Directory UMM :Data Elmu:jurnal:A:Agricultural Water Management:Vol44.Issue1-3.Apr2000:
Agricultural Water Management 44 (2000) 357±369
Modelling pesticide leaching in a sandy soil
with the VARLEACH model
M. Trevisana,*, G. Erreraa, C. Vischettib, A. Walkerc
a
Istituto di Chimica Agraria ed Ambientale, FacoltaÁ di Agraria, UniversitaÁ Cattolica del Sacro Cuore,
Via Emilia Parmense 84, 29100 Piacenza, Italy
b
Centro di Studio sulla Chimica e Biochimica dei Fitofarmaci, CNR, Borgo XX Giugno 72, 06121 Perugia, Italy
c
Horticulture Research International, Wellesbourne, Warwick CV35 9EF, UK
Abstract
Within this paper the ability of the VARLEACH model to simulate ®eld results is presented. The
evaluation was carried out in the framework of a European modelling validation exercise, adopting
a standardised modelling protocol. Simulations were performed with and without a calibrated dataset as identi®ed by independent model users. Finally a simulation with a consensus parameter set
was made. The model gave an accurate description of pesticide penetration in the soil pro®le,
although occasionally with some overprediction, but it did not simulate the absolute level of soil
residues. With bentazone, which is a weakly sorbed and moderately persistent compound, the
laboratory data on degradation did not describe the observed ®eld behaviour. Total residues of
ethoprophos were poorly simulated because the VARLEACH model does not take account of losses
by volatilisation. However, if a correction was applied for the potential vapour losses, the simulated
results were in agreement with those measured.
The modelling exercise with different users indicated how input value control is one of the most
important aspects to increase validity and use of models to forecast pesticide behaviour. # 2000
Elsevier Science B.V. All rights reserved.
Keywords: VARLEACH; Model validation; Model calibration; Ethoprophos; Bentazone
1. Introduction
EC directive 91/414 concerning the placing of plant protection products on the market
gives a new importance to the environment. As reported in annex II and annex III, the risk
assessment implies more than an expert judgement, and mathematical models have an
*
Corresponding author.
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 0 - 6
358
M. Trevisan et al. / Agricultural Water Management 44 (2000) 357±369
increasing role to predict environmental concentration (PEC). Furthermore, since field
studies to measure environmental concentration are so expensive and specific to a single
environmental scenario, interest has been developed in the use of models to predict
fate in a range of circumstances. The new legislation suggests that models are
indispensable tools to be used in the applied environmental sciences to pesticide fate.
However, knowledge of model assumptions and limitations is necessary for their proper
application.
The aim of this work was to evaluate the predictive ability of the VARLEACH model
(Walker and Hollis, 1994) using a data-set describing results from field and laboratory
experiments carried out in The Netherlands at Vredepeel (Boesten and Van der Pas, 2000)
in the framework of a European modelling validation exercise supported by the COST 66
Action `Pesticides in the soil environment' of DGXII-EU. The VARLEACH model was
considered by the European FOCUS working groups as a model useful for calculation of
predicted environmental concentrations (PEC) in soil and groundwater (Boesten et al.,
1995, 1997). It was classified by FIFRA in the United States as a secondary model
(FIFRA, 1994).
One main conclusion from the FOCUS work groups was that no pesticide leaching
model was adequately validated to permit widespread use in product registration. For this
reason an evaluation of model performances against a data-set of high quality was of
interest so that the ability of the model to forecast the behaviour of pesticide and the
confidence of the simulations could be established. In addition, different laboratories used
the same data-set, and this gave the opportunity to evaluate the influence of the users and
their choice of input parameters on the predictions. Three different groups performed the
simulations without exchange of information. Variability of input and output data were
compared. Individually calibrated runs of the model were also attempted, and finally, a
simulation was done with an input data file agreed after discussion among the users and
the providers of the data-set.
