Eur. J. Agron.• 1995, 4(4), 473-484

Modelling pesticide leaching in soils main aspects and main difficulties

R. Calvet

Institut National Agronomique Paris-Grignon, France, Centre de Grignon, 78850 Thiverval-Grignon Accepted: 14 September 1995.

Abstract The paper gives a review of the main models which have been proposed to describe the fate of pesticides in soils. Descriptions of processes taken into account by the models are compared and briefly discussed. Difficulties for modelling pesticide leaching are examined in relation to the pos­sible improvement of the description of involved processes and to model sensitivity and validation.

Key-words : pesticides, leaching, soil, models.

INTRODUCTION

Urban, industrial and agricultural activities produce increasing amounts of potential pollutants which are introduced to cropped and uncropped areas. There is growing concern about the fate of these chemicals, their consequences for the long-term soil health and quality of surface and ground-water. Water monitoring in many countries has revealed contamination by vari­ous toxic chemicals, particularly pesticides. A number of pesticides has recently been detected in groundwa­ters in western Europe and in the USA in the past years. Thus, nonpoint pollution due to pesticides may represent a threat to groundwater and to public water supply. As the need for water increases and the amount of potable water in the world is limited, people are increasingly conscious of the need to pro­tect water resources. The extent of leaching and the resulting alteration in water quality depend on soil and pesticide properties, climatic conditions, crop type and cropping practices, and water management methods. Considering the need of pesticide use in agriculture, the only acceptable solution for the prevention of groundwater contamination is improved pesticide management, leading to acceptable and safe applica­tion rates. Knowledge of the fate of pesticides in soil, in terms of basic phenomena such as transport, reten­tion and transformation, is essential to the develop­ment of predictive tools. One way to proceed is by numerical modelling of chemical transport, coupled with evaluation of sources and sinks. Models may be useful because they accomodate environmental and hydrological conditions, chemical properties, alterna­tive agronomic and management practices and the spa­tial variability of soil and chemical/media interactions.

There have been many attempts to model solute transport in soils and other porous media. Determinis­tic and mechanistic models based on miscible dis­placement theory (Nielsen and Biggar, 1962) have been widely used in transport modelling. Many math­ematical developments and field experiments have been reported in the literature (Wagenet and Rao, 1990 ; Wagenet, 1993). Transport models are classified according to several criteria (Addiscott and Wagenet, 1985). Modelling approaches can be classified as deterministic or stochastic models, with two subcat­egories, mechanistic and functional, according to the description of the processes. Closely related is the dis­tinction between rate and capacity models. From the point of view of their use, models have been devel­oped from various perspectives : research, manage­ment, regulation, screening and educational purposes. They present descriptions which vary greatly accord­ing to the phenomena considered and the nature of their mathematical formulation. These models are very different regarding the number of input data and dis­tinctions between different categories may appear arti­ficial. The general trend is to increase the complexity of models by increasing input parameters when going from educational to research models. However, research models may prove useful teaching tools. In principle, management models can relate the output results to cropping systems and cropping techniques.

The aim of this paper is to provide a general pre­sentation of some of the published pesticide leaching models and to discuss briefly some important difficul­ties encountered when using them. Mathematical and numerical aspects are beyond the scope of the paper and the reader is referred to publications of the model developers.

ISSN 1161-0301195/041$ 4.001© Gauthier-Villars - ESAg

474 R. Calvet

GENERAL CHARACTERISTICS OF PESTI­ them applied strictly to homogeneous media, others CIDE LEACHING MODELS can be used for describing transport in heterogeneous

