Evaluation of pesticide dynamics of the WAVE-modeldirectory.umm.ac.id/Data Elmu/jurnal/A/Agricultural Water... · 2010-03-29 · dInstitut fu ¨r Chemie und Dynamik der Geosphare,

Evaluation of pesticide dynamics of the WAVE-model

M. Vancloostera,*, S. Ducheyneb, M. Dustc, H. Vereeckend

aDepartment of Environmental Sciences and Land Use Planning, UniteÂ GeÂnie Rural,

UniversiteÂ Catholique de Louvain, Place Croix du Sud 2/2, B-1348 Louvain-la-Neuve, BelgiumbInstitute for Land and Water Management, Katholieke Universiteit Leuven, Vital Decosterstraat 102,

B-3000 Leuven, BelgiumcDuPont de Nemours SA, ERDC rue du Moulin 21, F-68740, Nambsheim, France

dInstitut fuÈr Chemie und Dynamik der GeosphaÈre, Forschungszentrum JuÈlich GmbH (KFA),

Wilhelm Johnen Strasse, D-5170 JuÈlich, Germany

Abstract

A validation study of the physical based pesticide leaching model WAVE is presented. The model

considers a mechanistic description of 1-D water, solute and heat transport. Linear sorption

isotherms and ®rst order degradation sub-models are used to simulate pesticide sorption and

transformation. The ®rst order degradation rates are reduced when temperature and moisture stress

in the soil pro®le occur. The model is conceived to describe pesticide fate within rigid mineral soils.

Model tests were therefore done using data collected at a sandy (Vredepeel) and a loamy soil

(Weiherbach). Both ®eld data and lysimeter data were used to evaluate the performance to describe

water, bromide, ethoprophos, bentazone and isoproturon transport in soil. The evaluation procedure

presented by Vanclooster et al. (Agric. Water Mgmt., Vol. 44, pp. 1±19) was completely adopted.

The measured soil moisture in the sandy soil could only successfully be described after

calibrating the hydraulic functions using ®eld observed soil moisture pro®les. In addition, the

predicted balance terms, such as the soil water drainage, were subject to a lot of uncertainty.

Bromide transport in the sandy soil was poorly described with the equilibrium solute transport

model. Anomalies were also observed when simulating the transport of the inert tracer in the

lysimeter at the loamy site. The fate of the weakly sorbing bentazone component was appropriately

described at the Vredepeel ®eld site. However, the retardation of the strongly sorbing ethoprophos

and isoproturon components was poorly simulated. Further, the pesticide dissipation varied

considerably in time, which could not be accounted for with the ®rst order degradation model.

The need for model calibration illustrates the constraints when using mechanistic models such as

WAVE to predict ®eld scale pesticide fate and transport. The adoption of a mechanistic model for

registration purposes may therefore be subjected to a lot of uncertainty. In addition, processes

affecting pesticide fate and transport are still poorly represented within the model. De®ciencies are

Agricultural Water Management 44 (2000) 371±388

* Corresponding author. Tel.: �32-10-473710; fax: �33-10-473833.

E-mail address: [email protected] (M. Vanclooster).

0378-3774/00/$ ± see front matter # 2000 Elsevier Science B.V. All rights reserved.

PII: S 0 3 7 8 - 3 7 7 4 ( 9 9 ) 0 0 1 0 1 - 8

related to the description of non-linear sorption, time dependent pesticide degradation, and pesticide

volatilisation. Future developments with the model should therefore envisage to improve the

parametrisation reduce the output uncertainty, and improve process descriptions of essential

processes. # 2000 Elsevier Science B.V. All rights reserved.

Keywords: Pesticide leaching modelling; Validation; Solute transport

1. Introduction

Pesticide residues are retrieved in groundwater bodies all over Europe (Leistra and

Boesten, 1989) and US. The impact of pesticide residues in groundwater is poorly

understood. Yet, it is generally accepted that leaching losses from agricultural soils

should be minimised as much as possible.

