubation
studies, SIMULAT underestimated the decay of isoproturon andoverestimated the mecoprop degradation in the soil. The
laboratory data seemed insufﬁcient for estimating sorption
and degradation in the ﬁeld (Aden and Diekkrüger, 2000).
Difﬁculties in applying PF models have triggered studies
on the calibration of chromatographic models. For example,
Scorza Junior and Boesten (2005) investigated whether the
CDE-based model PEARL could be calibrated to simulateleaching of Br−, mobile bentazone, and moderately sorbing
imidacloprid observed in drain water and groundwater in a
cracking clay soil. A large dispersivity (N60 cm) was calibrated
in PEARL, to simulate Br− concentrations. Bentazone concentrations could then be approximated with PEARL when using
a 2.5-times shorter half-life than derived from laboratory
studies. The transport of moderately sorbing imidacloprid
could not be simulated at all. It was concluded thatchromatographic models such as PEARL may sometimes becalibrated to approximate preferential transport of mobile
pesticides (Scorza Junior and Boesten, 2005). However inother case studies, CDE-based models could not be calibrated
to simulate transport of a tracer during PF (e.g., Gerke andKöhne, 2004). The applicability of chromatographic models
will depend on how ‘preferential’ water ﬂow at a given site is.
In many studies, degradation under ﬁeld conditions wasfound to be initially faster than predicted from laboratory
incubation tests followed by a slower decay phase (biphasic
degradation). Temperatures, water contents, nutrient concentrations and daylight vary in the ﬁeld and differ from constantlab conditions. The openness of the ﬁeld system causes various
dissipation processes to be lumped into a ‘pseudo-degradation’.
For instance, while initial rapid losses may occur by volatilization slower dissipation processes, such as photodecomposition
on soil and plant surfaces, degradation by adapted microorganisms, leaching and adsorption, or root uptake, may follow,
followed by slow biodegradation in the soil. In model applications, as well as in experimental measurements, it is often
nearly impossible to properly distinguish between degradationin the soil and other dissipation processes in the ﬁeld.
3.2.3. Non-localized stochastic applications
Roulier et al. (2006) used MACRO to identify controls on
atrazine leaching through luvisols and calcisols overlying
ﬁssured limestone at Brevilles, France. The site (100 m × 70 marea) had a large vertical scale (30 m deep). The time scale wasalso large (36 years). Calibrated MACRO predicted preferential
leaching at the base of a 30 m thick limestone sequence
within days after heavy rainfall following atrazine applicationin spring. A Monte-Carlo sensitivity analysis suggested thatthe degradation rate, sorption and macropore ﬂow in the soil
controlled leaching, while variations in the unsaturated rockhydraulic properties were less signiﬁcant. It was suggested
that further study of the sorption and degradation conditions
below the root zone would be useful (Roulier et al., 2006).
Stenemo et al. (2005) linked MACRO to the 3D groundwater DFM model FRAC3Dvs in order to simulate pesticide
pollution risk of groundwater beneath the fractured till.
Mecoprop was monitored in a moraine till and in a local sandaquifer (5 m depth) overlying a regional limestone aquifer

51(16 m depth) in Denmark. Water and pesticide ﬂuxes calculated by MACRO provided the upper boundary conditions
for FRAC3Dvs. While both spatially uniform and variable
upper boundary conditions (derived from Monte-Carlosimulations) were used with FRAC3Dvs, they did not show
any great effect on simulated mecoprop concentrations in thelocal sand aquifer. Full connectivity between macropores andfractures across the boundary between the two models wasfound to be more important. Moreover, the use of effective
pesticide fate parameters seemed to be a reasonable practical
approach. Furthermore, transient upper boundary conditions
resulted into pesticide leaching to the regional aquifer thatwas 20 times larger than when constant water ﬂow at thesame average rate was assumed. The results demonstrated
the importance of accounting for transient water ﬂows whenmodelling deep leaching through fractured porous media toregional groundwater aquifers (Stenemo et al., 2005).
3.2.4. Stochastic distributed approaches
Huang and Hu (2001) and Hu and Huang (2002) developed astochastic method for predicting contaminant transport
through heterogeneous, structured porous media. The mathematically rather intricate approach is built around the DPM ofGerke and van Genuchten (1993) and can consider 3D steadystate PF, linear kinetic sorption and ﬁrst-order degradation.
Hydraulic conductivities, sorption coefﬁcients and degradation
rates in the fracture and matrix regions, as well as the interregion mass diffusion coefﬁcient, were assumed to be randomvariables. Various correlation functions were assumed todescribe the spatial distributions of the parameters. The Euler
perturbation method was applied to obtain the analyticalsolution of mean concentrations in the fracture and matrix
regions, which were explicitly expressed by spatial Fourier andtemporal Laplace transforms, and which were numericallyinverted back to the real space solution via the Fast Fourier
Transform method. Exemplary results were shown for 2D
reactive transport calculations. The conclusions reveal thecomplexity of spatial variability effects on the preferential
transport of a reactive solute (Hu and Huang, 2002).
3.2.5. Model comparisons at plot and ﬁeld scales
A common theme of many model comparison studies
below is the assessment of model capabilities for an uncalibrated prediction of pesticide transport. This often involved a model calibration stage using hydrological and tracer
information, and a predictive model application using laboratory data on pesticide sorption and degradation.
Boesten (2000) reviewed applications and limitations ofpesticide behavior models for the soil–plant system, inparticular for assessment of leaching to ground water forpesticide registration purposes. The reviewed pesticide leaching models were either chromatographic (based on the CDE)
(PELMO, PRZM, GLEAMS, PESTLA, PESTRAS, LEACHP), considered PF (PLM, CRACK, MACRO, SIMULAT), or were of the hybrid
type (equilibrium water ﬂow and preferential solute movement) (WAVE and VARLEACH). The following progress was
identiﬁed: boundary conditions for water ﬂow had become
more adaptable, the description of PF in structured soils had
improved, linear sorption isotherms had been replaced with
Freundlich isotherms, and long-term sorption kinetics had been
included in many models. The pesticide degradation submodel

