Directory UMM :Data Elmu:jurnal:A:Agricultural Water Management:Vol44.Issue1-3.Apr2000:
Agricultural Water Management 44 (2000) 337±355
Modeling pesticide dynamics of four different sites
using the model system SIMULAT
K. Adena, B. DiekkruÈgerb,*
b
a
BASF Agricultural Center Limburgerhof, Postfach 120, 67114 Limburgerhof, Germany
Geographische 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
*
Corresponding author. Tel.: 49-228-732107; fax: 49-228-735393.
E-mail address: b.diekkrueger@uni-bonn.de (B. DiekkruÈger).
0378-3774/00/$ ± see front matter # 2000 Elsevier Science B.V. All rights reserved.
PII: S 0 3 7 8 - 3 7 7 4 ( 9 9 ) 0 0 0 9 9 - 2
338
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
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 onedimensional 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.
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
339
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) relationship. 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 pesticides k(T,y) (per day) is influenced by the soil water
content y and the soil temperature T. 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 parameters A and B are fitting parameters and the parameter
ycrit describes the water content at which the degradation rate is maximal.
k y AyB ;
"
#
y B
y B
exp 1 ÿ
k y
ycrit
ycrit
(1)
(2)
Furthermore, the user has the choice between two temperature response functions. One
is the well-known Arrhenius function (Eq. (3) with the activation energy Ea (J molÿ1), the
degradation rate k0 (Tÿ1) and the gas constant R (J Kÿ1 molÿ1).
Ea
:
(3)
k T k0 exp
RT
The other one is the O'Neill optimum curve (Richter et al., 1996) given in Eq. (4). The
value h denotes a step function, which takes the value of 1 for Tmax > T 08C, otherwise
it is zero. The maximal degradation rate rmax (Tÿ1) is achieved at the optimal temperature
Topt (8C). Tmax is the maximal temperature (8C) and the Q10-value describes the slope of
the curve.
X T ÿ Topt
Tmax ÿ T X
exp
k T hrmax
Tmax ÿ Topt
Tmax ÿ Topt
"
1=2 #
1
40
W2 1 1
and W Q10 ÿ 1 Tmax ÿ Topt : (4)
with X
400
W
340
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
The total degradation rate k(T,y) is a product of the temperature response function k(T)
and the humidity response function k(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
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
341
Table 1
Characteristics of the data sets
Soil
Scale
Experimental period
I
II
Pesticides, application
rate (kg haÿ1)
Weiherbach
Vredepeel
Brimstone
Tor Mancina
Loess
Field
Sand
Field
Clay
Field
Loam
Lysimeter
12/1993±4/1994
11/1990±3/1992
12/1990±1/1991
run 1
10/1990±3/1991
run 2
Isoproturon, 2.5
mecoprop, 2.4
5/1993±6/1996
5/1995±6/1995
No. applications
Tracer
No. applications
Sampling depth (m)
Isoproturon, 3.0
pendimethalin, 3.2
(only 1993)
2
Bromide
2
0.95
1
Bromide
1
Maximum 2
1
Groundwater
Irrigation
Description of
data sets
No in¯uence
Yes (1995)
Schierholz et al.,
2000
Fluctuating
No
Boesten and
Van der Pas, 2000
Fluctuating
No
Harris et al.,
2000
Bentazone, 0.8
ethoprophos, 3.4
1
±
Metolachlor, 1.2
2
Bromide
3
Leachate was
measured
±
Yes
Francaviglia and
Capri, 2000
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.
