Evaluation of the use of preferential flow models pesticides to water sources

Report to the U.K. Ministry of Agriculture, Food and Fisheries
MAFF project PL0516
Evaluation of the use of
preferential flow models
to predict the movement of
pesticides to water sources
under UK conditions
S. Beulke, C.D. Brown & I.G. Dubus
May 1998
Cranfield Centre for EcoChemistry
(formerly the Chemical Evaluation and Management Group of SSLRC)
Cranfield University, Silsoe, Beds MK45 4DT, UK
www.cranfield.ac.uk/ecochemistry
MAFF Project PL0516
Evaluation of the use of preferential flow models to
predict the movement of pesticides to water sources
under UK conditions
Final Report
Sabine Beulke, Colin Brown & Igor Dubus
Soil Survey and Land Research Centre
Cranfield University
Silsoe, Bedfordshire, MK45 4DT, UK
May 1998
DISCLAIMER
The opinions expressed and conclusions drawn in this report are those of the authors, not
necessarily of the project’s sponsor.
DECLARATION OF INTEREST
In evaluating the various preferential flow models, the authors have attempted to maintain
strict neutrality. However, SSLRC have been involved with two of the models as follows:
1. SSLRC were solely involved in development of the SWAT model;
2. SSLRC collaborated with Nick Jarvis in developing the pedo-transfer functions and
databases within MACRO_DB which allow soil hydraulic parameters to be estimated from
basic data.
COMMENTS BY MODEL DEVELOPERS
A first draft of this report was circulated to model developers for comment in February 1998
as follows:
Dr Adrian Armstrong, ADAS Gleadthorpe (CRACK-NP);
Prof. Nick Jarvis, Swedish University of Agricultural Sciences (MACRO);
Dr Peter Nicholls, Rothamsted Experimental Station (PLM).
All three authors were supportive of the overall conclusions of the report. A number of
comments for clarification, changes in emphasis and additional interpretation of the results
obtained have been incorporated into this final report. We are indebted to the authors for
their valuable contributions.
3
CONTENTS
SUMMARY
1
INTRODUCTION
1.1 OBJECTIVES
1.2 APPROACH TO MODEL EVALUATION
2
MODELS EVALUATED: OVERVIEW AND VALIDATION STATUS
2.1 LEACHP (benchmark model)
2.2 CRACK-NP
2.3 MACRO
2.4 MACRO_DB
2.5 PLM
2.6 SWAT
3
DATASETS FOR MODEL EVALUATION
3.1 Brimstone Farm
3.2 Cockle Park
3.3 SSLRC lysimeters
3.4 Wytham
4
MODEL EVALUATION
4.1 Brimstone Farm
4.1.1 LEACHP - Brimstone Farm
4.1.2 CRACK-NP - Brimstone Farm
4.1.3 MACRO - Brimstone Farm
4.1.4 MACRO_DB - Brimstone Farm
4.1.5 PLM - Brimstone Farm
4.1.6 SWAT - Brimstone Farm
4.1.7 Overview - Brimstone Farm
4.2 Cockle Park
4.2.1 LEACHP - Cockle Park
4.2.2 CRACK-NP - Cockle Park
4.2.3 MACRO - Cockle Park
4.2.4 MACRO_DB - Cockle Park
4.2.5 PLM - Cockle Park
4.2.6 SWAT - Cockle Park
4.2.7 Overview - Cockle Park
4.3 SSLRC lysimeters
4.3.1 LEACHP - SSLRC lysimeters
4.3.2 CRACK-NP - SSLRC lysimeters
4.3.3 MACRO - SSLRC lysimeters
4.3.4 MACRO_DB - SSLRC lysimeters
4.3.5 PLM - SSLRC lysimeters
4.3.6 SWAT - SSLRC lysimeters
4.3.7 Overview - SSLRC lysimeters
4
4.4 Wytham
4.4.1 LEACHP - Wytham
4.4.2 CRACK-NP - Wytham
4.4.3 MACRO - Wytham
4.4.4 MACRO_DB - Wytham
4.4.5 PLM - Wytham
4.4.6 SWAT - Wytham
4.4.7 Overview - Wytham
4.5 Overall evaluation
4.5.1 Non preferential flow benchmark (LEACHP)
4.5.2 CRACK-NP
4.5.3 MACRO
4.5.4 MACRO_DB
4.5.5 PLM
4.5.6 SWAT
4.5.7 Levels of predictive accuracy
5
REGULATORY IMPLICATIONS
6
CONCLUSIONS
ACKNOWLEDGEMENTS
REFERENCES
APPENDICES
APPENDIX 1:
APPENDIX 2:
APPENDIX 3:
APPENDIX 4:
Experimental details for the Brimstone site
Experimental details for the Cockle Park site
Experimental details for the SSLRC lysimeters
Experimental details for the Wytham site
5
SUMMARY
There has been rapid development in simulating the effects of preferential flow on pesticide
transport in soil. Applications for regulatory purposes appear desirable, but are limited by a
lack of information on the confidence which should be placed on results. Preferential flow
models were evaluated against four datasets (see below for combinations) and results were
compared with those from a non-preferential flow benchmark (LEACHP). MACRO was
evaluated both as the stand-alone model (MACRO) and as the database version which allows
automatic selection of soil hydraulic and crop parameters (MACRO_DB). Three of the
datasets were for movement of pesticide to drains (two heavy clay soils and a clay loam),
whilst the fourth was a lysimeter experiment investigating leaching to depth through five
representative soils.
Brimstone Farm
Cockle Park
SSLRC lysimeters
Wytham
LEACHP
(benchmark)
X
X
X
X
CRACK-NP
MACRO
MACRO_DB
PLM
SWAT
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Evaluation results for prediction of pesticide transport by the various models can be
summarised as follows:
LEACHP
The model failed to describe observed transport of pesticides in the
intermediate lysimeter soils as well as in the heavier clay soils. Predictive
application of non-preferential flow models to a wide range of soils may be
called into question.
CRACK-NP
The model was numerically unstable (the authors are actively working to
solve this problem) and a dramatic over-prediction was obtained for losses of
a strongly-sorbed compound from a clay loam soil. In very heavy clay soils,
results for a more mobile compound were very similar to those for MACRO,
but the model assumptions appear not to be applicable to soils with less than
50-60% clay. The model is not recommended for regulatory use.
MACRO
MACRO could be applied to all of the soils tested. Its predictive ability was
good in a range of intermediate soils, less good in the clay loam and very
variable in the two heavy clay soils. The model is user-friendly, welldocumented and there are many reports of model tests in the literature.
MACRO should be the preferred preferential flow model for regulatory
purposes and also showed equal or better predictive ability than LEACHP in
sandier soils. Parameter selection for MACRO is still problematic and the
model should only be applied by an experienced user. A comprehensive
calibration step should be included wherever possible.
MACRO_DB Automatic parameter selection reduced the emphasis on preferential flow
relative to the stand-alone version of MACRO. In a range of intermediate
soils and the clay loam, simulations of leaching with MACRO_DB were little
or no better than those with LEACHP. Simulations for the clay soils were
relatively more accurate, but serious mis-matches to observed behaviour
6
occurred in some circumstances. The philosophy behind MACRO_DB is
commendable, but the system is not recommended for regulatory use in its
current form.
PLM
The model generally requires calibration for the percentage of fast mobile
phase, but some predictive ability was demonstrated for heavy clay soils
where matrix flow can be considered negligible. In soils with less than 5060% clay, PLM is extremely sensitive to changes in the percentage of fast
mobile phase over a very small range. This makes selection of this parameter
extremely difficult even where calibration is possible and the application of
the model to all but the heaviest clays is not recommended for regulatory
purposes.
SWAT
The model gave good results for two of the three sites with drains, but was not
applicable to the lysimeter experiment with intermediate soils where water
impacts upon groundwater rather than surface water. Model output is rather
restricted, but as the model was not significantly out-performed by more
detailed models on the clay sites, this or similar approaches may have
applications at broad scales or screening levels.
Although some of the models showed reasonable predictive ability for the range of soils
covered, regulatory concerns over preferential flow may be best addressed through the
development of standard modelling scenarios. Further development of reliable methods to
select input parameters from basic information is required together with simple approaches to
describing preferential flow which are not data-intensive. A further report due in October
1998 will investigate the sub-routines and inherent assumptions of the various models and
consider a number of generic issues for preferential flow modelling.
7
1
INTRODUCTION
Mathematical modelling at a range of complexities has been given a prominent role in the fate
and behaviour section of the registration process by Council Directive 91/414/EEC
concerning the placing of plant protection products on the market. Although modelling
studies are frequently submitted as part of regulatory data packages, the weight which these
are afforded is restricted by a lack of information on the relative strengths and weaknesses of
current models. In 1995, SSLRC reported on an evaluation of the use of pesticide leaching
and runoff models available at the time (Brown & Hollis, 1995). The main models evaluated
were LEACHP, PRZM-2 and VARLEACH and the project concluded that these models may
be used to predict residues of pesticides in topsoil, but are not able to adequately simulate
leaching of pesticides to depth. The main reason for this failure was identified as the
importance of preferential flow in determining the extent of pesticide leaching and the lack of
any description of this process in the three models. The MACRO model which includes a
mechanistic description of flow through both micropores and macropores was briefly
evaluated and found to give improved predictions of pesticide leaching relative to nonpreferential flow models. Parameter estimation was identified as a major problem in using
MACRO and the need for much wider validation was proposed.
There is now evidence to suggest that preferential flow may be an important process for
pesticide transport through a wide range of soils including both clays (Harris et al., 1994;
Johnson et al., 1994; Brown et al., 1995a, b) and intermediate soils (Flury et al., 1995;
Aderhold & Nordmeyer, 1995; Brown et al., 1997). A number of mathematical models have
now been developed to simulate preferential flow and its influence on pesticide fate. The
incorporation of such models into the regulatory process appears desirable, but evidence of
their predictive ability is required and concerns over difficulties with robust selection of a
number of key input parameters need to be addressed. In 1995, the FOCUS leaching group
(Boesten et al., 1995) stated that:
“Current models that consider macropore flow require that soil parameters be
obtained by calibration. More advances are needed before predictions of
macropore flow can be made using soil parameters in existing data bases.”
The FOCUS surface water group (Adriaanse et al., 1997) were only slightly more optimistic
in their appraisal:
“Outputs from the macropore flow models MACRO and CRACK-NP are sensitive
to parameters related to the macropore region ... which are in turn difficult to
estimate. This may lead to high levels of predictive uncertainty compared to the
use of models in non-structured sandy soils.”
The aim of this study was to evaluate the predictive ability of preferential flow models against
pesticide datasets for a range of UK conditions and to assess the extent to which the concerns
raised above have been addressed by recent developments in this field such as the database
management tool, MACRO_DB.
1.1
Objectives
The study had three objectives:
1. to review existing information on the validation of preferential flow models;
8
2. to bring together recent UK datasets for pesticide experiments which are suitable
for evaluating preferential flow models;
3. to use the datasets to assess and, if possible quantify, the accuracy of the models
under UK conditions and to assess the implications of the findings for the
regulatory use of preferential flow models.
1.2
Approach to model evaluation
The purpose of this study was to evaluate the potential use of preferential flow models within
the regulatory process for pesticides. Much of the regulatory use of these models is likely to
be predictive in order to estimate potential movement to ground and surface waters with little
or no potential for calibration of the model against experimental data. Accordingly, the
evaluation has predominantly focused on comparing observed data with results from ‘blind’
predictive simulations. The temptation to correct the simulation of the water balance before
simulating pesticide behaviour was ignored, but it should be noted that a correct simulation of
the water balance is a fundamental requirement for accurate simulation of pesticide transport
(Armstrong et al., 1996). Where subsequent calibration has been carried out, this has been
clearly identified and has been restricted to the more uncertain parameters. Simulations
calibrated to a given year or solute have then been re-run for a second year or solute to test
how transferable are the input parameters.
A particular area of uncertainty for modellers is in the selection of values describing pesticide
sorption (organic carbon partition coefficient, Koc) and degradation (half-life), whether these
be from site-specific laboratory or field measurements or from literature sources. These two
parameters are considered fundamental in determining pesticide transport in soil (e.g.
Gustafson, 1989). For all of the models evaluated, selection of appropriate values for a given
combination of soil and pesticide can be the overriding factor in obtaining an accurate
simulation. As the models are sensitive to changes in pesticide Koc and half-life, the
uncertainty associated with these parameters offers an easy way to improve the fit between
observed and simulated data. Throughout this study, the temptation to do this has been
avoided, although occasionally results of simulations with site-specific values are compared
to those with literature values. The reader should be aware that the values used for modelling
cannot be considered correct and that changes in Koc and half-life may have quite large
effects upon simulation results. An increase in Koc for example would give smaller
concentrations leaving the soil profile in all soil types and a delay in breakthrough for
coarser-textured soils but not for clay soils. Half-life will have relatively little effect upon
simulations soon after application, but progressively more effect as time passes. With this
limitation in mind, the report tries to draw upon broad matches or discrepancies between
observed and simulated data rather than seeking an exact fit between the two.
Parameters for modelling have been selected on the basis of all the available experimental
information and using the experience of the modeller concerned to interpret that information
and fill any gaps. It should be remembered throughout that modelling is subjective and the
parameter sets selected by any given modeller are interpretations of reality. As such, they
cannot be considered either ‘correct’ or unique and will vary from modeller to modeller
according to personal experience and prejudice. The exception to this is MACRO_DB where
only a restricted amount of soils information is required to automatically select input values.
9
The layout of the report has been designed with the intention that the reader can take as much
or as little as required from the document. Thus each model and each dataset is described in
detail. This is followed by a section describing the simulations with each combination of
model and dataset. Once all simulations for a given dataset have been set out, an overview
comparing the performance of the various models for that site is provided. Finally,
conclusions on the overall ability of the models and implications for the regulatory use of
preferential flow models are given. Inevitably, there is some repetition between layers in the
hierarchy, but it is anticipated that not all readers will have the time or inclination to read the
document in full.
It has not been possible to list the input parameters for a given simulation because of the sheer
number of model runs. However, all input and output has been archived at SSLRC and could
be made available upon request. This would allow, for example, rapid re-evaluation of a
given model in the event of an update in either the model code or the methodology for
selection of input parameters.
2
MODELS EVALUATED: OVERVIEW AND VALIDATION STATUS
There have been a number of pesticide models published over the last few years which
incorporate descriptions of preferential flow. A total of four different models (CRACK-NP,
MACRO, PLM and SWAT) were selected for evaluation as being the most widely available
and relevant to the UK. Descriptions of preferential flow in these models range from highly
mechanistic to empirical and their scales range from profile/lysimeter up to the field scale.
MACRO was tested as either a stand-alone programme or as a component of the
MACRO_DB system which allows automatic selection of parameters for a given scenario.
This is the mode in which the model is most accessible to the non-expert and it was thus felt
particularly important to evaluate the performance of MACRO_DB. In addition to the
preferential flow models, LEACHP was used as a benchmark representative of models which
do not describe preferential flow. A summary of the main processes which are incorporated
into each of the models used in this study is given in Table 1 and the main input parameters
required for a basic simulation are listed in Table 2. The important features of the models are
described below.
2.1
LEACHP (benchmark model)
LEACHP Version 3.1 (Hutson & Wagenet, 1992) does not simulate preferential flow and was
used in this study as a benchmark against which the influence of the various descriptions of
preferential flow in the other models was assessed. In clay soils, it is clearly expected that the
inclusion of preferential flow will improve the simulation of observed behaviour. However,
this is less clear for coarser-textured soils where LEACHP and other models without
preferential flow will perform relatively better and there is the possibility that the inclusion of
preferential flow would lead to a poorer simulation of reality.
LEACHP considers the soil to be a homogeneous medium through which water and solute are
transported according to Richards’ equation and the convection-dispersion equation,
respectively. The model allows both chemical and biological degradation to be simulated
according to first-order kinetics and biological degradation to be corrected for temperature
and moisture effects. Two-site or instantaneous equilibrium sorption may be considered
10
together with a linear or Freundlich isotherm. The model has been extensively used and
tested since its first publication in 1987. It is ideally suited to simulating data from leaching
columns, but has also been found to simulate field behaviour in coarser-textured soils. In the
UK, Brown et al. (1996) demonstrated a very close fit between LEACHP simulations and soil
and soil water concentrations for a fungicide in a sandy loam soil. In broader evaluation
programmes with a range of soils for the UK and Europe, Brown & Hollis (1995) and Walker
et al. (1995) showed that LEACHP, in common with other models without preferential flow,
was able to simulate concentrations of pesticide in topsoil, but not the traces of pesticide
leaching to depth. LEACHP was considered to be more reliable overall than PRZM-2 or
VARLEACH, although any of the three models could best match observed behaviour in a
given situation.
