Atrazine leaching into groundwater: comparison of five simulation models

Ecological Modelling, 70 (1993) 239-261

239

Elsevier Science Publishers B.V., Amsterdam

Atrazine leaching into groundwater: comparison of five simulation models Danilo Persicani Centro di Ricerca sulla Geopedologia Ambientale (C.R.G.A.), Stradone Farnese 17, 29100 Piacenza, Italy (Received 3 July 1992; accepted 15 January 1993)

ABSTRACT Persicani, D., 1993. Atrazine leaching into groundwater: comparison of five simulation models. Ecol. Modelling, 70: 239-261. Five deterministic models (BAM, GLEAMS, HYDRUS, M O U S E and TETrans) were compared to evaluate, in first order of approximation, their ability to predict different soil vulnerabilities with respect to the atrazine leachability towards shallow groundwater. Two environmental scenarios were defined on the basis of the measured groundwater contamination. The former scenario corresponded to a vulnerable sandy loam soil, the latter to a moderately vulnerable sandy loam-organic soil. The pesticide was applied on both soils for corn weed control in early June 1986, while the groundwater samplings were made in February and September 1987. The small atrazine transport into groundwater through the sandy loam-organic soil, as it was detected from the groundwater sampling in late winter, was simulated only by TETrans model. With respect to the sandy loam soil vulnerability, satisfactory simulations were performed by both TETrans and HYDRUS. The applied herbicide was leached throughout this soil towards groundwater both in February and in September 1987. Conversely, atrazine leaching simulated by GLEAMS, M O U S E and BAM compared less consistently with the measured groundwater contaminations.

INTRODUCTION

The widespread groundwater contamination by pesticides used in agriculture (Cohen et al., 1984; Wehtje et al., 1984; Pionke et al., 1988; Hallberg, 1989; Leistra and Boesten, 1989; Isensee et al., 1990) requires the optimization of such soil-use with respect to both crop needs and environmental safety. Use of simulation models is becoming a very effective and little-time-consuming tool to assess the environmental vulnerability of an Correspondence to: D. Persicani, Centro di Ricerca sulla Geopedologia Ambientale (C.R.G.A.), Stradone Farnese 17, 29100 Piacenza, Italy. 0304-3800/93/$06.00 © 1993 - Elsevier Science Publishers B.V. All rights reserved

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D. PERSICANI

agricultural area and thereby also to evaluate the possibility of avoiding or minimizing the pollution risks of underlying water resources (Nicholls et al., 1982; Leistra, 1986; Sauer et al., 1990; Starr and Glotfelty, 1990; Boesten and van der Linden, 1991). However, existent models have not obtained exhaustive validation yet, in particular for actual field conditions and comprehensive environmental conditions other than the ones specifically used by the authors. Addiscott and Wagenet (1985), Pennell et al. (1990) and Wagenet and Rao (1990) pointed out the need that a major simulation model verification under different weather, pedological, and hydrological conditions should be made before expressing definitive considerations over their ability to predict pesticide fate in field soils. Nonetheless, the proper choice of the model, as compared with the specific soil-water system which has to be evaluated, is another fundamental aspect of the land-use planning accomplished by using mathematical models. In this regard, simulation models are currently divided into deterministic (Carsel et al., 1984; Nofziger and Hornsby, 1987; Wagenet and Hutson, 1987; Addiscott and Whitmore, 1991) and stochastic types (Jury, 1982; Villeneuve et al., 1990). However, the need of extending the basic physical concepts of deterministic models with more realistic evaluations including stochastic elements is generally accepted and has been applied, for example by using stochastic applications of deterministic models (AmoozegardFard et al., 1982; Petach et al., 1991). Both stochastic and deterministic models are usually divided into three different categories: (1) research models, (2) management models, and (3) screening models (Addiscott and Wagenet, 1985). All research models are mechanistic. In order to solve differential equations of water flow and solute transport, they require the knowledge of rate parameters, which are uneasy to determine either by field experiments or laboratory techniques. However, they warrant a quantitative approach to solute movement evaluation through a homogeneous porous medium. In contrast, all management models, even though physically oriented, require less input data. The water flow is usually evaluated through capacity parameters which are less variable and more accessible than the rate ones. For these reasons they provide only a qualitative or semi-quantitative prediction of the fate of agrochemicals in field soils. Screening models require few input data. However, only under very restrictive initial and boundary conditions can they successfully represent complicated and time-varying field situations. Therefore, the use of these models is constrained to an initial approximate environmental appraisal only. Hence, the simulation model to be used in each particular case depends on a large number of factors, among which: (1) the type of ecosystem which

ATRAZINE LEACHING INTO GROUNDWATER

241

has to be evaluated, (2) territorial extension, (3) available time and economic media, (4) the aim of the work, etc. Furthermore, a cost/benefit analysis of this subject should also take into account that at the present state of the art no numerical research model has implemented runoff and preferential flow processes, as in contrast some m a n a g e m e n t models have done. Considering the difficulty in verifying the actual ability of simulation models to predict agrochemical movement towards groundwater under field conditions, in this study two different soil-water systems were evaluated by means of simple field data. This approximate analysis represented the initial basis for a more complete environmental vulnerability analysis of an extensive agricultural area. Particular local hydrological conditions and different physicochemical soil characteristics were considered as potential interesting scenarios for a qualitative evaluation of some deterministic models (BAM, Jury et al., 1983, 1987; GLEAMS, Davis et al., 1990; H Y D R U S , Kool and van Genuchten, 1991; MOUSE, Steenhuis et al., 1984; TETrans, Corwin and Waggoner, 1990) even though their validation was impossible under such preliminary conditions. Some qualitative remarks on the model efficiency and comments on general cost/benefit ratio are provided for all models considering them only as a tool for preliminary environmental risk evaluation of an extensive agricultural area. M A T E R I A L S AND M E T H O D S

Field study From spring 1986 to summer 1987, a preliminary pedological survey (Persicani, 1988) was accomplished on farmland located in Northern Italy (Fig. 1). This area has been reclamed recently and is mainly cultivated to corn (Zea mais L.). Shallow, artificially drained groundwater is present to a depth of 50 to 100 cm. The above mentioned pedological survey located four main soil types: (1) sandy loam, homogeneous, Typic Fluvaquents, (2) sandy loam-organic, Thapto-Histic Fluvaquents, (3) sandy, layered, Typic Fluvaquents, and (4) organic, homogeneous, Fluvaquentic Haplaquolls. In this study only a Typic Fluvaquent (soil 1) and a Thapto-Histic Fluvaquent (soil 2) were examined from a hydrological point of view because they were considered representative of different environmental vulnerabilities. Fig. 2 shows the schematic profiles of both soils 1 and 2, while the main physicochemical characteristics of the upper and lower horizons are shown in Table 1. Considering the well-known relationships between s-triazine sorption on the solid soil phase and soil organic matter content (Khan, 1978), and also some previous short-term field trials on solute transport in similar field