2. Materials and methods
2.1. Model description
VARLEACH is a simple leaching model that incorporates subroutines to allow for the
effects of temperature and soil moisture on degradation rates in soil. The pesticide
leaching component of the model is an adaptation of the nitrate leaching program
described originally by Addiscott (1977). This was modified to allow for solute
adsorption by Nicholls et al. (1982a). The degradation subroutines in the model PERSIST
(Walker and Barnes, 1981) were then incorporated into the leaching model by Nicholls et
al. (1982b) and this was modified again by Walker (1987) to permit adsorption/desorption
to vary with residence time of the pesticide in the soil. Some recent updates permit
adsorption, degradation rates, and water holding properties of the soil to vary with depth,
and calculations are made of total water and solute flow across the lowest boundary as
described by Walker and Hollis (1994). In Fig. 1 flow diagram for operation of
VARLEACH is reported.
359
Fig. 1. Flow diagram for operation of VARLEACH with special features.
M. Trevisan et al. / Agricultural Water Management 44 (2000) 357±369
360
M. Trevisan et al. / Agricultural Water Management 44 (2000) 357±369
The model was developed initially to simulate mobility and persistence of preemergence applications of herbicides in the top layers (0±15 cm) of soil in relation to
their activity and selectivity between crop and weeds. The model was therefore developed
for bare soil situations and it is not able to simulate crop growth. Evaporation of water is
based on potential evaporation from an open water surface with a correction factor for
soil wetness, and no crop removal of water is allowed for. In addition there is no
allowance for pesticide volatilisation. The water flow routines use a tipping bucket type
approach and water equations are solved via stepwise integration. Only a single chemical
is considered in any model run (although a recent modification of the model allows for
repeated applications). The model does not include runoff or erosion routines. Boundary
conditions are set automatically by the program and the model sets the time and depth
increments (1 cm) for the calculations, which determine the amount of numerical
dispersion.
The main advantages of the model are that few input parameters are required, and there
is rapid run time. In terms of process descriptions, the positive features of the model are
the detailed simulation of temperature and moisture effects on degradation, and the
allowance for increasing sorption with time in the upper soil layers. Sensitivity analysis
carried out with VARLEACH (Walker et al., 1995) shown that degradation parameters,
soil field capacity, soil bulk density affected total residue level, sorption parameters (Kd
and increment with time) had a strong influence on leaching depth.
2.2. Data-set description
The data-set used was obtained at Vredepeel, The Netherlands, in a sandy soil and it
was described elsewhere (Boesten and Van der Pas, 2000). In this data-set, the results
refer to movement and persistence in soil of ethoprophos, bentazone and bromide.
Although the data-set is well characterised, a few problems were encountered in its
application to the VARLEACH model.
There were few measurements of residue distributions in soil, e.g. there were only
three sampling times when pesticide residue distribution profiles and soil moisture
contents were measured, and there just seven sampling dates to evaluate persistence
of ethoprophos. In addition there was no calculation of half-lives or sorption
coefficients of pesticides by the authors, and field capacity and wilting point of soil
horizons was not specified directly. Finally during the experiment time two crops were
grown.
2.3. Modelling parameters
The main objective was for the different users to simulate behaviour of ethoprophos
and bentazone at the Vredepeel site. Table 1 gives a summary of the VARLEACH input
parameters set by the different users. VARLEACH input data could be divided into
weather, soil and pesticide data and for each simulation, the number of input data is
approximately 20. Table 1 reports the range of input data selected independently by the
users, and also lists the values chosen for the consensus simulation which were agreed
following discussions between the various groups.