2.2
CRACK-NP
CRACK-NP is designed to simulate the movement of water, nitrate and pesticides in highlystructured, heavy clay soils where preferential flow in the form of bypass flow is the
dominant hydrological pathway. The model is derived from the hydrological model CRACK
(Jarvis & Leeds-Harrison, 1987; Jarvis, 1989) which divides the total soil porosity into that
within uniform aggregates and that in the cracks between. Water is assumed to move into
aggregates according to Philip’s infiltration theory and out of them in response to crop
extraction and/or evaporation. Downwards movement of water is assumed to occur only in
the cracks based on Hagen-Poisseuille’s equation with a correction for path tortuosity and
connectivity. The assumption that there is no net flux of water within the soil matrix means
that the model is only applicable for heavy clays where matrix flow can be considered a
negligible component of total flow.
In CRACK-NP, the hydrological descriptions in the model have been left unchanged, whilst
solute transport is modelled assuming mass flow in the cracks and diffusion both within the
aggregates and between cracks and aggregates. Pesticide sorption is described using a linear
isotherm. Degradation is modelled according to first-order kinetics with a (non-optional)
correction for temperature and moisture effects. An important feature of CRACK-NP is the
inclusion of a direct physical description of the macropore structure which can be observed in
the field or derived from standard descriptions of soil structure. CRACK-NP Version 2.0 was
evaluated in this study.
CRACK-NP has only previously been evaluated against the Brimstone Farm data for which it
was developed. Armstrong et al. (1995a,b) describe excellent fits to observed data for
Brimstone taken from 1985/86, 1989/90 and 1990/91 using only measured parameters. The
model has been modified since these fits were obtained and the same input parameters used
with Version 2.0 of the model do not produce such a good fit to observed behaviour.
2.3
MACRO
MACRO Version 4.0 (Jarvis, 1994) is a physically-based preferential flow model with the
total soil porosity divided into two flow domains (macropores and micropores), each
characterised by a flow rate and solute concentration. Soil water flow and solute transport in
the micropores is modelled using Richard’s equation and the convection-dispersion equation,
respectively, whilst fluxes in the macropores are based on a simpler capacitance-type
11
approach with mass-flow. Exchange between the two domains is calculated according to
approximate, physically-based expressions using an effective aggregate half-width which is a
crucial parameter. Whereas this parameter was empirical in early versions of the model, since
1994 it has been physically-based and can be derived from field observation. In situations
where preferential flow is unlikely to occur, the model reverts to the classical solution of
Richards’ equation and the convection-dispersion equation as in LEACHP. By varying the
input parameters, the model can be set up to simulate a soil with nothing but preferential flow
(as in CRACK-NP), a soil with no preferential flow at all (as in LEACHP) or any
combination of flow types between these two extremes. This means that the model is
appropriate to describe preferential flow in a variety of soils, but the processes of finger flow
and funnel flow in coarse-textured soils cannot be simulated. In MACRO, pesticide
degradation is modelled using first-order kinetics. Different half-lives can be specified for the
solid and the liquid phase of the macropores and micropores, respectively and these
degradation rates may be adjusted for temperature and moisture effects. Sorption is assumed
to be at instantaneous equilibrium and to be described by a linear isotherm. Strength of
sorption is the same in each pore domain, but the user must specify the distribution of
sorption sites between the two. Parameter values can be changed at any point during the run
and this allows time-dependent sorption or changes in rate of degradation to be simulated if
required.
MACRO has been evaluated in a number of recent field and lysimeter studies. In earlier
versions, MACRO could be run as both a one-domain model ignoring bypass flow and as a
two-domain model. In several studies these two options were compared to assess the
significance of preferential flow and to validate the description of macroporosity implemented
in the two-domain model. In sandy soils, the one-domain model was demonstrated to perform
well by Saxena & Jarvis (1995) and Brown et al. (1997) suggesting that preferential flow is
not important in these soils. However, dichlorprop and bentazone leaching through
lysimeters with Swedish sand soils could not be reproduced by the model, probably due to
finger flow (Jarvis et al., 1994). Finger flow was also identified as a possible reason for
discrepancies between simulated and observed leaching of alachlor through sandy loams by
Jarvis et al. (1995). In loam and clay soils in which preferential flow is of greater importance,
the mechanisms implemented in the two-domain version of MACRO have been shown to be a
clear improvement over the assumption that soil porosity is homogeneous. Drainflow, height
of the water table and chloride concentrations in drainage from an irrigated heavy clay marsh
soil were fairly well reproduced by MACRO in the two-domain case (Andreu et al., 1994).
The model performed less well if bypass flow was ignored. Brown et al. (1998) found a good
agreement between uncalibrated MACRO simulations and measured flow, bromide and
isoproturon leaching through heavy clay lysimeters. Water flow through three lysimeters
with Swedish loam or clay soils was closely matched by the two-domain option of MACRO
(Jarvis et al., 1994). Leaching of bentazone which was applied to one of these soils was also
well reproduced. However, in common with other models, MACRO failed to describe
dichlorprop leaching through two Swedish soils unless the degradation rate was markedly
decreased from the laboratory value. The one-domain version was not applicable to these
lysimeters. When preferential flow was ignored, MACRO also failed to describe leaching of
36
Cl through lysimeters with a clay soil, whilst the two-domain approach gave a reasonable
match to the observed data (Saxena et al., 1994). In a number of further studies, two-domain
simulations with MACRO agreed relatively well to field and lysimeter data (Jabro et al.,
1994; Jarvis, 1995; Bergström, 1996). However, MACRO did not always perform well. In
work by Brown et al. (1997) it failed to describe bromide and pesticide leaching through an
alluvial clay soil and the fits could not be markedly improved by calibrating parameters which
12
describe macroporosity. The model under-estimated the importance of preferential flow for
water and solute movement through two loamy soils which have been proposed to have a dual
flow system with important contributions from both preferential and matrix flow.
In most of the above mentioned studies some of the model parameters were calibrated. If
uncalibrated, MACRO performed less well, but still gave promising results. An uncalibrated
simulation with MACRO by Jarvis et al. (1995) reproduced the dissipation of alachlor in 0-10
cm of a clay loam field soil, but gave discrepancies to the observed concentrations in suction
cup samples due to selection of inappropriate parameters describing macroporosity. Work by
Brown et al. (1997) confirmed that these parameters are difficult to select. A model
evaluation against data for a clay loam soil reported by Brown (1996) showed that MACRO
performed better than any of the non-preferential flow models tested. However, some
significant discrepancies from observed data occurred late in the season.
In conclusion, validation studies with MACRO give promising results. However, marked
discrepancies from measured data are occasionally observed. A drawback of the model is its
complexity which leads to uncertainties in parameterisation. In particular, sensitive
parameters describing macroporosity are difficult to select. Calibrated simulations are often,
but not always, able to reproduce observed leaching of pesticides. Accurate simulations
without calibration are less frequent, indicating the continuing difficulties with selection of
appropriate input parameters. However, uncalibrated runs generally show improved match to
observed behaviour for a range of soils relative to models without preferential flow.
2.4
MACRO_DB
MACRO_DB (Jarvis et al., 1996, 1997) is a decision support tool which links various data
sources to the MACRO model (Version 4.0) by the use of parameter estimation algorithms.
The databases provided include pesticide properties, soils, cropping and weather. One of the
soils databases which can be accessed by MACRO_DB is that contained in SEISMIC (Hollis
et al., 1993) and the system will automatically select input parameters for any soil series in
England and Wales using a combination of simple rules and pedo-transfer functions. Thus
the sensitive parameters setting the boundary between micropores and macropores and
governing the rate of exchange of water and solute between regions can be set independent of
any user subjectivity. MACRO_DB has been designed for management applications by the
non-specialist user in making exposure and risk assessments for pesticides. Given the
complex nature of modelling preferential flow, it is likely that MACRO_DB will be used by
some companies to parameterise and run the model for selected scenarios. It is thus important
to evaluate the predictive ability of the complete system and this was done separately from the
evaluation of the stand-alone version of MACRO. Measured soils data for each dataset were
entered into MACRO_DB which was then allowed to select input parameters. Standard
parameter sets from within MACRO_DB were also selected for the crop of interest. These
input values were combined with site-specific data for pesticide properties, drainage
characteristics and weather to evaluate the predictive ability of MACRO_DB.
MACRO_DB has only been released for approximately one year. At present, no evaluations
of the predictive ability of the system have been presented in the literature.
13
2.5
PLM
The Pesticide Leaching Model (PLM) described by Hall (1993) is a functional model, based
upon an approach of Addiscott (1977) which divides the soil profile into 5-cm layers and the
soil water into a mobile and an immobile phase. In PLM, the mobile water is defined as the
water held at tensions between field capacity (5 kPa) and saturation (i.e. the air capacity).
This phase is further divided into a ‘slow’ and a ‘fast’ flow domain to account for both
convective flow of soil solution through water-filled pores and rapid transport through
macropores or fissures. The empirical parameter which characterises the percentage of the
mobile phase (air capacity) characterised as ‘fast’ needs to be specified by the user and is
constant for all horizons, irrespective of their different characteristics. The depth leached per
time interval in the fast and slow regions also needs to be specified. Pesticide degradation is
assumed to follow first-order kinetics with a bulked half-life for the solid and the three liquid
phases. This half-life is adjusted for variations in soil temperature and moisture during the
run. Sorption is restricted to the immobile soil water and the slow mobile water, whilst water
in the fast flow phase does not interact with soil surfaces. Instantaneous equilibrium between
the sorbed and solute phase is assumed together with a linear isotherm. The sorption
coefficient in the upper 5 cm is increased daily.
PLM can be considered semi-empirical as parameters describing the proportion of fast flow
and the depth leached per time interval in the fast and slow regions cannot be linked to soil
properties. The authors of PLM suggest that the model requires calibration for a given
dataset, but that this can often be limited to only one sensitive parameter (the percentage of
fast pores in the mobile phase). A fuller calibration was reported by Hall & Webster (1993)
in order to simulate transport of bromide and chloride through lysimeters with two different
soil types. Hall (1994) was able to calibrate PLM to describe dichlorprop leaching through
lysimeters with three Swedish soils but, in common with other models tested, it was necessary
to increase half-life by up to an order of magnitude relative to that measured in the laboratory.
2.6
SWAT
SWAT is a semi-empirical model which has been developed to predict concentrations of
agriculturally applied pesticides moving to surface waters via the combined pathways of
surface runoff, sub-lateral flow and drainflow (Brown & Hollis, 1996). It is based on a direct,
empirically-derived link between soil type and stream response to rainfall which has been
reported as the Hydrology of Soil Types (HOST) by Boorman et al. (1995). This system
groups all UK soil series into twenty-nine classes based upon hydrological characteristics of
the soil and the underlying substrate layer. Using the HOST system, soils have been grouped
according to their potential for soil run-off into five classes which form the basis for
prediction of the movement of water and associated pesticide to streams in response to
rainfall. Attenuation factors describe the decrease in concentrations of pesticide between
events. A modified version of SWAT has been incorporated into the Environment Agency’s
POPPIE programme to predict concentrations of pesticides in surface waters at the catchment
scale (Hollis & Brown, 1996).
SWAT has been evaluated against data from Cockle Park, Rosemaund and SSLRC
experiments on a sandy loam and clay loam soil at Temple Balsall, Warwickshire by Brown
& Hollis (1996). The model was shown to be capable of predicting to within one order of
magnitude the transient peak concentrations of a wide range of pesticides during rapid water
14
movement to streams in response to rainfall. Simulated concentrations were too great when
rainfall initiated water movement to streams very soon after application, particularly for the
more mobile pesticides, and some predictions for pesticides sorbed very strongly to soil were
relatively poor.
15
Hydrology model
Simulation prior to first
application of pesticide
Multiple pesticide
application possible
Soil model
Availability of needed
data
User friendliness
Assistance in determining
model parameters
Yes
Yes
Soil column divided into
homogeneous layers of
variable thickness
Two-domain model with
total pore space divided
into macropores and
micropores; solution of
Richards’ equation within
micropores, capacitance
approach within
macropores
Yes
Soil column divided into
homogeneous layers of
variable thickness
Two-region model with
aggregates and cracks
between aggregates;
Philips’s infiltration
equation for water entry
into aggregates, HagenPoisseuille’s equation for
water movement in cracks
MACRO
High
Some guidance provided
for a few parameters;
comprehensive help
system.
Weather data obtainable;
some soil data must be
estimated from pedotransfer functions or
expert judgement
Yes
Most data readily
available, some soil data
must be estimated
CRACK-NP
Low
No
Capacitance model over a
time-step of 1 day based
upon mobile and
immobile water fractions
with a division at 5 kPa
and a further empirical
division of mobile water
into a slow and fast flow
domain
No
Soil column divided into
homogeneous 5-cm layers
No
Weather data and soil
properties readily
available; parameter
describing macroporosity
needs to be calibrated
against experimental data,
other parameters need to
be estimated
PLM
High
Some guidance provided
for a few parameters
Vertical movement
according to a mean daily
flux
No
Topsoils only considered
as a simple mixing cell
No
Data readily available
SWAT
Low
No, but parameters are
relatively simple
Yes
Soil column divided into
homogeneous layers of
equal thickness
Solution of Richards’
equation
Yes
Weather data obtainable,
some soil data may need
to be estimated from
pedo-transfer functions
LEACHP
Low
Little guidance available
Table 1: Summary of process description by CRACK-NP, MACRO, PLM, SWAT and LEACHP (partly adapted from Boesten et al. (1995)
and Adriaanse et al. (1997))
Considered, but not
recommended for
predictive use
Seepage potential theory
Not considered
Diffusion within aggregates, mass flow in cracks
Linear in aggregates, no
sorption in cracks;
different Kd for each layer
Overland flow
Sublateral flow
Pesticide transport
Pesticide sorption
Drainage
Input of potential
evapotranspiration data
CRACK-NP
Two region model with
cracks; initiation of crack
flow if rainfall intensity
exceeds aggregate
sorption capacity
Evapotranspiration
Preferential flow
Table 1 (continued)
Convection-dispersion
equation in micropores,
mass flow in macropores
Linear; sorption sites
partitioned between
micro- and macropores;
different Kd for each
layer; Kd can be reset to a
new value at any time
during the run to account
for time-dependent
sorption
Not considered
MACRO
Two domain model with
macropore flow;
physically-based
description (effective
aggregate half-width,
boundary water tension,
water content and
hydraulic conductivity)
Input of potential evapotranspiration data or
estimation using PenmanMonteith’s equation
Considered, but not
recommended for
predictive use
Seepage potential theory
Mass flow transport
associated with mobile
water
Linear; sorption
increasing with time in top
5-cm layer; sorption only
in immobile and slow
mobile flow domain; Kd
can be set to 3 different
values down the profile
% of leached water which
moves to drains userspecified
Not considered
Estimation of potential
evapotranspiration from
measured or calculated
pan evaporation data
Not considered
PLM
Only when field capacity
is exceeded and porosity
associated with slow flow
is filled; empirical
description of macroporosity (percentage of fast
flow)
Linear time-dependent
sorption considered
according to Walker
(1987)
Considered as a
component of rapid runoff
Linked to water flux via a
retardation factor
Considered as a
component of rapid runoff
Considered as a
component of rapid runoff
Accounted for in mean
daily water flux during the
field capacity period
SWAT
Rapid flow described
using the Standard
Percentage Runoff value
from the HOST
classification
Linear or Freundlich
isotherm; two-site
sorption possible; single
Koc corrected according
to organic carbon content
Convection-dispersion
equation
Not considered
Weekly potential
evapotranspiration data
required, utility for
estimation provided
Not considered although
overflow is possible as a
check for water balance
Not considered
LEACHP
Not considered
No
Linear interpolation
between zero at
emergence and maximum
leaf area
Linear interpolation
between minimum at
emergence and maximum
when the crop has its
maximum leaf area; root
volume distributed
logarithmically with depth
Plant root growth
CRACK-NP
First-order; rate constants
corrected for temperature
and moisture effects (nonoptional, fixed parameters); degradation applies
to soil and water within
aggregates, no
degradation in cracks
No
Not considered
Pesticide uptake by plants
Plant shoot growth
Metabolites
Pesticide volatilisation
Pesticide degradation
Table 1 (continued)
Yes
Leaf area indices and form
factors specifying growth
curve; starting date of
regrowth of winter-sown
crops in spring may be
specified
Linear interpolation
between minimum at
emergence and maximum
when the crop has its
maximum leaf area; root
volume distributed
logarithmically with depth
MACRO
First-order; temperature
and moisture effects may
be modelled; different rate
constants for solid and
liquid phases of microand macropore domain
possible; different rate
constants for each layer
Yes (1)
Not considered
Growth 25 mm d-1 from
date of emergence (or
regrowth in spring) to a
crop-dependent maximum
depth; root volume
distributed logarithmically
with depth
No
Not considered
PLM
First-order; rate constants
corrected for temperature
and moisture effects (nonoptional, fixed parameters); degradation applies
to bulk soil; half-lives can
be set to 3 different values
down the profile
No
Not considered
Not considered
No
Considered within a
retardation factor
No
Not considered
SWAT
First-order field half-life
required
Based on Davidson et al.