Pesticide leaching towards subsurface groundwater bodies is controlled by a range of

soil and environmental conditions which are extremely variable in time and space. This

makes the quantification of pesticide leaching a tedious task (Brown et al., 1995). Yet, in

order to develop efficient farm management strategies, it is crucial to have correct

information on the amount of pesticide lost, and this in terms of variable soil conditions,

agricultural practices, meteorological and geo-hydrological conditions. Mechanistic

pesticide fate and transport models are accepted as being powerful tools to deliver such

information. Mechanistic models describe pesticide transport and dissipation based on

well established basic physical, biological and chemical laws. However, the use of

mechanistic models is jeopardised by badly defined model parameters which are hard to

identify in a statistical sense. In addition, there is a shortage of sufficiently detailed

experimental data to allow appropriate validation of mechanistic models. In a recent

review for instance, Jarvis et al. (1995) noted that eight popular pesticide leaching models

were only tested on 26 active components. This is definitely very few, given the amounts

of components currently used. The low validation level of pesticide leaching models is a

critical issue, especially if models are adopted within the pesticide registration process

(Boesten et al., 1995). Particular attention should therefore be devoted to improve the

general validation status of pesticide leaching models.

In this paper, a validation study is reported of the mechanistic±deterministic leaching

model WAVE (Vanclooster et al., 1994). The model has been conceived to describe

pesticide fate within rigid mineral soils. Earlier tests and application studies with the

model have been summarised by Muno-Carpena et al. (1998). Model tests in the present

study are performed on a dataset collected on a sandy and a loamy soil (Vredepeel site:

Boesten and Van der Pas, 2000; Weiherbach site: Shierholz et al., 2000). Both field and

lysimeter data were used to evaluate the performance of the different components of the

model. The evaluation procedure presented by Vanclooster et al. (2000) was completely

adopted. In order to elucidate the ability of the model to predict pesticide fate from

laboratory data, uncalibrated model results are shown. These results illustrate the validity

of the model when used for instance in a registration context. In addition, results with

calibrated model parameters are shown. The calibration allows to scale up laboratory-

scale input parameters to effective field-scale input parameters.

372 M. Vanclooster et al. / Agricultural Water Management 44 (2000) 371±388

2. Materials and methods

2.1. The model

A detailed description of the WAVE-model can be found in Vanclooster et al. (1994).

The WAVE-model combines different ad-hoc state-of-the-art models like SWATRER

(Belmans et al., 1983), SUCROS (Spitters et al., 1988) and modules of LEACHM

(Wagenet and Hutson, 1989). The model is a revised version of the SWATNIT-model

(Vereecken et al., 1991). The model is programmed in a modular way and can easily be

expanded to model the fate of other agro-chemicals in the soil-crop environment. The

WAVE-model is mechanistic and deterministic. It can handle different soil horizons

which are divided into equidistant soil compartments. A water, heat and solute mass

balance equation is developed for each compartment, taking into consideration different

sink/source terms. Physical transport equations are implemented which are solved

numerically using finite difference techniques.

Water transport is modelled using Richards' equation, which is obtained by combining

Darcy's law with the mass conservation equation:

C�h� @h

@t� @

@zK�h� @h

@t� 1

� �� ÿ Sinkwat (1)

where C(h) is the differential moisture capacity; K(h) the hydraulic conductivity

relationship; h [L] the soil water pressure head; Sinkwat [Tÿ1], the water sink term; and z

[L], t [T] the space and time co-ordinates. The water transport model assumes that soil

water ¯ow occurs in response to a hydraulic potential gradient which in this case obeys

the capillary ¯ow theory. This means that preferential water ¯ow, by-passing the soil

matrix, is not explicitly accounted for with the present model. Yet, it should be noted that

fast ¯ow in larger pores can partially be simulated by adopting a heterogeneous pore size

distribution, and hence a heterogeneous soil moisture retention characteristic and

hydraulic conductivity relationship (Durner, 1994). Alternatively, ®eld scale water

transport, and hence water ¯ow as well in large and small pores, can often successfully be

described using a stochastic description for the water transport parameters in a Monte

