Modeling pesticide dynamics of four different sites
using the model system SIMULAT
K. Aden
a, B. DiekkruÈger
b,*aBASF Agricultural Center Limburgerhof, Postfach 120, 67114 Limburgerhof, Germany bGeographische Institute, UniversitaÈt Bonn, Meckenheimer Allee 166, 53115 Bonn, Germany
Abstract
This study aimed to assess the accuracy of SIMULAT, a computer model primarily designed for predicting the fate of pesticides in soil. The evaluation was carried out by comparing simulated results on herbicide degradation with results obtained from ®eld and lysimeters experiments. For model validation four different data sets were available. The data sets included ®eld, lysimeter, and laboratory experiments from Germany (Weiherbach), The Netherlands (Vredepeel), Great Britain (Brimstone), and Italy (Tor Mancina). The applied herbicides and the determined soil and water parameters varied substantially among the four empirical data sets used for model evaluation.
In a ®rst step simulations were run with the model still being uncalibrated. Afterwards a calibration of hydraulic parameters was performed using measured water and bromide contents in soil (Weiherbach, Vredepeel and Brimstone) or leachate (Tor Mancina). In contrast to the hydraulic parameters, the sorption and degradation parameters were not calibrated. Simulations were run with the calibrated model and the results compared with those obtained from ®eld measurements.
The wide range of implemented boundary conditions such as lysimeter, free drainage or ¯uctuating groundwater table enabled applying SIMULAT to all four data sets. However, usage of parameters obtained in laboratory experiments gave no satis®able simulation of the degradation of the herbicides. In contrast to the macroporous loam soil at Tor Mancina, water and bromide transport were accurately simulated in the loess (Weiherbach) and in the sandy soil (Vredepeel). Severe problems occur while simulating the ¯uctuating groundwater and drain ¯ow in the clay soil at Brimstone.#2000 Elsevier Science B.V. All rights reserved.
Keywords:Simulation model; SIMULAT; Model calibration; Validation Agricultural Water Management 44 (2000) 337±355
*Corresponding author. Tel.:49-228-732107; fax:49-228-735393.
E-mail address: b.diekkrueger@uni-bonn.de (B. DiekkruÈger).
1. Introduction
Any model that is to be used for prediction purposes needs to be verified and validated using independent data sets. Validation requires data from field and laboratory experiments which are often not available. It seems easier to develop completely new models than to verify or to validate existing models (DiekkruÈger et al., 1995), because of the lack of public data. For that reason three workshops titled `Comparing and Evaluating Pesticide Leaching Models' took place. They were part of the EU-Project COST Action 66 `Pesticides in the Soil Environment'. Four data sets were provided to validate existing models.
Models used for the simulation of pesticide transport and degradation are often complex. Because of that a multistep validation is useful which is described by Armstrong et al. (1996). They consider five stages: (1) parameterization of the model using independent measurements, (2) hydrological validation, (3) solute movement validation, (4) fate of pesticides in the soil and (5) pesticide leaching validation. A guideline for model validation was published by Vanclooster et al. (2000) which is similar to the procedure mentioned before. All participants of the workshops should validate their models by following the suggested procedure.
This paper describes the validation of the model system SIMULAT 2.3 in the framework of the COST action 66 workshops 1996±1997.
2. Materials and methods
2.1. The model
SIMULAT 2.3 (DiekkruÈger, 1996, DiekkruÈger and Richter, 1996) has been developed at the Technical University of Braunschweig, Germany, within the Collaborative Research Program 179 `Water and Matter Dynamics in Agro-Ecosystems'. SIMULAT enables the calculation of transport and transformation of biodegradable substances as nitrogen, sulfur and pesticides in the unsaturated/saturated zone of the soil. It is an one-dimensional model which consists of submodels for the calculation of macropore flow, infiltration, runoff, evapotranspiration, plant growth, interception and heat flux in the soil. The submodels can be switched on and off by the user.