361
M. Trevisan et al. / Agricultural Water Management 44 (2000) 357±369
Table 1
Input parameters for the VARLEACH model
Input data
Users (range)
Consensus
Er (mm/day)
Soil depth (cm)
First layer (A) (cm)
Second layer (B) (cm)
Third layer (C) (cm)
Field capacity (ÿ5 kPa, w/w)
Water content (ÿ200 kPa, w/w)
Factor changing water content with depth (layer B)
Factor changing water content with depth (layer C)
Initial water content (w/w)
Bulk density (g/cm3)
Yes±no
120±160
0±25
25±100
100±(120±160)
15.9±37.8
9.5±22.7
0.712±1.044
0.308±0.500
13.0±27.0
1.33±1.40
Modi®ed
100
0±32
32±50
50±100
17.6
9.9
0.991
0.779
16.5
1.345
Bentazone
Adsorption coef®cient Kd (ml/g)
Adsorption time increment
Factor changing Kd with depth (layer B)
Factor changing Kd with depth (layer C)
Water solubility (mg/l)
Half life (days) (%moisture, T )
Factor changing half life with depth (layer B)
Factor changing half life with depth (layer C)
Applied dose (kg/ha)
0.11±0.13
0.002±0.01
0.048
0.052
500±1200
52.5±203.9 (15±14.6, 15±5)
4.00±29.00
0.47±2.50
0.73±0.80
0.105
0.011
0.218
0.048
500
206 (14.6, 5)
16.00
36.41
0.63
Ethoprophos
Adsorption coef®cient Kd (ml/g)
Adsorption time increment
Factor changing Kd with depth (layer B)
Factor changing Kd with depth (layer C)
Water solubility (mg/l)
Half life (days) (%moisture, T )
Factor changing half life with depth (layer B)
Factor changing half life with depth (layer C)
Applied dose (kg/ha)
2.67±3.62
0.049±0.13
0.048
0.052
750±1200
147.5±346.6 (15±14.6, 15±5)
1.82±2.50
1.25±1.50
3.00±3.35
4.23
0.049
0.218
0.048
750
193.4 (14.6, 10)
0.80
3.32
1.33
The main differences in input parameters among users (Table 1) were for those
parameters which did not have fixed values in data-set. The more evident discrepancies
were in the parameters that describe sorption and degradation of the pesticides, and the
parameters describing soil hydrology. In the consensus simulation, some parameters were
fixed by the provider of the data-set, and others were agreed among the model users. In
the weather data, there were differences between users in the way in which
evapotranspiration was described, because the data-set provided daily Makkink reference
crop evapotranspiration data (Er) and not daily values of pan evaporation. Two consensus
simulations were carried out using either Er, or Er corrected by crop factor (Van der Pas,
personal communication). However, one feature of VARLEACH is that it contains subroutines to estimate pan evaporation from air temperature data and these default subroutines were also used by the three user groups. With the soil data, the choice of horizon
362
M. Trevisan et al. / Agricultural Water Management 44 (2000) 357±369
thickness, field capacity and wilting point were the most variable. The data-set did not
clearly define the thickness of all soil horizons. The hydrology parameters used in
VARLEACH are expressed as weight on weight (w/w), and as such are not easy to find in
the data-set. Field capacity in VARLEACH is defined as the water content at a potential
of ÿ5 kPa, and the model also required water content at a potential of ÿ200 kPa. All
three users apparently chose different ways to estimate these values from the information
provided. Where the pesticide data is concerned, differences were most apparent in the
sorption and degradation parameters. These data had to be estimated from information in
the data-set and were not provided directly. Again. there were clear differences between
the users in the estimation methods used. The degradation routines in VARLEACH
compute the effects of temperature and moisture on rates of loss. In the Vredepeel dataset, only temperature effects on pesticide degradation were measured, again requiring
some subjective decisions by the users on how to quantify the effects of moisture
variations. Finally. the influence of the increment of sorption with time was evaluated
only in the data-set for ethoprophos.
2.4. Comparison
Evaluation of the correspondence between observed and simulated data was carried out
using both graphical methods and statistical indices. The statistical indices used were
chosen to evaluate the overall fit (Model Efficiency, EF), the prediction of total soil
residues (Coefficient of Residual Mass, CRM) (Vanclooster et al., 1998) and the
prediction of the distribution of residues in soil (Mean Depth Ratio, MDR) (Walker et al.,
1995). EF is a widely-used index to evaluate overall fit, and when EF becomes negative,
the fit is unacceptably poor. The best fit is when EF 1.0.
The CRM index is useful to evaluate the agreement between simulated and observed
total soil residues, i.e. the goodness of fit of the degradation simulation. When CRM > 0,
the model overpredicts soil residues; when CRM < 0, the model underpredicts soil
residues. A perfect fit is indicated by CRM 0.