242

D. PERSICANI

Fig. 1. Location of the study area in Northern Italy.

soils (unpublished data by Bonvini et al., 1986 on alachlor and Brleaching), two pedological scenarios were suggested: namely, Typic Fluvaquent (soil 1) represented a potentially vulnerable agricultural soil, whereas Thapto-Histic Fluvaquent (soil 2) represented a little vulnerable Typic Fluvaquent

Thapto-Histic Fluvaquent

'L''/

iiii/ii!l'i average

groundwater level (-70cm)

Fig. 2. Schematic profiles of the Typic Fluvaquent (soil 1) and Thapto-Histic Fluvaquent (soil 2).

ATRAZINELEACHINGINTOGROUNDWATER

243

TABLE 1 Main physicochemical characteristics of the Typic Fluvaquent (1) and Thapto-Histic Fluvaquent (2) Soil layer depth (cm)

pH (H20)

OC (%)

S (%)

L (%)

C (%)

0s

0fc

Or

p (g/ cm 3)

Ks (cm/ day)

• (cm)

(cm3/ cm 3)

(1) 0-30 30-70 (2) 0-60 60-70

7.6 7.7

1.7 0.5

63 67

22 25

15 8

0.38 0.36

0.24 0.20

0.05 0.03

1.38 1.42

44.4 48.0

4.0 3.5

7.5 7.3

1.9 18.5

56 -

26 -

18 -

0.40 0.60

0.27 0.06 0.45 0.16

1.38 0.80

38.7 2.0

4.0 6.0

o c (organic carbon), S - L - C (sand-silt-clay), 0 (soil-water content: saturation/field capacity/residual), p (bulk density), K s (saturated hydraulic conductivity), • (dispersivity).

field soil. T a k i n g into a c c o u n t the c o n t e x t o f t h e w o r k c h a r a c t e r i z e d by an initial a p p r o x i m a t e e n v i r o n m e n t a l a p p r a i s a l of the a g r i c u l t u r a l area, t h e p a r a m e t e r s for b o t h d e c a y and s o r p t i o n s u b m o d e l s s t e m m e d f r o m literat u r e d a t a ( P i o n k e a n d D e Angelis, 1980; J u r y et al., 1987). T a b l e 2 shows, for b o t h soils, the v a l u e s o f t h e d e g r a d a t i o n t i m e (T1/2) a n d l i n e a r distribution c o e f f i c i e n t (Kd) u s e d for r u n n i n g t h e s i m u l a t i o n models. P r e - e m e r g e n c e a t r a z i n e t r e a t m e n t s w e r e m a d e o n b o t h soils o n 2 J u n e 1986 (later t h a n t h e c u s t o m a r y d a t e b e c a u s e o f s o m e field w o r k s for local r e c l a m a t i o n ) with a b o u t 1.6 k g / h a a.i. o f a t r a z i n e . In the s e c o n d w e e k of F e b r u a r y 1987, a g r o u n d w a t e r s a m p l i n g was m a d e by m e a n s of two scout holes p i e r c e d directly in t h e soils 1 and 2. In t h e last w e e k o f S e p t e m b e r 1987, a new g r o u n d w a t e r sampling was m a d e . A f t e r e a c h s a m p l i n g date,

TABLE 2 Half-live (T1/2) and linear partitioning coefficients (Kd) used for the mathematical simulations Soils (1) Typic Fluvaquent 0-30 cm 30-70 cm (2) Thapto-Histic Fluvaquent 0-60 cm 60-70 cm

Ta/2

Kd

(days)

(ml/g)

60 70

1.20 0.60

60 40

1.80 15.00

244

D. PERSICANI

-25 -20 -15 -10 -5 -0

=; 1412" 10"

v

8.

E

0,M,j,j~A,S,OIN,D,j 1986

' F ' M [ A ' M I, fl

1987

Fig. 3. Weather data for the observation period.

the water samples were frozen and sent to the laboratory for the gas chromatography analyses. For the simulation period, Fig. 3 shows the monthly climatic pattern recorded at the closest weather station (ca. 10 kin) (Amministrazione Provinciale di Piacenza, 1987).

Leaching models The computer programs used in this study were, in order of increasing complexity: BAM (Jury et al., 1983, 1987), MOUSE version 5.0 (Steenhuis et al., 1984), TETrans version 1.5 (Corwin and Waggoner, 1990), GLEAMS version 1.8.55 (Davis et al., 1990), and H Y D R U S version 3.3 (Kool and van Genuchten, 1991). All models can be run on IBM-compatible personal computers and are intended to simulate relatively nonvolatile, nonpolar, organic compounds. The aim of the study, as above mentioned, was to evaluate a more general environmental predictive capacity, rather than a more specific agricultural system management potential of the present model use. Therefore, the models were used in the simplest manner, i.e. no meaningful calibration was made and furthermore simpler available options were chosen (e.g. linear adsorption isotherm, neglectable runoff, small number of soil layers, etc.). Furthermore, when possible, the same parameter values were chosen for all the models, independently of the values suggested by the relevant modellers.

A T R A Z I N E L E A C H I N G INTO G R O U N D W A T E R

245

Basic equations for pesticide fate modelling in field soils are related to the hydrologic mass balance and solute flux through soil profile. The general equation for the former process can be represented as P-Q-E-W I =

,

t

(1)

where I (L T -1) is the net infiltrating water into soil surface, P (L) is the precipitation or irrigation, Q (L) is the water loss by runoff, E (L) is the water loss by evapotranspiration, W (L) is a term considering the soil-water deficit to saturation (L), and t (T) is time of input water. As to the solute transport through the soil profile, it is usually assumed that the pesticide movement is a result of three processes (J0rgensen, 1990; Wagenet and Rao, 1990; among others): (1) convection of the pesticide due to movement of the bulk fluid phase in which the pesticide is dissolved, (2) pesticide diffusion in the aqueous phase along a solute-concentration gradient, and (3) pesticide diffusion in the gas phase in response to a gradient in gas-phase concentration, if the pesticide is volatile. Expressed mathematically, the terms of the pesticide flux Jp (M L -2 T-1) are