Carlo type of analysis. In this case, the 1-D ¯ow equation of WAVE is solved iteratively

for a representative sample of the adopted probability density function of the water

transport parameters (Mallants et al., 1996). Parametric forms of van Genuchten (1980)

were adopted to model moisture retention:

yys

� 1

1� �ah�n� �m (2)

where ys [±] is the saturated moisture content; a [Lÿ1] the inverse of the air entry value;

and n a shape parameter. Hydraulic conductivity was modelled with the Brooks and

Corey (1964) relationship at Vredepeel:

K

Ks

� yys

� �l

(3)

M. Vanclooster et al. / Agricultural Water Management 44 (2000) 371±388 373

and the van Genuchten±Mualem model (van Genuchten, 1980) at Weiherbach:

K

Ks

� yÿ yr

ys ÿ yr

� �t

1ÿ 1ÿ yÿ yr

ys ÿ yr

� �1=m !m" #2

(4)

where Ks [L Tÿ1] is the saturated hydraulic conductivity; yr [±] the residual soil moisture

content; and l, t shape parameters of the conductivity curve.

Water uptake by the crops is described with a macroscopic uptake term. The maximum

uptake rate by the roots defined by Belmans et al. (1983) was modified. In the present

study a weighing function, frac(x), is defined which is proportional to the root density

distribution: Z root depth

0

frac�x� dx � 1 (5)

where root_depth [L], is the actual rooting depth. The actual root uptake rate is de®ned as

the potential transpiration rate, Tpot [L Tÿ1] multiplied with the weighing function [±] and

reduced for water stress or

RTEX�x� � Tpot frac�x� a�h� (6)

where a(h) is an Arrhenius type of reduction function in terms of soil water pressure head

h [L].

A hybrid model, considering physical non-equilibrium solute transport, is available in

the code. The model considers convective dispersive flow in the mobile soil region

together with a rate limited exchange between the mobile and the immobile soil regions.

In the present validation study, equilibrium solute transport was considered such that the

transport equation reduces to the well known convection±dispersion equation:

@�yC�@t� @�rKdC�

@t� @

@zyD

@C

@z

� �ÿ @�qwC�

@z� Sinksol (7)

where C [M Lÿ3] is the volume averaged pesticide concentration of the soil solution; y[L3 Lÿ3] the volumetric water content; Kd [L3 Mÿ1] the distribution coef®cient; D

[L2 Tÿ1] the apparent dispersion coef®cient; qw [L Tÿ1] the Darcian water ¯ux; r[M Lÿ3] the apparent density and Sinksol [M Tÿ1] the solute sink term. The apparent

dispersion coef®cient is a composite of the chemical diffusion and hydrodynamic

dispersion coef®cient (Wagenet and Hutson, 1989):

D � 0:01D0 exp�10y�y

� lyw

y(8)

with D0 [L2 Tÿ1] the chemical diffusion constant in pure water and l [L] the

hydrodynamic dispersivity.

In view of regional applications of the model, and the limited availability of non-linear

sorption parameters, a pesticide retention model based on a simple equilibrium sorption

isotherm is adopted. The Sinksol term is approached with a first order decay model.


Potential first order degradation constants are reduced in terms of soil moisture and soil

temperature based on the approach suggested by Walker (1974):

kdec � fy fT kpot (9)

fy � 100yr

� �b

(10)

fT � Q10T ÿ Tb

10

� �(11)

Heat transport is modelled using Fourier's law as illustrated by Tillotson et al. (1980)

and Wagenet and Hutson (1989):

@T

@t� @

@z

l�y�@T

rCp@z

� �(12)

where T (K) is the soil temperature, KT(y) [M Lÿ2 Tÿ3 Kÿ1] the soil thermal

conductivity; and Cp [M Lÿ5 Tÿ2 Kÿ1] the volumetric heat capacity. The thermal

properties in the model are calculated as suggested by de Vries (1952).

For the Vredepeel dataset no attempt was made to model crop growth with the

available crop growth simulator. The evolution of crop leaf area and rooting depth and

distribution were estimated from the available literature data. On the contrary, for the

Weiherbach dataset, the integrated crop module was adopted to generate crop leaf area

index from meteorological and plant phenological data.