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J.M. Köhne et al. / Journal of Contaminant Hydrology 104 (2009) 36–60

was often based on the degradation model PERSIST (Walker andBarnes, 1981), which represents degradation as a ﬁrst-order
rate process that could depend on temperature (Arrhenius
function) and soil moisture. PERSIST was evaluated in over 100
studies of pesticide degradation in ﬁeld topsoils, and performed
satisfactorily except for a slight tendency for overestimation.
However, according to Boesten (2000), the overall validation
status of pesticide leaching models was still low, mostly due tothe PF processes, and particularly for small leaching levels of
b0.1% of the dose, which are usually relevant for pesticide
registration in the EU. Other obstacles to obtaining morereliable model results included the prediction of volatilization
ﬂuxes from plant and soil surfaces, and the subjective inﬂuenceof the individual modeller (Boesten, 2000). Gottesbüren et al.
(2000) compared applications of MACRO 3.1/4.0, SIMULAT 2.3,
WAVE, LEACHP, and GLEAMS 2.10 by different users for
simulating vertical movement of water, Br−, and isoproturon
in a silty loam ﬁeld soil proﬁle in the Weiherbach catchment,
Germany. The ﬁeld dataset (Schierholz et al., 2000) was split
into a calibration stage (1993/1994) and a prediction stage(1995). In SIMULAT, the macropore option was not chosen,because the calibration of bromide transport 1993/1994 in soil
was satisfactory using the MIM approach only. The only model
which was used with macropore ﬂow was MACRO. The results
showed that a good model calibration of Br− and isoproturon
concentrations in one experimental period (1993/1994) did not
imply uncalibrated good predictions for the second period(1995), although extensive and detailed laboratory measurements on the pesticide behaviour in the same soil wereavailable. It was not possible to estimate a parameter combination from the laboratory data to describe the ﬁeld behaviour ofisoproturon satisfactorily. Moreover, the dominant inﬂuence ofindividual model users on the modelling results was again
striking. Good (MACRO) and fairly good (SIMULAT) isoproturon
predictions, respectively, were possible after changing half-lives
and sorption coefﬁcients to realistic values as reﬂected byliterature. Furthermore, the authors suggest that comparison ofcomplex models should include evaluating the effects of submodels. For example, simulation results with and without
submodels for plant uptake, macropore ﬂow, or temperatureeffects on degradation, should be compared. This comparison
should be followed by a step-by-step validation parallel with
deﬁning model parameters, and initial and boundary conditions (Gottesbüren et al., 2000).
Vanclooster et al. (2000a) presented a test of 12 pesticideleaching models applied to four datasets from ﬁeld andlysimeter studies with unstructured and structured soils,
collected in the Netherlands, Germany, Italy and the UK.
Model types included those accounting for macropore ﬂow(CRACK-NP, MACRO, PLM), having a simpliﬁed description ofpreferential solute transport (SIMULAT, WAVE), or assuming
chromatographic transport (GLEAMS, LEACHP, PELMO, PESTLA, PESTRAS, PRZM-2, VARLEACH). As suggested by Gottesbüren et al. (2000), a stepwise approach was followed tocompare submodels simulating the movement of water,
solute, heat and pesticide. The experimental dataset wasagain split into calibration and prediction parts. It was
concluded that the predictive capabilities of tested models
were still limited due to the lack of information related tomacropore ﬂow, pesticide transformation (particularly in subsoil layers), hysteresis in adsorption/desorption isotherms,