342
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
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 measurements 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
Weiherbach
Vredepeel
Brimstone
Tor Mancina
Lower boundary condition
Free drainage
Dupuit±Forchheimer
Lysimeter
Sorption model
KF or Kd-value (l kgÿ1)
and Freundlich-coef®cient
n (ÿ)
Degradation model
Linear (equilibrium)
Isoproturon Kd: 4;
pendimethalin Kd: 20
Input measured groundwater
table
Freundlich (equilibrium)
Bentazone KF: 0.1, n: 1.1;
ethoprophos KF: 3.7, n: 0.8
Linear (equilibrium)
Isoproturon Kd: 2.9;
mecoprop Kd: 0.61
Linear (equilibrium)
Metolachlor Kd: 2.1
First-order
First-order
First-order
O'Neill
Optimum curve
Arrhenius
After Walker
Isoproturon (see text);
pendimethalin 65 (20/0.2)
O'Neill
No in¯uence
(0±0.5 m)
Bentazone 97 (10/*.);
ethoprophos 102 (10/*)
Isoproturon 60 (10/0.4);
mecoprop 7.5 (10/0.29)
Metolachlor 19 (20/0.23)
Given Van Genuchten
parameters
2.5
From retention conductivity
measurements
2.5/3
From pF-water content
relation
10
From pF-water content
relation PTF
5
Temperature response curve
Humidity response curve
Half-life (days) at (8C per
volumetric water content),
*®eld capacity
Estimation of hydraulic
parameters
Dispersion length (cm)
First-order, metabolic
(isoproturon)
O'Neill
Optimum curve
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
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)
343
344
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
Table 3
Hydraulic `van Genuchten' parameters used for the simulation of the Weiherbach data set
Depth (m)
ys (cm3 cmÿ3)
yr (cm3 cmÿ3)
Ks (cm per day)
a (h Paÿ1)
n (±)
0±0.3
>0.3
0.46
0.45
0.03
0.08
12
6
0.015
0.005
1.30
2.25
Table 4
Hydraulic 'van Genuchten' parameters used for the simulation of the Vredepeel data set
Depth (m)
ys (cm3 cmÿ3)
yr (cm3 cmÿ3)
Ks (cm per day)
a (h Paÿ1)
n (±)
0.0±0.3
0.3±0.5
0.5±2.0
0.42
0.44
0.32
0.05
0.05
0
7
14
0.076
0.023
0.026
0.026
2.16
1.91
2.58
Table 5
Hydraulic 'van Genuchten' parameters and the macropore volumes used for the simulation of the Brimstone data
set
Depth (m)
ys (cm3 cmÿ3)
yr (cm3 cmÿ3)
Ks (cm per day)
a (h Paÿ1)
n (±)
yMa (cm3 cmÿ3)
0.0±0.2
0.2±0.4
0.4±0.6
0.6±2.0
0.50
0.53
0.54
0.49
0.20
0.20
0.29
0.23
10
10
10
10
0.0003
0.0001
0.0204
0.0143
1.90
1.92
1.26
1.24
0.05
0.02
0.01
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 (cm3 cmÿ3)
yr (cm3 cmÿ3)
Ks (cm per day)
a (h Paÿ1)
n (±)
yMa (cm3 cmÿ3)
0.0±1.5
0.38
0.18
6
0.007
1.3
0.05
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.
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
345
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 measurements of percolated water volume and bromide concentration in leachate of the first two
years. In the end the saturated conductivity Ks was 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.
346
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
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).
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 kgÿ1 was taken from long-term desorption experiments,
which were reported in the data set. It would have been a better choice to consider the
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
347
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).
results of the batch experiments, where a Kd-value of 2 l kgÿ1 was 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 high Kd-value of 20 l kgÿ1.
348
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
Fig. 3. Simulated and measured bromide concentration in the soil at Vredepeel. The gray signature indicates
measured values standard deviation. Please notice that the x-axis cover different ranges. Bromide was applied
on 22 November 1990 (111 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 measurements standard
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
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
349
Fig. 4. Measured and predicted ethoprophos concentration in the soil at Vredepeel (logarithmic scale). The gray
signature indicates measured values standard deviation. Ethoprophos was applied on 22 November 1990
(3.4 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 underestimated, 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
350
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
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).
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).