Water and matter transport in the soil matrix is calculated by the Richards' equation and the convection±dispersion equation, respectively. Preferential flow in the soil can be simulated by a macropore model. Infiltration into the macropores occurs when the infiltration capacity of the matrix pores is exceeded. The infiltration capacity is calculated as the numerical solution of Richards' equation. Water flow in the macropores is pure gravitational. Solute transport in the macropores ignores dispersion. According to the timescale involved it is assumed that sorption and degradation can be neglected in macropores. Lateral interaction between the matrix and the macropore system is calculated according to Darcy's law assuming film flow in the macropores. For calculating solute concentration at the upper boundary of the macropore systems it is assumed that the concentration of the input is in equilibrium with the concentration of the upper numerical layer of the soil matrix.
For the Richards' equation information on the retention and conductivity curve are required. SIMULAT can either use a parameterization by van Genuchten/Mualem (van Genuchten, 1980) or by Brooks and Corey/Burdine (Brooks and Corey, 1964) re-lationship. These parameters can be estimated from measurements or derived from the soil texture using a pedotransfer-function of Rawls and Brakensiek (1985).
Potential evapotranspiration is calculated according to the Penman±Monteith equation. Although, potential evapotranspiration data can also be used directly as input. Actual evaporation is calculated according to Ritchie (1972). The approach of Feddes et al. (1978) is used for the calculation of the actual transpiration, in which the soil suction determines root water uptake. For an estimation of the actual transpiration a simple plant model was used which calculates, e.g. the plant leaf-area index as well as plant root depth and density. Further information concerning the water transport is given by DiekkruÈger and Arning (1995).
Sorption of pesticides can be described by an equilibrium or a kinetic form of a linear, a Freundlich- or a Langmuir-isotherm. Maximal three different binding sites can be considered.
The degradation rate of the pesticidesk(T,y) (per day) is influenced by the soil water
contenty and the soil temperatureT. The well known approach of Walker (Walker and
Allen, 1984) is given in Eq. (1). For many pesticides the degradation decreases at water content near saturation. This behavior can be described by an optimum curve given in Eq. (2) (Richter et al., 1996). The parametersAandBare fitting parameters and the parameter
ycritdescribes the water content at which the degradation rate is maximal.
k y AyB; (1)
Furthermore, the user has the choice between two temperature response functions. One is the well-known Arrhenius function (Eq. (3) with the activation energyEa(J molÿ1), the
degradation ratek0(Tÿ1) and the gas constantR(J Kÿ1molÿ1).
The other one is the O'Neill optimum curve (Richter et al., 1996) given in Eq. (4). The valuehdenotes a step function, which takes the value of 1 forTmax>T08C, otherwise
it is zero. The maximal degradation ratermax(Tÿ1) is achieved at the optimal temperature
The total degradation ratek(T,y) is a product of the temperature response functionk(T)
and the humidity response functionk(y). Normally,k(T,y) is used for ®rst-order kinetics.
Besides the exponential degradation, SIMULAT offers other approaches for describing pesticide degradation, e.g. Michaelis±Menten kinetic, metabolic or co-metabolic degradation. For the latter approach it is necessary to model the dynamics of microbial activity explicitly.
The fate of metabolites can be simulated with SIMULAT whereas volatilization and plant uptake are not considered in the current version.
SIMULAT includes different lower boundary conditions which guarantee a high range of applicability like lysimeter, prescribed water content, soil suction, water flux or gradient of soil suction. The values of the boundary condition may vary with time, e.g. in order to consider fluctuating groundwater table. For situations in which the groundwater table influences the dynamics in the root zone the base flow can be computed according to the Dupuit±Forchheimer assumption proportional to the thickness of the saturated zone (van Schilfgaarde, 1970).
In order to be able to compute the Brimstone data set, SIMULAT was completed by a tile drain model according to the Hooghoudt equation (Hooghoudt, 1940; Eggelsmann, 1981, pp. 127).
The simulated soil can be subdivided into different soil horizons and numerous computational layers. The user is able to choose the spatial and temporal discretization of the numeric model. As model input daily or hourly climatic data (air temperature, rainfall and global radiation or potential evapotranspiration) are necessary.