(1978) plus a scaling
factor
LEACHP
First-order; temperature
and moisture effects may
be modelled; biological
and chemical degradation
possible; degradation in
bulk soil or in solution
only; different rate
constants for each layer
Yes (maximum of 3)
Volatility across soil
surface
Yes
Empirical sigmoidal curve
CRACK-NP
Hourly max/min
temperatures, rainfall,
potential evapotranspiration, utility for
estimation from daily
values provided
Initial water contents, total
porosity, field capacity,
wilting point, stable crack
porosity, crack spacing
shrinkage factor, initial
depth to water table,
tortuosity factor, ped
sorptivity at wilting point,
hydraulic conductivity,
bulk density topsoil and
subsoil
Parameter grouping
Weather
Soil
MACRO
Daily max/min
temperatures, rainfall,
potential evapotranspiration or daily max/min
temperatures, rainfall,
solar radiation, vapour
pressure, wind speed and
height at which measured,
albedo, attenuation factor
for solar radiation in crop;
annual temperature amplitude, average annual temperature, average rainfall
intensity, latitude
Initial temperatures, initial
moisture, dispersivity,
effective aggregate halfwidth, shrinkage factor,
hydraulic conductivity and
water content and water
tension at boundary
between micro- and
macropores, saturated
hydraulic conductivity,
saturated water content,
residual water content,
pore size distribution
index, wilting point,
tortuosity factors for
micro- and macropores,
bulk density
Fraction of water moving
to next layer, % of fast
mobile phase, rates of fast
and slow drainage; total
porosity, water content at
5, 200 and 1500 kPa, bulk
density
PLM
Daily max/min
temperatures, rainfall, pan
evaporation, pan factor
Minimum standard
rainfall volume, water
content at 5, 200 and 1500
kPa, organic carbon
content, bulk density, air
space, interactive water,
hydraulic conductivity at
5 kPa
SWAT
Daily rainfall
Particle size distribution,
organic carbon content,
bulk density, hydraulic
conductivity at reference
tension, Campbell’s water
retention-conductivity
function parameters,
dispersivity, initial
moisture and temperature
LEACHP
Daily max/min
temperatures, rainfall,
weekly pan evaporation
and pan factor, or
potential
evapotranspiration
Table 2: Input parameters required for basic simulations using CRACK-NP, MACRO, PLM, SWAT and LEACHP (Data in italics may be
either input or calculated by the model)
CRACK-NP
Root adaptability factor,
canopy interception
capacity, correction factor
for wet canopy evaporation, critical soil air
content and water content
for root water uptake, date
of crop emergence,
harvest and date at which
maximum leaf area is
achieved, initial and
maximum root depth, root
distribution factor
Diffusion coefficient in
free water, half-lives,
reference temperature and
moisture, Kd, fraction of
sorption sites in macropores, canopy wash-off
coefficient, application
rate and date, concentration in rain, impedance
factor to control diffusion
within peds
Parameter grouping
Crop
Pesticide
Table 2 (continued)
MACRO
Root adaptability factor,
canopy interception
capacity, correction factor
for wet canopy evaporation, critical soil air
content and water tension
for root water uptake, date
of crop emergence,
harvest and date at which
maximum leaf area is
achieved, leaf area index
(LAI), root depth and
canopy height at a
specified date, LAI at
harvest, maximum LAI
and root depth, root
distribution factor, form
factors for growth curve
Diffusion coefficient in
free water, depth of
mixing within profile,
canopy degradation rate,
degradation rates in
solid/liquid phase of
micro- and macropores,
parameters for temperature, moisture
dependence, reference
temperature, Kd, fraction
of sorption sites in
macropores, canopy washoff coefficient, amount of
applied pesticide solution
and its concentration, fraction intercepted by crop,
concentration in rain
Hold-back factor (fraction
excluded from
equalisation of solute
concentration between
immobile and slow mobile
phase), exclusion zone for
anion sorption, Kd, halflife, reference moisture
and temperature,
application rate and date
PLM
Crop type, date of sowing
and harvest
Koc, half-life, Henry’s
constant, application rate
and date, crop interception
factor
SWAT
Crop not considered
Solubility in water, vapour
density, Koc, first-order
degradation constants,
application rate and date
LEACHP
Date of germination,
emergence, root maturity,
shoot maturity and
harvest, relative maximum
rooting depth and crop
cover
3
DATASETS FOR MODEL EVALUATION
Data have been collected from three field sites and one lysimeter experiment in the UK. Soils
at the field sites are either clays (Brimstone Farm and Wytham) or clay loams (Cockle Park)
in which preferential flow is likely to be an important pathway for water and solute movement
through the profile. The lysimeter experiment (SSLRC) was conducted with five contrasting
soil types with varying texture and potential for preferential flow. Movement of isoproturon
was monitored in all of the datasets and was used for model evaluation. The fate of trifluralin
at Cockle Park and the leaching of bromide through the SSLRC lysimeters was also
simulated. The studies were supplemented with either experimental or literature data on
pesticide sorption and degradation.
3.1
Brimstone Farm
The Brimstone Farm data set was collected within a four-year collaborative government and
industry-funded research programme conducted at the Brimstone Farm facility developed
jointly by ADAS and IACR-Rothamsted. A pesticide study was established on a heavy,
structured clay soil of the Denchworth series with a thick impermeable subsoil. Data were
available for the four control plots. Of these, two were conventionally mole drained (plots 5
and 20). On one plot, the drainage system consisted of gravel-filled moles (plot 15), whilst
the remaining plot had close-spaced pipes (plot 9). Pesticides were applied to winter cereals
in three successive seasons (1993/94, 1994/95, 1995/96) and data for isoproturon were
supplied. Rates of drainflow and isoproturon concentrations in drainflow were monitored for
the first two key rainfall events of each season. Experimental set-up and results for
Brimstone Farm are described by Nicholls et al. (1993), Harris et al. (1994, 1995) and Jones
et al. (1995). Information used for this study is summarised in Appendix 1.
3.2
Cockle Park
Data for Cockle Park were obtained from a collaborative, MAFF-funded project between the
Department of Agricultural and Environmental Science and Department of Agriculture,
University of Newcastle upon Tyne and the former ADAS Soil & Water Research Centre. A
pesticide experiment was carried out on an existing drainage trial on a clay loam soil of the
Dunkeswick series at the university farm at Cockle Park, Northumberland. Pesticides
including isoproturon and trifluralin were applied to winter wheat in two successive seasons
of which 1990/91 was chosen as the most comprehensively monitored. Losses of water and
pesticides from a mole-drained plot in surface-layer flow through the top 30 cm of the soil
profile and mole drainflow were recorded between November 1990 and March 1991. Details
of the Cockle Park experiment are given by Brown (1993) and Brown et al. (1995a, b). A
summary of the data used for model evaluation is provided as Appendix 2.
3.3
SSLRC lysimeters
Data were from a research project (PL0510) funded by the MAFF which was conducted at the
Soil Survey and Land Research Centre in Silsoe, Bedfordshire. Lysimeters were taken from
five soil types representative of each of the three High and the two Intermediate leaching
potential classes identified in the National Rivers Authority policy and practice for the
21
protection of groundwater (NRA, 1992). These soils were in descending order of
vulnerability: a clay loam alluvial soil with a potential for direct by-pass flow to a shallow
ground water table (Enborne series), a deep structureless sandy soil with small organic matter
content (Cuckney series), a moderately shallow loamy soil over gravel at about 60 cm depth
(Sonning series), a deep, weakly structured loamy soil (Ludford series) and a shallow peat
soil over structureless sand (Isleham series). Bromide and pesticides including isoproturon
were applied in two successive seasons (1994/95 and 1995/96) and their concentrations in
leachate were monitored throughout the leaching periods. The SSLRC lysimeter dataset is
described in detail by Brown et al. (1997). Data relevant to this study are summarised in
Appendix 3.
3.4
Wytham
The Wytham data were from a collaborative experiment between the Institute of Hydrology,
the Soil Survey and Land Research Centre and Horticultural Research International which
was funded by the Natural Environment Research Council, the Agriculture and Food
Research Council and others. Isoproturon was applied in spring 1994 to a winter barley crop
on a mole-drained clay of the Denchworth series at the Oxford University farm at Wytham,
Oxfordshire. During key events, isoproturon concentrations in drainflow, in interlayer flow
and occasionally also in overland flow were recorded together with the respective flow rates.
In addition, tensiometer data, capacitance probe data and soil temperatures were monitored
over an extended period. Further information on the Wytham experiment is provided by
Haria et al. (1994) and Johnson et al. (1994, 1995a,b, 1996). The data used in this study are
summarised in Appendix 4.
4
MODEL EVALUATION
The models described in Section 2 were applied to the four data sets. In all simulations,
degradation was assumed to occur in bulk soil according to first-order kinetics. Sorption was
considered to be characterised by a linear adsorption isotherm and to be at instantaneous
equilibrium. Half-lives and Koc values for the topsoil were set to measured values or to
literature data. Half-lives for deeper layers were derived from the top-layer value according
to the relationship proposed by Jarvis et al. (1997). Half-lives determined in laboratory
studies were corrected for changes in temperature and moisture content during the model run
according to literature relationships.
For LEACHP modelling, the Campbell’s parameters which characterise water release
characteristics and the relationship between hydraulic conductivity and water content were
estimated from the water retention data given in Appendices 1-4. The reference hydraulic
conductivity at a defined matric potential, which is also required for LEACHP simulations,
was derived from pedotransfer functions (Hollis & Woods, 1989) or set to 1 mm/day at field
capacity (J.L. Hutson, personal communication). As drainage is not explicitly considered in
LEACHP, observed rates of drainflow and associated pesticide concentrations were compared
to the simulated flow through the soil profiles at drain depth.
CRACK-NP simulations were based on an input file for Brimstone Farm which was provided
with the model. Attempts were made to change parameters to account for site-specific
conditions, but in many cases the appropriate changes were not possible because they
22
destabilised the model which then crashed. For the same reason, simulations were usually
started on the day of application, although a pre-run of the model would have been preferred.
The parameters which caused the instability were identified by systematic modifications from
the original values. Thereafter, the input parameters were adapted to the evaluated dataset as
far as possible. Full details are provided in the relevant results sections. As measured values
for parameters defining macroporosity (crack spacing, stable drainable porosity) were not
available, these were selected according to expert judgement. For the heavy clay soil at
Wytham which is very similar to that at Brimstone, the default values were used (crack
spacing in top layer = 0.05 m, stable drainable porosity = 5%). Crack spacing for the more
moderate clay loam at Cockle Park was set to a smaller value (0.02 m), whilst stable drainable
porosity was set to a larger value (9%) to effectively reduce aggregate size. CRACK-NP
enables the user to specify half-lives and Kd values for each soil layer. These were derived
from experimental values or literature data. If field half-lives were used, these had to be
corrected for temperature and moisture effects as this subroutine cannot be switched off in
CRACK-NP and parameters describing these effects cannot be changed by the user.
Wherever possible, measured data were used to select input parameters for the stand-alone
version of MACRO or default values were retained to avoid introducing unnecessary usersubjectivity. One exception is for the parameter describing the relative proportion of sorption
sites in the micropore and macropore regions (FRACMAC). The default value for this
sensitive parameter is 0.1 (10% of sorption sites are in the macropores), but the value should
be adjusted for any given soil. Logically, FRACMAC should equate to the macroporosity as
a fraction of the total porosity so that sorption is set equal in each domain. In practice, this
results in values of FRACMAC which are rather large (generally 0.02-0.30) and which
artificially restrict movement of pesticide in the macropores. A value of 0.01-0.04 (1-4% of
sorption sites in the macropores) is considered more realistic for many soils. In the absence
of reliable guidance on the selection of this parameter, FRACMAC was empirically set to
0.01 for soils with a topsoil air capacity (total porosity - field capacity) of 4% or less
(Brimstone Farm, Wytham, Ludford and Enborne series from the SSLRC lysimeters) and to
0.04 for soils with an air capacity of 14% or greater (Cockle Park, Cuckney, Sonning and
Isleham series from the SSLRC lysimeters). There were no soils with air capacities in the
range 4-14%.
Other MACRO parameters which are difficult to select are those describing the soil hydraulic
properties. The pore size distribution index in the micropores (ZLAMB) was calculated by
fitting the Brooks & Corey function (equation 11; Jarvis, 1994) to the measured water release
curve. Expert judgement was used to establish the water tension at the boundary between the
two flow domains (CTEN) as this cannot readily be independently estimated. Values ranged
from 50 cm water tension (5 kPa) for heavy clay soils to 10 cm water tension (1 kPa) for
coarse sands. The water content equivalent to this tension (XMPOR) was then derived from
the measured water release curve, whilst the conductivity at the boundary (KSM) was
estimated from the above values using the equation given by Laliberte et al. (1968). The pore
size distribution index in the macropores (ZN) was calculated from CTEN using equations
built into MACRO_DB . Saturated conductivity (KSATMIN) was derived using the pedotransfer functions for soils in England and Wales described by Hollis & Woods (1989).
Aggregate half-widths (ASCALE) control the movement of water and solute between the
micropore and macropore domains. These were selected from basic descriptions of soil
structure using the rules built into MACRO_DB (see Section 2.4) so that an area for
subjectivity was eliminated as far as possible. Where field half-lives were used for a given
23
pesticide, functions correcting rate of degradation for effects of soil temperature and moisture
content were minimised.
Simulations with MACRO_DB retained the weather, pesticide, application and site hydrology
(drain depth, drain spacing, depth of profile) parameters from the simulations with the standalone version of MACRO. A set of crop input parameters was selected from the validated
database provided with MACRO_DB based on the closest to the desired crop and locality.
Only the dates of emergence and harvest were altered to match those at each site. Soil
hydraulic parameters were calculated within MACRO_DB using the automatic procedures
based on pedo-transfer functions. The soils database was updated for each of the soils to be
modelled with basic soils information - content of sand, silt, clay and organic carbon, bulk
density, pH and description of soil structure. The system then automatically calculates all the
soils properties required as input for MACRO and these were used without any changes at all
to simulate observed behaviour. The parameter describing the relative proportion of sorption
sites in the micropore and macropore regions (FRACMAC) is automatically set within the
system according to the soil properties and values are given in the results section for each
dataset.
For PLM modelling, the rate of slow and fast water movement through the soil has to be
specified. The former was set to 5 cm/day, whilst the fast flow rate was set to a value which
allowed the soil profile to drain within one day (e.g. 100 cm/day for a 100-cm profile).
Profile depths for Brimstone, Cockle Park, the SSLRC lysimeters and the Wytham site were
70 cm, 75 cm, 105 cm and 100 cm, respectively. The percentage of fast mobile phase was set
according to expert judgement for the clay soils based upon knowledge of the hydrology of
the various soils. For the SSLRC lysimeter dataset, there was felt to be insufficient evidence
to support an independent estimate of the percentage of fast mobile phase in the five
intermediate soils and the parameter was set by calibration to observed results. If a drainage
system was present, all water leaching from the bottom of the soil profile was assumed to be
intercepted by the drains.
SWAT has relatively simple input requirements. All values were taken from measured data
apart from hydraulic conductivity at field capacity which was derived from a pedo-transfer
function for soils in England and Wales (Hollis & Woods, 1989).
4.1
Brimstone Farm
A subset of the Brimstone Farm dataset was available for model evaluation. Results consisted
of point rates of drainflow and isoproturon concentrations (maximum nine per event) together
with total drainflow monitored on four plots at Brimstone Farm over the first two key events
of three successive years. It should be noted that only two plots (5 and 20) were true
replicates (see Appendix 1). For simplicity, model simulations are initially compared to
results for just one plot (5). However, there was considerable variability between plots and
this is treated in the overview of modelling for Brimstone Farm (Section 4.1.7). Rainfall,
maximum and minimum air temperature were supplied at a daily resolution and potential
evapotranspiration was estimated using Linacre’s equation (Linacre, 1977). If not otherwise
stated, the experimental Koc (81 ml/g) and half-life (75 days at 10oC and 80% field capacity)
were used and degradation was corrected for temperature and moisture effects.
24
4.1.1
LEACHP - Brimstone Farm
Simulation by LEACHP of rates of drainflow at Brimstone for the two events in each of three
seasons is shown in Figure 1. Note that there is no experimental information between the
storm events and it is impossible to evaluate overall model performance for these years.
Simulation of rates of drainflow was poor for the 1993/94 season, but somewhat better for the
next two seasons. The timing of the events was well simulated, but peaks in rate of flow were
under-estimated apart from a single occasion in December 1995. Concentrations of
isoproturon simulated in drainflow were greatly below those observed (maximum simulated
in any of the three years 0.02 µg/l) confirming the expected poor performance of LEACHP on
this soil type where preferential flow through cracks and fissures is the dominant hydrological
pathway.