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
351
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).
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
352
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
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.
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
353
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).
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
354
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
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.
References
Armstrong, A.C., Aden, K., Amraoui, N., DiekkruÈger, B., Jarvis, N., Mouvet, C., Nicholls, P., Wittwer, C., 2000.
Comparison of the performance of pesticide-leaching models on a cracking clay soil: results using the
Brimstone Farm data set. Agric. Water Mgmt. 44, 85±104.
Armstrong, A.C., Portwood, A.M., Leeds-Harrison, P.B., Harris, G.L., Catt, J.A., 1996. The validation of
pesticide leaching models. Pestic. Sci. 48, 47±55.
Boesten, J.J.T.I., Van der Pas, L.J.T., 2000. Movement of water, bromide and the pesticides ethoprophos and
bentazone in a sandy soil. The Vredepeel data set. Agric. Water Mgmt. 44, 21±42.
Brooks, R.H., Corey, A.T., 1964. Hydraulic properties of porous media. In: Hydrology Paper, vol. 3. Colorado
State University, Fort Collins, CO, pp. 22±27.
Botterweg, P., 1995. The user's in¯uence on model calibration results: an example of the model SOIL
independently calibrated by two users. Ecol. Model. 81, 71±81.
DiekkruÈger, B., 1996. SIMULAT Ð Ein Modellsystem zur Berechnung der Wasser- und Stoffdynamik
landwirtschaftlich genutzter Standorte. In: Richter, O., SoÈndgerath, D., DiekkruÈger, B. (Eds.),
Sonderforschungsbereich 179 `Wasser- und Stoffdynamik in AgraroÈkosystemen'. Selbstverlag, Institut fuÈr
Geographie und GeooÈkologie der Technischen UniversitaÈt Braunschweig, Braunschweig, pp. 30±47.
DiekkruÈger, B., Arning, M., 1995. Simulation of water ¯uxes using different methods for estimating soil
parameters. Ecol. Model. 81, 83±95.
DiekkruÈger, B., Richter, O., 1996. Modellierung der Pestiziddynamik mit dem Modellsystem SIMULAT. In:
Richter, O., SoÈndgerath, D., DiekkruÈger, B. (Eds.), Sonderforschungsbereich 179 `Wasser- und Stoffdynamik
in AgraroÈkosystemen'. Selbstverlag, Institut fuÈr Geographie und GeooÈkologie der Technischen UniversitaÈt
Braunschweig, Braunschweig, pp. 93±103.
DiekkruÈger, B., SoÈndgerath, D., Kersebaum, K.C., McVoy, C.W., 1995. Validity of agroecosystem models Ð a
comparision of results of different models applied to the same dataset. Ecol. Model. 81, 3±29.
Eggelsmann, R., 1981. DraÈnanleitung. Paul Parey, Berlin, 288 pp.
Feddes, R.A., Kowalik, P.J., Zaradny, H., 1978. Simulation of ®eld water use and crop yield. Simulations
Monograph. Pudoc., Wageningen.
Francaviglia, R., Capri, E., 2000. Lysimeter experiments in Tor Mancina. Agric. Water Mgmt. 44, 63±74.
Francaviglia, R., Capri, E., Klein, M., Hosang, J., Aden, K., Trevisan, M., Errera, G., 2000. Comparing and evaluating
pesticide leaching models. Results for the Tor Mancina data set (Italy). Agric. Water Mgmt. 44, 135±151.
GottesbuÈren, B., 1991. Konzeption, Entwicklung und Validierung des wissenbasierten Herbizid-Beratungssystems HERBASYS. Doctoral Thesis. UniversiaÈt Hannover, 212 pp.
GottesbuÈren, B., Aden, K., BaÈrlund, I., Brown, C., Dust, M., GoÈrlitz, G., Jarvis, N., Rekolainen, S., SchaÈfer, H.,
2000. Comparison of pesticide leaching models: results using the Weiherbach data set. Agric. Water Mgmt.
44, 153±181.
