Modelling pesticide leaching in a sandy soil
with the VARLEACH model
M. Trevisan
a,*, G. Errera
a, C. Vischetti
b, A. Walker
caIstituto di Chimica Agraria ed Ambientale, FacoltaÁ di Agraria, UniversitaÁ Cattolica del Sacro Cuore,
Via Emilia Parmense 84, 29100 Piacenza, Italy
bCentro di Studio sulla Chimica e Biochimica dei Fitofarmaci, CNR, Borgo XX Giugno 72, 06121 Perugia, Italy
cHorticulture 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 data-set as identi®ed by independent model users. Finally a simulation with a consensus parameter data-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
Agricultural Water Management 44 (2000) 357±369
*Corresponding author.
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
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The model was developed initially to simulate mobility and persistence of pre-emergence 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 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 eitherEr, orErcorrected by crop factor (Van der Pas,
personal communication). However, one feature of VARLEACH is that it contains routines to estimate pan evaporation from air temperature data and these default sub-routines were also used by the three user groups. With the soil data, the choice of horizon
Table 1
Input parameters for the VARLEACH model
Input data Users (range) Consensus
Er(mm/day) Yes±no Modi®ed
Soil depth (cm) 120±160 100
First layer (A) (cm) 0±25 0±32
Second layer (B) (cm) 25±100 32±50
Third layer (C) (cm) 100±(120±160) 50±100 Field capacity (ÿ5 kPa, w/w) 15.9±37.8 17.6 Water content (ÿ200 kPa, w/w) 9.5±22.7 9.9 Factor changing water content with depth (layer B) 0.712±1.044 0.991 Factor changing water content with depth (layer C) 0.308±0.500 0.779 Initial water content (w/w) 13.0±27.0 16.5
Bulk density (g/cm3) 1.33±1.40 1.345
Bentazone
Adsorption coef®cient Kd (ml/g) 0.11±0.13 0.105 Adsorption time increment 0.002±0.01 0.011 Factor changing Kd with depth (layer B) 0.048 0.218 Factor changing Kd with depth (layer C) 0.052 0.048
Water solubility (mg/l) 500±1200 500
Half life (days) (%moisture,T) 52.5±203.9 (15±14.6, 15±5) 206 (14.6, 5) Factor changing half life with depth (layer B) 4.00±29.00 16.00 Factor changing half life with depth (layer C) 0.47±2.50 36.41
Applied dose (kg/ha) 0.73±0.80 0.63
Ethoprophos
Adsorption coef®cient Kd (ml/g) 2.67±3.62 4.23 Adsorption time increment 0.049±0.13 0.049 Factor changing Kd with depth (layer B) 0.048 0.218 Factor changing Kd with depth (layer C) 0.052 0.048
Water solubility (mg/l) 750±1200 750
Half life (days) (%moisture,T) 147.5±346.6 (15±14.6, 15±5) 193.4 (14.6, 10) Factor changing half life with depth (layer B) 1.82±2.50 0.80
Factor changing half life with depth (layer C) 1.25±1.50 3.32
Applied dose (kg/ha) 3.00±3.35 1.33
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 ofÿ200 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 data-set, 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 EF1.0.
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 CRM0.
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 <1 the model tends to underpredict penetration; when MDR1.0, there is perfect agreement between observation and
simulation in terms of penetration into the soil.
MD
Table 2
Statistical model performance indicators for simulated ethoprophos and bentazone simulation
Index Bentazone profile Ethoprophos profile Index Total ethoprophos
Uncalibrated Calibrated Uncalibrated Calibrated Uncalibrated Calibrated
Global EF Poora ÿ0.533 EF Poor
CRM 0.614
User 1 EF Poora b Poor b EF Poor b
MDR 0.476 b 1.193 b CRM 0.992 b
User 2 EF Poor Poor Poor Poor EF Poor Poor
MDR 1.089 1.451 0.980 1.109 CRM 0.948 0.899
User 3 EF Poor Poor Poor Poor EF Poor Poor
MDR 0.184 1.112 0.985 1.201 CRM 1.030 0.809
Consensus EF Poorc Poord 0.986c 0.986d EF 0.565c Poord
MDR 1.039c 1.018d 1.088c 1.067d CRM 0.565c
ÿ0.185d aNegative values, see text.
bUser 1 perform only the uncalibrated simulation. cUsingEr, data-set.
dUsingE
r, values correct by crop factor.