The MDR index is useful for evaluation of the prediction of the distribution of residues
in soil. The mean depth is a measure of the penetration of compound into the soil, and
represents the centre of mass of the pesticide distribution along the soil profile. The MDR
is the ratio between simulated and observed mean depths. When MDR > 1, the model
tends to overpredict the penetration; when it is
Modelling pesticide leaching in a sandy soil
with the VARLEACH model
M. Trevisana,*, G. Erreraa, C. Vischettib, A. Walkerc
a
Istituto di Chimica Agraria ed Ambientale, FacoltaÁ di Agraria, UniversitaÁ Cattolica del Sacro Cuore,
Via Emilia Parmense 84, 29100 Piacenza, Italy
b
Centro di Studio sulla Chimica e Biochimica dei Fitofarmaci, CNR, Borgo XX Giugno 72, 06121 Perugia, Italy
c
Horticulture Research International, Wellesbourne, Warwick CV35 9EF, UK
Abstract
Within this paper the ability of the VARLEACH model to simulate ®eld results is presented. The
evaluation was carried out in the framework of a European modelling validation exercise, adopting
a standardised modelling protocol. Simulations were performed with and without a calibrated dataset as identi®ed by independent model users. Finally a simulation with a consensus parameter set
was made. The model gave an accurate description of pesticide penetration in the soil pro®le,
although occasionally with some overprediction, but it did not simulate the absolute level of soil
residues. With bentazone, which is a weakly sorbed and moderately persistent compound, the
laboratory data on degradation did not describe the observed ®eld behaviour. Total residues of
ethoprophos were poorly simulated because the VARLEACH model does not take account of losses
by volatilisation. However, if a correction was applied for the potential vapour losses, the simulated
results were in agreement with those measured.
The modelling exercise with different users indicated how input value control is one of the most
important aspects to increase validity and use of models to forecast pesticide behaviour. # 2000
Elsevier Science B.V. All rights reserved.
Keywords: VARLEACH; Model validation; Model calibration; Ethoprophos; Bentazone
1. Introduction
EC directive 91/414 concerning the placing of plant protection products on the market
gives a new importance to the environment. As reported in annex II and annex III, the risk
assessment implies more than an expert judgement, and mathematical models have an
*
Corresponding author.
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 0 - 6
358
M. Trevisan et al. / Agricultural Water Management 44 (2000) 357±369
increasing role to predict environmental concentration (PEC). Furthermore, since field
studies to measure environmental concentration are so expensive and specific to a single
environmental scenario, interest has been developed in the use of models to predict
fate in a range of circumstances. The new legislation suggests that models are
indispensable tools to be used in the applied environmental sciences to pesticide fate.
However, knowledge of model assumptions and limitations is necessary for their proper
application.
The aim of this work was to evaluate the predictive ability of the VARLEACH model
(Walker and Hollis, 1994) using a data-set describing results from field and laboratory
experiments carried out in The Netherlands at Vredepeel (Boesten and Van der Pas, 2000)
in the framework of a European modelling validation exercise supported by the COST 66
Action `Pesticides in the soil environment' of DGXII-EU. The VARLEACH model was
considered by the European FOCUS working groups as a model useful for calculation of
predicted environmental concentrations (PEC) in soil and groundwater (Boesten et al.,
1995, 1997). It was classified by FIFRA in the United States as a secondary model
(FIFRA, 1994).
One main conclusion from the FOCUS work groups was that no pesticide leaching
model was adequately validated to permit widespread use in product registration. For this
reason an evaluation of model performances against a data-set of high quality was of
interest so that the ability of the model to forecast the behaviour of pesticide and the
confidence of the simulations could be established. In addition, different laboratories used
the same data-set, and this gave the opportunity to evaluate the influence of the users and
their choice of input parameters on the predictions. Three different groups performed the
simulations without exchange of information. Variability of input and output data were
compared. Individually calibrated runs of the model were also attempted, and finally, a
simulation was done with an input data file agreed after discussion among the users and
the providers of the data-set.