increase in sorption with time, plant uptake and volatilization(Vanclooster et al., 2000a).
Armstrong et al. (2000a) compared the performance of
MACRO, CRACK-NP, SIMULAT and PLM for simulating drainﬂow and pesticide concentrations in a cracking clay soil at theBrimstone site, UK. PLM was not applicable to a subset of datawith an hourly time resolution, since it uses a daily calculationinterval. SIMULAT was unable to describe the observed
pesticide leaching, reﬂecting the model's failure to represent
the hydrological conditions of the site. Models developed for
macroporous soils, CRACK-NP and MACRO, performed better
than the more generalized PF models SIMULAT and PLM(Armstrong et al., 2000a).
Beulke et al. (2001a) evaluated the PF models CRACK-NP,
MACRO/MACRO_DB, PLM, and SWAT (Brown and Hollis, 1996)
for their uncalibrated predictive ability to simulate tile-drain
ﬂow rates and associated isoproturon concentrations in a
cracking clay soil at the Brimstone site, UK, during three
winter seasons. No advantage was found in using mechanistic
models (CRACK-NP, MACRO) over simpler models (PLM,
SWAT), since no model could consistently predict the data.
It was concluded that drainage outﬂow and pesticideconcentrations in drain outﬂow of structured clay soils
under ﬁeld conditions could not be predicted withoutmodel calibration (Beulke et al., 2001a,b).
In contrast, Beulke et al. (1998) conducted a PF model
evaluation allowing for some calibration using ﬁeld andlysimeter datasets from different sites in the UK. For mostsoils, MACRO either gave results that were similarly good, or
outperformed CRACK-NP, PLM, and SWAT. Accordingly,
MACRO was the single model recommended for regulatory
use. The MACRO_DB system was deemed a useful conceptual
development. However, evaluation results suggested that
output from MACRO_DB in its (then) present form could not
be relied upon for regulatory purposes (Beulke et al., 1998).
Garratt et al. (2003) compared the pesticide leaching models
LEACHP, MACRO, PLM, PELMO, PESTLA, PRZM and VARLEACH,
and their parameterization, using data involving aclonifen andethoprophos transport obtained over the course of three years
obtained from a 0.3-ha arable ﬁeld site near Bologna, Italy.
Aclonifen is a rather immobile and persistent compound, while
ethoprophos is moderately mobile and less persistent. Calibrated and uncalibrated simulations of soil water contents andpesticide concentrations in the soil and ground water wereconducted. None of the models could accurately simulate therapid leaching of pesticide to ground water. The user inﬂuenceon the parameter selection often played a greater role than themodel type in model simulations. Garratt et al. (2003) concluded
that the parameterization of macropore ﬂow models needed
further improvements. This may include reducing user subjectivity by protocols with methods for determination of eachmodel parameter by measurements, inverse determination(data requirements), or databases. Information about sensitivity
and possible ranges of parameters, as depending on the scale
and boundary conditions of a model application, would also
improve modelling.
Gärdenäs et al. (2006) compared four 2D transport models
in HYDRUS-2D (Šimůnek et al., 1999) for predicting PF and theleaching of the herbicide MCPA in a 50-m long transect
through a sloping, heterogeneous, tile-drained ﬁeld soil inSouth Sweden. The simulated time covered six weeks

J.M. Köhne et al. / Journal of Contaminant Hydrology 104 (2009) 36–60

following the spray application. The 2D approaches included a
Richards-CDE chromatographic model with hydraulic properties modiﬁed near saturation, a MIM (Richards water ﬂow,
mobile and immobile solute transport regions, diffusive masstransfer), a dual-porosity model (mobile and immobile
regions for both water ﬂow and solute transport, advectivediffusive mass transfer), and a DPM (two mobile regions). The
soil heterogeneity was represented deterministically in 11
different soil zones. Pedotransfer functions derived from thesite, lab experiments, literature data and previous ﬁeld
experiments at the site were used to parameterize the ﬂow,
transport and reaction processes in the model. The 2D DPM
best predicted drainage ﬂow. Only the dual-permeability anddual-porosity models reproduced the pesticide concentration
patterns in drain outﬂow. Furthermore, drains near the hilltop
always remained dry, and macropore ﬂow moved laterally
through the saturated zone to drains in the midslope andhollow positions (Gärdenäs et al., 2006).
Jarvis et al. (2006) compared simulation results carried out
with four CDE-based 1D models (traditional CDE, or CDE plus
ﬁnger ﬂow, particle-facilitated transport, and kinetic sorption)
against imazapyr concentrations observed in 1-m deep soil
proﬁles and groundwater observation wells in the coarse sandand gravel materials of a railway embankment in central
Sweden. Only the particle-facilitated transport model (withoutPF) could reproduce short-term transport observations, but it
did not adequately predict imazapyr residues in the soil one
year after application. This mismatch suggests that the longterm sorption process, in this case the formation of boundresidues, was neglected in all models. The observed muchslower degradation of imazapyr after one year supported thenotion of ‘protected’ residues formation (Jarvis et al., 2006).
Fox et al. (2006) compared uncalibrated RZWQM and PRZM
predictions against observed Br− and pesticide concentrationsin pore water (from suction samplers), and in the soil at depths
down to 210 cm, in two ﬁelds in North Carolina and Georgia,
USA. Both, RZWQM and PRZM slightly overestimated pesticide
displacement through the soil proﬁle. RZWQM generally
outperformed or was equivalent to PRZM in simulating concentrations in soil pore water (Fox et al., 2006).