soil profiles. Transport of pesticides can take place through run­

Several methods are available for modelling the fate off, erosion, leaching and volatilization. Runoff and of pesticides in the environment, depending on the erosion models are not described here, the paper being global character of the description and on the pro­ limited to the most recent and frequently used leach­cesses taken into account. Global or multimedia mod­ ing models (Table 1). els calculate the distribution of pesticides among vari­ Relationships describing water and solute transport ous environmental compartments (soil, air, water, and water balance in soil constitute the master set of fauna, plants, etc ..). Basically, these models use pesti­ equations. These equations are coupled with other cide fugacity taken as representative of the ability of a equations representing sorption/desorption, transfor­compound to escape from a given compartment mation/degradation, plant uptake and volatilization. (Mackay, 1979; Wania et al., 1993). Thus, they allow simulation of the fate of chemicals in large and com­plex ecosystems. Others models are limited to the Transport Phenomena description of pesticide transport with or without cou­pling to sinks and sources. These models can be used Table 2 gives an overview of transport phenomena in water saturated and unsaturated media, some of included in various models. In addition to water and

Table 1. Some pesticide leaching models.

Acronym Model Category Reference

BAM Behaviour Assessment Model screening Jury et al., 1983, 1987

CMLS Chemical Movement in Layer Soils educational Nofziger and Hornsby, 1986

PRZM2 Pesticide Root Zone Model management Carse! et al., 1984, 1992*

VULPEST Vulnerability to Pesticides management Villeneuve et al., 1990

VARLEACH Model for describing transport and research/management Nicholls et al., 1982a, ! 982b degradation of pesticides Walker and Barnes, 1981

Walker and Welch, 1989

LEACHMP2 Leaching Estimation and Chemistry Model research Wagenet and Hutson, 1989, 1992*

x Mathematical model for describing research Piver and Lindstrom, 1990 transport in the unsaturated zone of soils

y Modeling the influence of sorption and research Boesten and van der Linden, 1991 transformation on pesticide leaching and persistence

PESTFADE Pesticide fate and transport model research Clemente et al.. 1993

* Date of the last published version.

Table 2. Modelling of transport phenomena.

Water transport Solute transport Heat

Model Transport in the soil transport Downward flow Upward fow Volatilization in the soil

Convection Convection/dispersion

BAM x x x CMLS x x PRZM2 x x x x x VULPEST x x VARLEACH x x x LEACHMP2 x x x x x x x x x x x y x x x PESTFADE x x x x x

Eur. J. Agron.

475 Modelling pesticide leaching in soils

solute transport, PRZM2 and PESTFADE calculate the the soil to the atmosphere are obtained by coupling amounts of soil and pesticide transported by runoff the liquid/gas partition of the pesticide (Henry's law) and erosion. Some models, not cited in Table 2, are with diffusion into the soil gas phase and into the designed to simulate runoff and erosion (e.g. atmosphere just above the soil surface. GLEAMS; Leonard et al., 1987). All models cited above simulate water and solute

transport in the unsaturated zone for given sets of Water transport boundary conditions. These boundary conditions

(Table 4) allow several field situations to be dealt with but require the Richards equation and theTable 3 indicates how water transport is described convection/dispersion equatioon to be solved numeri­in the models. When the Richards equation is resolved cally.for transient flow, hydraulic conductivity-water content

relations and matric potential-water content relations are needed and this is often a serious limitation to Sink/Source Phenomenamodel application. In contrast, PRZM2, VARLEACH and CMLS demand only some water content values such as field moisture capacity, wilting point and the Table 5 indicates the various sink/source phenom­water content at some specified matric potentials ena included in models cited in Table I. It is interest­(e.g. : - 10 kPa and - 200 kPa). ing to note some additional information :

When a water table is present, capillary rise can be 1) Concerning sorption. In the X model, the linear simulated and the corresponding flux calculated only sorption coefficient is expressed as a function of the with models using a convection/dispersion equation. particle-size composition and the organic matter con­

tent of the soil. A first order rate irreversible sorption is also introduced. In LEACHMP2, two-site sorption Solute transport kinetics are included ; a fraction of sites displays a local chemical equilibrium and another is character­

Solute transport is calculated by resolution of the ized by a kinetically controlled sorption and desorp­dispersion/convection equation (LEACHMP, X, Y, tion which are described by linear isotherms. In PEST­PESTFADE), by multiplying a water flux by a solu­ FADE, a two-region, two-site approach is also used tion concentration (VULPEST, BAM) or by piston (Nkeddi-Kizza et al., 1984); the two types of sites are flow (CMLS, PRZM, VARLEACH). For PRZM2 characterized by a first order rate kinetics but with dif­hydrodynamic dispersion is simulated by a numerical ferent localization, one being available in micropores, dispersion calculation. Volatilization and losses from the other in macropores. A sorption kinetics model

Table 3. Modelling of water transport.