Comparison between measured rates of drainflow from Plot 5 at Brimstone
Farm and those simulated by LEACHP
0.35
Drainage (mm/hour)
0.30
0.25
0.20
0.15
0.10
0.05
0.00
02/11/93
12/11/93
22/11/93
02/12/93
12/12/93
0.50
Drainage (mm/hour)
0.40
0.30
0.20
0.10
0.00
07/12/94
12/12/94
17/12/94
22/12/94
27/12/94
01/01/95
22/12/95
27/12/95
01/01/96
06/01/96
11/01/96
3.50
3.00
Drainage (mm/hour)
Figure 1
2.50
2.00
1.50
1.00
0.50
0.00
17/12/95
observed
25
LEACHP
4.1.2
CRACK-NP - Brimstone Farm
Water and solute movement observed at Brimstone Farm in previous studies were described
successfully with CRACK-NP 1.0 (Armstrong et al., 1995a, b), although simulations with
Version 2.0 are not as good (see Section 2.2). It was expected, therefore, that the
experimental data from later seasons at Brimstone might be relatively well simulated by the
model. The input file set up by the model authors to simulate drainflow and isoproturon
concentrations during winter 1990/91 is provided with the model and was applied to data
from 1993/94, 1994/95 and 1995/96 with only minor changes (application rates and dates,
pesticide Kd and half-life, crop dates). As no measured data were available, initial water
contents were set to field capacity at the start of the simulation.
Simulated drainflow agreed well with that measured for the first event in 1995/96 (Figure 2).
The simulated drainage in the other two seasons, however, was close to zero (these
simulations are not shown in Figure 2). Changing the initial depth of the water table from the
default value (0.99 m) to drain depth (0.55 m) had no marked effect. The simulated drainflow
is influenced by the water input due to rainfall and by losses of water via evapotranspiration.
An over-estimation of evapotranspiration may result in under-estimation of drainflow. The
model simulated 55 and 74 mm of evaporation from the soil over the 1993/94 and 1994/95
monitoring periods, respectively Given that the relevant periods were rather short (39 and 45
days, respectively), the temperatures were rather low and the crop small at that time of the
year, these volumes appear to be too large. The actual predicted evaporation even exceeded
potential evapotranspiration (49 and 67 mm, respectively) which can be explained as follows:
the model assumes that rainfall is stored on the canopy surface up to a maximum value and
only water exceeding this amount enters the soil profile. This canopy interception capacity is
considered to increase linearly from zero at emergence to its maximum at the date of
maximum leaf area. The maximum was set to a rather large value (5 mm) and although the
actual interception capacity was not greater than 1.5 mm at the relevant intervals, this meant
that the crop surface was wet over a considerable time. As the evaporation from a wet canopy
was assumed to exceed potential evapotranspiration by a factor of 1.5, total water loss by this
pathway amounted to unreasonably large volumes. The assumption of a linear increase of
crop interception capacity from emergence onwards is unrealistic for winter sown crops. In
addition, the default values for both crop interception capacity and the correction factor for
wet canopy evaporation appeared to be too large. When these parameters were decreased to
more realistic values, the model destabilised and crashed. In an attempt to improve the
simulation in spite of this problem, water loss through the crop canopy was artificially
decreased by assuming that the soil was bare over the simulation periods 1993/94 and
1994/95. Simulated drainflow for the first two key events in 1993/94, 1994/95 (without crop)
and 1995/96 (with crop) compared to that measured from Plot 5 are given in Figure 2.
26
Figure 2
Comparison between rates of drainflow and isoproturon concentrations from
Plot 5 at Brimstone Farm and those simulated by CRACK-NP (application on 02/11/93,
17/11/94 and 30/10/95)
0.35
simulated assuming bare soil
500
0.25
Isoproturon (µg/l)
Drainage (mm/hour)
0.30
600
simulated assuming bare soil
0.20
0.15
0.10
300
200
100
0.05
0.00
02/11/93
400
12/11/93
22/11/93
02/12/93
0
02/11/93
12/12/93
1.20
12/11/93
22/11/93
simulated assuming bare soil
500
0.80
400
Isoproturon (µg/l)
Drainage (mm/hour)
1.00
0.60
0.40
0.20
200
100
12/12/94
17/12/94
22/12/94
27/12/94
0
07/12/94
01/01/95
25
2.0
20
Isoproturon (µg/l)
Drainage (mm/hour)
300
2.5
1.5
1.0
0.5
0.0
17/12/95
12/12/93
600
simulated assuming bare soil
0.00
07/12/94
02/12/93
12/12/94
17/12/94
22/12/94
27/12/94
01/01/95
27/12/95
01/01/96
06/01/96
11/01/96
15
10
5
22/12/95
27/12/95
01/01/96
06/01/96
11/01/96
0
17/12/95
observed
22/12/95
CRACK-NP
27
In 1993/94, the onset of drainflow was simulated to occur earlier than that observed and the
drainflow intensities monitored from 13-14 November and 7-8 December 1993 were underestimated. However, no drainage was simulated on 4 and 5 November 1993 if the run was
started two months before application. In 1994/95, the simulated maximum flow rate during
the first leaching event (1.16 mm/hour) exceeded that observed from plot 5, but a better
agreement was achieved for plots 9, 15 and 20 (maximum flow rates 2.2, 1.9 and 1.4
mm/hour). In 1995/96, the hydrograph of the first event was well matched by CRACK-NP,
whilst flow during the second event was under-estimated. Simulated isoproturon
concentrations in drainflow agreed relatively well with those observed in 1993/94 although
the simulation of hydrology was very poor over this period (Figure 2). In the second and
third season, for which the hydrograph was better matched by CRACK-NP, measured
isoproturon concentrations were markedly over-estimated.
In conclusion, the model could not predict water and solute movement at Brimstone Farm
with sufficient accuracy, even though the simulations were based on an input file set up for
the same site. In each season, the model was able to represent either drainflow or isoproturon
leaching, but not both. Discrepancies between simulated and measured data showed no
underlying trends or consistency between years. Minor changes of input parameters
destabilised the model which then crashed. Parameterisation of the model according to expert
judgement was, thus, not possible even for a situation very similar to that for which the
default file was provided.
The mis-match between CRACK-NP simulations and observed drainflow and pesticide
concentrations might to some extent be attributed to uncertainties involved in the estimation
of hourly rainfall which is required as a model input. A utility (metconv.exe) is provided
outside the model to calculate hourly data from the daily values. These calculations are based
on the simplifying assumption of a triangular distribution of daily rainfall around a maximum.
This maximum is defined as a proportion of total daily rainfall which is specified by the user.
Based on data analyses for different sites in the UK, a proportion of 0.3 is suggested by the
manual (i.e. the peak rainfall intensity equals 30% of total daily rainfall) and this value was
used for the simulations reported in Figure 2. To test the sensitivity of the model output for
changes in this parameter, further simulations were carried out using a value for this
proportion of either 0.1 or 0.6. The results are demonstrated in Figure 3 for the first and the
second event in 1993/94 together with the hourly rainfall calculated with the metconv.exe
utility.
28
Figure 3
CRACK-NP simulations of drainflow and isoproturon concentrations in
drainflow from the Brimstone site over the first and second event in 1993/94
for different hourly rainfall patterns (maximum hourly rainfall = 0.1, 0.3 or
0.6 x total daily rainfall)
6
6
0.1
0.3
0.6
4
3
2
1
0
13/11/93
4
3
2
1
14/11/93
15/11/93
16/11/93
0.05
0
08/12/93
17/11/93
0.04
09/12/93
10/12/93
0.14
0.1
0.3
0.6
0.03
0.02
11/12/93
0.1
0.3
0.6
0.12
Drainage (mm/hour)
Drainage (mm/hour)
0.1
0.3
0.6
5
Rainfall (mm/hour)
Rainfall (mm/hour)
5
0.10
0.08
0.06
0.04
0.01
0.00
13/11/93
0.02
14/11/93
15/11/93
16/11/93
0.00
08/12/93
17/11/93
250
10/12/93
11/12/93
150
0.1
0.3
0.6
0.1
0.3
0.6
Isoproturon (µg/l)
200
Isoproturon (µg/l)
09/12/93
150
100
100
50
50
0
13/11/93
14/11/93
15/11/93
16/11/93
17/11/93
29
0
08/12/93
09/12/93
10/12/93
11/12/93
Changing the hourly rainfall pattern had a marked influence on the simulations by CRACKNP (Figure 3). However, the results shown are extremely complex with no clear relationship
between the pattern of hourly rainfall generated and either rate of drainflow or concentrations
of pesticide simulated. Maximum isoproturon concentrations, which were simulated on the
basis of the input values 0.1, 0.3 or 0.6, varied by factors of 5.3 and 2.4 for the two events,
respectively. A comparison of this variation to differences between simulated and observed
concentrations at Brimstone Farm (factors up to 79) demonstrates that the uncertainty in the
tested parameter alone cannot be responsible for the variable model performance. In addition,
it should be noted that the values used (0.1 and 0.6) are outside the range of experimental
values given by the model authors (0.199-0.467). The analysis suggests that 0.3 is probably
the most appropriate value for the proportion of maximum rainfall.
4.1.3
MACRO - Brimstone Farm
Uncalibrated simulations were carried out with MACRO for the three seasons of data at
Brimstone Farm. The boundary between micropores and macropores was set to 5 kPa
according to expert judgement and then hydraulic parameters were selected around this
boundary according to measured properties. The same parameter set was used for each of the
three seasons with only weather data, time of application and crop growth varied. In the
absence of measured values, initial soil water content was set to establish drainage
equilibrium (i.e. fully wetted but without initiating drainflow) on 1 September of each season.
This moisture condition is too wet for September, but at least three months initiation period
was simulated before any of the measured events. Nevertheless, by varying the initial soil
moisture content, it might be possible to better simulate the first event of the season (but not
the second).
Results of the various simulations are compared with observed data for plot 5 in Figure 4.
The timing of drainage events was well matched by MACRO, particularly given that only
daily rainfall data were available. The magnitude of drainflow was also well matched in two
of the three seasons (93/94 and 95/96), but there appears to be a considerable over-estimate of
peak drainflow rates from plot 5 for 94/95. As for CRACK-NP (Section 4.1.2), it should be
noted that simulated drainflow for the first event of the 94/95 season matched that from the
other three plots much better (observed maxima 2.2, 1.9 and 1.4 mm/hour). Point
concentrations of isoproturon in drainflow were not well simulated. Results for the 93/94
season were best, but under-estimated peak concentrations by factors of 4 and 2 for the first
and second events, respectively. In both subsequent seasons, peak concentrations were
greatly over-estimated by MACRO. The model gave maximum values of 566 and 57 µg/l in
94/95 and 95/96, respectively, whereas observed maxima from any of the four plots were 84
and 1.2 µg/l, respectively.
30
Figure 4
Comparison between rates of drainflow and isoproturon concentrations from
Plot 5 at Brimstone Farm and those simulated by MACRO (application on
02/11/93, 17/11/94 and 30/10/95)
Drainage
Isoproturon
0.35
600
500
0.25
Isoproturon ( µg/l)
Drainage (mm/hour)
0.30
0.20
0.15
0.10
400
300
200
100
0.05
0.00
02/11/93
0
12/11/93
22/11/93
02/12/93
12/12/93
02/11/93
12/11/93
22/11/93
02/12/93
12/12/93
600
2.00
1.80
500
1.40
Isoproturon ( µg/l)
Drainage (mm/hour)
1.60
1.20
1.00
0.80
0.60
0.40
400
300
200
100
0.20
0.00
07/12/94
0
12/12/94
17/12/94
22/12/94
27/12/94
07/12/94
01/01/95
12/12/94
17/12/94
22/12/94
27/12/94
01/01/95
60
2.0
1.8
50
1.4
Isoproturon ( µg/l)
Drainage (mm/hour)
1.6
1.2
1.0
0.8
0.6
0.4
40
30
20
10
0.2
0.0
17/12/95
0
27/12/95
06/01/96
16/01/96
17/12/95
observed
MACRO
31
27/12/95
06/01/96
16/01/96
The results given in Figure 4 suggest that the input parameters selected for MACRO were not
transferable between seasons. In part, this is accounted for by the absence of information on
initial water contents at the start of the simulation and the use of daily rainfall with an average
intensity rather than hourly data. However, even with this information, the enormous
variability in concentrations of isoproturon leaving the site in drainflow would not be
accurately simulated for all three seasons. The rate of application in the final season was one
tenth of that in the first two, but even if concentrations are normalised to a single application
rate, the range of maximum concentrations observed over the three seasons still spans two
orders of magnitude. Calibration of the model to reproduce results for any selected season
would be relatively simple, but it was not possible to produce a single parameter set to
adequately simulate all three seasons. The four plots at Brimstone Farm for which results are
available are not replicates in terms of drainage treatment and there is additional variability in
results as a consequence of the highly heterogeneous structure in this soil type. Even so,
results of simulations with MACRO suggest that some important processes determining the
behaviour of isoproturon at Brimstone Farm are not being described by the model and/or are
not being represented in the monitoring programme at the site.
4.1.4
MACRO_DB - Brimstone Farm
Measured soil properties were entered into MACRO_DB and used to generate a parameter
file for the Denchworth soil at Brimstone Farm. The proportion of sorption sites within the
macropore region (FRACMAC) was set by the system to 4%. The results of the simulations
with MACRO_DB are shown in Figure 5.
The principal difference between the input parameters used with the stand-alone version of
MACRO and those selected automatically by MACRO_DB was in the position of the
boundary between micropores and macropores. In the former, this was set at 5 kPa water
tension (field capacity), whereas the automatic procedure set the boundary at 2 kPa in the
topsoil and 3.2 kPa in the subsoil. The consequence of this difference for the MACRO_DB
simulation is that the soil would have to be relatively wetter before macropore flow would be
initiated, the conductivity of the micropore domain would be greater and the overall
importance of macropore flow would be reduced. Figure 5 demonstrates the effect of this
change with MACRO_DB simulating smoother drainflow hydrographs than MACRO (see
Figure 4). Peak rates of drainflow are smaller for MACRO_DB and a number of events are
either not simulated or greatly reduced in importance. Each event is followed by a
considerable tail in the hydrograph resulting from slower drainage through the micropores.
Although the point data provided for Brimstone Farm do not allow a full evaluation of the
two methods, it is considered that the simulation of drainflow from MACRO is more
representative of the rapid responses to drainflow observed from this soil than the simulation
using MACRO_DB. MACRO_DB gave a better simulation of rates of drainflow than
MACRO for the first event of the 1994/95 season for plot 5 (and consequently a worse
simulation for the other three plots).
32
Figure 5
Comparison between rates of drainflow and isoproturon concentrations from
Plot 5 at Brimstone Farm and those simulated by MACRO_DB (application on
02/11/93, 17/11/94 and 30/10/95)
Isoproturon
600
0.30
500
0.25
Isoproturon ( µg/l)
Drainage (mm/hour)
Drainage
0.35
0.20
0.15
0.10
400
300
200
100
0.05
0.00
0
12/11/93
22/11/93
02/12/93
02/11/93
12/12/93
1.40
70
1.20
60
1.00
50
Isoproturon ( µg/l)
Drainage (mm/hour)
02/11/93
0.80
0.60
0.40
0.20
17/12/94
22/12/94
27/12/94
01/01/95
12/12/93
40
30
20
07/12/94
12/12/94
17/12/94
22/12/94
27/12/94
01/01/95
40
1.8
35
1.6
30
1.4
Isoproturon ( µg/l)
Drainage (mm/hour)
02/12/93
0
12/12/94
2.0
1.2
1.0
0.8
0.6
25
20
15
10
0.4
5
0.2
0.0
17/12/95
22/11/93
10
0.00
07/12/94
12/11/93
0
27/12/95
06/01/96
16/01/96
17/12/95
observed
27/12/95
MACRO_DB
33
06/01/96
16/01/96
MACRO_DB simulated smaller concentrations of isoproturon in drainflow from Brimstone
Farm than MACRO. This was a direct result of the lesser influence of macropore flow in the
model run with MACRO_DB where hydraulic parameters were selected according to pedotransfer functions . In addition, sorption within the macropores was four times as great for
runs with MACRO_DB and losses to drains in rapid bypass flow would thus be further
decreased. Zero loss to drains was simulated in the first season when the largest
concentrations of isoproturon were observed. In 1994/95, the maximum concentration in the
first event was under-estimated, whilst that in the second event was over-estimated by a factor
of 20. In common with all other models, concentrations in the third season were overestimated by more than an order of magnitude. It is not possible to say whether parameter
selection using expert judgement (MACRO) or pedo-transfer functions (MACRO_DB)
resulted in the more accurate simulation of observed behaviour of isoproturon . However,
MACRO_DB failed to predict any concentrations of isoproturon at all in drainflow during the
two events in 1993/94 when the largest concentrations (up to 280 µg/l) were observed.
Coupled with the overall weaker simulation of water flow, this suggests that simulations with
MACRO_DB may be relatively poor for this soil type in certain seasons.
4.1.5
PLM - Brimstone Farm
For PLM simulations of the Brimstone Farm data set, the percentage of fast mobile phase was
set to 95% to reflect the domination of bypass flow at the site. This value was subsequently
decreased to 80% in an attempt to improve the fit to observed concentrations of isoproturon in
drainflow. Initial soil moisture deficits for the three seasons (i.e. the amounts of water
required to increase soil moisture to field capacity) were calibrated to values whereby the
simulated and observed drainflow started approximately on the same date. These were 20
mm for 1993/94 and 1994/95 and 90 mm for 1995/96. The measured half-life (75 days at
10oC and 80% field capacity) and Kd (2.9 ml/g) for isoproturon were used and the output
compared to simulations based on the literature-derived data (half-life = 30 days at 10oC, field
capacity and Koc = 100 ml/g).