2.2. Description of data sets
The four data sets were named Weiherbach (Germany), Vredepeel (The Netherlands), Brimstone (Great Britain) and Tor Mancina (Italy) in the text. Table 1 shows the main characteristics of these data sets. The following chapter describes special characteristics of each data set which are not listed in the table.
The herbicide and bromide concentration and the water content in the soil were measured on all field plots (except bromide in Brimstone). Because a lysimeter study was installed in Tor Mancina, the herbicide and bromide concentration in the soil could not be measured during the experiment. Instead of that, the percolated water volume, the concentration of bromide and metolachlor in leachate were observed.
Measurements of hydraulic parameters were made for each soil, but the number of measurements and methods were different. The parameters of the van Genuchten/ Mualem retention curve were published for the Weiherbach soil while for the sandy soil in Vredepeel a lot of measurements of the retention and conductivity curve were available. The data sets of Tor Mancina and Brimstone only included some measurements of the relation between the water content and the soil suction. But for the Brimstone soil hydraulic properties were reported in detail.
All data sets included laboratory experiments for the estimation of sorption and degradation parameters. The original data from degradation and sorption studies were published for the Vredepeel and Weiherbach soil (only limited information concerning pendimethalin). Whereas half-life values and sorption coefficients were
reported for the herbicides applied on the soil in Tor Mancina and Brimstone (Nicholls et al., 1993).
For each data set climate data were provided. The daily rainfall, global radiation, minimum and maximum temperature and air humidity were reported. Additionally, the soil temperature and volumetric water content were measured daily at the Weiherbach site.
Two different irrigation regimes were conveyed at Tor Mancina. Two lysimeters were irrigated with a total volume of 452 mm (irrig. 1) and two others with 602 mm (irrig. 2) during the whole experiment. The field of the Weiherbach project was irrigated in year 1995, too.
The experiments can be differentiated by time and number of applications (see Table 1). For example during a short period (run 1) the groundwater table and the drainflow was measured hourly at Brimstone Farm. These data could be used for testing the ability of models to predict fast processes. In contrast to that the lysimeter experiments of Tor Mancina could test the long-term behavior of models. The fate and transport of metolachlor were observed over nearly 1000 days. Three tracer and two herbicide applications allowed a partition of the data set into a calibration part (first two years) and a validation part (third year). In the same way the experiments on the Weiherbach site could be used. The experiments in year 1993/1994 were used for calibration and the results of 1995 for model validation. To achieve a real validation of the computer model only the results of the calibration part were available for the model user. The remaining measurements of the validation period were made available for the model users after finishing all computer simulations.
No. applications 2 1 1 2
Tracer Bromide Bromide ± Bromide
No. applications 2 1 3
Sampling depth (m) 0.95 Maximum 2 1 Leachate was
measured
Groundwater No in¯uence Fluctuating Fluctuating ±
Irrigation Yes (1995) No No Yes
Description of
2.3. Application of SIMULAT
First a general description is given how model parameters were derived from the data sets. Then more details of the parameter estimation process for each data set are reported. Additionally, values for the hydraulic, sorption and degradation parameters used in the model are provided.
The initial hydraulic parameters were derived from measurements of water retention and hydraulic conductivity or they were calculated from the soil texture using a pedotransfer-function. Sorption and degradation parameters were estimated from the laboratory experiments using the ModelMaker 2.0 software package or, if not available, from literature. With these parameters a simulation run was performed. The results of the simulation using the uncalibrated model were the first part of the validation procedure (Vanclooster et al., 2000).
Afterwards, a calibration procedure of the hydraulic parameters followed. The mea-surements of the water content in the soil, the leaching volumes of lysimeter studies and measurements of the bromide concentration in soil or leachate were used for calibrating the water transport parameters. A trial and error method was used because an optimization tool was not available. Measurements of pesticide concentrations in the soil or in the leachate were not used for calibration according to the suggested procedure (Vanclooster et al., 2000). In the following, detailed information is given about the model parameters used for the simulation of each data set. An overview of the sorption and degradation (sub)models used in SIMULAT is listed in Table 2. Additionally, boundary conditions, sorption coefficients and half-life values for the top soil are given in the table. Tables 3±6 show the hydraulic parameters which were used in the model before calibration.