Model Modelling water transport Comments

BAM The Richards equation is resolved for steady flow the soil profile is assumed to be at a uni vorm water content

CMLS piston displacement between field water capacity and wilting point

PRZM2 calculation of water flow based on 'tipping bucket' a fraction of water held in each layer may be allowed method between field water capacity and wilting point to drain; two empirical drainage rules may be used

VULPEST no calculation for water transport amount of water which infiltrates is an average monthly value of the difference between rainfall and evapotranspiration

VARLEACH mass balance applied to water in soil layer; flow drainage distinction is made between mobile and immobile is calculated as the excess of water compared to field water for calculating the flow of the soil solution water capacity

LEACHMP2 generalized Richards equation possible choice between steady and transient flow x generalized Richards equation equation can also be resolved for transport in the

vapor phase y generalized Richards equation PESTFADE generalized Richard equation; water flow is simulated by macropore flow may be introduced for describing

the model SWACROP solute transport

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476 R. Calvet

Table 4. Various boundary conditions (B.C.) used in the models. The choice between several B.C. is possible with some models.

Model Water transport Solute transport upper B.C. lower B.C. upper B.C. semi-infinite

BAM steady state water flux semi-infinite flux condition semi-infinite

CMLS variable flux given by the free drainage given amount of applied free drainage difference : rainfall-evapo­ solute transpiration

PRZM2 water balance taking into free drainage diffusion through a limited free drainage account the flux in and film at the soil/atmos­out the soil surface layer phere interface

VULPEST variable flux given by the not specified constant concentration zero concentration at infi­difference rainfall-evapo­ nite transpiration

VARLEACH water balance taking into free drainage given amount of applied free drainage account the flux in and solute out the soil surface layer

LEACHMP2 ponded, non-ponded infil­ permanent water table, volatilization, diffusion water table, unit gradient tration, water evaporation, free drainage, zero flux, through a surface soil drainage zero flux at the soil/atmos­ zero flux + constant film and a stagnant phere interface potential, fluctuating water atmospheric film; non

table volatile chemicals : zero flux

x as for LEACHMP2 fixed water table as for LEACHMP2 fixed water table

y variable flux given by the fixed water table at I m zero flux only convection flow is difference : rainfall-evapo­ depth allowed out of the system transpiration at 3 m depth

PESTFADE B.C. of SWACROP B.C. of SWACROP zero concentration at the zero flux at a given depth soil surface

Table 5. Characteristics of sink/source phenomena taken into account in the models.

Model PU SORPTION BIOTIC Transformation and Degradation

ABIOTIC Transformations*

Equil. NE Liquid Gas Liq. Sol.

Model L NL NE Ist. 8 T z

BAM x x x CMLS x x PRZM2 x x x x VULPEST x x x VARLEACH x x x x x LEACHMP x x x x x x x x ? x x x x x x x x x y x x x x x x PESTFADE x x x x x x x x

PU : solute plant uptake ; T : temperature dependent rate constant; 0 : water content dependent rate constant ; z : depth dependent rate constant ; liq. : abiotic transforma­tion in the liquid phase; sol. : abiotic transformation at the soil constituent surfaces ; Equil. : sorption equilibrium; L : linear isotherm; NL : non linear sorption i&otherm ; !st: overall first order reaction; *: explicitely described in the model.

Eur. J. Agron.

477 Modelling pesticide leaching in soils

formulated by Gamble and Khan (Clemente et al., 1993) may be incorporated. Sorption coefficient is allowed to vary with depth in some models (CMLS, LEACHMP2, VARLEACH, PRZM2).