2.2. Initial model parametrisation and input estimation

The data used to test the model are the field data collected at Vredepeel, The

Netherlands and Weiherbach, Germany. A detailed description of the dataset can be found

in Boesten and Van der Pas (2000) and Shierholz et al. (2000). For the Vredepeel soil,

numerical grids of 100 mm were used. The Weiherbach soil was descretised in 38 soil

layers of 50 mm.

2.2.1. The water balance component

Climatological data measured at Vredepeel (precipitation, air temperature) were

collected from the nearby meteorological stations in Beek and Arcen (global radiation,

wind speed) and were processed to calculate the potential reference evapotranspiration

according to an update of the Penman±Monteith method (Allen et al., 1994). The total

potential reference evapotranspiration obtained for the simulation period (23/11/1990±3/

10/1992) was 754 mm. This value overestimates substantially the total Makkink

reference evapotranspiration of 599 mm as provided in the dataset report by the

Koninklijk Nederlands Meteorologisch Instituut (Fig. 1). For the Weiherbach site, the

available daily potential Penman evapotranspiration rates were directly used as model

input (Shierholz et al., 2000). Measured moisture contents at the onset of the simulation

were used to initialise the model at Vredepeel. Initial soil moisture conditions for


Fig. 1. Comparison of the FAO and Makkink reference evapotranspiration for the Vredepeel dataset.

37

6M

.V

an

cloo

steret

al./A

gricu

ltura

lW

ater

Ma

nagem

ent

44

(2000)

371±388

Weiherbach were obtained through previous simulations starting from 1 January

assuming ÿ100 hPa tension.

The development of crop LAI for winter wheat at Vredepeel was taken from literature

data collected in similar conditions (Groot and Verberne, 1991). Musterd grass LAI was

set equal to 2. The crop factors Kc were estimated from relevant data sources (Feddes,

1987). The crop growth parameters reported in the WAVE manual for winter wheat

(1993±1994) and summer barley (1995) were used as input for the crop growth simulator

at the Weiherbach site. For the lysimeter data, the literature inferred Kc factor was

multiplied with 1.25 in order to account for increased evapotranspiration that often occurs

in small lysimeters compared to the field situation (Boesten, 1994).

Available laboratory measured data of the drying moisture release curve and the

hydraulic conductivity curve were fitted to Eqs. (2) and (3) to yield an initial estimate of

the hydraulic properties at Vredepeel. Results are given in Tables 1 and 2. Following

Fuentes et al. (1992) the shape parameter m was set equal to 1 ÿ (2/n). The saturated

hydraulic conductivities as measured on duplicate soil cores were not used to parametrise

the hydraulic conductivity model since these values are subjected to a huge variability

and prone to experimental artefacts.

For the two soil horizons at the Weiherbach site, the hydraulic parameters of Eqs. (2)

and (6) as reported by Shierholz et al. (2000) were directly used. For this site m was set

Table 1

Moisture retention parameters for the Vredepeel and Weiherbach ®eld site

Dataset Layer (cm) Uncalibrated Calibrated

ys [±] yr [±] a (cmÿ1) n [±] ys [±] yr [±] a (cmÿ1) n [±]

Vredepeel 0±30 0.369 0 0.037 2.655 0.369 0 0.02 2.655

30±60 0.393 0 0.033 2.623 0.393 0 0.06 2.623

60±100 0.29 0 0.020 3.327 0.29 0 0.025 3.327

100±140 0.29 0 0.020 3.327 0.35 0 0.02 3.327

Weiherbach 0±30 0.46 0.03 0.015 1.30 0.46 0.03 0.015 1.30

30±200 0.45 0.08 0.005 2.25 0.45 0.08 0.005 2.25

Table 2

Hydraulic conductivity parameters for the Vredepeel and the Weiherbach ®eld site

Dataset Layer (cm) Uncalibrated Calibrated

Ksat (cm per day) Z Ksat (cm per day) Z

Vredepeel 0±30 10 4.2 10 4.9341

30±60 10 6.5 10 5.2235

60±100 10 3.1 10 2.36

100±140 10 3.1 10 2.437

Weiherbach 0±30 12.0 0.5 12.0 0.5

30±195 7.2 0.5 7.2 0.5

195±200 7.2 0.5 3.6a 0.5

aOnly for the lysimeters.


equal to 1 ÿ (1/n) and t to 0.5. Saturated water content (water content at 15 538 hPa) and

saturated hydraulic conductivity were measured directly. The shape parameters were

estimated using inverse modelling based on the measured outflow curves according to the

method reported by Van Dam et al. (1994).