Jp=Jc + JD + JG, (2) where the subscripts C, D, and G refer to points 1, 2, and 3, respectively. Neglecting the diffusive fluxes and considering the prevailing effects of the hydrodynamic dispersion, sorption, degradation and plant uptake, Eq. 2 can be explicitely rewritten in the fundamental convective-dispersive equation of the solute transport ac Ot

a2Dc aZ 2

OVc aZ

pas OOt

tXC +__b ,

(3)

where c (M L -3) is the pesticide concentration in solution, D (L 2 T ~) is the dispersion coefficient, V (L T -1) is the pore water velocity, s (M M-~) is the adsorbed concentration, p (M L -3) is the soil bulk density, 0 (L 3 L -3) is the soil water content, tx (T -1) is a first-order decay coefficient, ~h (M L -3 T-1) represents a sink solute term, z (L) is soil depth assumed to increase in the downward direction, and t (T) is time. As pointed out by Addiscott and Wagenet (1985) and Wagenet and Rao (1990), Eq. 3 can be solved either analytically, assuming a steady-state water flow condition, or numerically with a transient flux. However, unsaturated soil hydraulic functions are needed to adequately consider the fundamental equation of continuity a0 aq at az' (4) where q (L T -1) is the water flux density.

246

D. PERSICANI

The Behaviour Assessment Model (BAM) is a screening model in which the chemical is assumed to undergo linear, reversible equilibrium adsorption, and a first-order biochemical decay while moving downwards through soil by piston flow (no dispersion or diffusion). The analytical solution of the classic convection-dispersion equation (Eq. 3) is thereby constrained to conditions of steady water flow and constant soil-water content over time, which are remote from actual field soil conditions. In this screening model, the soil profile is divided into three zones: (1) a surface zone within which the biological decay constant,/x, and the microbial population density are both constant, (2) a lower vadose zone wherein the microbial population density is exponentially declining from its surface value to a residual value, and (3) a deep zone where both the microbial population density and decay constant are at residual constant values. However in this study, considering the above specified soil characteristics, the presence of a shallow groundwater, and the lack of specific field data on effective microbial population distribution along the soil profile, a fixed degradation constant for atrazine, proportional to upper horizon characteristics of soils 1 and 2, was assumed. In fact, no meaningful decrease with the depth of the biological activity was supposed for Typic Fluvaquent profile 1, whereas a hypothetical increase could be thought for Thapto-Histic Fluvaquent profile 2. In the BAM model, Eq. 3 is simplified to the following form ~C T ----'~

0t

~C T We - -

0z

]A,C T ,

(5)

where Ca- (M L -3) is the total pesticide concentration in the soil and Ve (L T -a) is the effective solute velocity (V/R, where R is a dimensionless retardation factor which considers the sorption effect). Eq. 5 is solved for the assumption of zero initial solute in soil, and for the case of a single flux of pesticide mass M 0 applied to the soil at t = 0 the general solution becomes

ex[-

].

(6)

Thus, as the solute pulse reaches depth z at time t = z/Ve, the fraction M(z)/M o of the pesticide mass applied at the surface which has not degraded is calculated by BAM model by means of Eq. 6. G L E A M S (Groundwater Loading Effects of Agricultural Management Systems) is a mathematical model developed for field-size areas to assess the effects of agricultural management systems on the movement of agrochemicals within and through the plant root zone. It consists of a single computer program of interactive processes and consists of three separate

ATRAZINE LEACHING INTO GROUNDWATER

247

submodels with intermediate output files from hydrology (1), and erosion (2) for use in the erosion and chemistry (3) components, respectively. GLEAMS management model takes into account Eqs. 1 through 4, but for the last equation several approximations are assumed. In particular, any unsaturated soil hydraulic function is used to define the hydraulic conductivity-pressure-head-soil water content relationships under transient flow conditions. The dispersive effect stemmed from the numerical technique of the convective flux, and the solute transport calculation is taken into account to simulate the actual solute dispersion during the vertical movement along the soil layers. However, GLEAMS model allows the water and solute to move upwards by capillary flow. Linear, reversible equilibrium sorption with a first-order kinetic decay of solute are also assumed in GLEAMS. Therefore, due to such simplified assumptions for the transport processes, a mass balance approach is used to calculate water and solute flow between soil layers. The soil profile is compartimentalized in seven fixed layers among which the first (equal 1 cm) represents the surface zone for which the conceptual Eq. 1 is solved considering both runoff and erosion components. Combining the Eqs. 1 through 3, the general mass balance equation used in GLEAMS to calculate the pesticide transport in field soils can be summarized as follows OC T

. . . . Ot

0

Oz

+ J . +JT +Ju +IR +JE),

(7)

where Jc and Ju are as previously defined, and other fluxes are defined by subscripts denoting transformation (T), plant uptake (U), runoff of dissolved pesticide (R), and loss of pesticide sorbed to eroded soil particles (E). However, in this study, the sophisticated erosion submodel, based on several overland-channel-impoundment combinations, and runoff based on classic SCS curve number method, were strongly simplified because of the very fiat soil surface. MOUSE (Method Of Underground Solute Evaluation) is a management model which is able to simulate both the vertical solute leaching along the soil profile and horizontal transport within underlying groundwater. The following four submodels are used to provide subsequent output files starting from synthetic climatic input data: (1) climate data, (2) vadose water balancer, (3) vadose solute transporter, and (4) water and solute transporter in the saturated zone. This model includes water losses by runoff calculated using the SCS curve number method, whereas vertical flux and solute transport into soil are solved assuming several approximations, namely, water drainage occurs under unit hydraulic gradient, whereas