53

3.3. Scale III (catchment)
Catchment scale pesticide models were developed as tools
to quantify the agricultural impact on groundwater quality(Herbst et al., 2005b), to estimate ground water contamination risk and to select high-risk areas for ground waterobservation networks (Holman et al., 2004; Sinkevich et al.,2005), and to improve the cost-effectiveness of measuresneeded to achieve the ‘good status’ of groundwater quality as
required, for example, by the European Water Framework
Directive (Holman et al., 2004). Until recently, pesticide
models at this scale typically did not account for bypass
ﬂow and their applications are still rather rare (Table 4). A few
different approaches were applied at this scale, including 1Dmodels used with soil and pesticide databases or with stochastic parameter sampling for scenario simulations, 1D
distributed modelling, or coupled 1D unsaturated–3D groundwater models. Model validations at scale III face a signiﬁcantdeﬁcit of measurements.
3.3.1. Stochastic applications of 1D models
Lindahl et al. (2005) used MACRO (Jarvis,1994) with a Monte
Carlo technique to analyze the leaching of the herbicide MCPAin the small Vemmenhög catchment (9 km2) in South Sweden.
The authors justiﬁed using a 1D approach by pointing to thelimited relevance of lateral transport processes (surface runoff,
spray drift, groundwater ﬂow) and a rapid drainage routing to
the catchment outlet in the Vemmenhög area. MACRO withstochastic parameter sets could predict the hydrologic catchment response, while MCPA concentrations in the stream were
mostly underestimated. In particular, the model could notaccount for high observed concentrations that were apparently
caused by the point-source pollution from ﬁlling or cleaning
spraying equipment (Lindahl et al., 2005). Such farmyard losses
may cause short-term high concentration peaks in surface
water (Leu et al., 2004), particularly in regions with smaller
agricultural ﬁelds and a correspondingly larger number offarmyards, such as in SW Germany (Müller et al., 2002).
In a subsequent sensitivity analysis for the Vemmenhög
catchment, Lindahl et al. (2005) identiﬁed the mass transfer

Table 4
Applications of models simulating preferential pesticide (or reactive solute) transport at scale III
Scale

Model type; name

W (F) DPM; MACRO—stochastic

Dimension, equation name or description

Authors

Stochastic MACRO simulations parameterized from measured
probability distributions
Groundwater: 3D Darcy, surface: 2D overland ﬂow, 1D channels,
vadose zone (MACRO): Richards, CDE (matrix); kinematic wave,
advection (macropores), sorption
1D—distributed MACRO, GIS compatible
3D (Richards, CDE) with nonlinear sorption

Lindahl et al. (2005)

W

DPM a (distributed); Mike She/Daisy + MACROW
W

DPM; MACRO + spatializing tool
SPM; TRACE (ﬂow), SUCROS (plant), 3DLEWASTE(transport)
DPM; Generalized Preferential Flow Model and GIS 1D distributed, steady-state ﬂow. Transmission zone: CDE in PF
paths (no matrix), distribution zone: exponential loss due to leaching
and biodegradation.
DPM; MACRO Emulator coupled with AQUAT
Tabular database of over 4000 MACRO scenario simulations are used
Systemby AQUAT to predict maximum annual concentrations reaching groundwater
surface below the base of the soil proﬁle.

W

Wb

Watershed (W), Field (F).
aSynthetic model parameters.
bNational scale of Great Britain.

Christiansen et al.
(2004)
Esposito et al. (2005)
Herbst et al. (2005a,b)
Sinkevich et al. (2005)

Holman et al. (2004)

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J.M. Köhne et al. / Journal of Contaminant Hydrology 104 (2009) 36–60