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
355
Harris, G.L., Catt, J.A., Bromilow, R.H., Armstrong, A., 2000. Evaluating pesticide leaching models: The
Brimstone Farm data set. Agric. Water Mgmt. 44, 75±83.
Hooghoudt, S.B., 1940. Bijdragen tot de kennis van einige natuurkundige grootheden van de grond No. 7.
Algemene beschouwing van het probleem van de detailontwatering en de in®ltratie door moddel van parallel
lopende drains greppels sloten und kanalen, vol. 46. Verslagen van landbouwkundige Onderzoekingen, The
Hague, Netherlands, pp. 515±707.
Nicholls, P.H., Evans, A.A., Bromilow, R.H., Howse, K.R., Harris, G.L., Rose, S.C., Pepper, T.J., Mason, D.J.,
1993. Persistence and leaching of isoproturon and mecoprop in the Brimstone Farm plots. Brighton Crop
Protection Conference. Weeds 2, 849±854.
Perkow, W.C., 1994. Wirksubstanzen der P¯anzenschutz- und SchaÈdlingsbekaÈmpfungsmittel (Loseblattsamml.).
Paul Parey, Berlin.
Rao, P.S.C., Bellin, C.A., Brusseau, M.L., 1993. Coupling biodegradation of organic chemicals to sorption and
transport in soils and aquifers: paradigms and paradoxes. In: Linn, D.M., Carski, T.H., Brusseau, M.L.,
Chang, F.-H. (Eds.), Sorption and Degradation of Pesticides and Organic Chemicals in Soil, Proceedings of a
Symposium, Denver, CO, 30 October 1991, SSSA Special Publication No. 32, pp. 1±25.
Rawls, W.J., Brakensiek, D.L. 1985. Prediction of soil water properties for hydrological modelling. In: Jones, E.
(Ed.), Proceeding of the Symposium `Watershed Management in the Eighties'. 30 April to 2 May 1985,
Denver, CO, ASCE, New York, pp. 293±299.
Richter, O., NoÈrtersheuser, P., DiekkruÈger, B., 1996. Environmental Fate Modelling of Pesticides: From the
Laboratory to the Field Scale. VCH, Weinheim, 281 pp.
Ritchie, J.Z., 1972. Model for predicting evaporation from a row crop with incomplete cover. Water Resources
Res. 8, 1204±1213.
Schierholz, I., SchaÈfer, D., Kolle, O., 2000. The Weiherbach data set Ð an experimental data set for pesticide
model testing on the ®eld scale. Agric. Water Mgmt. 44, 43±61.
Tiktak, A., 2000. Application of pesticide leaching models to the Vredepeel data set. II. Pesticide fate. Agric.
Water Mgmt. 44, 119±134.
Vanclooster, M., Boesten, J.J.T.I., 2000. Application of pesticide leaching models to the Vredepeel data set. I.
Water, solute and heat transport. Agric. Water Mgmt., this issue.
Vanclooster, M., Boesten, J.J.T.I., Trevisan, M., Brown, C., Capri, E., Vacek, O., Eklo, O.-M., GottesbuÈren, B.,
Gouy, V., van der Linden, A.M.A., 2000. A European test of pesticide-leaching models: methodology and
major recommendations. Agric. Water Mgmt. 44, 1±19.
van Genuchten, M.Th., 1980. A closed-form equation for predicting the hydraulic conductivity of unsaturated
soils. Soil Sci. Soc. Am. J. 44, 892±898.
van Schilfgaarde, J., 1970. Theory of ¯ow to drains. In: Chow, V.T. (Ed.), Advances in Hydroscience, pp. 43±
106.
Walker, A., Allen, R., 1984. In¯uence of soil and environmental factors on pesticide persistence. Proceedings of
the Symposium on Soils and Crop Protection Chemicals. BCPC Monograph 27, 89±100.