248

o. PERSICANI

an unsaturated hydraulic conductivity is calculated using an exponential function. However, a solute dispersion is also calculated using a simple error function. Partitioning of chemicals between liquid and solid phases is assumed to be instantaneous, linear, and reversible with first-order kinetics for herbicide degradation. This approach of water and solute transport modelling under unsaturated field soil conditions, even though approximate, is an appreciable effort of considering the conceptual Eqs. 2 and 4 through a rate parameter as the unsaturated hydraulic conductivity (Ko). Unfortunately, in the computer program there are few options allowing the basic parameters (i.e. K, 0, q) to fit the examined field conditions. TETrans (Trace Element Transport) is a management model which simulates the preferential flow in the unsaturated zone of the soil. Freundlich/Linear, Langmuir, or Keren boron, reversible equilibrium submodels are available in TETrans to compute the chemical partitioning between the liquid and solid soil phases. In contrast, solute losses by runoff and soil particle erosion and pesticide degradation are not taken into account by this functional model. Water flow and solute transport are based on a simple mass-balance approach in which the soil profile is divided in layers, each characterized by mobile and immobile soil-water regions. Water and solute move through the mobile region of the soil driven by piston-type displacement while dispersion-diffusion effects are neglected. Water losses by evapotranspiration are assumed as a sink term, whereas no pesticide uptake is calculated as on the contrary G L E A M S model does. Bypass flow in capacity-based models, such as TETrans which employs a simple mass-balance approach, can be approximated by the spatial variation in the quantity of the resident pore-water (0im) that is not involved in piston-type displacement following a precipitation or irrigation event. The quantity, or more specifically, the fraction of the total resident soil solution which is not miscibly displaced by incoming precipitation or irrigation water is subject to bypass. In order to address the problem posed by bypass in the most simplistic manner, a single term, the mobility coefficient (a), which accounts for the effects of bypass due to the presence of immobile water and preferential flow through large pores and cracks, is used in TETrans. The mobility coefficient is defined as the fraction of the resident soil water that is subject to displacement; therefore, 1 - a represents the fraction of soil water that is bypassed. As the authors outlined (Corwin et al., 1991), TETrans assumes that a fraction of the incoming water entering each and every depth increment or layer is subject to bypass rather than bypassing the complete soil profile to enter the groundwater, namely, preferential flow occurs in TETrans from one layer to the next and not for the entire soil profile. This allows the modelling of low intensity rainfalls or light

249

A T R A Z I N E LEACH1NG INTO G R O U N D W A T E R

irrigations over extended time periods with subsequent downward movement of solute. Basic equations assumed by TETrans for defining pesticide leaching towards groundwater under field soils subject to preferential flow can be derived from simple mass balance considerations. After a precipitation event and drainage to field capacity the total amount of pesticide Tp (M) in a volume V (L 3) of soil can be represented by the following equation Tp = T,)+ T~. - Tout = T 0 + minCin -- VoutCout,

(8)

where the subscripts 0, in, out represent initial, incoming, and outcoming values, respectively, and C (M L -3) is the pesticide concentration. Assuming 0 < a ~< 1, if Vin > Vfc - (1 - o~)V0, then Vou t = Vin - Vfc -~- V 0 ,

(9)

and Cou, =

~VoC 0 -

KcCin

-I- V i n C i n + (1 -

Vout

a)Vofin

(10)

Analogously, the basic piston-flow equation to consider the position of the solute pulse (Rao et al., 1976) has to be modified to account for the bypass flow conditions

Z,=Zi_,+[i-O/OmR)],

I>D

(ll)

Zi=Zi

D>I,

(12)

1,

where Z i and Zi 1 (L) are the depths at which the pesticide pulse is located after the ith and ( i - 1)th events, I (L) is the infiltrating water amount, D (L) is the soil water deficit amount, 0m (L 3 L -3) is the volumetric water content in the mobile soil region, and R is a dimensionless retardation factor to account for sorption effects. As for the soils investigated, the percentage of bypass flow required by this model was empirically set to 15% and 20% for Typic Fluvaquent and Thapto-Histic Fluvaquent, respectively. In order to compare the atrazine leaching values drawn out using TETrans with other deterministic models, a first-order kinetic for solute biochemical transformation was externally computed. The H Y D R U S computer program is a research model which simulates one-dimensional variably saturated water flow and solute transport in porous media. It solves Richards' equation considering also the effects of root water uptake and hysteresis in the unsaturated soil hydraulic properties. Therefore, H Y D R U S requires the knowledge of soil water retention

250

D. PERSlCANI

and hydraulic conductivity curves that can be described by the parametric functions of van Genuchten (1978). The solute transport equation incorporates the effects of ionic or molecular diffusion, hydrodynamic dispersion, Freundlich/Linear equilibrium adsorption, and first-order decay. Both flow and transport equations are solved employing fully implicit, Galerkintype linear finite elements. No runoff submodel is taken into account. Hence, H Y D R U S research model neglects Eq. 1 but adequately solves Eq. 3 under Eq. 4 conditions. The governing equation for one-dimensional, vertical water flow through variably saturated soil profile (the Richards' equation) is

c--=

-K

at

- 4 4 z , t),

(13)

where h (L) is the pressure head, C (L-1) is the soil water capacity, K (L T -1) is the hydraulic conductivity, and ~b(z,t) is the sink term to account for plant uptake of water. For solving Eq. 13, H Y D R U S assumes that the soil hydraulic properties (i.e. O(h) and K(O)), can be described by the parametric functions of van Genuchten (1978)

O(h)=O~+

0s - Or ( l + [ a h [ b ) c'

(14)

and K ( S e ) -~- KsSle/2[1

- (1

-

S1/c~' C]2e, 1 '

(15)

in which S e is relative saturation 0 -

Or

(16)

Se = 0Os- - -__ --~,

and

c = 1 - l/b,

(17)

where the subscripts r and s refer to residual and saturated conditions, respectively, whereas a (L-a) and b (dimensionless) are shape parameters of the unsaturated soil hydraulic function. As to the sink term, th(z, t) of Eq. 13, in the H Y D R U S computer program, it is determined by the potential evapotranspiration rate, E, a normalized root uptake distribution function, f, and a pressure-salinity stress response function, or, as follows

&(z, t ) = E ( t ) f ( z ) t r ( h ,

ho),

where h o is the osmotic head.

(18)

ATRAZINE LEACHING INTO G R O U N D W A T E R

251

In this study, neither hysteresis nor root water uptake were considered. Hydraulic properties of both soils used for running H Y D R U S stemmed from simple field trials interpreted using a nonlinear least-squares optimization computer program (RECT) developed by van Genuchten et al. (1991). As to the Eq. 3, H Y D R U S requires explicite input data for dispersivity e (L) of the soil and the molecular diffusion coefficient D o (L 2 T-I). The dispersion coefficient D is then calculated through the following equation D = • [VI +Do~,

(19)

where ~- is a dimensionless tortuosity factor evaluated in H Y D R U S as a function of the water content using the Millington-Quirk relationship 010/3 r02 (20) In this preliminary field study, • values ranging between 3.5 and 6.0 cm were assumed for examined soil layers. RESULTS AND DISCUSSION The verification of the simulation model ability to adequately predict the pesticide fate under uncontrolled field soil conditions is a difficult task as compared with verifications based on lysimeters or soil column leaching under controlled laboratory conditions. Thus, simulation results, in the above defined conditions, may be essentially considered to be of qualitative or quantitative type, respectively. For example, the acceptance of qualitative or semi-quantitative results for a territorial analysis is practically implicit when a probabilistic approach is also used to consider the soil spatial heterogeneity a n d / o r annual rainfall events. As to the evaluation of the mathematical model ability to forecast the groundwater vulnerability, other problems arise linked to the difficulty to exactly determine some field data. In particular, it may be sometimes difficult to exactly establish the leachate concentration in the groundwater and whether such solute comes from the leaching of the soil around the examined well or from more distant leaching areas (e.g. meters or kilometers). The topic complexity has favoured the generation of simple parametric methods of land vulnerability evaluation as the Leaching Index (Laskowski et al., 1982), D R A S T I C (Aller et al., 1985), and GUS (Gustafson, 1989), which have little flexibility and are often too generic in the assessing of soil vulnerability. However, such parametric techniques can be useful for some extensive territorial evaluation (Banton and Villeneuve, 1989).