2.4. Weiherbach
Because an evident lag phase in the laboratory experiments with isoproturon was measured a metabolic degradation model was chosen in SIMULAT. The half-life value obtained from this degradation model could not be compared with half-life values for first-order kinetics. Because of that, in Table 2, no half-life is given for isoproturon. In contrast to isoproturon the pendimethalin degradation was calculated by using first-order kinetics. In addition to the degradation studies with pendimethalin, described in the data set, further information were taken from GottesbuÈren (1991). The sorption parameter of pendimethalin was obtained from literature (Perkow, 1994).
SIMULAT was calibrated using soil water content and bromide concentration from the experiments in 1993/1994. During the calibration procedure the saturated conductivity was increased in the top soil from 12 to 72 cm per day and from 6 to 96 cm per day for deeper layers, and the van Genuchten parameters were varied. Additionally, a mobile± immobile water approach was tested with an immobile water content of 7 vol.%.
2.5. Vredepeel
The main information about the simulation parameters of the Vredepeel experiment are listed in Table 2. During the calibration some of the parameters of the van Genuchten
Table 2
Simulation parameters and models used in SIMULAT for the simulation of the data sets (sorption coef®cients and half-lives are only given for the top soil)
Weiherbach Vredepeel Brimstone Tor Mancina
Lower boundary condition Free drainage Inputmeasured groundwater
table
Dupuit±Forchheimer Lysimeter
Sorption model Linear (equilibrium) Freundlich (equilibrium) Linear (equilibrium) Linear (equilibrium)
KForKd-value (l kgÿ1)
Temperature response curve O'Neill O'Neill O'Neill Arrhenius
Humidity response curve Optimum curve No in¯uence Optimum curve After Walker
(0±0.5 m)
Dispersion length (cm) 2.5 2.5/3 10 5
retention curve were modified and the saturated conductivity was increased to 76 cm per day in the entire soil column.
2.6. Brimstone
The experiments at Brimstone show a distinct bypass flow. Because of that the macropore model included in SIMULAT was activated. The parameters for the macropore model were derived from measurements. Furthermore, it was necessary to use the tile drain model according to Hooghoudt (1940). Model calibration was performed by comparing measured and predicted groundwater level and drain flow. Only minor modifications of the van Genuchten parameters were made, but the lateral flow from the soil matrix into the macropores was increased. Additionally, the saturated conductivity of the first 0.1 m was reduced to 1 cm per day to obtain higher macropore flow.
Table 3
Hydraulic `van Genuchten' parameters used for the simulation of the Weiherbach data set
Depth (m) ys(cm3cmÿ3) yr(cm3cmÿ3) Ks(cm per day) a(h Paÿ1) n(±)
0±0.3 0.46 0.03 12 0.015 1.30
>0.3 0.45 0.08 6 0.005 2.25
Table 4
Hydraulic 'van Genuchten' parameters used for the simulation of the Vredepeel data set
Depth (m) ys(cm3cmÿ3) y
r(cm3cmÿ3) Ks(cm per day) a(h Paÿ1) n(±)
0.0±0.3 0.42 0.05 7 0.023 2.16
0.3±0.5 0.44 0.05 14 0.026 1.91
0.5±2.0 0.32 0 0.076 0.026 2.58
Table 5
Hydraulic 'van Genuchten' parameters and the macropore volumes used for the simulation of the Brimstone data set
Depth (m) ys(cm3cmÿ3) yr(cm3cmÿ3) Ks(cm per day) a(h Paÿ1) n(±) yMa(cm3cmÿ3)
0.0±0.2 0.50 0.20 10 0.0003 1.90 0.05
0.2±0.4 0.53 0.20 10 0.0001 1.92 0.02
0.4±0.6 0.54 0.29 10 0.0204 1.26 0.01
0.6±2.0 0.49 0.23 10 0.0143 1.24 0.01
Table 6
Hydraulic `van Genuchten' parameters and the macropore volume used for the simulation of the Tor Mancina data set
Depth (m) ys(cm3cmÿ3) y
r(cm3cmÿ3) Ks(cm per day) a(h Paÿ1) n(±) yMa(cm3cmÿ3)
0.0±1.5 0.38 0.18 6 0.007 1.3 0.05
2.7. Tor Mancina
The macropore model was chosen to calculate water flow and matter transport in the loam soil. Only few measurements of the water retention in the soil were available. For that reason hydraulic parameters (cf. Table 6) were estimated from the soil texture using a pedotransfer-function. A calibration of these parameters was made by using measure-ments of percolated water volume and bromide concentration in leachate of the first two years. In the end the saturated conductivityKswas increased in the first 0.1 m (from 1 to
1.9 cm per day) and decreased in deeper layers (from 6 to 4.5 cm per day). The lateral flow between the soil matrix and the macropores has been switched off. A dispersion length of 10 cm was taken, because measurements of the soil bromide content were not available for calibration.