coefﬁcient as the most inﬂuential variable, as opposed to studies
at smaller spatial and temporal scales that found degradation,
sorption, and—when PF was involved—hydraulic parameters tobe the most inﬂuential parameters for pesticide transport (e.g.,
Roulier and Jarvis, 2003a,b). The variation in precipitation
during the 2.5 weeks following application and the organiccarbon content were the other sensitive variables, whereas thesmall measured variability in the herbicide degradation rate
had little effect on MCPA losses (Lindahl et al., 2005).
Brown et al. (2004) used MACRO to predict the long-term
fate of the herbicide sulfosulfuron in 20 environmental
scenarios devised for the target area of wheat-growing land in
England and Wales (1.7 × 106 ha). The scenarios comprised
discrete classes of the soil type and climate, using the SEISMIC
database (Hallet et al., 1995). The sulfosulfuron concentrations
in a receiving ditch estimated using MACRO were weighted
according to the likelihood of each scenario to produce aprobability distribution of daily exposure (Brown et al., 2004).
3.3.2. Spatially distributed model applications
Holman et al. (2004) developed a model system forpredicting large-scale pesticide losses to groundwater
through the matrix and macropores. The 1D distributedmodel system was based on MACRO 4.1 for the top 1 m of soil
proﬁle, and the attenuation factor leaching model AQUAT(Hollis, 1991) for the deeper part of the soil proﬁle. The
objective was to develop a diffuse source groundwatercontamination calculator for the Prediction of Pesticide
Pollution in the Environment (POPPIE) system, which isused by the Environment Agency of England and Wales fordriving and reﬁning pesticide monitoring programs (Brown etal., 2002). A fast MACRO emulator was created that uses lookup tables based on the results of 4704 MACRO simulations ofpesticide leaching scenarios for different soils, pesticide halflives and KOC values, weather (excess winter rainfall) andapplication seasons (spring or fall). The MACRO emulator
could predict pesticide losses from 12 combinations oflysimeters and pesticides in a qualitative sense (‘leaching ornot’) with actual concentrations reproduced within an orderof magnitude. The linked MACRO emulator/AQUAT system
was applied for the entirety of England and Wales at a spatial
resolution of 2 × 2 km. When tested against national monitoring data for pesticides in UK aquifers, realistic regionalleaching patterns of atrazine, isoproturon, chlorotoluron andlindane were predicted (Holman et al., 2004).
Esposito et al. (2005) developed a distributed approach tomodelling pesticide leaching on the catchment scale using
MACRO. Their tool assembles input data for MACRO in a grid,runs MACRO for each grid cell, and creates output ﬁles for GIS
or other environmental software. Thus, the tool may be used as
an interface between MACRO and regional 3D ground watermodels. It was successfully tested to simulate isoproturon fate
during 10 years in the Zwischenscholle test area (20 km2),
Germany, which is characterized by intense agricultural use
and shallow groundwater (Esposito et al., 2005).
Christiansen et al. (2004) extended the MIKE SHE/Daisy
code with a macropore ﬂow description derived from MACRO.
They described spatially distributed pesticide transport in asmall part (1.5 km2) of a catchment in Denmark. The model
was applied in a telescopic approach (1.5 km2, 13.7 km2,
62.3 km2) to account for lateral groundwater exchange. A

large spatial variation of macropore ﬂow was found, due tothe variability in topography and depth to the groundwatertable. Simulation results suggested a signiﬁcant effect ofmacropores on pesticide leaching to groundwater at thecatchment scale (Christiansen et al., 2004).
Herbst et al. (2005b) developed and applied an integrated
modelling approach of pesticide transport. The 3D numericalmodel TRACE (Vereecken et al., 1994), based on the Richards
equation, was linked with 3DLEWASTE (Yeh et al., 1992) thatsolves the CDE, and with a plant module. The resulting modelaccounts for spatial heterogeneity, though not explicitly for
PF. Isoproturon transport in the 20-km2 Zwischenscholle area
(Germany) was described for a 10-year period. The 3D model
domain comprised 20,000 nodes and had a horizontal gridsize of 200 m, vertical intervals ranging from 1 cm (top) to
10 cm (bottom of the proﬁle) for the soil proﬁle and variable
thicknesses for the aquifer grid cells. Parameters were derived
from measurements, pedotransfer functions and literature.
Measured piezometric heads were reproduced by model
results. Relatively high isoproturon concentrations in groundwater were predicted for locations with thin layered andpermeable soils (Herbst et al., 2005b).
Sinkevich et al. (2005) developed and tested a GIS-basedrisk assessment model for ground water contamination bypesticides. The Generalized Preferential Flow Model by Kim
et al. (2005) was implemented in a GIS framework. In the topsoil layer, an exponential loss was assumed due to leaching
and biodegradation, while in the subsoil transmission zone,
solute transport via steady-state PF is calculated utilizing
analytical solutions of the CDE. The model system uses land
cover data and information about chemical properties andground water recharge to estimate the chemical concentration reaching the ground water. The distributed risk assessment tool was tested by comparing the model-predicted risk
with observed atrazine and nitrate concentrations from 40
sampling wells in Cortland County, New York, USA. Nitrate
was considered as an indicator of agricultural pollution. The
predictions agreed well with observed nitrate concentrations
and pesticide detections and suggested that the high-riskareas constituted only 5% of the catchment. Focusing groundwater monitoring in only these areas would be more effective
than implementing an evenly distributed country-wide
program (Sinkevich et al., 2005).
4. Comparing model strengths and weaknesses
A number of models have been developed, tested, andapplied that consider the effect of variably-saturated, transient PF on transport of agrochemicals, such as pesticides.
Most models included in this review are 1D approaches basedon the two- (or three-) region concept (e.g. RZWQM, MACRO,HYDRUS-1D, CRACK-NP, SIMULAT, PLM, S1D DUAL, SWAT).
Model comparisons have demonstrated that all of the above
models are clearly superior to chromatographic models intheir ability to simulate pesticide displacement in structured
soils. With regard to their PF description, the models differ intheir philosophy, complexity, and equations used for (i)displacement of water and tracer in and between the domains(see companion paper), and (ii) sorption and degradation in
the domains. The ﬁrst part of the list (i) indicates theprevalent type of the soil structure targeted by the models;