252

D. PERSICANI

For these reasons, an approximate capacity verification of some deterministic models to evaluate agricultural soil vulnerability with respect to atrazine application was thought to be of some interest even though based on a few measured data of local groundwater contamination. On the other hand, this reclamed area is characterized for both particular hydrological conditions (i.e. shallow water table with small water exchange with other aquifer and slow horizontal water flow) and differenciated pedological behaviours (i.e. soils with different permeability and chemical sorption), which makes this ecosystem favourable for a qualitative model evaluation. As to the laboratory analyses on atrazine content in groundwater samples, they showed different results for the two samplings in February and September 1987. The former indicated that both soils had allowed for atrazine leaching throughout the profile, even though it was greater for Typic Fluvaquent (soil 1) (ca. 1.5/zg/1) than Thapto-Histic Fluvaquent (soil 2) (ca. 0.1 ~ g / l ) . Taking into account that in 1987 for corn planting only alachlor-cyanazine treatments were made on both soils, the later groundwater sampling showed a low atrazine content (ca. 0 . 1 / z g / l ) for soil 1 only, whereas no atrazine contamination resulted from soil 2. Previous approximate hydrological investigations (Bonvini et al., unpublished data, 1986) seemed to suggest that the average residence time of groundwater in such reclamation area was shorter than 3 months. Since no atrazine was used in 1985 treatments on examined soils, in this study groundwater contaminations resulting from soil herbicide leaching rather than from previous immobile residues were considered. Thereby, due to the above mentioned limitations of the few available data, they cannot be used for a quantitative model validation. Therefore, the measured groundwater contamination values were only used in a qualitative manner to fix two different vulnerabilities of the examined soils. Then, the mathematical models were evaluated with respect to their ability to forecast the potential groundwater pollution. A relatively high vulnerability was related to the sandy loam, Typic Fluvaquent (soil 1), which allowed a fair atrazine leaching into underlying groundwater in winter 1987, but only little atrazine contamination in summer 1987. In contrast, a relatively low vulnerability was related to the sandy loam-organic, ThaptoHistic Fluvaquent (soil 2), which allowed only a low groundwater contamination in winter 1987. Graphic results of all model simulations for Typic Fluvaquent are shown in Fig. 4, whereas no graphic comment turned out to be interesting for the less-vulnerable Thapto-Histic Fluvaquent. BAM model predicted potential groundwater contamination for soil 1 only. It simulated the solute pulse arrival to average groundwater level ( - 7 0 cm) in early spring with little delay with respect to observed data. The high atrazine concentration of the pulse ( 1 / x g / m l ) was consequent on

ATRAZINE

LEACHING

253

INTO GROUNDWATER

z

BAM

o-

•

Z _o b--

'~

HYDRUS

© GLEAMS [] TETrans • MOUSE

--1-

Z W ¢3 Z

~ Groundwater sampling

°o

-2-

O ff~

-3-

-~lM, J ,j 'A'S'O'N'DIj'FIM~A'Mbj 1986

' j ~A=S~O I 1987

Fig. 4. Atrazine breakthrough curves simulated for the Typic Fluvaquent (soil 1).

the piston flow approach used in the BAM screening model. In contrast, M O U S E management model simulated very little atrazine leaching into groundwater for sandy loam soil 1, and no contamination for drainage water coming from sandy loam-organic soil 2. Only traces of solute were predicted at the end of the simulation period (August-September 1987). Therefore, the management model did not agree with the measured groundwater pollution, which makes it similar to the results obtained by Pennell et al. (1990) who pointed out a smaller sensitivity of MOUSE to pesticide leaching throughout the soil than that shown by other deterministic models not considered in this study. TETrans was the only model predicting atrazine leaching into shallow groundwater for Thapto-Histic Fluvaquent (soil 2) in late winter 1987. A small amount of solute started leaching in November 1986 up to June 1987 with a peak in February 1987. With regard to soil 1, TETrans simulated two pulse phases: the former, isolated in July 1986, and the latter continuously from October 1986 to September 1987, with a maximum of solute leaching in February 1987. GLEAMS model predicted solute leaching into underlying groundwater only for sandy loam soil 1 in the period February-July 1987. Due to the obvious atrazine concentration dilution from soil-water drainage to local groundwater, the leachate amounts simulated by GLEAMS were lower than those actually measured in the sampling in late winter. In general, however, these results agree with the evaluations on the low sensitivity to the solute leaching of GLEAMS made in the work by Pennel et al. (1990). The H Y D R U S research model predicted atrazine leaching into local groundwater from only Typic Fluvaquent (soil 1). The simulation showed