2.2.2. The solute and heat balance components

The default parametrisation of the heat transport model as reported in the WAVE

manual was adopted in the study. The hydrodynamic dispersivity was set equal to 3 cm

for the sandy soil and 10 cm for the loamy soil. The soil chemical diffusion was set to

1.6 mm2 per day for bromide, 35 mm2 per day for ethoprophos and bentazone, and 5 mm2

per day for isoproturon. Since bromide is an anion, root uptake of the `̀ tracer'' was

considered for Vredepeel (51 kg total uptake in the winter wheat crop and 14 kg in the

mustard).

2.2.3. The pesticide balance component

To account for pesticide volatilisation, total pesticide input was reduced in Vredepeel

as suggested by Van den Bosch and Boesten (1994). Bentazone and ethoprophos

distribution constants were inferred from the Kom and fom measurements reported by Van

der Pas (1994). For isoproturon, the batch experiments reported by Shierholz et al. (2000)

were used (Table 3).

The potential first order degradation rate constants were inferred from the batch

experiments reported by Van der Pas (1994) and Shierholz et al. (2000) (Table 4). The

base temperature within fT (Eq. (11)) was set to 158C for ethoprophos and bentazone and

258C for isoproturon. The Q10 value was set equal to 2. The exponent within fy (Eq. (10))

was set equal to 0.053 for Vredepeel, while the batch experiments measured at 20, 40 and

60% of ysat enabled this exponent to be set equal to 0.2 for the Weiherbach soil. Uptake of

pesticide was never considered but lumped within the decay process.

2.3. Model calibration

For illustrating the impact of using effective calibrated field parameters instead of

laboratory scale parameters, calibrated modelling results are shown as well. Calibration

was performed on a trial and error basis, using the field scale observed moisture content,

bromide content, and pesticide content as objects.

Table 3

Pesticide sorption properties for the Vredepeel and Weiherbach ®eld sites

Dataset Layer (cm) Kd (uncalibrated) (l kgÿ1) Kd (calibrated) (l kgÿ1)

Vredepeel±ethoprophos 0±30 3.871 9.000

30±180 0.158 0.158

Vredepeel±bentazone 0±30 0.1030 0.1030

30±80 0.0042 0.0042

Weiherbach±isoproturon 0±30 2 2

30±200 1.69 1.69


At Vredepeel, the field data of 1990 and 1991 was used for calibration, while the data

of 1992 were used for the evaluation. Observed soil moisture profiles at day 103 and 278

were used to refine initial estimates of the hydraulic properties. The a value (Eq. (2)) for

all layers was calibrated assuming equilibrium with the groundwater table at day 103.

Further, the soil porosity of the layer below 1 m was increased and the conductivity of the

30±60 cm soil layer was augmented as recommended by Van den Bosch and Boesten

(1994). The final parameter estimates are also given in Tables 1 and 2.

The initial retardation of the strongly sorbing ethoprophos was underestimated which

was compensated by calibrating the Kd constant. The laboratory determined decay rate of

ethoprophos was calibrated to account for the overestimated dissipation at the soil

surface.

For the Weiherbach site, data for the year 1993±1994 were used to calibrate the model,

while the results for the year 1995 were used to validate the model. For the lysimeter data

at Weiherbach, the Ksat in the lowest layer (0.05 m thick) was set to 36 mm per day, i.e.

50% of the measured value to account for decreased conductivity due to the nature of the

lysimeter boundary construction.

3. Results and discussion

The uncalibrated and calibrated soil moisture and soil bromide profiles at days 103,

278 and 474 at Vredepeel are given in Fig. 2. The simulated concentration profiles of

bentazone and ethoprophos are given in Fig. 3.