2) Concerning transformation and degradation. In BAM, the degradation rate constant may vary with depth according to an empirical reduction factor. In Y model the degradation rate constant may also vary with depth according to an empirical numerical func­tion proposed by the authors. LEACHMP2 can simu­late simultaneously the fate of several chemicals and their daughter products undergoing biotic and abiotic reactions, all being of the first order. The several rate constants are allowed to vary with depth and soil moisture content according to the formulation of Walker and Barnes (1981) and the soil temperature (Arrhenius formulation). Transformations in the gas phase are also introduced. Daughter product degrada­tion is also described in PRZM2.

DIFFICULTIES ENCOUNTERED WITH PESTICIDE LEACHING MODELS

As briefly described earlier, some models incorpo­rate a variety of phenomena, sometimes with detailed descriptions. However, models generally need to be improved to simulate field observations properly and one of the difficulties lies in the introduction of these improvements. Other difficulties are related to model sensitivity to variations in input parameters which generally determine their possible applications and validation procedures.

Principal improvements to better represent the fate of pesticides

Preferential flow

Many field experimental results are poorly simu­lated by pesticide leaching models. Some strongly and weakly adsorbed pesticides are transported to great depths as if they were subject to accelerated move­ment. Two examples are given below to iHustrate this observation.

Ghodrati and Jury ( 1992) designed a field experi­ment on an irrigated loamy sand soil. They studied the transport of atrazine, napropamide and prometryn each with two formulations, two soil surface preparations (undisturbed and repacked), under four flow condi­tions obtained by continuous or intermitent sprinkler and flood irrigation. Irrigation was started immediately after pesticide application and the soil was sampled 6 days later. (Figure 1 shows their partial results). The data show transport to depth of the three herbicides

irrespective of the irrigation method and soil surface preparation. According to their retardation factor val­ues, the three compounds should have migrated to shallower depths. The authors have attributed this dis­crepancy to preferential flow.

The second example concerns an experiment designed to study the influence of sewage sludge application on atrazine leaching behaviour (Barriuso et al., 1993). The soil of the experimental site was an hydromorphic clay soil drained at 0.8-1.0 m depth and cropped with maize treated with atrazine 5 months after sludge application. The distribution of atrazine in the soil profile and the time-variation of atrazine con­centration in drainage water are given in Figure 2. In this case, observation of atrazine in drainage water indicated deep transport not predicted by the CMLS, VARLEACH and LEACHMP models.

Preferential flow seems to be observed in various soils, often in structured clay soils, but also in sandy soils. Flow mechanisms are not yet completely under­stood. Channeling through macropores and cracks in clay soils and through saturated zones of high hydrau­lic conductivity in sandy soils have been proposed as possible mechanisms. If such flow patterns occur, sol­ute transport may deviate greatly from a piston-like displacement. Attempts have been made to model such a phenomenon, mainly by distinguishing mobile and immobile water, the former being partially or totally responsible for the rapid transport. Several models have been proposed to account for preferential flow in laboratory columns (Coats and Smith, 1964 ; Van Genuchten and Alves, 1982) and in the field (Addis­cott, 1977 ; Corwin et al., 1991 ; Jarvis, 1991 ; Hutson and Wagenet, 1993). This is certainly an improvement for modelling pesticide leaching. However, besides the mathematical difficulty due to complex flow patterns, another question remains which does not have a satis­factory answer, i.e. how soil heterogeneities can be characterized and how the partition between mobile and immobile water can be assessed to feed the mod­els with pertinent input data ? This is a future research challenge.

Concerning the transport of pesticides, colloidal materials and hydrosoluble humic substances may play a role. These compounds can bind pesticide mol­ecules and make them mobile and readily transport­able by water (Ballard, 1971 ; Vin ten et al., 1983). This point merits greater attention, particularly in rela­tion to soil organic matter transformations.