Drainflow from the four plots at the Brimstone site during the first two key events in three
successive years is compared to flow simulated by PLM assuming 95% fast flow in Table 3.
Table 3
Comparison between drainflow (mm) from four plots at Brimstone and that
simulated with PLM
Year
Event
1993/94
1994/95
1995/96
1
2
1
2
1
2
Plot 5
4.3
8.8
5.8
22.4
57.8
9.1
Observed
Plot 9
Plot 15
3.9
5.5
13.4
8.7
12.0
12.8
15.5
26.7
59.9
45.1
12.2
2.7
34
Simulated
Plot 20
6.6
10.5
2.5
6.9
43.0
3.0
3.8
8.6
3.5
24.4
44.3
10.8
PLM outputs are given at a daily resolution. Therefore, data measured at a higher resolution
such as those from Brimstone farm may not be represented in detail by the model. Taking
this into account, the observed drainflow is represented relatively well by the model. In
contrast, isoproturon concentrations were markedly over-estimated (Figure 6). Thus, whilst
CRACK-NP and MACRO under-estimated concentrations in the first season and overestimated them in the two subsequent seasons, PLM over-estimated concentrations by a
considerable margin in all three seasons. Decreasing the half-life from 75 to 30 days and
increasing Kd in the topsoil from 2.9 to 3.6 ml/g gave slightly smaller concentrations, but
there is still a considerable over-estimation of observed data.
Isoproturon concentrations in drainflow from Plot 5 at Brimstone Farm and
those simulated by PLM (application on 02/11/93, 17/11/94 and 30/10/95)
1200
Isoproturon (µg/l)
1000
800
600
400
200
0
02/11/93
12/11/93
22/11/93
02/12/93
12/12/93
900
800
Isoproturon (µg/l)
700
600
500
400
300
200
100
0
07/12/94
12/12/94
17/12/94
22/12/94
27/12/94
01/01/95
80
70
60
Isoproturon (µg/l)
Figure 6
50
40
30
20
10
0
17/12/95
observed
27/12/95
06/01/96
PLM (DT50 = 75 d, Kd = 2.9 ml/g)
35
16/01/96
PLM (DT50 = 30 d, Kd = 3.6 ml/g)
Decreasing the percentage of fast mobile phase from 95 to 80% had no significant effect on
PLM simulations for the 1993/94 season, but greatly reduced the amount of drainflow. The
associated loss of isoproturon was also reduced between observed events in 1994/95 and
1995/96. However, concentrations of isoproturon simulated during the events were almost
unchanged and still greatly over-predicted observed concentrations in all cases.
4.1.6
SWAT - Brimstone Farm
SWAT does not simulate rates of drainflow, but rather the total flow to surface water over a
given event (results from the model are compared with observed totals in Section 4.1.7).
Comparison of the maximum concentration of isoproturon observed and simulated (Table 5)
shows that SWAT followed a similar pattern to the other models with maxima underestimated in the first season and over-estimated in the second and third season. However,
SWAT was the only other model apart from PLM to correctly simulate the relative magnitude
of maximum concentrations in each of the three seasons (i.e. 1993/94 > 1994/95 > 1995/96).
A dominant factor used by SWAT to estimate concentrations moving to surface waters is the
time from application to the storm event. As this was shortest in 1993/94, the largest
concentrations were simulated in this year. Clearly, CRACK-NP and MACRO include
additional effects which resulted in much larger concentrations simulated for the second
season than for the first.
4.1.7
Overview - Brimstone Farm
Point rates of drainflow and associated concentrations of isoproturon were available for four
plots for two storm events in each of three successive seasons. In addition, total drainflow but
not loss of isoproturon was available for each event. There was considerable variation
between the four plots which arose partly from the natural variability expected in this highlystructured soil and partly from the fact that they were not replicates in terms of drainage
treatment (see Section 3.1). There was no information between the two events so that it was
only possible to evaluate model performance over the duration of the storm. The observed
maximum rate of flow and concentration may not have matched the actual maximum over the
storm event, but the graphs depicted naturally draw the reader to make the comparison with
the equivalent maxima simulated by the model.
Notwithstanding the above comments, the Brimstone Farm experiment provides a high
quality dataset on a soil which is extremely vulnerable to preferential flow through cracks and
fissures to the artificial drainage system. Table 4 summarises simulated flows for each of the
six events and compares them with the mean and standard deviation for the four plots at
Brimstone. There is no consistent pattern of one model performing better than another. PLM
and SWAT were best in the first season, all models performed reasonably in the second and
MACRO was particularly accurate in the third. Comparing the simulated flow with the mean
± one standard deviation shows that CRACK-NP and MACRO_DB were accurate for three of
the six events, MACRO and SWAT for four events and PLM for five events. It should be
noted that although estimations of initial water contents were made for all of the models,
PLM was the only one for which a genuine calibration of this parameter was performed.
36
Table 4
Comparison between total flow (mm) observed for the six events at Brimstone
Farm and those simulated in uncalibrated runs with the models
Event
Observed*
CRACK-NP
MACRO
MACRO_DB
PLM
SWAT
93/94 First event
Second event
5.1 (1.2)
10.4 (2.2)
0.5
1.7
3.6
2.1
1.9
0.6
3.8
8.7
5.3
6.3
94/95 First event
Second event
8.3 (5.0)
17.9 (8.6)
11.6
13.5
10.0
15.1
5.9
11.7
3.5
24.4
9.8
16.4
95/96 First event
Second event
51.5 (8.6)
6.8 (4.7)
52.2
0.8
56.9
5.4
47.6
1.1
44.3
10.8
33.0
6.4
* Mean of values for plots 5, 9, 15 and 20 together with the standard deviation in parentheses
There was much greater variation in concentrations of isoproturon simulated to leave
Brimstone in drainflow. Maximum values are given in Table 5, again with the mean observed
maximum from the four plots and the associated standard deviation. Of the 20 simulated
maximum concentrations reported in Table 5, only one (CRACK-NP for the second event in
1993/94) falls within one standard deviation of the mean observed, indicating the difficulty of
simulating this site without calibration. Taking a broader measure of acceptability of within
one order of magnitude of the observed mean, CRACK-NP, MACRO and SWAT were
acceptable for both events in 1993/94 and the first event in 1994/95. For the same three
events, PLM was acceptable for two events and MACRO_DB for one. None of the models
gave acceptable simulations for the second event in 1994/95 or either event in 1995/96.
Table 5
Comparison between maximum concentrations of isoproturon (µg/l) observed
in the six events at Brimstone Farm and those simulated in uncalibrated runs
with the models
Event
Observed*
CRACK-NP
MACRO
MACRO_DB
PLM
SWAT
93/94 First event
Second event
465 (132)
134 (47)
156
141
69.1
55.7
0
0
967
682
140
43.4
94/95 First event
Second event
65.1 (14.7)
2.6 (2.3)
527
206
566
524
8.2
50.1
808
613
80.2
37.3
95/96 First event
Second event
0.64 (0.41)
0.21 (0.28)
24.4
5.3
58.5
12.4
35.1
2.9
51.3
35.4
8.2
3.9
* Mean of values for plots 5, 9, 15 and 20 together with the standard deviation in parentheses
Maximum pesticide concentrations at Brimstone show extreme variability between seasons
which the models were not able to simulate. Only PLM and SWAT correctly ranked the
seasons in terms of maximum concentrations (i.e. 1993/94 > 1994/95 > 1995/96) and SWAT
appeared to give the best simulation of maximum concentrations over all six events. All of
the simulations reported above are vast improvements on those obtained with non-preferential
37
flow models such as LEACHP (Section 4.1.1). However there are clearly important processes
controlling pesticide losses at Brimstone Farm which are either not accurately treated by the
preferential flow models or are not evident from the dataset supplied (e.g. structural
variations, shrink-swell status). It can be concluded that simulation of such a heavy clay soil
without calibration is a relatively hazardous exercise which carries a high risk of inaccuracy.
4.2
Cockle Park
The clay loam soil at Cockle Park is less extreme in terms of bypass flow than the clay soils at
Brimstone Farm and Wytham. This is demonstrated by the significant component of flow
between storm events and the smaller concentrations of isoproturon detected in drainflow.
Daily rates of drainflow and point concentrations of isoproturon and trifluralin in drainflow
throughout the winter of 1990/91 were used for model evaluation. There were no site-specific
measurements for the water release curve at Cockle Park so, where required, these were taken
from the SEISMIC database for the representative profile of Dunkeswick series under arable
cultivation (Hollis et al., 1993). Rainfall was supplied on a daily resolution together with
maximum and minimum temperature. Potential evapotranspiration was calculated using
Linacre’s equation (Linacre, 1977). If not otherwise stated, Kd values used as input
parameters for Cockle Park modelling were calculated from the soil organic carbon content
and literature-derived Koc values for isoproturon (100 ml/g) and trifluralin (4000 ml/g).
Isoproturon degradation was modelled using the field DT50 of 35 days or a literature value
(30 days) for the upper horizon and corrected values for deeper layers. For trifluralin, the
field DT50 of 180 days and a literature value of 60 days were considered. For field DT50’s,
the reference temperature and moisture were set to 8oC and water content at 5 kPa,
respectively.
4.2.1
LEACHP - Cockle Park
LEACHP was used to simulate drainflow and associated losses of isoproturon and trifluralin
at Cockle Park. Input values were taken from measured data wherever possible. Drainflow
was approximated by simulating leaching to drain depth (50 cm) and assuming all leachate
was intercepted by the drainage system. Field half-lives of 35 and 180 days for isoproturon
and trifluralin, respectively, were set constant throughout the model run.
Although LEACHP does not include any description of preferential flow, simulated drainflow
matched reasonably well to that observed for this clay loam soil (Figure 7). Peak rates of
flow were under-estimated, whereas flow between events was over-estimated. Total
drainflow simulated for the period was 248 mm (90% of observed). Preferential flow is
known to be extremely important for pesticide transport in such soils and LEACHP
completely failed to describe the movement of either compound to drains. The major events
for isoproturon loss were missed and only a small breakthrough at the end of the season was
simulated (Figure 8). This pattern of breakthrough is typical of models without a description
of preferential flow. No losses of trifluralin were simulated because of the strong sorption to
soil, whereas concentrations up to 0.06 µg/l were observed in drainflow over the winter
period.
38
Figure 7
Comparison between measured rates of mole drainflow from Cockle Park and
those predicted by LEACHP
18
observed
16
Drainage (mm/day)
14
12
10
8
6
4
2
0
0
20
40
60
80
100
120
140
Time from application (d)
18
predicted
16
Drainage (mm/day)
14
12
10
8
6
4
2
0
0
20
40
60
80
100
120
140
Time from application (d)
Comparison between observed concentrations of isoproturon in mole drainflow
from Cockle Park and those predicted by LEACHP
Isoproturon
6
5
Isoproturon ( g/l)
Figure 8
4
3
2
1
0
0
20
40
60
80
100
Time from application (d)
observed
39
LEACHP
120
140
4.2.2
CRACK-NP - Cockle Park
The original hydrological model CRACK was developed to describe leaching through
cracking clay soils and was only applied to heavy clay soils of the Denchworth and Evesham
series (≈ 60% clay). Following incorporation of nitrate and pesticide sub-routines, CRACKNP has only previously been applied to the Denchworth soil at Brimstone. Therefore, it was
interesting to check the ability of the model to describe water and solute movement in the
more moderate Dunkeswick clay loam over clay soil at the Cockle Park site (38% clay at 60
cm depth). The input file provided for the Denchworth soil at Brimstone Farm was modified
to account for soil properties and the experimental design at Cockle Park. The simulation was
started on the day of application and the initial water contents were set to greater values than
those measured, because the model destabilised and crashed if actual soil water values were
used or the simulation was started prior to application. The model requires information about
the initial depth of a water table which has to be smaller than or equal to the total depth of the
profile. As significant drainflow does not start before the water table has risen above the
drain depth, this is a sensitive parameter. At Cockle Park no water table was present and this
parameter was set to an artificial value (0.79 m). Smaller values were not possible due to
instability of the model. For the same reason, aggregate sorptivity had to be set to the default
value.
The observed and simulated drainflow are given in Figure 9. The model gave a good match
to both the timing of drainflow and the peak flow rate. However, cumulative drainflow from
CRACK-NP was only 69% of that observed because flow between events was underestimated. Isoproturon concentrations were over-estimated by more than two orders of
magnitude (Figure 10). The assumptions inherent in CRACK-NP (i.e. negligible movement
of water and solute in the micropores) are probably not valid for the clay loam soil at Cockle
Park (N.Jarvis, personal communication) and this appears to be confirmed by the results
presented for isoproturon. An alternative explanation is that preferential flow in cracks is not
initiated at the soil surface as simulated by CRACK-NP, but at the base of the plough layer
where a temporary perched water table is known to develop (A. Armstrong, author’s
comments). Preferential flow generated at depth will clearly contain less pesticide than
simulated by the model which assumes initiation in the pesticide-rich upper layers of the soil.
Whatever the mechanism responsible for the mis-match between observed and simulated
results, it can be concluded that the model in its current form should not be applied to soils
where clay content decreases significantly below 50-60%.
Maximum concentrations of trifluralin simulated by CRACK-NP over-estimated observed
values by four orders of magnitude and were only slightly smaller than those for isoproturon
despite the very large difference in adsorption properties. Simulated total losses of trifluralin
were actually larger than those of isoproturon, presumably because the former is more
persistent. These results were checked by simulating a hypothetical application of trifluralin
at Brimstone Farm and comparing the model output to simulated behaviour of isoproturon.
The very large over-estimate of transport of the more strongly sorbed compound was
confirmed. CRACK-NP assumes that sorption of pesticides is limited to the soil aggregates
with no sorption within the cracks (Adriaanse et al., 1997). Once pesticide enters preferential
flow in the cracks, there is thus little if any potential for further attenuation. As results with
trifluralin at Cockle Park were reproduced for Brimstone Farm, it seems likely that this is the
principal reason for the large over-estimate of concentrations of this strongly-sorbed
pesticide. If this is the case, then this assumption would appear not to be valid for Cockle
Park.
40
Figure 9
Comparison between measured rates of mole drainflow from Cockle Park and
those predicted by CRACK-NP
18
observed
16
Drainage (mm/day)
14
12
10
8
6
4
2
0
0
20
40
60
80
100
120
140
Time from application (d)
18
predicted
16
Drainage (mm/day)
14
12
10
8
6
4
2
0
0
20
40
60
80
100
120
140
Time from application (d)
Figure 10
Comparison between observed concentrations of isoproturon and trifluralin in
mole drainflow from Cockle Park and those predicted by CRACK-NP
Isoproturon
Trifluralin
500
800
450
700
400
600
Trifluralin (µg/l)
Isoproturon (µg/l)
350
500
400
300
300
250
200
150
200
100
100
50
0
0
0
20
40
60
80
100
120
0
140
20
40
60
80
Tim e from application (d)
Tim e from application (d)
observed
CRACK-NP
41
100
120
140
4.2.3
MACRO - Cockle Park
Soil and hydraulic parameters were selected according to the method detailed in Section 4.
Expert judgement was used to set the boundary between micropore and macropore domains at
1.5 kPa in the topsoil and 2.5 kPa in the subsoil. As there was no description of soil structure
at the site, the values for Dunkeswick series incorporated into MACRO_DB were taken.
Initial soil moisture contents were based on measured values, but simulations were also run
for approximately 14 months prior to the application date to allow the model to equilibrate.
Half-lives for the two pesticides were based on field values for Cockle Park over the 1990/91
season and variation in rate according to soil temperature and moisture content was
minimised.
MACRO gave a reasonable simulation of the measured hydrograph, particularly early in the
season (Figure 11). An event 45-50 days after application was not simulated and peak flows
from 90 to 130 days after application were under-estimated. This suggests that simulated
evapotranspiration was greater than that observed and this is reinforced by the total simulated
drainflow (225 mm) which was only 81% of that observed. As the simulation covered the
winter months when actual evapotranspiration approximated to potential evapotranspiration,
it is most likely that the values for potential evapotranspiration used as input to the model
were an over-estimate.
Comparison between measured rates of mole drainflow from Cockle Park and
those predicted by MACRO
18
observed
16
Drainage (mm/day)
14
12
10
8
6
4
2
0
0
20
40
60
80
100
120
140
Time from application (d)
18
predicted
16
14
Drainage (mm/day)
Figure 11
12
10
8
6
4
2
0
0
20
40
60
80
100
Time from application (d)
42
120
140
Concentrations of isoproturon in drainflow for the first significant event after application
were well simulated by the model (Figure 12), although the maximum value was overestimated by a factor of three. Subsequently, concentrations but not their pattern were well
matched from 40 to 70 days after application. From 90 days after application, all
concentrations were greatly over-estimated (factors of between ten and twenty). As flow was
greatest during this period, the total loss of isoproturon in drainflow was also over-estimated
by a factor of ten. Thus, although MACRO gave reasonably accurate simulation of the first
pulse of pesticide reaching drainflow, there was a considerable error associated with the
simulation of total loss of pesticide over the season. It is not clear why the model simulates
such large concentrations of isoproturon late in the season. One possibility is that this results
from slower leaching through the micropores implying that the input parameters place too
much emphasis on matrix flow relative to conditions at the site. However, such large
concentrations were not observed for simulations with MACRO_DB (see Section 4.2.4)
where hydraulic parameters were expected to place even greater emphasis on flow through
the micropores. A second explanation is that pesticide moved down the soil profile in
preferential flow early in the season resides in the soil micropores at depth before acting as a
source for movement to drains later in the season (N. Jarvis, author’s comments). Changes to
parameter values made to address the problem had little effect on the simulation, but this
aspect will receive further attention in an extension to the programme of work.