J.M. Köhne et al. / Journal of Contaminant Hydrology 104 (2009) 36–60

cracked clay soils: CRACK-NP, MACRO; soils with large
continuous macropores: RZWQM, MACRO, and soils withhierarchical structure: HYDRUS-1D, S1D DUAL, SIMULAT.
It is not possible to identify a particular PF-pesticidetransport model that is clearly superior for all purposes. Having
more model comparisons using benchmark data sets would be
useful to compare the calibrated or uncalibrated model
performance for different pesticides (including water ﬂow
and tracer transport), for different soils, and agricultural
management practices. However, three model systems, i.e.,
MACRO, RZWQM, and the HYDRUS-1D/-2D/(2D/3D) model set,appear to stand out regarding their (i) ease-of-use: graphical(Windows) user interfaces, pedotransfer functions, inverse
parameter estimation procedures, and detailed documentation,(ii) complexity: physics-based processes and options (Table 1),boundary conditions, (iii) up-to-dateness: continued upgrades,
availability, and support (e.g., discussion forums), and (iv)
distribution: large number of published applications.
The RZWQM model (e.g., Ahuja et al., 2000a,b) was
developed within the US Department of Agriculture, Agricultural Research Service (USDA-ARS), and was applied particularly in the USA. As an agricultural systems model, RZWQM
exceeds the other two models, and maybe any other model, in
its options for considering agricultural management including
tillage and schemes for crop rotation, irrigation, fertilizer,
pesticide and manure applications. Furthermore, it can beused to estimate crop yield and herbicide transport in surface
runoff (Malone et al., 2004a; Ma et al., 2007). Different
submodels are included to describe equilibrium and kinetic
sorption of pesticide in the sil matrix. However, by comparison
with the MACRO and HYDRUS models, the description ofpreferential solute transport in the matrix and macropores andof lateral transfer is more empirical. Examples thereof are theassumed exponential decrease of concentrations in macroporeswith depth and the assumptions of one way (macropore-tomatrix) water transfer, and of instant partial solute mixing,
between both domains. Sorption and degradation are notconsidered in the macropore region, which is a ‘worst-case’assumption. Experimental and modelling evidence showedthat despite of limited contact time and contact area, sorption
affected preferential pesticide leaching (e.g., Jørgensen et al.,
1998; Malone et al., 2001; Rahman et al., 2004) within the PF
domain (e.g. Mallawatantri et al., 1996; Stehouwer et al., 1994).
This semi-empiricism in the description of PF related processes
may not be critical for site-speciﬁc calibration, but might
potentially limit the development of methods to estimate
RZWQM soil hydraulic and transport parameters from soil
properties. Finally, RZWQM was linked with GIS for catchment
modelling (Wang and Cui, 2004; Ascough et al., 2005).
MACRO (Jarvis, 1994) was often applied, particularly inEurope, to simulate pesticide transport in agricultural soils.
For example, MACRO has been coupled to one of the European
scenarios to estimate leaching of pesticides to ground waterfor regulatory purposes (FOCUS, 2000). Compared with theRZWQM, the descriptions of ﬂow, transport, bi-directional
lateral transfer, and sorption and degradation in both thematrix and macropores permit a more ﬂexible and physically
based description of preferential ﬂow effects on pesticidetransport. Reverse (matrix-to-macropore) solute transfer in
its advective part proceeds instantaneously for excess matrix
saturation. This is not necessarily a realistic concept (Köhne