Pesticide retention in the soil

Retention is the transfer of chemical species from the liquid phase or from the gas phase to a solid phase due to reversible (sorption) and/or irreversible phe­nomena. Sorption is always included in models, even in simpler ones, but irreversible retention is pratically

Vol. 4, n° 4 - 1995

478 R. Calvet

Atrazinc Napropamidc Prometryn30 60 100

CP CP CP50u D u 20 TG 40 TG \VP

6030

4010 20 2010

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50 80 80 JP cs cs{40 ~D D u60 60 :{TC EC ti TG30

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\YP TG TG30 30~ 40 ­' 20 20 20 1010 l;

0 ·.· 0 0 0 25 50 75 100 125 150 0 25 50 75 100 125 150 0 25 50 75 I!){) 125 1.50

Soil Depth (cm)

Figure 1. Examples of preferential flow observed for three pesticides in irrigated plots. Characteristics of irrigation : C = continuous ; I = intermittent; P = ponding ; S = sprinkling. Applied product : TG = technical grade ; EC = emulsifiable concentrate ; WP = wettable powder. Soil structure: U = undisturbed; D = disturbed (from Ghodrati and Jury, 1990).

never introduced. The only model where it is accounted for is the model developed by Piver and Lindstrom ( 1990). This is not a satisfactory situation since an increasing body of observations shows that this phenomenon does occur.

There are many unanswered questions about mol­ecules retained by irreversible retention (bound resi­dues) as regarding their nature, properties and mobil­ity. Modelling the fate of pesticides in soil should incorporate such a phenomenon. Nevertheless, whether the questions are fully answered or not, pesti­cide leaching models would be improved with a better description of sorption. Two points of view have to be considered. The first, probably connected with bound residue formation, is the time dependence of the sorp­tion coefficient. Estimation of sorption coefficients for modelling is a difficult problem (Green and Karick­hoff, 1990 ; Calvet, 1993a), further complicated by its relationship to time. As a matter of fact, sorption coef­ficients seem to increase with time (Walker, 1987 ;

Lehmann et al., 1990; Barriuso et al., 1993). Unfortu­nately, experimental and theoretical procedures are not yet available to evaluate sorption-time relationships.

The second point deals with the coupling of sorp­tion to transport and transformation-degradation pro­cesses. Sorption which is rate-limited by transport is related to the presence of heterogeneous flow domains and particularly to more rapid flow in macropores which prevent pesticide molecules reaching adsorption sites in micropores. Some modelling approaches have been proposed to describe this coupling (Pignatello, 1989). Sorption may also be limited by intra-particle and intra-sorbent diffusion, as described by Brusseau et al., 1991). Sorption may modify biotic transforma­tions and degradation in two ways. The first way is by reducing the amount of pesticide capable of being degraded because sorbed molecules are not mobile and thus not accessible to .microorganisms. The second way concerns a kinetic effect due to the necessary dif­fusion of molecules out of the micropores. Models

Eur. J. Agron.

479 Modelling pesticide leaching in soils

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Figure 2. a) atrazine distribution in the soil profile 12 months after the application. b) variation of atrazine concentration in water drainage; • = control ; " sludge applied 5 months before atrazine (from Barriuso et al., 1993).

have been recently developed to account for this kinetic effect by Scow and Hutson (1992), Scow and Alexander (1992) and by Duffs et al. (1993). Introduc­tion of such a coupling, at least in research models, would allow more precise simulation of the fate of pesticides.

Model Sensitivity

Assessment of model sensitivity to variations in input parameter values is of key importance in appli­cation of pesticide leaching models. This is because input parameters are highly variable, however they are measured or estimated. The variability of measured values has two components, one due to uncertainties associated with protocols and analytical methods, the other one being the result of the spatial variability of soil properties. This spatial variability has been described by Rao et al. (1986) in terms of factors linked to site pedogenesis (intrinsic factors) and of cultivation and pesticide application practices (extrin­

sic factors). As a result, physical, chemical and bio­logical properties of the soil display a large in-situ variability. Published results show that spatial variabil­ity for adsorption coefficient (Kd) and half life (Tl/2) values determined in surface cropped soils is about 30 per cent (Rao et al., 1986; Wood et al., 1987 ; Allen and Walker, 1987). This is similar to the magni­tude observed for physical static characteristics ; hydrodynamic characteristics display a greater vari­ability (Vauclin, 1990).