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groundwater contamination starting from November 1986 to September 1987 with a major solute pulse in April and a secondary solute pulse in July 1987. As expected, the comparison between atrazine leaching simulations and observed groundwater contamination in the agricultural ecosystem studied is not homogeneous for both soils and models. In this regard, it is generally accepted that nonvolatile, nonpolar, organic chemicals are more strongly adsorbed by soil organic fraction than mineral components of soil (Harris and Warren, 1964; Talbert and Fletchall, 1965; Khan, 1978; Huang et al., 1984). In this study such different soil buffer capacities were confirmed by analyses of the underlying groundwater samples. A unique exception to this general pedological behaviour was the groundwater contamination detected in the winter period below the soil 2. However, a direct atrazine leaching through the organic horizon of the Thapto-Histic Fluvaquent (soil 2) may be questionable. For example, a raised groundwater level above the organic horizon could allow an occasional contact between soil solution and water table or a groundwater contamination by lateral atrazine flow coming from other mineral soils. However, also an occasional solute pulse reaching the water table through a whole of nonequilibrium sorptiontransport processes (Brusseau and Rao, 1990) cannot be excluded. In this regard, TETrans was the only of the examined models predicting such a limited groundwater contamination, attributing this high atrazine leachability to the bypass flow process. However, the disagreement between the qualitative predictions of the other models and the real environmental vulnerability conditions inferred from preliminary field trials can be related to factors other than effective model ability to predict atrazine leaching. Namely, input data must be considered as serious sources of error in mathematical model use, in particular when complex soil-water systems are considered. Undoubtly, the use of literature data, extrapolations and approximations in the soil data coming from field a n d / o r laboratory techniques plays a basic role in the correct use of simulation models. For these reasons an adequate analysis of each model sensitivity could be of great concern for assessing the priority of the data demand. For example, low atrazine leaching predicted in this study by MOUSE and GLEAMS could depend on their method of estimating evapotranspiration. In fact, these management models internally compute losses of water by evapotranspiration starting from simple temperature data, whereas in BAM, H Y D R U S and TETrans, the evapotranspiration values are directly assigned by the user. In this specific field case, with shallow groundwater, the potential capillary rises had to be taken into account as a positive component of soil-water balance, thus making up for, at least partially, the negative component E. Although these approximate mass-balance assumptions were

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made for all the models here used, the atrazine leaching simulated by M O U S E and G L E A M S was lower than that simulated by BAM, H Y D R U S and TETrans. With regard to qualitative comparisons among the examined models, three different behaviours can be identified. The BAM screening model turns out to be relatively conservative in the case of soil 1, but non-conservative for soil 2; furthermore, it predicted a slightly delayed solute pulse. The c o s t / b e n e f i t ratio, from a general point of view, may be considered moderately satisfactory if evaluated with respect to the soils here examined, which showed meaningful differences in their own sorption capacity. On the other hand, the relative ability of BAM to predict the different vulnerabilities of soil 1 and 2 could be assumed a priori considering the simple analytical scheme of the model in which the sorption process (taking into account in the effective flux Ve of Eqs. 5 and 6) is one of the basic components. Hence, such analytical simplicity could make the BAM model unable to correctly predict less differenciated pedological conditions in which the agrochemical leachability depends on specific conditions as the presence of macropores and soil cracks or few isolated heavy rainfalls. M O U S E and G L E A M S models underestimated the potential groundwater contamination, thus turning out to be relatively non-conservative from an environmental point of view. Probably the particular hydrological condition of the examined area has not been adequately represented by the climatic parameter values used for running the models (e.g. the above mentioned evapotranspiration data). Both these m a n a g e m e n t models, in contrast to the others here examined, take in great consideration the global hydrological balance of Eq. 1. In the case of flat soils, this characteristic can lead to a general underestimation of the infiltrating water into the soil profile and to simulating smaller pesticide leaching to groundwater. As to the M O U S E program specifically, a significant difficulty to correctly manage the fundamental soil hydraulic parameters based on an indirect 0 evaluation could account for the inadequate pesticide leaching predictions. For example, the possibility of entering directly the value of the water content at the field capacity and using a better-known shape parameter of the unsaturated hydraulic conductivity, possibly available in literature too, could be a relevant improvement to the M O U S E program. Unlike this, some G L E A M S significant characteristics seem more conceptual and thereby less changeable. In particular, the presence of a very thin surface layer (1 cm), which is rapidly saturable, can meaningfully increase the runoff component and so decreasing the infiltrating water. Furthermore, G L E A M S allows the pesticide upward flow along the soil profile. This process probably occurs in the examined field conditions, as well as a local

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preferential flow takes place. It is obvious that the lack of the latter process in the GLEAMS program implementation, the only solute upward effect yields a comprehensively negative leaching evaluation. However, MOUSE and GLEAMS management models offer very interesting options for assessing the impact of different soil uses, particularly in ondulate morphologic conditions. Furthermore, GLEAMS, where there are three distinct submodel components (i.e. for hydrology, erosion and pesticides), could provide a better prediction in field conditions in which runoff and soil erosion play a significant role in determining infiltrating water and solute into the soil profile. Nevertheless, when a specific vulnerability estimate (e.g. with respect to the ground water pollution) of a flat agricultural area is based essentially on the inherent structure of the soil-water system (i.e. the physicochemical and hydraulic soil characteristics and the specific weather conditions), then MOUSE and GLEAMS models could be characterized by a unfavourable cost/benefit ratio. For example, in this study, in which an adequate and subjective model validation was not possible, the cost/benefit ratio could be considered more favourable for BAM than MOUSE even though all the remarks above cited underline the difficulty to evaluate each model in a general context. On the contrary, in the examined case, the GLEAMS management model could be considered qualitatively more suitable than the BAM analytical model because the former predicted more correctly the atrazine leaching throughout the soil 1 into the local groundwater from February to July 1987. In this preliminary field study, in which the model evaluation was based only on the ability to forecast the pesticide leaching to groundwater, TETrans and H Y D R U S seemed to be the best mathematical models among the ones examined (Fig. 4). TETrans and HYDRUS, as compared with the other numerical models, MOUSE and GLEAMS, simulated more atrazine reaching the water table, probably because they neglected surface flow component of Eq. 1 and allowed a more direct fitting of the hydrological input data to the specific soil-water systems investigated. Between the two models, TETrans gave the best predictions because it correctly simulated also the atrazine leaching into groundwater throughout the ThaptoHistic Fluvaquent (soil 2). The ability of simulating also a low atrazine leaching, as really occurred for the soil 2, is related to the implementation in the TETrans computer program of an algorithm for the preferential flow calculation (Eq. 10). In fact, in this simulation model, a portion of infiltrating water is allowed to bypass the soil matrix, so rapidly transporting the herbicide downward the groundwater. From a simple conceptual point of view, it is obvious that neglecting both Q and E components of Eq. 1 and JD and J~ components of Eq. 2, besides considering 0m < 0fc in Eq. 11,