55

et al., 2004), but has no effect when reverse solute transfer
proceeds by diffusion. Since both RZWQM and MACRO
assume gravity-driven ﬂow in the macropores, the preferential ﬂow component cannot easily be generalized to two or
three dimensions. However, the concepts in MACRO were
included in the 2D/1D (matrix-macropores) M-2D model(Kohler et al., 2001). MACRO was also coupled with othermodels for regional scale applications (section 3.3.).
HYDRUS-1D (Šimůnek et al., 1998, 2003, 2005, 2008a,
2008b; Šimůnek and van Genuchten, 2008) has been used innumerous applications worldwide. However, despite of recent
extensions of its PF options (Šimůnek et al., 2003; Šimůnek andvan Genuchten, 2008), the model has not yet been tested asoften as RZWQM and MACRO for describing pesticide transport
experiments. Among the models in this review, HYDRUS
contains the most wide-ranging options regarding (twodomain) boundary conditions, ﬂow-transport concepts (one tothree domains), degradation and sorption in the soil (including
kinetic sorption and degradation in different soil pore systems),
transport of metabolites, and particle transport. The more often
applied HYDRUS-1D submodels consider the Richards equation
and CDE also for the PF system, instead of gravity-driven ﬂow
and convection as considered in RZWQM and MACRO. A
HYDRUS-1D variant was developed to describe gravity-driven
ﬂow and convection (Novák et al., 2000). The Richards-CDE
submodel is conceptually better suited to loamy soils, than toheavy clay soils with shrinkage cracks (see companion paper fora discussion). The two or three dimensional versions HYDRUS2D (2D/3D) (Šimůnek et al., 1999, 2006b) additionally provide
tools for representing soil heterogeneity and can consider
preferential transport in different directions.
All three models contain some provisions for estimating
parameters from basic data. However, even the most ambitious database, the MACRO_DB (Jarvis et al., 1997) system forthe selection of soil hydraulic and crop parameters, still needs
improvement before it can be recommended for regulatoryuse (Beulke et al., 1998). Further research in that direction has
shown promise (Jarvis et al., 2007). By comparison, the otherthree models shown in Table 1 (SIMULAT, PLM and CRACK-NP)
are less comprehensive, more empirical, and have not been
applied as often. SIMULAT does not consider sorption anddegradation in macropores, nor volatilization and plant solute
uptake. PLM does not simulate a ground water table, and isempirical in its capacity description of water ﬂow with acalculation time interval of 1 day. However, in applications
where model parameters are unknown, the more physically
based models were not always superior.
One also needs to keep in mind that not all soils show
macropore ﬂow and preferential pesticide leaching: as just two
out of many examples not reviewed here, both successful
application of RZWQM without macropore option (Jaynes andMiller, 1999) and of the Pesticide Root Zone Model (PRZM),
which does not consider PF (e.g., Carbone et al., 2002), werereported. PRZM-3 is used by the United States Environmental
Protection Agency (US EPA) for Tier II screening of pesticideleaching into groundwater, even though it does not consider PF.
5. Conclusions: progress, problems, outlook
The fate and transport of pesticides in structured ﬁeld soils
is often affected by PF. Considerable progress in modelling

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J.M. Köhne et al. / Journal of Contaminant Hydrology 104 (2009) 36–60

pesticide transport under such conditions has been achievedin the past decade using two- (or three-) region models (e.g.
RZWQM, MACRO, HYDRUS-1D, CRACK-NP, SIMULAT, PLM,
S1D DUAL, SWAT). Scientiﬁc progress in model development
comprises descriptions of domain-separated equilibrium and/or kinetic sorption and degradation for variably-saturated
ﬂow conditions, and other surface and subsurface processes.
More practical aspects of progress in the model applicability
include development of graphical user interfaces, provisions
for inverse parameter and uncertainty estimation, continued
availability, upgrading and support, documented tests andapplications. When all above aspects are taken together,
MACRO, HYDRUS-1D, and RZWQM are the leading model
systems for simulating preferential pesticide transport inagricultural structured ﬁeld soils.
Progress in utilizing inverse and uncertainty parameter
estimation techniques for simulating preferential pesticidetransport can yield suggestions for future research (seebelow). For uncalibrated modelling of preferential pesticidetransport, using a model calibrated to water and tracer
observations and laboratory sorption and degradation data, asatisfactory prediction of concentrations within a factor 3–5 ofthe measured concentrations was reported in a few cases
usually involving MACRO. More often, particularly for blind
validation, deviations between observed and predicted concentrations of one or several orders of magnitude were found.
Identifying conditions for successful predictions could also givesome guidance for future applications (see below). Improvements in the applicability of PF pesticide models proceed
through an iterative process between experiments and model
evaluations. Measurement techniques need to be improved forderiving PF-transport parameters. Model process descriptions
need to be further enhanced. On the other hand, since model
complexity cannot increase ad inﬁnitum, options to use
simpliﬁed descriptions should be also identiﬁed for certain
settings. Based on the model application studies reviewed here,
the following needs in PF pesticide modelling and in further
research are proposed.
5.1. Improving predictive model capabilities
1) Any research experiment should, as far as possible, have aclosed pesticide mass balance (of the parent compound and its
metabolites) by including time-series of ﬂux concentrations,ﬁnal soil resident concentration proﬁle, losses via plant-uptake
and volatilization. The use of tracer and compounds of different
mobility would be helpful. 2) On the soil column scale,development of PF-related pedotransfer functions, e.g., for themass transfer coefﬁcient, based on basic soil properties isneeded. 3) The preceding approach will have limitations, so
additionally, computer tomography should be combined withhydraulic and solute transport measurements to identify
characteristic ‘form-and-function’ relations between the soil
structure and ﬂow-transport response. 4) Degradation andsorption in the macropores and soil matrix should be
distinguished, e.g. by in-situ measurements of degradation inearth-worm burrows (tedious) or lab measurements of sorption
and degradation using the soil material sampled from thematrix or PF paths material. Techniques for identifying therelation between soil structure, their biochemical characteristics, and resulting sorption or degradation functions should be