Modeling pesticide dynamics of four different sites
using the model system SIMULAT
K. Adena, B. DiekkruÈgerb,*
b
a
BASF Agricultural Center Limburgerhof, Postfach 120, 67114 Limburgerhof, Germany
Geographische 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
*
Corresponding author. Tel.: 49-228-732107; fax: 49-228-735393.
E-mail address: b.diekkrueger@uni-bonn.de (B. DiekkruÈger).
0378-3774/00/$ ± see front matter # 2000 Elsevier Science B.V. All rights reserved.
PII: S 0 3 7 8 - 3 7 7 4 ( 9 9 ) 0 0 0 9 9 - 2
338
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
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 onedimensional 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.
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
339
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) relationship. 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 pesticides k(T,y) (per day) is influenced by the soil water
content y and the soil temperature T. 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 parameters A and B are fitting parameters and the parameter
ycrit describes the water content at which the degradation rate is maximal.
k y AyB ;
"
#
y B
y B
exp 1 ÿ
k y
ycrit
ycrit
(1)
(2)
Furthermore, the user has the choice between two temperature response functions. One
is the well-known Arrhenius function (Eq. (3) with the activation energy Ea (J molÿ1), the
degradation rate k0 (Tÿ1) and the gas constant R (J Kÿ1 molÿ1).
Ea
:
(3)
k T k0 exp
RT
The other one is the O'Neill optimum curve (Richter et al., 1996) given in Eq. (4). The
value h denotes a step function, which takes the value of 1 for Tmax > T 08C, otherwise
it is zero. The maximal degradation rate rmax (Tÿ1) is achieved at the optimal temperature
Topt (8C). Tmax is the maximal temperature (8C) and the Q10-value describes the slope of
the curve.
X T ÿ Topt
Tmax ÿ T X
exp
k T hrmax
Tmax ÿ Topt
Tmax ÿ Topt
"
1=2 #
1
40
W2 1 1
and W Q10 ÿ 1 Tmax ÿ Topt : (4)
with X
400
W
340
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
The total degradation rate k(T,y) is a product of the temperature response function k(T)
and the humidity response function k(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
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
341
Table 1
Characteristics of the data sets
Soil
Scale
Experimental period
I
II
Pesticides, application
rate (kg haÿ1)
Weiherbach
Vredepeel
Brimstone
Tor Mancina
Loess
Field
Sand
Field
Clay
Field
Loam
Lysimeter
12/1993±4/1994
11/1990±3/1992
12/1990±1/1991
run 1
10/1990±3/1991
run 2
Isoproturon, 2.5
mecoprop, 2.4
5/1993±6/1996
5/1995±6/1995
No. applications
Tracer
No. applications
Sampling depth (m)
Isoproturon, 3.0
pendimethalin, 3.2
(only 1993)
2
Bromide
2
0.95
1
Bromide
1
Maximum 2
1
Groundwater
Irrigation
Description of
data sets
No in¯uence
Yes (1995)
Schierholz et al.,
2000
Fluctuating
No
Boesten and
Van der Pas, 2000
Fluctuating
No
Harris et al.,
2000
Bentazone, 0.8
ethoprophos, 3.4
1
±
Metolachlor, 1.2
2
Bromide
3
Leachate was
measured
±
Yes
Francaviglia and
Capri, 2000
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.