MACRO did not predict any leaching to drain depth of the strongly-sorbed compound
trifluralin although very small concentrations of this pesticide were detected in drainflow
throughout much of the season. Further evaluation is recommended to investigate the
predictive capacity of MACRO for more strongly-sorbed pesticides - all of the validation
studies reported in Section 2.3 have been carried out with relatively mobile pesticides. It is
likely that parameters controlling the predominance of preferential flow (e.g. FRACMAC the proportion of sorption sites in the macropore domain) will be especially sensitive for more
strongly-sorbed pesticides. This will be investigated in an extension to this programme of
work.
Comparison between observed concentrations of isoproturon in mole drainflow
from Cockle Park and those predicted by MACRO
Isoproturon
14
12
10
Isoproturon ( g/l)
Figure 12
8
6
4
2
0
0
20
40
60
80
100
Time from application (d)
observed
43
MACRO
120
140
4.2.4
MACRO_DB - Cockle Park
The MACRO input file described in Section 4.2.3 was adjusted to include soil/hydraulic and
crop parameters derived from MACRO_DB. Soil/hydraulic parameters were derived using
the pedo-transfer functions incorporated into MACRO_DB and based on data for bulk
density, sand, silt, clay and organic carbon content set out in Appendix 2. As at other sites,
the boundary between the two pore domains (CTEN) was set closer to saturation by
MACRO_DB (0.9 kPa in the topsoil and 1.4 kPa in the subsoil) than using expert judgement
with the stand-alone version of MACRO (see Section 4.2.3). This would reduce the effect of
preferential flow in MACRO_DB relative to MACRO. In the absence of a full soil survey for
the site, information on structure sizes was taken from a standard description for the
Dunkeswick series contained within MACRO_DB. Crop parameters were taken from within
the system for a winter wheat crop at a German site with the dates of emergence and harvest
adjusted to those at Cockle Park. Pesticide parameters and weather data were the same as
those used with the stand-alone version of MACRO except that the proportion of sorption
sites in the macropores (FRACMAC) was set to 0.06 by MACRO_DB whereas a value of
0.04 was used in the stand-alone version.
Comparison between measured rates of mole drainflow from Cockle Park and
those predicted by MACRO_DB
18
observed
16
Drainage (mm/day)
14
12
10
8
6
4
2
0
0
20
40
60
80
100
120
140
Time from application (d)
18
predicted
16
14
Drainage (mm/day)
Figure 13
12
10
8
6
4
2
0
0
20
40
60
80
100
Time from application (d)
44
120
140
As with LEACHP, MACRO_DB gave a reasonable approximation to the observed pattern of
drainflow, but under-estimated peak flow and over-estimated that between events (Figure 13).
Total flow for the period was again under-estimated (83% of observed). The simulation of
the pattern of isoproturon concentrations in drainflow was poor (Figure 14). Although timing
of the first major event was well matched, the maximum concentration observed (4.2 µg/l)
was under-estimated by a factor of 30 (0.14 µg/l). All subsequent observed concentrations of
isoproturon were under-estimated by the model and the total observed loss of isoproturon
(0.141 mg/m2) was under-estimated by a factor of 35 (0.004 mg/m2). MACRO_DB failed to
predict any losses of trifluralin to drains over the monitoring period. It can be concluded that
the parameter set selected using MACRO_DB under-estimated the effect of macropore flow
on pesticide transport at the site.
Figure 14
Comparison between observed concentrations of isoproturon in mole drainflow
from Cockle Park and those predicted by MACRO_DB
Isoproturon
6
Isoproturon ( g/l)
5
4
3
2
1
0
0
20
40
60
80
100
120
140
Time from application (d)
observed
4.2.5
M ACRO _DB
PLM - Cockle Park
To simulate pesticide movement at Cockle Park, the percentage of fast mobile phase was set
to 60%. This was assumed to be a reasonable value for the Dunkeswick soil. Measured
drainflow was reasonably well simulated by the model although rates of flow between events
were consistently over-estimated as the model simulated a slow leaching to the drains (Figure
15). Concentrations of both isoproturon and trifluralin were markedly over-estimated (factors
of up to 950). In an attempt to improve simulation of pesticide concentrations, the percentage
of fast mobile phase was reduced to 30%. With this value, simulation of drainflow patterns
deviated considerably from those observed (Figure 15). There was a significant reduction in
predicted concentrations of the two herbicides in drainflow, but these still greatly exceeded
measured values (Figure 16). Using a literature half-life of 60 days for trifluralin instead of
the experimental value (180 days) gave no significant improvement of the fit to observed
concentrations. The reasons for the very poor simulation of pesticide losses at Cockle Park
are not clear, but may relate to an interaction between the parameter for the percentage of fast
mobile phase and the air capacity of the soil (see Section 4.3.5). Topsoil air capacity was
15.6% at Cockle Park, compared to 4.0 and 3.0% at Brimstone Farm and Wytham,
45
respectively. It is concluded that PLM is not suitable for application to the clay loam soil at
Cockle Park without calibration.
Figure 15
Comparison between measured rates of mole drainflow from Cockle Park and
those predicted by PLM
30% fast flow
16
14
14
12
12
Drainage (mm/day)
Drainage (mm/day)
60% fast flow
16
10
8
6
10
8
6
4
4
2
2
0
0
0
20
40
60
80
100
120
140
0
20
40
Time from application (d)
observed
Figure 16
60
80
100
120
140
Time from application (d)
PLM
Comparison between observed concentrations of isoproturon and trifluralin in
mole drainflow from Cockle Park and those predicted by PLM (30% fast
mobile phase)
Isoproturon
Trifluralin
10
120
9
8
7
80
Trifluralin (µg/l)
Isoproturon (µg/l)
100
60
40
6
5
4
3
2
20
1
0
0
0
20
40
60
80
100
120
0
140
Time from application (d)
20
40
60
80
Time from application (d)
observed
46
PLM
100
120
140
4.2.6
SWAT - Cockle Park
An evaluation of SWAT against data from Cockle Park has previously been reported by
Brown & Hollis (1996) and only a brief reprise is given here. SWAT is only intended to
predict rapid flow during storm events although a catchment version (SWATCATCH)
describing the complete water balance is available and is incorporated into the Environment
Agency’s POPPIE system (Hollis & Brown, 1996). Figure 17 shows that SWAT was able to
match the timing of peak flows although actual values were consistently under-estimated.
Two simulated events 85-90 days after application were not actually observed because the
ground was frozen over this period. Total rapid flow predicted by SWAT was 83 mm (30%
of the total observed).
Figure 17
Comparison between measured rates of mole drainflow from Cockle Park and
losses of rapid throughflow over storm events predicted by SWAT
18
16
Drainage (mm/d)
14
12
10
8
6
4
2
140
120
100
80
60
40
20
0
0
Time f rom application (d)
Observed
SWAT rapid flow
Concentrations of isoproturon and trifluralin in drainflow were reasonably well simulated by
SWAT (Figure 18). Peak concentrations were over-estimated by a factor of 2.5 for
isoproturon and under-estimated by a factor of 2 for trifluralin. Concentrations more than 100
days after application were very well matched for isoproturon, but less so for trifluralin where
a small increase in concentration with time was observed. Although the model only simulated
flow during storm events, larger observed concentrations generally coincided with times at
which rapid flow was simulated. Results suggest that the simple approach adopted by SWAT
may be appropriate for two contrasting pesticides at this site. In particular, SWAT was the
only one of the models evaluated which was able to simulate the small concentrations of the
strongly-sorbed compound trifluralin detected in drainflow.
47
Figure 18
Comparison between observed concentrations of isoproturon and trifluralin in
mole drainflow from Cockle Park and those predicted by SWAT
Isoproturon
12
10
0.08
8
Trifluralin ( g/l)
Isoproturon ( g/l)
Trifluralin
0.1
6
4
0.06
0.04
0.02
2
0
0
0
20
40
60
80
100
120
140
0
20
Time from application (d)
60
80
100
120
140
Time from application (d)
observed
4.2.7
40
SWAT
Overview - Cockle Park
There was great variability in the accuracy with which the various models simulated results at
Cockle Park (see Table 6). Using the same inputs for potential evapotranspiration, all of the
models under-estimated total drainflow, but the discrepancy with the observed total was
smaller for LEACHP (29 mm) than for the preferential flow models (44-90 mm).
Table 6
Summary of the major outputs of the simulations with each model and
comparison to observed results.
Parameter
Observed LEACHP CRACK-NP MACRO MACRO_DB
PLM1
SWAT
Total drainflow (mm)
277
248
187
225
231
233
83.1*
Loss of IPU (mg/m2)
0.141
0.019
20.6
1.53
0.0004
26.2
0.171
4.2
0.3
729
13.1
0.14
506
10.3
Loss of trifluralin (mg/m2)
0.001
0
44.0
0
0
1.21
0.0001
Max conc of trifluralin (µg/l)
0.06
0
499
0
0
10.2
0.02
Max conc of IPU (µg/l)
1
results for 60% fast mobile phase,* Fast flow only
For losses of isoproturon, the best simulation was obtained with SWAT. Despite its
simplicity in simulating only fast flow (30% of the total observed), SWAT gave an excellent
simulation of the total loss of IPU and over-estimated the maximum concentration by a factor
of 2.5. The stand-alone version of MACRO gave the next best simulation. However,
although the maximum concentration was only over-estimated by a factor of 3, larger losses
late in the season than those observed (concentrations over-estimated by factors of ten to
twenty) meant that the total loss of IPU was an order of magnitude larger than that observed.
48
Parameters selected using MACRO_DB reduced the effect of preferential flow on the loss of
isoproturon to drains which was greatly under-estimated. Both CRACK-NP and PLM greatly
over-estimated the observed loss of isoproturon. This suggests that CRACK-NP should not
be applied to such a clay loam soil with 30% clay in the topsoil and a significant component
of matrix flow. Results for PLM were more surprising, but the large proportion of fast mobile
phase necessary to simulate patterns of drainflow resulted in maximum concentrations of
isoproturon which were two orders of magnitude larger than those observed. When the
proportion of fast mobile phase was halved, flow was not acceptably simulated, whilst
concentrations of isoproturon were still over-estimated by a factor of 25. LEACHP which
was used as a benchmark for non-preferential flow models did simulate a small breakthrough
of isoproturon late in the season, but this did not match the pattern of losses which were
dominated by movement soon after application.
Cockle Park was the only dataset where information on the movement of a strongly-sorbed
pesticide was available. Results for trifluralin (Koc = 4000 ml/g) divide the models into three
groups. CRACK-NP and PLM again over-estimated concentrations. For CRACK-NP, this
was particularly marked with maximum concentrations of trifluralin only slightly smaller than
those of isoproturon and total losses of the more persistent trifluralin actually larger. This
result raises serious questions over the pesticide routines incorporated into CRACK-NP and
was confirmed by hypothetical simulations of trifluralin for the Brimstone dataset which
again resulted in concentrations almost as large as those of isoproturon. Neither MACRO,
MACRO_DB, nor LEACHP simulated any loss of trifluralin to drains. Although
concentrations were relatively small, the ability of the models to simulate leaching of morestrongly sorbed compounds requires further attention (almost all validation studies have been
carried out with relatively mobile pesticides). The only model to accurately simulate the trace
concentrations of trifluralin moving in drainflow throughout the season was SWAT. Coupled
with the accurate simulation of isoproturon, it can be concluded that this extremely simple
model performed best for Cockle Park.
4.3
SSLRC Lysimeters
Replicate, undisturbed lysimeters from five contrasting soil types were monitored over two
successive seasons. Volumes of leachate and concentrations of bromide and isoproturon were
measured at intervals of 1-4 weeks. Rainfall and air temperature were monitored on-site and
potential evapotranspiration was estimated using Linacre’s equation (Linacre, 1977). As no
degradation or sorption studies were carried out with these soil types, average literature
values were used for isoproturon (half-life 30 days at 10oC in the topsoil and Koc 100 ml/g).
Plant uptake of bromide, but not isoproturon was considered in all simulations where
possible.
49
4.3.1
LEACHP - SSLRC lysimeters
The simulated amounts of water draining from the lysimeters between 18/11/94 and 18/08/96
agreed reasonably well with those observed (Table 7). Concentrations of bromide in leachate
(Figure 19, Table 7) were very well simulated for three of the five soils (Cuckney, Sonning,
Isleham) and also closely predicted for the second season in Ludford soil. The match
between observed and simulated bromide concentrations was not as good for the first season
in the Ludford soil or for either period in Enborne (the most clay-rich of the five soils used).
This would be expected as these were the soils most susceptible to preferential flow.
The observed leaching of isoproturon was not well simulated (Table 7) with total losses
greatly under-estimated for the Sonning, Ludford and Enborne soils and over-estimated by
more than a factor of ten in the Cuckney soil (sandiest soil for which LEACHP should
perform best). The observed timing of isoproturon leaching through the Cuckney soil was
represented relatively well by the model (Figure 20). The model was rather sensitive to
changes in dispersivity and a decrease of this parameter from 100 to 50 mm decreased
isoproturon losses from the Cuckney soil by a factor of 3 to 1.6 mg/m2. Uncertainties about
the Kd value may have contributed to the discrepancies between simulated and observed data
in the Cuckney soil. No sorption experiments were carried out for this soil and an average
literature Koc value of 100 was used instead. Based on this value, the compound is very
mobile and persistent once it has passed out of the topsoil due to the very low organic carbon
contents in deeper layers. Increasing the Koc from 100 to 120 reduced the simulated
isoproturon loss from the Cuckney lysimeters from 5.05 to 2.9 mg/m2. There was no leaching
of isoproturon from the Isleham soil and this was correctly simulated by LEACHP.
Table 7
Comparison between observed flows, bromide and isoproturon loads over two
seasons from each of the SSLRC lysimeters and those simulated by LEACHP
Soil
Flow
observed* simulated
(mm)
(mm)
Bromide load
observed* simulated
(g/m2)
(g/m2)
Isoproturon load
observed* simulated
(mg/m2) (mg/m2)
Cuckney
Sonning
Ludford
Enborne
Isleham
474; 477
417
412
339
335; 356
11.36; 9.97
10.52
6.01; 6.33
3.44; 2.97
4.02; 3.94
0.36; 0.20
0.92
1.53; 4.94
2.77; 5.04
0; 0
416
395
399
347
368
* where available, values are given for replicate lysimeters
50
11.80
9.44
7.95
3.78
5.77
5.05
8.9 x 10-5
4.4 x 10-2
2.2 x 10-3
4.4 x 10-7
Figure 19
Comparison between the observed leaching of bromide through the five
SSLRC lysimeter soil types and LEACHP simulations
Sonning
80
70
70
60
60
50
50
Bromide (mg/l)
Bromide (mg/l)
Cuckney
80
40
30
30
20
20
10
10
0
0
0
100
200
300
400
500
0
100
200
300
Time from first application (d)
Time from first application (d)
Enborne
Ludford
50
50
40
40
Bromide (mg/l)
Bromide (mg/l)
40
30
20
10
500
400
500
30
20
10
0
0
0
100
200
300
400
500
0
100
Time from first application (d)
200
300
Time from first application (d)
Isleham
40
35
30
Bromide (mg/l)
400
25
20
15
10
5
0
0
100
200
300
400
500
Time from first application (d)
Lysimeter 1
Lysimeter 2
51
LEACHP
Figure 20
Comparison between the observed leaching of isoproturon through the
Cuckney lysimeters and LEACHP simulations
Cuckney
35
30
Isoproturon (µg/l)
25
20
15
10
5
0
0
100
200
300
400
500
Time from first application (d)
Lysimeter 1
4.3.2
Lysimeter 2
LEACHP
CRACK-NP - SSLRC lysimeters
The concepts built into CRACK-NP mean that the model is only applicable to very heavy
clay soils where net water movement within the soil matrix can be considered negligible. All
of the soils tested in the lysimeter experiment were groundwater soils with intermediate
textures and thus fall outside the range of soils to which CRACK-NP applies. The model was
therefore not evaluated against this dataset.
4.3.3
MACRO - SSLRC lysimeters
MACRO was run uncalibrated for leaching of water, bromide and isoproturon. Detailed soil
descriptions and physical/chemical analyses for each of the five soil types were available for
selection of input parameters. The boundary between the two flow domains was set to 0.9-1.1
kPa for Cuckney, Sonning, Ludford and Isleham soils), whilst for the clay loam Enborne soil
it was set to 1.5 kPa in the topsoil and 3.5 kPa in the subsoil. As set out in Section 4, the
proportion of sorption sites in the macropores was set to 0.01 for Ludford and Enborne
lysimeters and to 0.04 for the remaining soils. There were no data for initial moisture
contents, so these were varied to match the onset of leaching in the autumn of the first season.