2. Materials and methods
2.1. Model description
VARLEACH is a simple leaching model that incorporates subroutines to allow for the
effects of temperature and soil moisture on degradation rates in soil. The pesticide
leaching component of the model is an adaptation of the nitrate leaching program
described originally by Addiscott (1977). This was modified to allow for solute
adsorption by Nicholls et al. (1982a). The degradation subroutines in the model PERSIST
(Walker and Barnes, 1981) were then incorporated into the leaching model by Nicholls et
al. (1982b) and this was modified again by Walker (1987) to permit adsorption/desorption
to vary with residence time of the pesticide in the soil. Some recent updates permit
adsorption, degradation rates, and water holding properties of the soil to vary with depth,
and calculations are made of total water and solute flow across the lowest boundary as
described by Walker and Hollis (1994). In Fig. 1 flow diagram for operation of
VARLEACH is reported.
359
Fig. 1. Flow diagram for operation of VARLEACH with special features.
M. Trevisan et al. / Agricultural Water Management 44 (2000) 357±369
360
M. Trevisan et al. / Agricultural Water Management 44 (2000) 357±369
The model was developed initially to simulate mobility and persistence of preemergence applications of herbicides in the top layers (0±15 cm) of soil in relation to
their activity and selectivity between crop and weeds. The model was therefore developed
for bare soil situations and it is not able to simulate crop growth. Evaporation of water is
based on potential evaporation from an open water surface with a correction factor for
soil wetness, and no crop removal of water is allowed for. In addition there is no
allowance for pesticide volatilisation. The water flow routines use a tipping bucket type
approach and water equations are solved via stepwise integration. Only a single chemical
is considered in any model run (although a recent modification of the model allows for
repeated applications). The model does not include runoff or erosion routines. Boundary
conditions are set automatically by the program and the model sets the time and depth
increments (1 cm) for the calculations, which determine the amount of numerical
dispersion.
The main advantages of the model are that few input parameters are required, and there
is rapid run time. In terms of process descriptions, the positive features of the model are
the detailed simulation of temperature and moisture effects on degradation, and the
allowance for increasing sorption with time in the upper soil layers. Sensitivity analysis
carried out with VARLEACH (Walker et al., 1995) shown that degradation parameters,
soil field capacity, soil bulk density affected total residue level, sorption parameters (Kd
and increment with time) had a strong influence on leaching depth.
2.2. Data-set description
The data-set used was obtained at Vredepeel, The Netherlands, in a sandy soil and it
was described elsewhere (Boesten and Van der Pas, 2000). In this data-set, the results
refer to movement and persistence in soil of ethoprophos, bentazone and bromide.
Although the data-set is well characterised, a few problems were encountered in its
application to the VARLEACH model.
There were few measurements of residue distributions in soil, e.g. there were only
three sampling times when pesticide residue distribution profiles and soil moisture
contents were measured, and there just seven sampling dates to evaluate persistence
of ethoprophos. In addition there was no calculation of half-lives or sorption
coefficients of pesticides by the authors, and field capacity and wilting point of soil
horizons was not specified directly. Finally during the experiment time two crops were
grown.
2.3. Modelling parameters
The main objective was for the different users to simulate behaviour of ethoprophos
and bentazone at the Vredepeel site. Table 1 gives a summary of the VARLEACH input
parameters set by the different users. VARLEACH input data could be divided into
weather, soil and pesticide data and for each simulation, the number of input data is
approximately 20. Table 1 reports the range of input data selected independently by the
users, and also lists the values chosen for the consensus simulation which were agreed
following discussions between the various groups.