342
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
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 measurements 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
Weiherbach
Vredepeel
Brimstone
Tor Mancina
Lower boundary condition
Free drainage
Dupuit±Forchheimer
Lysimeter
Sorption model
KF or Kd-value (l kgÿ1)
and Freundlich-coef®cient
n (ÿ)
Degradation model
Linear (equilibrium)
Isoproturon Kd: 4;
pendimethalin Kd: 20
Input measured groundwater
table
Freundlich (equilibrium)
Bentazone KF: 0.1, n: 1.1;
ethoprophos KF: 3.7, n: 0.8
Linear (equilibrium)
Isoproturon Kd: 2.9;
mecoprop Kd: 0.61
Linear (equilibrium)
Metolachlor Kd: 2.1
First-order
First-order
First-order
O'Neill
Optimum curve
Arrhenius
After Walker
Isoproturon (see text);
pendimethalin 65 (20/0.2)
O'Neill
No in¯uence
(0±0.5 m)
Bentazone 97 (10/*.);
ethoprophos 102 (10/*)
Isoproturon 60 (10/0.4);
mecoprop 7.5 (10/0.29)
Metolachlor 19 (20/0.23)
Given Van Genuchten
parameters
2.5
From retention conductivity
measurements
2.5/3
From pF-water content
relation
10
From pF-water content
relation PTF
5
Temperature response curve
Humidity response curve
Half-life (days) at (8C per
volumetric water content),
*®eld capacity
Estimation of hydraulic
parameters
Dispersion length (cm)
First-order, metabolic
(isoproturon)
O'Neill
Optimum curve
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
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)
343
344
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
Table 3
Hydraulic `van Genuchten' parameters used for the simulation of the Weiherbach data set
Depth (m)
ys (cm3 cmÿ3)
yr (cm3 cmÿ3)
Ks (cm per day)
a (h Paÿ1)
n (±)
0±0.3
>0.3
0.46
0.45
0.03
0.08
12
6
0.015
0.005
1.30
2.25
Table 4
Hydraulic 'van Genuchten' parameters used for the simulation of the Vredepeel data set
Depth (m)
ys (cm3 cmÿ3)
yr (cm3 cmÿ3)
Ks (cm per day)
a (h Paÿ1)
n (±)
0.0±0.3
0.3±0.5
0.5±2.0
0.42
0.44
0.32
0.05
0.05
0
7
14
0.076
0.023
0.026
0.026
2.16
1.91
2.58
Table 5
Hydraulic 'van Genuchten' parameters and the macropore volumes used for the simulation of the Brimstone data
set
Depth (m)
ys (cm3 cmÿ3)
yr (cm3 cmÿ3)
Ks (cm per day)
a (h Paÿ1)
n (±)
yMa (cm3 cmÿ3)
0.0±0.2
0.2±0.4
0.4±0.6
0.6±2.0
0.50
0.53
0.54
0.49
0.20
0.20
0.29
0.23
10
10
10
10
0.0003
0.0001
0.0204
0.0143
1.90
1.92
1.26
1.24
0.05
0.02
0.01
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 (cm3 cmÿ3)
yr (cm3 cmÿ3)
Ks (cm per day)
a (h Paÿ1)
n (±)
yMa (cm3 cmÿ3)
0.0±1.5
0.38
0.18
6
0.007
1.3
0.05
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.
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
345
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 measurements of percolated water volume and bromide concentration in leachate of the first two
years. In the end the saturated conductivity Ks was 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.
346
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
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).
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 kgÿ1 was taken from long-term desorption experiments,
which were reported in the data set. It would have been a better choice to consider the
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
347
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).
results of the batch experiments, where a Kd-value of 2 l kgÿ1 was 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 high Kd-value of 20 l kgÿ1.
348
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
Fig. 3. Simulated and measured bromide concentration in the soil at Vredepeel. The gray signature indicates
measured values standard deviation. Please notice that the x-axis cover different ranges. Bromide was applied
on 22 November 1990 (111 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 measurements standard
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
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
349
Fig. 4. Measured and predicted ethoprophos concentration in the soil at Vredepeel (logarithmic scale). The gray
signature indicates measured values standard deviation. Ethoprophos was applied on 22 November 1990
(3.4 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 underestimated, 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
350
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
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).
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).
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
351
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).
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
352
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
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.
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
353
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).
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
354
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
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.