It is important to be aware of the variability of esti­mated parameter values frequently used for prediction purposes. Some important parameters such as the organic-carbon-normalized sorption coefficient K

0c and

the half-life degradation time T 112 have to be esti­mated when measured values are missing. The only way to do this is to refer to published values which are often very variable. Table 6 gives a series of Koc values compiled by Gerstl (1990) together with their associated coefficients of variation. For T112 the situa­tion is more critical because no method exists to esti­mate this parameter from soil and pesticide properties.

Vol. 4, n° 4- 1995

480 R. Calvet

Table 6. Examples of average K 0

c values and their associated coefficients of variation (from Gerst!, 1990).

Compound n Koc CV

atrazine 217 227 158 carbofuran 52 78 229 diuron 156 384 74 lindane 94 1160 176 napropamide 36 487 71 trifluralin 22 1135 72

n : number of observations ; K0 c : average value (I/kg) ; CV : coefficient of variation (%).

Thus, it is important to know the consequences of such variability on predictions. This is useful for defining the precision of input parameter measure­ments and absolutely necessary for interpreting simu­lated data. These consequences are given by sensitiv­ity analysis which indicates the following general features for pesticide leaching models.

The sensitivity of pesticide leaching models to variations of input parameters is often high, particu­larly for sorption and degradation parameters (Ville­neuve et al., 1988 ; Boesten, 1991 ; Calvet, l 993b ; Walker and Hollis, 1993). Studying the simulation of aldicarb leaching by the PRZM model, Villeneuve et

percentage of dose leached

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al. (1988) showed that an uncertainty of 15 per cent in degradation rate and 24 per cent in sorption coefficient modify by 100 per cent the predicted cumulative quantity of pesticide reaching the water table after 3 years. Boes ten ( 1991) has carefully examined the sensitivity of his model to variations in several param­eters. Figure 3 illustrates some of his results concern­ing K c and He has also observed that non­

0 T 112•

linearity could be critical and that the sensitivity is greater for low leaching rates. Figure 4 shows other sensitivity effects for VARLEACH applied to a drained soil (Calvet, 1993b). As the models are highly sensitive to variations in sorption and degradation parameters, these parameters must be known as pre­cisely as possible, including their variation through the soil profile.

From a general point of view, sensitivity assessment must be discussed carefully, taking into account the nature of the parameters and the period corresponding to the simulation. It is also important to be aware of the different possible criteria for assessing model sen­sitivity : the distribution of the chemical in the soil profile, the amount remaining in a given soil layer, the leached amounts and the amount transported beyond a given depth. They may lead to different conclusions, so that comparison between performances of different models should be done with the same criteria for each model.

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Figure 3. Examples of sensitivity behaviour; model developed by Boesten. The graph indicates the percentage of applied amount of pesticide leached below Im depth as a function of Korn for various half-lives (a), as a function of half-live for various values of Kam (b) (from Boesten, 1990).

Eur. J. Agron.

200

481 Modelling pesticide leaching in soils

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Figure 4. Example of sensitivity behaviour; model VARLEACH applied to a field experiment. The graph indicates the amount of pesticide leached beyond 1.5 matt= 256 days in a sandy loam soil (Calvet, 1993b).

Model Validation

Model validation is an important step in modelling and is a prerequisite for model application. There are relatively few published papers reporting validation investigations and, generally, they only concern one site and one season. Thus, it is not easy to know pre­cisely how various models describe the fate of pesti­cides.