For modelling the soil moisture profiles in Vredepeel, calibration was needed to

account for the scale gap between the laboratory measured retention and hydraulic

conductivity curves and the effective field hydraulic properties. The porosity (ys) of the

bottom layers was considered to be higher in order to describe the moisture content at the

bottom of the soil profile on 27/8/91 (Fig. 2c). The air entry value of all layers was

changed assuming hydraulic equilibrium with the shallow groundwater on 5/3/1991,

similar to Van den Bosch and Boesten (1994). Given these calibrations, the soil moisture

profile of the spring of 1992 could reasonably well be predicted. It should be mentioned,

however, that the calibration of the soil moisture profiles proved to be a tedious job. In

total, 18 trials were made before the calibration was accepted. This large number of

Table 4

Pesticide degradation parameters

Dataset Layer (cm) kdec (uncalibrated) (per day) kdec (calibrated) (per day)

Vredepeel±ethoprophos 0±40 0.089 0.012

40±180 0.00371 0.00371

Vredepeel±bentazone 0±40 0.049 0.049

40±100 0.000 0.000

100± 0.039 0.039

Weiherbach±isoproturon 0±30 0.047 0.047

30±200 0.005 0.005


simulation runs justifies the adoption of more automated calibration procedures in future

studies. Further, the most significant calibration was based on an indirect estimate of the

field scale moisture retention curve while the laboratory measured saturated hydraulic

conductivities were ignored. Therefore, one could question the usefulness of laboratory

measured retention and hydraulic conductivity data in predicting field scale water behaviour.

The measured soil bromide profiles at Vredepeel could not be described with the

considered solute transport model. The high mass of bromide at the soil surface in 1991

and 1992 was not predicted. The overestimated bromide content in the 20±40 cm soil

layer of 27/8/1991 could be calibrated by considering bromide uptake by the plant, but

the maximal concentrations of bromide deeper in the soil profile was subsequently

Fig. 2. Simulated soil moisture and bromide content at Vredepeel on days: (a) 103, (b) 278 and (c) 474.


underestimated. The measured bromide at the soil surface could be a result of the internal

entrapment of bromide within soil immobile zones upon its application. These solutes,

situated at the soil surface, slowly get released from the immobile towards the mobile

regions by a diffusion controlled process. In addition, mineralisation of structural root

bio-mass after harvest or root exudates can explain the presence of bromide close to the

soil surface. We believe that the adoption of a more appropriate non-equilibrium solute

Fig. 3. Simulated bentazone and ethoprophos pro®les at Vredepeel on days: (a) 103, (b) 278 and (c) 474.


transport concept with appropriate descriptions of the solute boundary conditions, and a

more mechanistic approach for describing bromide turn-over in the rhizosphere would

improve the simulation of the solute transport component at Vredepeel.

The simulated ethoprophos and bentazone concentration profiles are given in Fig. 3.

The overall migration of ethoprophos was overestimated given the kom and fom.

Increasing the Kd value did allow a correct description of the centre of mass of the

ethoprophos plume but not its dispersion. The measured ethoprophos profiles show a

rather sharp boundary, which is typical for non-linear sorbing pesticides. Non-linear

sorption is however not accounted for in the present version of the model, and the model

will therefore fail to describe appropriately the migration of non-linear sorbing pesticide

components if laboratory sorption data are used.

The dissipation rate of ethoprophos in the early season was overestimated. This could

be due to an inappropriate estimation of the pesticide volatilisation (which was arbitrarily

set equal to 50% of the pesticide applied), or due to an appropriate modelling of the biotic

and abiotic transformation processes in the early season. The overestimation of the

dissipation rate was corrected by calibrating kdec. Given these corrections, the

ethoprophos content was still overestimated in the summer season. This could again be

corrected by calibrating the Tb value of Eq. (11). The time dependent degradation

resulting from this calibration could probably account for the adaptation of the soil bio-

mass, a mechanism which is not considered in the present version of the model.

Fig. 4. Soil water contents, pro®les of bromide and isoproturon at the Wieherbach ®eld plot, 1993/1994,

experimental results and simulation: calibration.