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leads TETrans to be more conservative than the other models after the pesticide degradation effect has been externally calculated. On the other hand, TETrans computes the bypass flow without meaningfully increasing the input demand, although the capacity parameter defining the short-circuiting in the field soil can be estimated from only empirical evaluations. This remark is important for the general soil-water system vulnerability assessment because many cases of groundwater contamination by pesticides seem to derive from the preferential solute flow along the largest biopores and soil cracks (Nicholls et al., 1982; Bouma, 1990; Isensee et al., 1990; Sauer et al., 1990; Starr and Glotfelty, 1990; Hall et al., 1991; Kladivko et al., 1991). Unfortunately, in this study based upon preliminary field investigations, the precise influence of the bypass flow on the herbicide transport could not be assessed due to a lack of groundwater sampling in early s u m m e r - a u t u m n 1986. As to the H Y D R U S research model, its relatively good predictions were obtained with some more-intensive input demand than other deterministic models in comparison to the unsaturated soil hydraulic functions. On the other hand, options allowing both the hysteresis and root water uptake effects to be neglected, aimed to balance the lack of nonequilibrium transport considerations; so, such little calibration also simplified the input data demand. However, H Y D R U S simulations delayed the atrazine leaching by about 1 or 2 months with respect to TETrans, whereas the latter generated a very significant early pulse in summer 1986. These different simulation results were clearly due to the different approach to water and solute flow in the two models. Even though the bypass flow was considered to be little pronounced in the Typic Fluvaquent (soil 1), the earlier atrazine pulse simulated by TETrans in July 1986 is typical of such a hydrological process, whereas the matrix flow is conceptually slower and more regular. CONC L US I ONS

The use of simulation models can be considered an easy and quick tool for the evaluation of the hydrological vulnerability of agricultural areas. However, when the environmental vulnerability analysis is related to large field or basin scales with several different territorial unities, only little model calibrations are enabled, in particular if the sensitivity of the mathematical model vs specific input parameters is unknown. Large territorial extension, different soil-water system characteristics, and few available data coming directly from the area that has to be evaluated, can be considered a very probable context when using simulation models as a preliminary approach to land-use planning. U n d e r such conditions of work, not considering any cost/benefit ratio, it is obvious that

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each deterministic model will yield only qualitative vulnerability evaluations of the examined area. In this study, five deterministic models were approximately evaluated with respect to their ability to correctly predict two different environmental scenarios. The results obtained indicated the following decreasing order of predictive ability of the examined deterministic models: TETrans > H Y D R U S > G L E A M S > BAM >~ MOUSE. The qualitative evaluations of the models seemed to indicate that the bypass flow process implemented within TETrans, enabled to perform simulations in which the atrazine amounts leaching toward groundwater were essentially in agreement with the measured groundwater pollution. H Y D R U S research model seemed less conservative than TETrans m a n a g e m e n t model, but more conservative as compared with G L E A M S m a n a g e m e n t model. Both H Y D R U S and G L E A M S had intensive input demand; nevertheless the former allowed a simpler consideration of the specific soil-water conditions. The latter model instead showed an input data d e m a n d (e.g. a complete erosion submodel) which seemed little useful for the specific purpose of the study applied on a fiat agricultural area. Hence, the preliminary model calibration aims to increase the general atrazine transport down toward groundwater gave some positive results for TETrans and H Y D R U S but they did not for G L E A M S and M O U S E models. This last management model gave too conservative predictions indicating both soil profiles to be little vulnerable if compared with the measured atrazine leaching into local groundwater. Furthermore, M O U S E showed more intensive input d e m a n d than the simplest BAM screening model. However BAM, with reference to this study, can be considered only occasionally a suitable predictive model for a qualitative environmental vulnerability appraisal, namely when there are different physicochemical soil characteristics. ACKNOWLEDGEMENTS The author thanks Profs. Fontana, Del Re and Silva for their laboratory assistence, Dr. Bonvini for his precious help in the field work, and Drs. van Genuchten, Corwin, Knisel and Steenhuis for providing a copy of their computer programs. REFERENCES Addiscott, T.M. and Wagenet, R.J., 1985. Concepts of solute leaching in soils: a review of modelling approaches. J. Soil Sci., 36: 411-424. Addiscott, T.M. and Whitmore, A.P., 1991. Simulation of solute leaching in soils of differing permeabilities. Soil Use Manage., 7: 94-102.

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Aller, L., Bennet, T., Lehr, J.H. and Petty, R., 1985. DRASTIC: A standardized system for evaluating groundwater pollution potential using hydrogeologic settings. EPA/600/285/018. Amministrazione Provinciale di Piacenza, 1987. Dati agrometeorologici giornalieri degli anni 1986-1987. Piacenza, Italy. Amoozegar-Fard, A., Nielsen, D.R. and Warrick, A.W., 1982. Soil solute concentration distributions for spatially varying pore water velocities and apparent diffusion coefficients. Soil Sci. Soc. Am. J., 46: 3-9. Banton, O. and Villeneuve, J.-P., 1989. Evaluation of groundwater vulnerability to pesticides: a comparison between the pesticide DRASTIC index and the PRZM leaching quantities. J. Contam. Hydrol., 4: 285-296. Boesten, J.J.T.I. and van der Linden, A.M.A., 1991. Modeling the influence of sorption and transformation on pesticide leaching and persistence. J. Environ. Qual., 20: 425-435. Bouma, J., 1990. Using morphometric expressions for macropores to improve soil physical analysis of field soils. Geoderma, 46: 3-11. Brusseau, M.L. and Rao, P.S.C., 1990. Modeling the transport in structured soils: a review. Geoderma, 46: 169-192. Carsel, R.F., Smith, C.N., Mulkey, L.A., Dean, J.D. and Jowise, P., 1984. Users manual for the pesticide root zone model (PRZM). EPA-600/3-84-109, 216 pp. Cohen, S.Z., Creeger, S.M., Carsel, R.F. and Enfield, C.G., 1984. Potential pesticide contamination of groundwater from agricultural uses. ACS Symp. Ser., 259: 297-325. Corwin, D. and Waggoner, B., 1990. Trace Element Transport (TETrans) user's guide. USDA-ARS, United States Salinity Laboratory, Research Report, 123. Corwin, D.L., Waggoner, B.L. and Rhoades, J.D., 1991. A functional model of solute transport that accounts for bypass. J. Environ. Qual., 20: 647-658. Davis, F.M., Leonard, R.A. and Knisel, W.G., 1990. Groundwater Loading Effects of Agricultural Management Systems (GLEAMS) user manual. USDA-ARS Southeast Watershed Research Lab and University of Georgia, Tifton, GA. Gustafson, D.I., 1989. Groundwater ubiquity score: a simple method for assessing pesticide leachability. Environ. Toxicol. Chem., 8: 339-357. Hall, J.K., Mumma, R.O. and Watts, D.W., 1991. Leaching and runoff losses of herbicides in a tilled and untilled field. Agric. Ecosyst. Environ., 37: 303-314. Hallberg, G.R., 1989. Pesticide pollution of groundwater in the humid United States. Agric. Ecosyst. Environ., 26: 299-367. Harris, C.I. and Warren, G.F., 1964. Adsorption and desorption of herbicides by soil. Weeds, 12: 120-125. Huang, P.M., Grover, R. and McKercher, R.B., 1984. Components and particle size fractions involved in atrazine adsorption by soils. Soil Sci., 138: 20-24. Isensee, A.R., Nash, R.G. and Helling, C.S., 1990. Effect of conventional vs. no-tillage on pesticide leaching to shallow groundwater. J. Environ. Qual., 19: 434-440. Jcrgensen, S.E., 1990. Modelling in Ecotoxicology. Elsevier, Amsterdam. Jury, W.A., 1982. Simulation of solute transport using a transfer function model. Water Resour. Res., 18: 363-368. Jury, W.A., Spencer, W.F. and Farmer, W.J., 1983. Behavior assessment model for trace organics in soil. I. Model description. J. Environ. Qual., 12: 558-564. Jury, W.A., Focht, D.D. and Farmer, W.J., 1987. Evaluation of pesticide groundwater pollution potential from standard indices of soil-chemical adsorption and biodegradation. J. Environ. Qual., 16: 422-428. Khan, S.U., 1978. The interaction of organic matter with pesticides. In: M. Schnitzer and