References
Armstrong, A.C., Aden, K., Amraoui, N., DiekkruÈger, B., Jarvis, N., Mouvet, C., Nicholls, P., Wittwer, C., 2000.
Comparison of the performance of pesticide-leaching models on a cracking clay soil: results using the
Brimstone Farm data set. Agric. Water Mgmt. 44, 85±104.
Armstrong, A.C., Portwood, A.M., Leeds-Harrison, P.B., Harris, G.L., Catt, J.A., 1996. The validation of
pesticide leaching models. Pestic. Sci. 48, 47±55.
Boesten, J.J.T.I., Van der Pas, L.J.T., 2000. Movement of water, bromide and the pesticides ethoprophos and
bentazone in a sandy soil. The Vredepeel data set. Agric. Water Mgmt. 44, 21±42.
Brooks, R.H., Corey, A.T., 1964. Hydraulic properties of porous media. In: Hydrology Paper, vol. 3. Colorado
State University, Fort Collins, CO, pp. 22±27.
Botterweg, P., 1995. The user's in¯uence on model calibration results: an example of the model SOIL
independently calibrated by two users. Ecol. Model. 81, 71±81.
DiekkruÈger, B., 1996. SIMULAT Ð Ein Modellsystem zur Berechnung der Wasser- und Stoffdynamik
landwirtschaftlich genutzter Standorte. In: Richter, O., SoÈndgerath, D., DiekkruÈger, B. (Eds.),
Sonderforschungsbereich 179 `Wasser- und Stoffdynamik in AgraroÈkosystemen'. Selbstverlag, Institut fuÈr
Geographie und GeooÈkologie der Technischen UniversitaÈt Braunschweig, Braunschweig, pp. 30±47.
DiekkruÈger, B., Arning, M., 1995. Simulation of water ¯uxes using different methods for estimating soil
parameters. Ecol. Model. 81, 83±95.
DiekkruÈger, B., Richter, O., 1996. Modellierung der Pestiziddynamik mit dem Modellsystem SIMULAT. In:
Richter, O., SoÈndgerath, D., DiekkruÈger, B. (Eds.), Sonderforschungsbereich 179 `Wasser- und Stoffdynamik
in AgraroÈkosystemen'. Selbstverlag, Institut fuÈr Geographie und GeooÈkologie der Technischen UniversitaÈt
Braunschweig, Braunschweig, pp. 93±103.
DiekkruÈger, B., SoÈndgerath, D., Kersebaum, K.C., McVoy, C.W., 1995. Validity of agroecosystem models Ð a
comparision of results of different models applied to the same dataset. Ecol. Model. 81, 3±29.
Eggelsmann, R., 1981. DraÈnanleitung. Paul Parey, Berlin, 288 pp.
Feddes, R.A., Kowalik, P.J., Zaradny, H., 1978. Simulation of ®eld water use and crop yield. Simulations
Monograph. Pudoc., Wageningen.
Francaviglia, R., Capri, E., 2000. Lysimeter experiments in Tor Mancina. Agric. Water Mgmt. 44, 63±74.
Francaviglia, R., Capri, E., Klein, M., Hosang, J., Aden, K., Trevisan, M., Errera, G., 2000. Comparing and evaluating
pesticide leaching models. Results for the Tor Mancina data set (Italy). Agric. Water Mgmt. 44, 135±151.
GottesbuÈren, B., 1991. Konzeption, Entwicklung und Validierung des wissenbasierten Herbizid-Beratungssystems HERBASYS. Doctoral Thesis. UniversiaÈt Hannover, 212 pp.
GottesbuÈren, B., Aden, K., BaÈrlund, I., Brown, C., Dust, M., GoÈrlitz, G., Jarvis, N., Rekolainen, S., SchaÈfer, H.,
2000. Comparison of pesticide leaching models: results using the Weiherbach data set. Agric. Water Mgmt.
44, 153±181.