The profiles of the mobile bentazone component could successfully be simulated

without any calibration. The surface accumulation which was pronounced for the

bromide profiles becomes nearly insignificant in the spring of 1991. The successful

simulation of the bentazone profile in contrast to the bromide profile indicates the

dominant sensitivity of degradation and sorption parameters to describe pesticide

behaviour in soil.

In contrast to the Vredepeel site, good accordance was found between simulated and

field measured soil moisture profiles during the first growing season using the laboratory

measured hydraulic parameters at Weiherbach (Fig. 4). This could be due to a better

performing parameter identification procedure based on inverse modelling. It should also

be noted that only winter data were used to assess the model performance during this

season. Total mass and distribution of bromide were well predicted without further

calibration of the solute transport parameters. Under soil moisture conditions of 1993/

1994 the laboratory derived value of 0.715 of the Walker-parameter b (Eq. (10)) predicted

unrealistic dissipation of the pesticide in the 0±0.95 m soil layer. Decreasing b to 0.2 led

to adequate mass predictions of isoproturon. Downward transport of the herbicide was

slightly overpredicted during the 141 days of the field experiment whereas dissipation at

the top 15 cm of the soil was underpredicted. Again, invoking a non-linear sorption

isotherm would improve this simulation. Using the calibrated dissipation parameters it

was possible to simulate realistic residue profiles of isoproturon.

Fig. 5. Drainage volumes, bromide and isoproturon loads in leachates from lysimeters at the Wieherbach site,

1993/1994, experimental results and simulation: calibration.


Drainage fluxes and accordingly bromide loads observed in the lysimeters displayed a

considerable variation (Fig. 5). The model predicted leachate volumes are within the

experimental range. The starting of drainage was also matched. Timing of bromide

breakthrough was also well predicted, but total loads were matched only once and

underpredicted for the other three lysimeters. Accordingly, we were not able to predict

the isoproturon load in the drainage water. In three replicates less isoproturon was

measured than simulated, revealing an underestimation of the pesticide retardation. In the

fourth lysimeter, most probably preferential flow contributed to an early breakthrough.

Considering the experimental variations the mechanistic±deterministic modelling

approach proved to be limited to predict leaching processes in the lysimeter system

under investigation.

In 1995 the field plot in Weiherbach received an irrigation of 260 l mÿ2 in addition to

the rainfall of 285 l mÿ2. Soil hydraulic parameters proven to be valid for the 1993/1994

did no longer allow to predict accurately the moisture profiles. In the 36 days period of

this experiment soil moisture was overpredicted (Fig. 6). Bromide profiles were

accurately predicted in the first 22 days of the simulation, but dissipation of bromide in

the 0.95 m soil profile was underestimated at the end of the study. Obviously, the change

of porosity due to tillage or other processes that affected water flow and solute transport

in 1995 is not accounted for within the present model concept. In addition, the model did

not accurately predict total amounts of isoproturon in the 1995 soil profile. Dissipation of

Fig. 6. Soil water content, pro®les of bromide and isoproturon at the Weiherbach ®eld plot 1995, experimental

results and simulations: evaluation.


the pesticide was faster in the field than simulated. As for the Vredepeel dataset,

enhanced microbial activity and increased degradation rates could be invoked.

On the lysimeters two irrigation regimes were imposed. Group I received 140 l mÿ2

and group II 280 l mÿ2. Drainage volumes were always overpredicted (Fig. 7). However,

the prediction of the start of bromide breakthrough was successful. Again, transport

mechanisms other than convection dispersion must have occurred since in the lysimeter 3

a smaller bromide load was detected than in lysimeter 4 which had a smaller drainage.

Breakthrough of isoproturon was observed in all lysimeters. In group I the duplicates

behaved similar, but the model overestimated pesticide loads and predicted a late start of

the isoproturon breakthrough. Again, non-linear sorption mechanisms and enhanced

degradation could be invoked. Huge differences of isoproturon loads in the leachates

were observed in group II that do not correspond to the observations of the bromide loads.

More information of the governing transport mechanism is needed to understand

pesticide fate in these conditions.