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S.U. Khan (Editors), Soil Organic Matter, Developments in Soil Science, Vol. 8. Elsevier, Amsterdam, pp. 137-168. Kladivko, E.J., Van Scoyoc, G.E., Monke, E.J., Oates, K.M. and Pask, W., 1991. Pesticide and nutrient movement into subsurface tile drains on a silt loam soil in Indiana, J. Environ. Qual., 20: 264-270. Kool, J.B. and van Genuchten, M.Th., 1991. HYDRUS One-dimensional variably saturated flow and transport model, including hysteresis and root water uptake. U.S. Salinity Laboratory USDA-.ARS, Riverside, CA, 101 pp. Laskowski, D..A., Goring, C.A.I., McCall, P.J. and Swarm, R.L., 1982. Terrestrial environment. In: R.A. Conway (Editor), Environmental Risk Analysis for Chemicals. van Nostrand Reinhold Co., New York, NY, pp. 198-240. Leistra, M., 1986. Modelling the behaviour of organic chemicals in soil and ground water. Pestic. Sci., 17: 256-264. Leistra, M. and Boesten, J.J.T.I., 1989. Pesticide contamination of groundwater in western Europe..Agric. Ecosyst. Environ., 26: 369-389. Nicholls, P.H., Walker, A. and Baker, R.J., 1982. Measurement and simulation of the movement and degradation of atrazine and metribuzin in a fallow soil. Pestic. Sci., 12: 484-494. Nofziger, D.L. and Hornsby, A.G., 1987. Chemical Movement in Layered Soils: User's Manual. Computer Series, IFAS University of Florida, 44 pp. Pennell, K.D., Hornsby, A.G., Jessup, R.E. and Rao, P.S.C., 1990. Evaluation of five simulation models for predicting aldicarb and bromide behavior under field conditions. Water Resour. Res., 26: 2679-2693. Persicani, D., 1988. Incidenza del fattore pedologico suUa percolazione dell'atrazina nell'acqua di falda. Acque Sotterranee, 20: 25-31. Petach, M.C., Wagenet, R.J. and De Gloria, S.D., 1991. Regional water flow and pesticide leaching using simulations with spatially distributed data. Geoderma, 48: 245-269. Pionke, H.B. and De Angelis, R.J., 1980. Method for distributing pesticide loss in field runoff between the solution and adsorbed phase. In: W.G. Knisel (Editor), CREAMS Manual. USDA Conservation Research Report, 26: 607-643. Pionke, H.B., Glotfelty, D.E., Lucas, .A.D. and Urban, J.B., 1988. Pesticide contamination of groundwaters in the Mahantango Creek watershed. J. Environ. Qual., 17: 76-84. Rao, P.S.C., Davidson, J.M. and Hammond, L.C., 1976. Estimation of nonreactive and reactive solute front locations in soils. In: W.H. Fuller (Editor), Residue Management by Land Disposal, Proc. Waste Research Syrup., Univ. Arizona, Tucson. USEP.A Rep. 600/9-76-015, Washington, DC, pp. 235-242. Sauer, T.J., Fermanich, K.J. and Daniel, T.C., 1990. Comparison of the Pesticide Root Zone Model simulated and measured pesticide mobility under two tillage systems. J. Environ. Qual., 19: 727-734. Starr, J.L. and Glotfelty, D.E., 1990. Atrazine and bromide movement through a silt loam soil. J. Environ. Oual., 19: 552-558. Steenhuis, T.S., Hughes, H.B.F., Pacenka, S. and Gross, M., 1984. MOUSE user's manual. Northeast Regional -Agricultural Engineering Service, Cornell University, Ithaca, NY, 43 pp. Talbert, R.E. and Fletchall, O.H., 1965. The adsorption of some s-triazine in soils. Weeds, 13: 46-51. van Genuchten, M.Th., 1978. Calculating the unsaturated hydraulic conductivity with a new closed-form analytical model. Research Report 78-WR-08, Dept. of Civil Engineering, Princeton Univ., Princeton, NJ, 63 pp.

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van Genuchten, M.Th., Leij, F.J. and Yates, S.R., 1991. The RETC code for quantifying the hydraulic functions of unsaturated soils. U.S. Environmental Protection Agency, R.S. Kerr Environmental Research Laboratory Office of Research and Development, Ada, OK, 81 pp. Villeneuve, J.P., Banton, O. and Lafrance, P., 1990. A probabilistic approach for the groundwater vulnerability to contamination by pesticides: the VULPEST model. Ecol. Modelling, 51: 47-58. Wagenet, R.J. and Hutson, J.L., 1987. LEACHM: A Finite-Difference Model for Simulating Water, Salt, and Pesticide Movement in the Plant Root Zone, Continuum 2. New York State Resources Institute, Cornell University, Ithaca, NY. Wagenet, R.J. and Rao, P.S.C., 1990. Modeling pesticide fate in soils. In: H.H. Cheng (Editor), Pesticides in the Environment: Processes, Impacts, and Modeling, Soil Series Science Vol. 2. Soil Science Society of America, Madison, WI, pp. 351-399. Wehtje, G., Mielke, L.N., Leavitt, J.R.C. and Schepers, J.S., 1984. Leaching of atrazine in the root zone of an alluvial soil in Nebraska. J. Environ. Qual., 13: 507-513.