3. Results and discussion
The results of the simulations are given by Figs. 1±9 and are described qualitatively. Additional information about the simulation results are given in accompanying papers. In these papers the simulations obtained by SIMULAT were also compared with other models. For this comparison, Goodness-of-fit statistics were reported for the Weiherbach (GottesbuÈren et al., 2000) and the Tor Mancina data set (Francaviglia et al., 2000). Whereas the simulation results of the Vredepeel data set (Vanclooster and Boesten, 2000; Tiktak, 2000) and Brimstone data set were compared by showing figures and discussing input parameters.
3.1. Weiherbach
The calculation of the water and bromide (1995) content in the soil is only described qualitatively. In the same way, the simulation results of isoproturon were reported. The measured and simulated soil concentrations of bromide 1993/1994 and of pendimethalin are given in Figs. 1 and 2.
The water content in the soil 1993/1994 was overestimated in the first 0.25 m and underestimated in deeper layers before calibration. Differences between measured and simulated water content up to 0.08 kg kgÿ1 (0.15 kg kgÿ1 in 1995) occurred. After calibration SIMULAT showed a good prediction of the water content in the soil for the calibration period 1993/1994 as well as for the validation period 1995. The deviations between observed and predicted water content were always smaller than 0.04 kg kgÿ1.
The uncalibrated model showed an acceptable prediction of the bromide transport for 1993/1994 while the calibrated model gave a good prediction of the bromide transport (Fig. 1). These results are not surprising, because the measurements of 1993/1994 were used for calibration. In contrast to 1993/1994, the bromide transport in year 1995 was very fast. Bromide was nearly leached out completely (below 0.9 m) during the four weeks after application while the model predicted a maximum peak at a depth of 0.4 m. The observed fast leaching of bromide could only be explained by transport through macropores. Because the 1993/1994 measurements gave no hint on macropores only transport in the soil matrix was considered.
In addition to bromide two herbicides were applied. Isoproturon disappeared completely during the calibration period 1993/1994. In contrast to the measurements, the simulation shows herbicide residues in the soil at the end of the experiments. The remaining mass was about one third of the applied amount. The transport and degradation of the herbicide was underestimated. Similar results could be observed for the validation period 1995. Isoproturon was measured up to the depth of 0.5 m and after one month no pesticide could be detected. In contrast to the measurements, SIMULAT predicted one third of the initial content at the end of experiments in the upper 0.3 m. The underestimation of isoproturon transport can be explained by the choice of sorption parameters. A Kd-value of 4 l kgÿ1was taken from long-term desorption experiments,
which were reported in the data set. It would have been a better choice to consider the Fig. 1. Bromide content in the soil measured at four sampling dates at the Weiherbach ®eld plot VIII. Bromide was applied on 6 December 1993 (150 kg haÿ1).
results of the batch experiments, where aKd-value of 2 l kgÿ1was measured for the top
soil. For further information one is referred to GottesbuÈren et al. (2000), who reported simulation results with even lower sorption coefficients (1 l kgÿ1). The degradation of isoproturon was underestimated by the model. GottesbuÈren et al. (2000) showed that the half-lives obtained from the laboratory experiments were too high. Because of that they suggest to use half-lives which were 50% lower than the measured ones.