4. Conclusions

In this study, the different components of the integrated pesticide leaching model

WAVE were systematically evaluated using two datasets collected on a sandy (Vredepeel,

Netherlands) and a loamy (Weiherbach, Germany) soil.

Fig. 7. Drainage volumes, bromide and isoproturon loads in leachates from lysimeters at the Weiherbach site,

1995, experimental results and simulation: evaluation.


Predicting field measured soil moisture profiles from laboratory measured soil

moisture retention and hydraulic conductivity relationships is limited. The laboratory

inferred model parameters do not consider the same heterogeneity as present within the

field. In addition, temporal dynamics (effect of soil tillage, etc.) are not correctly

accounted for when using parameters inferred from laboratory cores sampled at a fixed

time. From the difference in performance of the uncalibrated soil water flow model

between Vredepeel and Weiherbach, it can be learned that hydraulic parameters inferred

from dynamic flow experiments, such as the multi-step outflow method, are more

appropriate than parameters inferred from classical soil physical set-ups.

No direct measurements of the different water balance terms are available in

Vredepeel. Identifying the correct atmospheric evapotranspiration demand was proven to

be problematic for this dataset, yielding a serious uncertainty on predicted drainage fluxes

at this site. Predicting the balance terms measured within lysimeters at the Weiherbach

field proved also to be limited. Lysimeters are point samples collected within a

heterogeneous field. Large variations are therefore observed in the measured drainage

fluxes. These variations could not be described with the presented deterministic

modelling approach.

The classical convection dispersion equation did not allow to explain the accumulation

of the bromide at the soil surface of the sandy soil. Apparently, some soil regions at the

soil surface catch the bromide within an immobile zone which slowly releases its tracer to

the mobile soil solution. In addition, bromide±crop interaction was proven to be

important and more information on the solution±crop interaction is needed to understand

the fate of this `̀ inert'' ionic tracer. Solute profiles in loamy soil on the other hand were

successfully described with the equilibrium transport model. Yet, when analysing the

lysimeter flow terms at this site, again non-equilibrium phenomena become more

pronounced, resulting in high bromide load for lysimeters showing small drainage.

The linear sorption isotherm model was inadequate for describing isoproturon and

ethoprophos retardation. This modelling approach resulted in an overestimate of the

dispersion of the pesticide in the profile and did not correctly describe the self-sharpening

migration front of highly sorbing pesticides. This modelling approach was however more

successful for the mobile bentazone component. The rather good description of the

pesticide fate in comparison to the tracer fate is an indication of the small sensitivity of

pesticide transport to solute transport parameters.

A first order degradation model is limited for describing pesticide dissipation.

Temporal dynamics of dissipation rates can partly be accounted for by manipulating the

temperature and soil moisture stress reduction function of the first order degradation

constant. Yet, it is our belief that a more mechanistic model of the micro-biological

activity is needed to appropriately describe the enhanced or retarded pesticide

degradation. The overestimate of the ethoprophos content in the early summer season

at the Vredepeel site could also be due to an inappropriate estimate of the volatilisation

losses. More appropriate descriptions for pesticide volatilisation losses would therefore

definitely increase the validation status of the present model.

The validation level of the integrated pesticide model remains low. Calibration of

model components was needed, especially for the hydrological part of the model. This

weakens the prediction capacity of the model to a large extent. In addition, no uncertainty


on the model parameter and model input estimates were considered in the present study.

A more advanced validation strategy, including sensitivity analysis and uncertainty

propagation analysis should be considered in future in order to be able to compare ranges

of measurements with ranges of simulation. In addition, more objective and automated

calibration procedures based on direct field measurement should be further developed.

Using deterministic models helps to identify crucial processes with regard to water, solute

and pesticide fate in soils, but their potential for accurate predictions is currently still

limited.

Acknowledgements

We thank Peter Viaene for programming the pesticide module within the existing

WAVE code. The European Communities are acknowledged for their support of this work

through the COST66 action on `Pesticide fate in the soil environment'. We thank Dr.

Boesten and Dr. Shierholz for providing the experimental data which helped to carry out

this study.

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