K. Aden, B. DiekkruÈger / Agricultural Water Management 44 (2000) 337±355
355
Harris, G.L., Catt, J.A., Bromilow, R.H., Armstrong, A., 2000. Evaluating pesticide leaching models: The
Brimstone Farm data set. Agric. Water Mgmt. 44, 75±83.
Hooghoudt, S.B., 1940. Bijdragen tot de kennis van einige natuurkundige grootheden van de grond No. 7.
Algemene beschouwing van het probleem van de detailontwatering en de in®ltratie door moddel van parallel
lopende drains greppels sloten und kanalen, vol. 46. Verslagen van landbouwkundige Onderzoekingen, The
Hague, Netherlands, pp. 515±707.
Nicholls, P.H., Evans, A.A., Bromilow, R.H., Howse, K.R., Harris, G.L., Rose, S.C., Pepper, T.J., Mason, D.J.,
1993. Persistence and leaching of isoproturon and mecoprop in the Brimstone Farm plots. Brighton Crop
Protection Conference. Weeds 2, 849±854.
Perkow, W.C., 1994. Wirksubstanzen der P¯anzenschutz- und SchaÈdlingsbekaÈmpfungsmittel (Loseblattsamml.).
Paul Parey, Berlin.
Rao, P.S.C., Bellin, C.A., Brusseau, M.L., 1993. Coupling biodegradation of organic chemicals to sorption and
transport in soils and aquifers: paradigms and paradoxes. In: Linn, D.M., Carski, T.H., Brusseau, M.L.,
Chang, F.-H. (Eds.), Sorption and Degradation of Pesticides and Organic Chemicals in Soil, Proceedings of a
Symposium, Denver, CO, 30 October 1991, SSSA Special Publication No. 32, pp. 1±25.
Rawls, W.J., Brakensiek, D.L. 1985. Prediction of soil water properties for hydrological modelling. In: Jones, E.
(Ed.), Proceeding of the Symposium `Watershed Management in the Eighties'. 30 April to 2 May 1985,
Denver, CO, ASCE, New York, pp. 293±299.
Richter, O., NoÈrtersheuser, P., DiekkruÈger, B., 1996. Environmental Fate Modelling of Pesticides: From the
Laboratory to the Field Scale. VCH, Weinheim, 281 pp.
Ritchie, J.Z., 1972. Model for predicting evaporation from a row crop with incomplete cover. Water Resources
Res. 8, 1204±1213.
Schierholz, I., SchaÈfer, D., Kolle, O., 2000. The Weiherbach data set Ð an experimental data set for pesticide
model testing on the ®eld scale. Agric. Water Mgmt. 44, 43±61.
Tiktak, A., 2000. Application of pesticide leaching models to the Vredepeel data set. II. Pesticide fate. Agric.
Water Mgmt. 44, 119±134.
Vanclooster, M., Boesten, J.J.T.I., 2000. Application of pesticide leaching models to the Vredepeel data set. I.
Water, solute and heat transport. Agric. Water Mgmt., this issue.
Vanclooster, M., Boesten, J.J.T.I., Trevisan, M., Brown, C., Capri, E., Vacek, O., Eklo, O.-M., GottesbuÈren, B.,
Gouy, V., van der Linden, A.M.A., 2000. A European test of pesticide-leaching models: methodology and
major recommendations. Agric. Water Mgmt. 44, 1±19.
van Genuchten, M.Th., 1980. A closed-form equation for predicting the hydraulic conductivity of unsaturated
soils. Soil Sci. Soc. Am. J. 44, 892±898.
van Schilfgaarde, J., 1970. Theory of ¯ow to drains. In: Chow, V.T. (Ed.), Advances in Hydroscience, pp. 43±
106.
Walker, A., Allen, R., 1984. In¯uence of soil and environmental factors on pesticide persistence. Proceedings of
the Symposium on Soils and Crop Protection Chemicals. BCPC Monograph 27, 89±100.