Table 8 shows that MACRO gave a reasonable simulation of total flow from the various
lysimeters. With the exception of Ludford soil, the soils were correctly ranked in terms of
relative flow, although the difference between the soils with greatest and least flow was
under-estimated by the model. Generally, the model gave a very good simulation of total
flow for the first season, but either under- or over-estimated leaching in the second. This
suggests that the simulation of the drying and wetting cycle over the period between the two
winters was less reliable than the simulation of leaching over the winter.
52
Table 8
Comparison between observed flows, bromide and isoproturon loads over two
seasons from each of the SSLRC lysimeters and those simulated by MACRO
Soil
Flow
observed* simulated
(mm)
(mm)
Bromide load
observed* simulated
(g/m2)
(g/m2)
Isoproturon load
observed* simulated
(mg/m2) (mg/m2)
Cuckney
Sonning
Ludford
Enborne
Isleham
474; 477
417
412
339
335; 356
11.36; 9.97
10.52
6.01; 6.33
3.44; 2.97
4.02; 3.94
0.36; 0.20
0.92
1.53; 4.94
2.77; 5.04
0; 0
451
421
454
366
353
11.78
10.76
10.37
7.40
8.26
1.00
0.52
4.91
1.33
0
* where available, values are given for replicate lysimeters
Simulations of bromide leaching with MACRO are shown in Figure 21. Comparison with
LEACHP simulations (Figure 19) allows the effect of incorporating preferential flow to be
examined for the five soils. For the two sandiest of the five soils (Cuckney and Sonning) and
the peat soil (Isleham), MACRO gave a worse simulation than LEACHP in the first season
when the model over-estimated the observed concentrations of bromide. However, in the
second season, MACRO was better able to simulate the larger concentrations of bromide in
leachate than LEACHP. Without calibration, MACRO was not able to simulate the pattern of
bromide concentrations from the clay loam Enborne lysimeter, although the magnitude of
concentrations were much better matched for each season than with LEACHP. Surprisingly,
LEACHP gave the better simulation for both seasons for the other soil where extensive
preferential flow was observed (Ludford).
It is known that leaching of pesticides is much more influenced by preferential flow than that
of the non-interactive tracer bromide. Table 8 and Figure 22 show that MACRO gave much
better simulations of isoproturon leaching than LEACHP for all four of the soils from which
leaching occurred (neither model simulated leaching from the peaty Isleham soil). Mean total
loss of isoproturon was very well matched for the Sonning and Ludford lysimeters, whilst that
from Cuckney and Enborne soils was over- and under-estimated, respectively, each by a
factor of approximately three. The pattern of isoproturon concentrations was also relatively
well simulated for the Ludford, Cuckney and Enborne soils, although magnitude of
concentrations was consistently under-estimated for the latter. By contrast, pattern of
leaching was not well described for the Sonning lysimeter where breakthrough occurred
earlier than simulated and observed concentrations were under-estimated in the first season
and over-estimated in the second. Overall, results of simulations of isoproturon leaching with
MACRO through the five soils are extremely encouraging, particularly given that no
calibration was undertaken apart from adjustment of initial water contents to match the
observed onset of flow.
53
Figure 21
Comparison between the observed leaching of bromide through the five
SSLRC lysimeter soil types and MACRO simulations
Cuckney
70
70
60
60
50
50
40
30
40
30
20
20
10
10
0
0
0
100
200
300
400
500
0
100
200
Time from first application (d)
Enborne
Ludford
50
50
40
40
30
20
10
400
500
400
500
30
20
10
0
0
0
100
200
300
400
500
0
Time from first application (d)
100
200
35
30
25
20
15
10
5
0
0
100
200
300
300
Time from first application (d)
Isleham
40
Bromide (mg/l)
300
Time from first application (d)
Bromide (mg/l)
Bromide (mg/l)
Sonning
80
Bromide (mg/l)
Bromide (mg/l)
80
400
500
Time from first application (d)
Lysimeter 1
Lysimeter 2
54
MACRO
Figure 22
Comparison between observed concentrations of isoproturon in leachate from
four soils and those predicted by MACRO
Cuckney
Sonning
10
18
9
16
8
14
7
Isoproturon ( g/l)
Isoproturon ( g/l)
20
12
10
8
6
5
4
6
3
4
2
2
1
0
0
0
100
200
300
400
500
0
100
Time from first application (d)
Enborne
90
90
80
80
70
70
60
50
40
400
500
400
500
60
50
40
30
30
20
20
10
10
0
0
0
100
200
300
400
500
0
100
Time from first application (d)
Lysimeter 1
4.3.4
300
Ludford
100
Isoproturon ( g/l)
Isoproturon ( g/l)
100
200
Time from first application (d)
200
300
Time from first application (d)
Lysimeter 2
MACRO
MACRO_DB - SSLRC lysimeters
MACRO_DB was used to set up input files for the five soil types based on the parameter
estimation techniques built into the system. All soil parameters required for the simulation
were automatically calculated within MACRO_DB from the base soils information (texture,
organic carbon, bulk density and aggregate characterisation) obtained from soil descriptions
for each pair of lysimeters. As with MACRO (Section 4.3.3), the soils parameters placed
little emphasis on preferential flow in either Cuckney or Isleham soil. For the remaining three
soil types, the simulations resulted in preferential flow being less important than in
simulations with the stand-alone version of MACRO based on measured hydraulic properties
of the soils together with the conception of each soil’s behaviour held by the modeller. The
boundary between the two flow regions (CTEN) selected by MACRO_DB was almost
identical to that used with MACRO for four of the five soils. For the clay loam Enborne soil,
the boundary was set much closer to saturation by MACRO_DB (1.0 kPa in the topsoil and
1.6 kPa in the subsoil) thus reducing the impact of preferential flow. The biggest difference
between simulations with MACRO_DB and those with MACRO was in the proportion of
sorption sites in the macropore region. In MACRO this was set to 1-4% depending upon soil
properties, whereas MACRO_DB gave values as follows: Cuckney (27%), Sonning (22%),
55
Ludford (17%), Enborne (11%) and Isleham (22%). The result of these larger values would
be to increase sorption within the macropore region and thus reduce the impact of preferential
flow on pesticide transport under conditions where preferential flow is important. Where
matrix flow is more important, sorption within the micropores would be less and thus overall
transport of pesticide might be greater.
Apart from the Ludford soil, MACRO_DB ranked the soils correctly for total volume of
leachate (Table 9) although the range of differences between soils was again under-estimated.
MACRO gave a better estimate of the range of different leachate volumes seen from the five
soils.
Table 9
Comparison between observed flows, bromide and isoproturon loads over two
seasons from each of the SSLRC lysimeters and those simulated by
MACRO_DB
Soil
Flow
observed* simulated
(mm)
(mm)
Bromide load
observed* simulated
(g/m2)
(g/m2)
Isoproturon load
observed* simulated
(mg/m2) (mg/m2)
Cuckney
Sonning
Ludford
Enborne
Isleham
474; 477
417
412
339
335; 356
11.36; 9.97
10.52
6.01; 6.33
3.44; 2.97
4.02; 3.94
0.36; 0.20
0.92
1.53; 4.94
2.77; 5.04
0; 0
434
424
435
384
397
10.45
8.81
10.14
9.69
7.28
1.74
0
0
0
0
* where available, values are given for replicate lysimeters
Total loss of bromide was under-estimated for the Sonning soil and over-estimated for
Ludford, Enborne and Isleham lysimeters. Although the general magnitude of concentrations
of bromide leaching from the five lysimeters was accurately simulated by MACRO_DB
(Figure 23), the actual patterns of leaching were less well matched than by either LEACHP or
MACRO. For Cuckney and Sonning lysimeters, timing of the peaks in breakthrough was
delayed and this was accentuated in the second season. This is likely to have resulted from
the smaller total volume of leachate simulated relative to that from MACRO and less
dispersivity from default parameters for MACRO_DB relative to those for LEACHP. The
two soils found to be most prone to preferential flow were not well simulated for bromide
with both seasons mis-matched for Enborne and an over-estimation of concentrations from
the sandy clay loam Ludford soil during the first season. Patterns of bromide from the
Isleham soil were reasonably well simulated.
56
Figure 23
Comparison between the observed leaching of bromide through the five
SSLRC lysimeter soil types and MACRO_DB simulations
Cuckney
70
70
60
60
50
50
40
30
40
30
20
20
10
10
0
0
0
100
200
300
400
500
0
100
200
300
Time from first application (d)
Time from first application (d)
Enborne
Ludford
50
50
40
40
Bromide (mg/l)
Bromide (mg/l)
Sonning
80
Bromide (mg/l)
Bromide (mg/l)
80
30
20
10
400
500
400
500
30
20
10
0
0
0
100
200
300
400
500
0
Time from first application (d)
100
200
300
Time from first application (d)
Isleham
40
35
Bromide (mg/l)
30
25
20
15
10
5
0
0
100
200
300
400
500
Time from first application (d)
Lysimeter 1
Lysimeter 2
MACRO_DB
Total losses of isoproturon simulated by MACRO_DB (Table 9) demonstrated that the input
parameters selected made preferential flow relatively unimportant. The model simulated no
loss of isoproturon from four of the five soils whereas only the peaty Isleham soil was
observed to show no consistent leaching over the experimental period. However, for the
57
Cuckney soil where matrix flow can be expected to dominate, the total loss was overestimated by a factor of six because sorption in the micropore region was decreased by the
system giving a large value to FRACMAC. The pattern of concentrations of isoproturon in
leachate was also not well matched for Cuckney soil (Figure 24), particularly in the second
season when observed concentrations were greatly over-estimated. It can be concluded that
the use of the automatic techniques for parameter selection built into MACRO_DB did not
produce reliable simulations for the three intermediate soils where preferential flow was
found to have a significant impact upon pesticide leaching (Sonning, Ludford, Enborne). For
all three soils, the simulations did not place sufficient emphasis upon preferential flow.
Although the system was better able to select parameters suitable for simulating the sandy
Cuckney soil and the organic Isleham soil, the use of the system in its current form for a range
of intermediate soils cannot be recommended.
Figure 24
Comparison between observed concentrations of isoproturon from the
Cuckney lysimeter and those simulated by MACRO_DB
Cuckney
20
18
16
Isoproturon ( g/l)
14
12
10
8
6
4
2
0
0
100
200
300
400
500
Time from first application (d)
Lysimeter 1
4.3.5
Lysimeter 2
MACRO_DB
PLM - SSLRC lysimeters
There was felt to be insufficient guidance available to support an independent selection of the
percentage of fast mobile phase for the various soils, so this parameter was calibrated. The
model was first run against results for bromide leaching through the five soil types with the
percentage of fast mobile phase calibrated to optimise the fit to observed concentrations. The
calibrated values were then used for simulations of isoproturon movement through the
lysimeters. If necessary, a second calibration was carried out to improve the fit to observed
data for isoproturon. A relative weakness in PLM is that multiple solute applications cannot
be simulated and that a pre-run of the model to allow equilibration of soil hydrology is not
possible. Hence, PLM was re-started at the date of application in the second season whilst all
other models were run for the entire monitoring period. This might have introduced some
errors concerning leachate concentrations simulated by PLM, especially for the first date after
the second application (= 374 days).
Total flow through the five soils simulated is given in Table 10 on the basis of the percentage
of fast flow calibrated for bromide. Surprisingly, the flows simulated for isoproturon differed
from those for bromide by not more than 1.4 mm although the percentages of fast flow were
58
changed markedly for three of the soils (Table 11). Total flow through all soils was underestimated by PLM. To some extent, this can be attributed to the model assumption that
percolation of water does not occur before water contents for the whole soil profile are above
field capacity. However, simulated evapotranspiration is the dominant factor controlling
leaching through the lysimeters. The model should be used with pan evaporation data which
are then reduced to give potential evapotranspiration (PET) using an empirical pan factor
(0.7-1.0). As PLM was run with PET data, the pan factor should be set to 1.0. In fact, this
factor was set to 0.8, effectively reducing PET by 80%, but the model still over-estimated
evapotranspiration and under-estimated leaching. It would be possible to calibrate the pan
factor to better simulate total leaching, although the pan factor is included in the model
specifically to adjust pan evaporation.
Table 10
Comparison between observed flows, bromide and isoproturon loads over two
seasons from each of the SSLRC lysimeters and those simulated by PLM
(calibrated runs)
Soil
Flow
observed* simulated**
(mm)
(mm)
Bromide load
observed* simulated
(g/m2)
(g/m2)
Isoproturon load
observed* simulated
(mg/m2) (mg/m2)
Cuckney
Sonning
Ludford
Enborne
Isleham
474; 477
417
412
339
335; 356
11.36; 9.97
10.52
6.01; 6.33
3.44; 2.97
4.02; 3.94
0.36; 0.20
0.92
1.53; 4.94
2.77; 5.04
0; 0
345
329
360
273
281
10.51
6.03
4.96
5.10
4.62
0.24
1.09
5.01
2.99
0
* where available, values are given for replicate lysimeters, ** = based on % fast flow calibrated for bromide
Bromide leaching through two of the five soils (Cuckney and Ludford) was reasonably well
represented by PLM (Figure 25, Table 10). For these soils, the calibrated percentage of fast
mobile phase was zero, suggesting that preferential flow was not an important pathway for
leaching of this solute. The ‘best-fit’ percentage of fast mobile phase for the Sonning soil was
also zero, but in this soil, total bromide loss was under-estimated and the pattern of bromide
leaching in the second season was not well represented. For the Enborne soil, the optimum
value for the percentage of fast mobile phase was found to be 95% corresponding to the great
importance of preferential flow in this soil. However, on the basis of this value, total bromide
loads were still under-estimated and leaching over the second season was not well matched by
the model. For the Isleham soil, the fit to bromide results could only be improved by setting
the model to include an effect of anion exclusion.
59
Figure 25
Comparison between the observed leaching of bromide through the five
SSLRC lysimeter soil types and PLM simulations
Sonning
80
70
70
60
60
50
50
Bromide (mg/l)
Bromide (mg/l)
Cuckney
80
40
30
30
20
20
10
10
0
0
0
100
200
300
400
500
0
100
200
300
Time from first application (d)
Time from first application (d)
Enborne
Ludford
50
50
40
40
Bromide (mg/l)
Bromide (mg/l)
40
30
20
10
500
400
500
30
20
10
0
0
0
100
200
300
400
500
0
100
Time from first application (d)
200
300
Time from first application (d)
Isleham
50
40
Bromide (mg/l)
400
30
20
10
0
0
100
200
300
400
500
Time from first application (d)
Lysimeter 1
Lysimeter 2
60
PLM
Table 11
Percentage of fast mobile phase used to model the leaching behaviour of
bromide and isoproturon through the five SSLRC lysimeter soil types with
PLM calibrated to give the best fit to observed results
Soil type
Calibrated percentage of fast mobile
phase for bromide leaching
Calibrated percentage of fast mobile
phase for isoproturon leaching
Enborne
95
40
Cuckney
0
0
Sonning
0
72
Ludford
0
57
Isleham
0*
-
* Anion exclusion set to 75% of the water below wilting point
The percentage of fast mobile phase calibrated for bromide was not transferable to
isoproturon (Table 11). It was necessary to include fast flow into the isoproturon simulations
for three of the four soils for which the model was calibrated (Enborne, Sonning, Ludford).
Calibrations against isoproturon leaching through the Enborne soil gave an optimum value of
40% which was much less than that found for bromide (95%). The Cuckney soil was the only
one for which the best fit to measured concentrations in leachate was achieved without any
fast flow for both bromide and isoproturon. Total isoproturon loads from this soil were
matched closely by the model and patterns of leaching were reasonably well represented.
Simulated isoproturon losses from the Sonning soil agreed closely with those measured, but
the observed pattern of concentrations was not well matched. For both Enborne and Ludford
soils, PLM gave isoproturon losses similar to those from one of the replicate lysimeters
(Table 10). Timing of breakthrough was well simulated and maximum concentrations were
also well simulated in the second season (Figure 26). The Isleham soil was not calibrated as
there was no leaching of isoproturon.