The simulation results of pendimethalin are shown in Fig. 2. An acceptable agreement between the observed and predicted pendimethalin concentration in the soil was obtained. The high sorption of the herbicide in the upper soil layer was simulated by the model, using a highKd-value of 20 l kgÿ1.
Fig. 2. Pendimethalin concentration in the soil during the experimental period 1993/1994 at the Weiherbach ®eld plot VIII. Pendimethalin was applied on 6 December 1993 (3.2 kg haÿ1).
A model calibration according to the guidelines of the COST workshop, which ignores the fast transport of bromide 1995, was the wrong decision. It seems that the measurements of bromide transport 1993 are not representative for the hydraulic behavior of the soil. Better simulation results could be achieved using the macropore model of SIMULAT.
3.2. Vredepeel
The simulation results of the water content in the soil were only described qualitatively while the measurements and predictions of bromide, bentazone and ethoprophos are shown in diagrams.
The model was not able to calculate the water content in a satisfying way for depths greater than 0.3 m before calibration. Difference between the measured and calculated values up to 20 vol.% for some layers below 0.5 m were observed. After calibrating the hydraulic parameters the water content could be simulated well for the whole soil column. Nearly all predicted values were in the range of the measurementsstandard deviation. The influence of the calibration on the simulation results of bromide is given in Fig. 3, although SIMULAT computed only small differences between the calculated bromide concentration before and after calibration. The depth of the peak maximum was described better with the calibrated model, but the measured retention of bromide in the top soil could not be computed at all. This can be explained by the model assumption that Fig. 3. Simulated and measured bromide concentration in the soil at Vredepeel. The gray signature indicates measured valuesstandard deviation. Please notice that thex-axis cover different ranges. Bromide was applied on 22 November 1990 (111 kg haÿ1).
bromide acted as an ideal tracer without sorption. Furthermore, root uptake of bromide was neglected. The latter process could explain the retention of bromide in the upper soil. In order to show low as well as high concentrations the results are presented in the diagrams using a logarithmic scale. The degradation of this herbicide was under-estimated, but the transport overestimated by SIMULAT (Fig. 4). The sorption coefficient derived from the laboratory experiments seemed to be high for this sandy soil. The estimation of the degradation parameters bases on a few incubation studies only. Only two temperatures and one soil moisture content were considered in the degradation experiments with the top soil.
The transport of bentazone was only partly reproduced by the model. However, the differences between the calibrated and the uncalibrated versions were small (Fig. 5). Therefore, the worse prediction of the bentazone fate is due to sorption and degradation, but not due to transport.
3.3. Brimstone
The output variables which had to be simulated for the Brimstone soil differ from all other experiments: the groundwater table and the concentration of herbicides in drain flow should be calculated. The model predicted the groundwater level in a satisfactory way. Additionally, the model was able to describe the drainflow, but the temporal pattern Fig. 4. Measured and predicted ethoprophos concentration in the soil at Vredepeel (logarithmic scale). The gray signature indicates measured valuesstandard deviation. Ethoprophos was applied on 22 November 1990 (3.4 kg haÿ1).
differs significantly compared to the measurements. This is caused by the assumed high hydraulic conductivity (10 cm per day), which enabled a fast interaction between the water table and drain flow. For further information about the simulation results of the groundwater table and drain flow in comparison to the measurements one is referred to Armstrong et al. (2000).
The isoproturon concentration in the drainflow was strongly underestimated by a factor of 10±50 because the macropore model was not linked to the tile drain model. Therefore, water from macropores could not flow directly into the drainage system. First, the water has to infiltrate from the macropores into the soil matrix before it could reach the drainage system. Because of that, high concentration of pesticides, typical for cracked soils, could not be simulated. SIMULAT underestimated the decay of isoproturon and overestimated the mecoprop degradation in the soil as shown in Figs. 6 and 7. The information concerning degradation obtained in the laboratory seemed to be insufficient for estimating sorption and degradation parameters.
3.4. Tor Mancina
Only the results of the simulation of the lysimeter irrigation system 1 is discussed here. A detailed description of the simulation results of both irrigation systems is given by Francaviglia et al. (2000).