It should be noted that these indices must be used carefully and the values should be interpreted with caution. However they do provide a useful method of comparing the degree of fit of model outputs produced by different users to measured data.
The graphical evaluation of the data was performed using the confidence limit approach. We have calculated the confidence levels for both the experimental and predicted data atp0.05. All of the predicted data were used in order to obtain sufficient replication, and the end result therefore probably provides a good representation of the true variability between users. The statistical indices were calculated for each individual model run and also for all simulations combined (global; Table 2). We have included the consensus simulation in the graphical data to indicate probably the best performance of the VARLEACH model.
3. Results and discussion
User 1 performed only uncalibrated simulations while users 2 and 3 calibrated water flow and bromide transport, changing field capacity, water content at ÿ200 kPa and
initial soil moisture values, to obtain better fit between observed and simulated data. In this paper we will discuss only the results for the pesticide simulations and not those for water flow or bromide transport. Graphical comparisons of the observed and simulated data for ethoprophos and bentazone are shown in Figs. 2±4. Statistical indices derived from the data are given in Table 2.
There were large differences between measured and simulated distribution profiles for bentazone (Fig. 2). According to the statistical criteria, the bentazone distribution profiles were not accurately simulated, although user 2, who used a higher half life value, predicted the overall distribution of residues in soil with reasonable accuracy (MDR
Fig. 2. (Continued).
Fig. 4. Observed versus predicted areic mass of ethoprophos as a function of time. Mean and 95% con®dence interval of observed data and of all simulations provided by the participants are shown. Model results with the consensus parameters are also shown.
Fig. 3. (Continued).
index1.089). The consensus simulation appeared to predict better distributions even though it overestimated the experimental data in terms of absolute residues (MDR1.039 or 1.018) (Table 2). As expected, the simulated behaviour of bentazone,
a mobile pesticide, was strongly influenced by the variability in sorption and degradation rate parameters chosen by the different users. The sorption parameters were clearly defined in the Vredepeel data-set set and hence the degradation parameters must have played a particularly significant role; in fact the degradation rate in the second horizon measured in laboratory experiments gave very long half-lives which were clearly much longer than those for dissipation time measured in the field experiment. It seems probable that this discrepancy is the most likely reason for the pronounced differences in bentazone profile concentration between simulated and measured data.
Total ethoprophos residues in soil were largely influenced by the initial rate of volatilisation from soil. VARLEACH does not have routines to describe these losses, and it was only in the consensus simulation that volatilisation was taken into account by reducing the applied dose to 1.33 kg/ha instead of the actual amount of 3.0 kg/ha. Although somewhat arbitrary, this procedure gave much improved simulations of later soil residues (Fig. 4) indicating that ethoprophos degradation was well described by the model. Ethoprophos residue distributions in the soil profile were also well predicted in the consensus simulations, but always overestimated in the others. This was particularly so for total residues but not for penetration in soil (MDR index near to 1.0 for all users; Table 2). Ethoprophos behaviour was therefore well forecast by the VARLEACH model with the exception of the early losses by volatilisation.
Ethoprophos consensus simulations were always within the confidence limits of the experimental data, except for those in the first day for total mass comparison curve. In contrast, bentazone consensus simulations were occasionally outside the confidence level limits of the experimental data.
4. Conclusion
must be reduced if the models are to be used for regulatory goals with strict guidelines and/or user friendly tools for deriving model inputs.
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