developed; organic tracers might play a role in this effort. 5) Ona soil proﬁle scale, the ‘form–pattern–function’ relation shouldbe established, between ‘form’ (structure: layers, layer interfaces, heterogeneity, macropores), ‘transport patterns’ (PFnetwork formation, lateral redistribution, dispersion), andfunctional descriptions of this relation. Promising approaches
are the ‘scaleway’ (Vogel and Roth, 2003) or classiﬁcation oftransport patterns in soil horizon sequences (Kulli et al., 2003).
7) On a ﬁeld scale, the effect of spatial variation of hydraulic,sorption and degradation properties (in macropores andmatrix) can be considered by uncertainty estimation. 8)
Seasonal variability of the agricultural soil–plant system, andthe short-term variability of rain intensity, need to beconsidered in adequate time resolutions. 9) Both physical andchemical nonequilibrium should be considered in PF and matrix
domains, as e.g. done in HYDRUS-1D. 10) Particle facilitatedtransport for strongly sorbing pesticides could be added. 11)
Further model comparisons (including DFM approaches)
exploring the capability and importance of individual model
subcomponents should be performed based on benchmark data
sets. 12) Closely related, the relative importance of different
processes (PF, leaching, sorption on plant residues and in soil,
volatilization, overland ﬂow) for the overall pesticide loss fromthe ﬁeld should be further compared. 13) Calibrated parametervalues and their ranges, improved pedotransfer functions, andother information facilitating parameter determination, should
be assembled in meta-databases for different structured soil
types, crops, and agricultural management. Some of theseissues will be addressed in a European collaborative research
project named ‘footprint’ (http://www.eu-footprint.org/ataglance.html–Sep 2008).
5.2. Improving calibrated model capabilities
This includes the above efforts for reducing model errors by
improving process descriptions, and furthermore requires
improved inverse procedures and data. 1) Inverse parameter
estimation should simultaneously use all data (not sequentiallyuse water, tracer, and pesticide data). Proper identiﬁcation
requires ﬂux and resident concentrations and probably somedomain-speciﬁc information on sorption and degradation.
Standard batch or incubation techniques are usually not
representative of in-situ unsaturated matrix conditions, even
if they (by volume) approximately represent the matrix.
2) Parameter sensitivities: many studies have analyzed parameter sensitivity. Results could be used to systematize model
parameter sensitivity as depending on few structured soil
categories, compounds (mobile versus strongly sorbed), andlength and time scales (e.g., a 2-wk column study versus a 2-yr
lysimeter experiment). 3) In order to reduce the effect of user
subjectivity, ‘correct’ example simulations and/or a protocol
with rules for model setup for different scales and settings
should be included with the model code. 4) Model calibration
preceding model validation should focus on matching thoseparts of the tracer data (usually time and peak of initial tracer
breakthrough) that contain the most PNE information forpesticide breakthrough. Advanced corresponding weighting
schemes could be developed in automated inverse procedures.
Moreover, there are indications of a signiﬁcant effect ofmacropores on pesticide leaching to groundwater at thecatchment scale. At this scale, 1D models used with soil andJ.M. Köhne et al. / Journal of Contaminant Hydrology 104 (2009) 36–60

pesticide databases or with stochastic parameter sampling, 1D
distributed modelling, or coupled 1D unsaturated–3D groundwater models were used. However, model applications at thisscale faced a signiﬁcant deﬁcit of measurements and appear to
be in an early research stage.
Generally, the model analysis of preferential pesticideleaching has made signiﬁcant progress in the past decade andwill undoubtedly be developed further. By assessing the safetyof new compounds in the regulatory process, or identifying hot
spots of high groundwater vulnerability that would require
reductions in pesticide dose or use of organically grown crops,
such model applications can assist in reducing the environmental (and economic) impact of pesticide applications onwater resources.
Acknowledgements
The ﬁrst author was supported by the German Research
Foundation (DFG) under project KO 2899/1-1. The authors are
grateful to Nick Jarvis for his numerous insightful comments on
the draft manuscript. The comments by the editor-in-chief, Emil
Frind, the managing guest editors for this special issue ‘Flow
domains’, Miroslav Kutilek and Antonio Coppola, and an anonymous reviewer were also most helpful in preparing this paper.
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