There are two main difficulties in validation. To properly validate a model, it is necessary to have many experimental results obtained under conditions allowing the determination of all input data. This may seem obvious but complete published experimental data sets are not very numerous. The reason lies prin­cipally in the great number of measurements needed and this is always expensive. Nethertheless, any mod­elling work should be accompanied by well-designed field experiments allowing observed results and simu­lation uncertainties to be correctly estimated. Under these conditions, the validation procedure can be based on sound comparisons between observed and simulated data. Figure 5 gives an example of such a comparison (Cal vet, l 993b ). Variation of the sorption coefficient Koc' of the half-life T 112 and of the applied amount was taken into account for a first estimate of the uncertainty of simulated values. Model users must be aware that calibrating a model against a given field data set is not a validation. This only shows that the model outputs can numerically represent this data set.

The second difficulty arises from the interpretation of the comparison between simulated results and observed data. Several theoretical treatments have been published on this subject and examples of appli­cations have been given by Pennel et al. (1990). How­ever, these treatments do not give an answer to the question : what is the maximum difference between observed and simulated data which is acceptable for

predictive and for regulatory purposes ? A complete and definite answer probably does not exist today, and this is a fundamental problem. A better knowledge of transport, of transport/sink-source phenomena cou­pling mechanisms and of their relations to soil struc­ture would contribute greatly to answering the ques­tion.

CONCLUDING COMMENTS

Whatever their complexity, existing models are not able to provide an accurate description of the fate of pesticides in soils. For a particular situation, some of them seem to predict correctly the center-of-mass­position of the solute, the pesticide degradation and the maximum leaching depth. However, none provides a good simulation of the solute distribution in the soil profile and a feasible prediction of the amount of pes­ticide transported to groundwater. Thus, model improvements are necessary and this is a challenge for future research. This leads to a question about devel­oping more detailed models. As discussed by Decour­sey (1992), the answer is not straightforward and the quality of the prediction cannot be related to model complexity. Nonetheless, it is true that improvements in process understanding will lead to improved simu­lations. The problem is that each increase in model complexity requires more input information.

Whatever the purpose of modelling, users must be aware of model uncertainty due to data quality (sam­pling, measurement errors), model structure and parameter estimation methods. The spatial variability of input and observed data and the adequacy of the simulation scale to the problem to be resolved (Wagenet, 1993) are also important.

What can be said about modelling the fate of pesti­cides and particularly modelling pesticide leaching in

Vol. 4, n° 4- 1995

482 R. Calvet

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Figure 5. Examples of comparisons between observed (full lines) and simulated (dotted lines) values. Mmax and Mmin indicate the range of variation of observed values ; QSmax and QSmin indicate the range of variation of simulated values : a) dissipation kinetics of atrazine in the 0-20 cm layer in a hydromorphic clay soil (at Rambouillet, Ile de France). Simulation with model CMLS. b) dissipation kinetics of atrazine in the 0-10 cm layer in a sandy loam soil (at Mont Saint Michel, France). Simulation with VARLEACH. c) dissipation kinetics of atrazine in the 0-20 cm layer in a hydromorphic clay soil (at Rambouillet, Ile de France). Simulation with model VARLEACH (from Calvet, 1993b).

soils ? Research and educational models are useful because they can serve initially as an evaluation for conducting field experiments. They provide a conve­nient way to account for climatic variations (simulat­ing many seasons), and soil variability (simulating many sites). For research they allow fictitious experi­ments to be performed to evaluate the relative role of various factors, thereby providing a powerful tool for defining protocols for field and laboratory experi­ments. For teaching they enable the simulator to illus­trate a variety of conditions.

The usefulness of management and regulatory mod­els are more difficult to assess. At best, a calibrated model for a given site and a given climate may pro­vide a basis for management decisions but it is impos­sible to safely extrapolate the simulation. Improving simulation quality has beeen discussed by Wauchope (1992). Probabilistic model analysis may be more use­ful using Monte Carlo techniques (Villeneuve et al,

1990). It is clear that modelling will only allow classi­fication of pesticides according to their potential mobility and comparison of diversity of agro-pedo­climatic scenarios. However, quantitative prediction of transport to groundwater using pesticide leaching models, seems impossible at present.

REFERENCES

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