361
M. Trevisan et al. / Agricultural Water Management 44 (2000) 357±369
Table 1
Input parameters for the VARLEACH model
Input data
Users (range)
Consensus
Er (mm/day)
Soil depth (cm)
First layer (A) (cm)
Second layer (B) (cm)
Third layer (C) (cm)
Field capacity (ÿ5 kPa, w/w)
Water content (ÿ200 kPa, w/w)
Factor changing water content with depth (layer B)
Factor changing water content with depth (layer C)
Initial water content (w/w)
Bulk density (g/cm3)
Yes±no
120±160
0±25
25±100
100±(120±160)
15.9±37.8
9.5±22.7
0.712±1.044
0.308±0.500
13.0±27.0
1.33±1.40
Modi®ed
100
0±32
32±50
50±100
17.6
9.9
0.991
0.779
16.5
1.345
Bentazone
Adsorption coef®cient Kd (ml/g)
Adsorption time increment
Factor changing Kd with depth (layer B)
Factor changing Kd with depth (layer C)
Water solubility (mg/l)
Half life (days) (%moisture, T )
Factor changing half life with depth (layer B)
Factor changing half life with depth (layer C)
Applied dose (kg/ha)
0.11±0.13
0.002±0.01
0.048
0.052
500±1200
52.5±203.9 (15±14.6, 15±5)
4.00±29.00
0.47±2.50
0.73±0.80
0.105
0.011
0.218
0.048
500
206 (14.6, 5)
16.00
36.41
0.63
Ethoprophos
Adsorption coef®cient Kd (ml/g)
Adsorption time increment
Factor changing Kd with depth (layer B)
Factor changing Kd with depth (layer C)
Water solubility (mg/l)
Half life (days) (%moisture, T )
Factor changing half life with depth (layer B)
Factor changing half life with depth (layer C)
Applied dose (kg/ha)
2.67±3.62
0.049±0.13
0.048
0.052
750±1200
147.5±346.6 (15±14.6, 15±5)
1.82±2.50
1.25±1.50
3.00±3.35
4.23
0.049
0.218
0.048
750
193.4 (14.6, 10)
0.80
3.32
1.33
The main differences in input parameters among users (Table 1) were for those
parameters which did not have fixed values in data-set. The more evident discrepancies
were in the parameters that describe sorption and degradation of the pesticides, and the
parameters describing soil hydrology. In the consensus simulation, some parameters were
fixed by the provider of the data-set, and others were agreed among the model users. In
the weather data, there were differences between users in the way in which
evapotranspiration was described, because the data-set provided daily Makkink reference
crop evapotranspiration data (Er) and not daily values of pan evaporation. Two consensus
simulations were carried out using either Er, or Er corrected by crop factor (Van der Pas,
personal communication). However, one feature of VARLEACH is that it contains subroutines to estimate pan evaporation from air temperature data and these default subroutines were also used by the three user groups. With the soil data, the choice of horizon
362
M. Trevisan et al. / Agricultural Water Management 44 (2000) 357±369
thickness, field capacity and wilting point were the most variable. The data-set did not
clearly define the thickness of all soil horizons. The hydrology parameters used in
VARLEACH are expressed as weight on weight (w/w), and as such are not easy to find in
the data-set. Field capacity in VARLEACH is defined as the water content at a potential
of ÿ5 kPa, and the model also required water content at a potential of ÿ200 kPa. All
three users apparently chose different ways to estimate these values from the information
provided. Where the pesticide data is concerned, differences were most apparent in the
sorption and degradation parameters. These data had to be estimated from information in
the data-set and were not provided directly. Again. there were clear differences between
the users in the estimation methods used. The degradation routines in VARLEACH
compute the effects of temperature and moisture on rates of loss. In the Vredepeel dataset, only temperature effects on pesticide degradation were measured, again requiring
some subjective decisions by the users on how to quantify the effects of moisture
variations. Finally. the influence of the increment of sorption with time was evaluated
only in the data-set for ethoprophos.
2.4. Comparison
Evaluation of the correspondence between observed and simulated data was carried out
using both graphical methods and statistical indices. The statistical indices used were
chosen to evaluate the overall fit (Model Efficiency, EF), the prediction of total soil
residues (Coefficient of Residual Mass, CRM) (Vanclooster et al., 1998) and the
prediction of the distribution of residues in soil (Mean Depth Ratio, MDR) (Walker et al.,
1995). EF is a widely-used index to evaluate overall fit, and when EF becomes negative,
the fit is unacceptably poor. The best fit is when EF 1.0.
The CRM index is useful to evaluate the agreement between simulated and observed
total soil residues, i.e. the goodness of fit of the degradation simulation. When CRM > 0,
the model overpredicts soil residues; when CRM < 0, the model underpredicts soil
residues. A perfect fit is indicated by CRM 0.
The MDR index is useful for evaluation of the prediction of the distribution of residues
in soil. The mean depth is a measure of the penetration of compound into the soil, and
represents the centre of mass of the pesticide distribution along the soil profile. The MDR
is the ratio between simulated and observed mean depths. When MDR > 1, the model
tends to overpredict the penetration; when it is