Fig. 5. Measured and predicted bentazone concentration in the soil at Vredepeel (logarithmic scale). The gray signature indicates measured values standard deviation. Bentazone was applied on 22 November 1990 (0.80 kg haÿ1).
The water and solute dynamics of the lysimeters were not described well by the model. The percolating water volume and mass of bromide were underestimated in the first two years and strongly overestimated in last year. Figs. 8 and 9 show the measurements and the simulations over a period of three years. The incorrect prediction of the leached water volume was reflected directly in the poor simulation of the bromide concentration in leachate.
The model could predict the appearance of metolachlor in the percolate. The simulated concentrations were within the range of measurements except at the beginning where SIMULAT overestimated the concentration. Furthermore the model calculation did not show the same temporal pattern of metolachlor concentrations in leachate. One of the reasons why SIMULAT failed to simulate this experiment correctly may be that the soil properties within the lysimeters change with time. While it seems that in the beginning of the experiment preferential flow occurred this could not be observed at the end. The simulation results reflect the lack of information concerning water flow and initial conditions in the lysimeters. No initial water content and only few measurements of water retention and conductivity were available. It was not possible to calibrate SIMULAT satisfactorily without further information.
It was possible to use SIMULAT for the simulation of all four data sets. The choice of lower boundary conditions and submodel, e.g. macropores or drain flow allowed to simulate a wide range of laboratory and field situations.
The simulation results of the water and bromide content after calibration (Weiherbach, Vredepeel) showed that the water transport model in SIMULAT was able to reproduce the Fig. 6. Isoproturon concentration in the soil as measured at Brimstone in comparison to the prediction (logarithmic scale). Isoproturon was applied on 8 October 1990 (2.5 kg haÿ1).
Fig. 7. Measured and predicted mecoprop content in the soil as measured at Brimstone farm, represented in a logarithmic scale. Mecoprop was applied on 8 October 1990 (2.4 kg haÿ1).
Fig. 8. Cumulative leaching from a lysimeter at Tor Mancina. The whole period is divided in a calibration and validation phase which are separated by a vertical line. In addition to natural rainfall of 2410 and 452 mm were irrigated.
hydraulic behavior in soils. Problems appeared by predicting the water transport in the cracked soils at Brimstone and Tor Mancina. This is due to the fact the SIMULAT was not developed for such soils.
The results of the simulations runs showed that it was not possible to predict the pesticide dynamics in all cases. But one has to keep in mind that according to the proposed procedure sorption as well as degradation parameters were not calibrated in this study.
The fact that the simulation results deviated from the herbicide measurements could have the following reasons. The direct transfer of parameters obtained from incubation and sorption experiments in the laboratory to outdoor conditions is often difficult (Rao et al., 1993). Additionally, the data base for the estimation of degradation parameters was too small in some cases for obtaining reliable parameters. Another fact could have a great influence on the quality of simulation result: the user experience (Botterweg, 1995). The handling of such a complex model like SIMULAT needs a lot of training due to the high number input parameters and submodels available. So it is often not possible to distinguish between model quality and user experience.
4. Conclusions
SIMULAT was a suitable tool for the prediction of water and solute transport in soils. By the use of reliable sorption and degradation parameters it is also possible to simulate the behavior of pesticides in most soils.
In order to compare different simulation models exactly defined input parameters are useful (GottesbuÈren et al., 2000). Otherwise the effect of the individual influence of user Fig. 9. Sum of leached in comparison to predicted bromide. The whole period is divided in a calibration and validation phase which are separated by a vertical line. Bromide was applied on May 1993, July 1994 and June 1995 (always 100 kg haÿ1).
on the simulation results could be large, resulting in errors or misinterpretation during the estimation procedure.
The transfer of information obtained from laboratory studies to outdoor conditions should be improved. One approach could be a new design of laboratory studies, e.g. the use of small lysimeters or incubation studies under variable temperature and soil moisture.
Acknowledgements
The financial support of the COST 66 Action `Pesticides in the soil environment' of DGXII-EU is gratefully acknowledged.
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