61
Figure 26
PLM simulations of isoproturon leaching through all soils except Isleham
calibrated to the observed behaviour of isoproturon (no calibration required for
Cuckney)
Cuckney
Sonning
9
9
8
8
7
7
Isoproturon (µg/l)
10
Isoproturon (µg/l)
10
6
5
4
6
5
4
3
3
2
2
1
1
0
0
0
100
200
300
400
500
0
100
200
Time from first application (d)
Enborne
400
500
400
500
Ludford
110
110
100
100
90
90
80
80
70
70
Isoproturon (µg/l)
Isoproturon (µg/l)
300
Time from first application (d)
60
50
40
60
50
40
30
30
20
20
10
10
0
0
0
100
200
300
400
500
0
Time from first application (d)
100
200
300
Time from first application (d)
Lysimeter 1
Lysimeter 2
PLM
The best-fit percentage of fast mobile phase for modelling of isoproturon leaching increased
in the order Cuckney<Enborne<Ludford<Sonning. This order is slightly surprising as
patterns of leaching suggested that preferential flow was a more dominant process for the
Enborne and Ludford soils than for Sonning. This can partly be attributed to the fact that
within PLM the relative predominance of preferential flow is determined for any given soil by
a combination of the empirical parameter setting the percentage of fast mobile phase and the
maximum amount of mobile water (i.e. the air capacity). Thus, the larger value for
percentage of fast mobile water reported in Table 11 for the Sonning soil relative to the
Ludford soil is a function of the large air capacity of the Sonning soil and should not be
considered to indicate that preferential flow is more important in this soil than in the Ludford
soil. However, even if the relationship to air-capacity is considered, the small proportion of
fast mobile water for the Enborne lysimeter suggests that preferential flow is less dominant in
this than in the Sonning soil. This does not correspond to our findings on the importance of
preferential flow in these two soils. Soils with a relatively large air capacity (total porosity water content at field capacity) such as the Cuckney and Sonning series have a very large
potential for transmitting water via matrix flow and this fraction must be filled before
preferential flow is initiated. Thus, increasing the percentage of fast mobile phase will have
62
virtually no effect on simulations until a critical value is exceeded. At this point, a dramatic
increase in pesticide leaching is observed. The extreme sensitivity of isoproturon
concentrations for the Sonning soil in the range 70-73% fast flow is illustrated in Figure 27.
In soils with smaller air capacities (e.g. Ludford and Enborne series) this effect is somewhat
reduced and sensitivity occurs at a lower percentage of fast mobile phase. Figure 28 contrasts
for the Sonning and Enborne soils the effect of variation in the percentage of fast mobile
phase on the maximum concentration of isoproturon simulated by PLM at any time over the
two seasons of monitoring. The interaction between the proportion of fast mobile water and
the air capacity makes parameter estimation very difficult. The extreme sensitivity of model
output to changes in the percentage of fast mobile phase makes it impossible to establish an
appropriate value for this parameter and the use of PLM for intermediate soils is not
recommended. The finding, that parameters calibrated for bromide were not transferable to
simulations of isoproturon behaviour, suggests that use of PLM for such soils should not be
recommended even where a calibration step is possible.
Effect of variations in the percentage of fast mobile phase in the range 70-73%
on PLM simulations for leaching of isoproturon through the Sonning lysimeter
25
70%
72%
73%
20
Isoproturon (µg/l)
Figure 27
15
10
5
0
0
100
200
300
400
Time from first application (d)
63
500
Figure 28
Sensitivity analyses contrasting the effect of variations in the percentage of fast
mobile phase on the maximum concentration of isoproturon simulated by PLM
for the Sonning and Enborne lysimeters in each of the two seasons of
monitoring
900
Maximum concentration (µg/l)
800
first year
second year
700
600
500
400
300
200
Sonning
100
0
0
10
20
30
40
50
60
70
80
90
100
80
90
100
Percentage of fast mobile phase
900
Maximum concentration (µg/l)
800
first year
second year
700
600
500
400
300
200
Enborne
100
0
0
10
20
30
40
50
60
70
Percentage of fast mobile phase
4.3.6
SWAT - SSLRC lysimeters
SWAT is designed to predict lateral transport of water and solute to surface waters.
Therefore, this model is not appropriate to describe leaching through lysimeters and was not
applied to the SSLRC lysimeter data set.
4.3.7
Overview - SSLRC lysimeters
In considering the relative merits of models with or without preferential flow, it can be
generally stated that a description of preferential flow is not a prerequisite for simulating
sandy soils, particularly as current preferential flow models cannot describe either finger or
funnel flow. Furthermore, models without preferential flow cannot describe movement of
pesticides in heavy clay soils whereas preferential flow models offer improved simulation
with appropriate selection of input parameters and/or calibration. The SSLRC dataset gave
the chance to investigate the need for a description of preferential flow to accurately simulate
a range of intermediate soils overlying aquifers which are typical of arable use in England and
64
Wales. As such intermediate soils make up the biggest part of our arable resource, results
will have important implications, although it should be noted that regulatory modelling most
frequently concentrates on either very sandy or clay-rich soils. The assumptions built into
two of the models (CRACK-NP and SWAT) meant that they were not suitable for simulating
this dataset. As there was no guidance available on selecting the percentage of fast mobile
phase for PLM simulations in the various soil types, the model was evaluated following
calibration for this parameter. Values obtained with PLM are shown in parentheses in Tables
12-14 to distinguish them from the results of uncalibrated simulations with the other models.
As seen for other datasets, there were large differences in the total flow predicted by the four
models (Table 12), even though all simulations used the same data for potential
evapotranspiration. Relative amounts of flow simulated for the different soil types also varied
from model to model, although all models showed greater leaching from the Cuckney,
Sonning and Ludford lysimeters than from the Enborne and Isleham cores. Overall, MACRO
best simulated the range between maximum and minimum total flow from the various soil
types (98 mm compared to 130 mm observed). LEACHP (69 mm) and MACRO_DB
(50 mm) under-estimated the range suggesting that they were less able to simulate the
hydrological differences between this range of soils. PLM under-estimated total flow from all
lysimeters due to an over-estimation of evapotranspiration caused by the use of pan
evaporation data adjusted using an empirical pan factor (see Section 4.3.5). As previously
discussed, it would be possible to calibrate the pan factor to better simulate total leaching.
Table 12
Soil type
Cuckney
Sonning
Ludford
Enborne
Isleham
Comparison between observed total flow from the five soil types and that
simulated by the four models tested
Total flow over two winter wheat seasons (mm)
Mean observed
LEACHP
MACRO
MACRO_DB
476
416
451
434
417
395
421
424
412
399
454
435
339
347
366
384
346
368
353
397
PLM
(345)
(329)
(360)
(273)
(281)
LEACHP gave the best simulation of losses of bromide from the five soils although totals
were generally over-estimated (Table 13). This suggests that preferential flow was not an
important process for the transport of bromide and this was confirmed by calibrated
simulations with PLM where four of the five best-fit simulations had the percentage of fast
mobile phase set to zero. MACRO and MACRO_DB gave reasonable simulations of bromide
transport through the coarse-textured Cuckney and Sonning soils, but over-estimated total
losses from the remaining three soils by factors of up to two. This might have implications
for the use of MACRO to simulate leaching of exceptionally mobile pesticides.
65
Table 13
Comparison between observed total loss of bromide from the five soil types
and that simulated by the four models tested
Soil type
Total loss of bromide over two winter wheat seasons (g/m2)
Mean observed
LEACHP
MACRO
MACRO_DB
PLM
10.7
11.8
11.8
10.5
(10.5)
10.5
9.4
10.8
8.8
(6.0)
6.2
8.0
10.4
10.1
(5.0)
3.2
3.8
7.4
9.7
(5.1)
4.0
5.8
8.3
7.3
(4.6)
Cuckney
Sonning
Ludford
Enborne
Isleham
Despite the poor simulation of total flow, it was possible to calibrate PLM to accurately
simulate total losses of isoproturon from all five soils (Table 14) although patterns of
concentrations were not always well matched. Parameters calibrated for bromide leaching
were not transferable to simulations of isoproturon transport. A proportion of fast flow had to
be included in simulations for Sonning, Ludford and Enborne soils, demonstrating that
preferential flow was important for the transport of isoproturon. It is thus not surprising that
LEACHP was unable to simulate leaching of isoproturon through the Sonning, Enborne and
Ludford lysimeters (LEACHP did simulate a very small loss for the latter), although the large
over-estimation in the total loss from the sandy Cuckney soil was not expected. The
simulations generated using MACRO_DB were rather moderate with respect to preferential
flow and there was no improvement in results relative to those from LEACHP except for the
Cuckney soil where the loss was within a factor of six of that observed. Of the models tested
without calibration, MACRO was best able to simulate the observed leaching of isoproturon
through the five intermediate soils. The model gave reasonable estimates of the total loss of
isoproturon from all five soils with estimates within a factor of two for three of the soils and
within a factor of four for the remaining two soils. Although the model failed to simulate the
actual pattern of concentrations of isoproturon from one of the soils (Sonning), results suggest
a relatively high predictive ability for MACRO in a range of intermediate soils. It should
however be considered that the modellers involved were relatively experienced in selecting
parameters for MACRO.
Table 14
Soil type
Cuckney
Sonning
Ludford
Enborne
Isleham
Comparison between observed total loss of isoproturon from the five soil types
and that simulated by the four models tested
Total loss of isoproturon over two winter wheat seasons (mg/m2)
Mean observed
LEACHP
MACRO
MACRO_DB
PLM
0.28
5.05
1.00
1.74
(0.24)
0.92
0
0.52
0
(1.09)
3.24
0.04
4.91
0
(5.01)
3.91
0
1.33
0
(2.99)
0
0
0
0
(0)
66
4.4
Wytham
Drainflow (three events) and isoproturon concentrations and loads (two events) were used for
model evaluation. Soils information for the site was taken from a description made by
SSLRC in autumn 1993. Comprehensive weather data including potential evapotranspiration
estimated according to the Penman equation (Penman, 1963) was provided for March 1993 to
October 1994. This allowed a substantial period for model equilibration prior to application
(March 1994) for those models with that facility. Rainfall was provided at an hourly
resolution and comparisons were made between modelling with daily and hourly rainfall for
MACRO (the only one of the models evaluated which can use both). In general, isoproturon
half-life was set to the measured value (18.2 days at 15oC and 33% w/w) in topsoil. A Koc of
77 ml/g was calculated from the mean of two measured Kd values and the organic carbon
content in the topsoil (Appendix 4). With some models, additional simulations were carried
out using literature values of 30 days at 10oC and field capacity and 100 ml/g.
A significant problem was identified with the water balance at Wytham. Over the period 14
December 1993 - 30 April 1994, total rainfall at the site was 306 mm, surface runoff was 1.7
mm and drainflow was 76.4 mm. The estimated potential evapotranspiration over this period
was 133.4 mm, whilst MACRO estimated direct evaporation of intercepted rainfall from the
crop canopy to be 25.2 mm. Even assuming that actual evapotranspiration meets the potential
amount (MACRO predicted a reduction of 13 mm), the total water lost from the soil and crop
is 159 mm and the total accounted for at the site is 237 mm, leaving 69 mm of rainfall
unaccounted for. Assuming that all flow gauges at the site were functioning correctly, the
most likely explanation for this missing water is that it was leaving the site via a secondary
drainage system which was not monitored. The alternative explanation of slow seepage to
groundwater is not a possibility at this site. All six of the models tested support the secondary
drainage system explanation with a significant over-estimate of the volume of drainflow both
over the winter and for the short period of monitoring following application of isoproturon.
Simulations of drainflow have been kept for information, but this discrepancy should be
considered when judging the match between observed and simulated flow.
4.4.1
LEACHP - Wytham
The measured half-life for isoproturon of 18.2 days was used and corrected for temperature
and moisture effects. Koc was set to the experimental value of 77 ml/g. The simulation was
started on 01/10/93 to allow model equilibration prior to application in March 1994.
Figure 29 demonstrates the observed drainflow and the simulated amount of water leaving the
profile at 50 cm depth together with rainfall for a period of 30 days after application.
LEACHP simulated drainflow starting immediately after application with rate of flow roughly
proportional to rainfall intensity. In contrast, drainflow from the Wytham site was initiated
only by the most intense rainfall and occurred over very short periods. This discrepancy
between observed and simulated drainflow was seen to varying degrees for all of the models
used and resulted from the water not accounted for in the water balance for the site.
Maximum concentrations of isoproturon leaching to 50 cm depth did not exceed 0.003 µg/l
and were far below those observed (maximum 290 µg/l). As expected, the model was not
applicable to this very heavy clay soil where bypass flow is dominant.
67
Comparison between observed rates of drainflow from the Wytham site and
those simulated by LEACHP along with daily rainfall
1.4
0
1.2
5
1
10
0.8
15
0.6
20
observed
0.4
Rain (mm/day)
Drainage (mm/hour)
Figure 29
25
0.2
0
30
0
5
10
15
20
25
30
1.4
0
1.2
5
1.0
10
0.8
15
0.6
20
predicted
0.4
Rain (mm/day)
Drainage (mm/hour)
Time from application (d)
25
0.2
30
0.0
0
5
10
15
20
25
30
Time from application (d)
4.4.2
CRACK-NP - Wytham
The soil and experimental design at Wytham were comparable to those at Brimstone Farm.
Therefore, the Wytham data set was suitable to test the ability of CRACK-NP to describe a
situation very similar to that for which the model was demonstrated to be valid (Armstrong et
al., 1995a, b) In a first run, the input file for Brimstone Farm simulations provided with the
model was only slightly changed (application rate and date, pesticide half-life and Kd, crop
dates, drain depth and spacing) to calculate drainage and isoproturon concentrations at
Wytham.
With the input file from Brimstone Farm, no drainage was predicted to occur from the
Wytham site. As for Brimstone, this could be attributed to an over-estimation of
evapotranspiration which was estimated to be 63 mm between 12/03/94 and 10/04/94
although potential evapotranspiration was only 48.5 mm. For this dataset, the discrepancy
between actual and potential evapotranspiration was even greater than for Brimstone, because
the simulation period was several months after crop emergence. The assumption of a linear
increase of crop interception capacity from emergence (early November 1993) onwards lead
to a maximum possible storage of 2.7-3.4 mm rainfall in March/April 1994. This meant, that
the crop surface was wet over a considerable time within the simulation interval. As
evaporation from a wet canopy was 1.5 greater than potential evapotranspiration,
68
unreasonably large water losses from the crop were simulated. To overcome this problem,
attempts were made to modify the maximum crop interception capacity and the correction
factor for wet canopy evaporation. However, these changes destabilised the model.
Therefore, CRACK-NP was subsequently run assuming a bare soil to artificially decrease the
amount of evapotranspiration. In contrast to the previous run, the simulation was started 5½
months before application to allow equilibration of water contents (initial water contents were
set to default values) and depth of water table. These changes decreased evapotranspiration
to 44 mm (12/03-10/04/94).
Peaks in simulated drainflow for the run with bare soil agreed relatively well with that
observed (Figure 30). However, measured total drainage from 0 to 30 days after application
was over-estimated by a factor of eight due to flow simulated between events when none was
observed. In addition, CRACK-NP failed to predict significant drainflow during the first
event. Isoproturon concentrations measured over the first event were greatly under-estimated
because of the failure to simulate drainflow accurately, whereas those observed during the
third event after application (day 27/28) were very well matched (Figure 31). If the half-life
in topsoil was set to a literature value of 30 days at 8oC and field capacity together with a
literature Koc of 100 ml/g, simulated isoproturon concentrations were slightly higher
(maximum 114.3 µg/l compared to 94.2 µg/l for the experimental half-life and Koc).
Comparison between observed drainflow from the Wytham site and that
simulated by CRACK-NP (bare soil run) together with daily rainfall
0
0.9
0.8
5
0.7
10
0.6
0.5
15
0.4
20
0.3
Rain (mm/day)
Drainage (mm/hour)
Figure 30
0.2
25
0.1
30
0.0
0
5
10
15
20
25
30
Time from application (d)
observed drainage
CRACK-NP
69
rainfall
Figure 31
Comparison between observed concentrations of isoproturon in drainflow from
the Wytham site (first and third events after application only) and those
simulated by CRACK-NP (bare soil run ; DT50 = 18.2 days, Koc = 77 ml/g)
300
Isoproturon (µg/l)
250
200
150
100
50
0
0
5
10
15
20
25
30
Time from application (d)
observed
4.4.3
CRACK-NP
MACRO - Wytham
Hydraulic input parameters for MACRO were selected as set out in Section 4. The boundary
between micropores and macropores was set using expert judgement to 5 kPa. Initial water
content was set to establish drainage equilibrium (i.e. fully wetted but without initiating
drainflow). The simulation was started several months before application. Pesticide Koc and
half-life were set to measured values of 77 ml/g and 18.2 d at 15oC, respectively. The
proportion of sorption in the macropores was set to 1% for all simulations (see Section 4).
Rainfall was available on an hourly resolution for Wytham allowing a comparison to be made
between a simulation with hourly rainfall where intensity will vary each hour and one with
daily rainfall where intensity was set to a constant value of 2 mm/h (the default in the model).
Results for the simulation of drainflow using daily and hourly rainfall are shown in Figure 32.
Both simulations considerably over-estimated the amount of flow over the period due to
greater peak flows than those observed and flow simulated between events when none was
actually observed. The measured flow over the period shown was 1.8 mm, whereas that
predicted using daily and hourly rainfall was 18.4 and 16.0 mm, respectively. As discussed
above, a significant proportion of rainfall is not accounted for by the water balance measured
at Wytham and this makes evaluation of simulated drainflow impossible. MACRO does
allow water to seep from a saturated bottom boundary. This was used to approximate a
secondary drainage system at the site and an extremely good simulation of drainflow was then
achieved through calibration.
70
Figure 32
Comparison between observed drainflow from the Wytham site and that
simulated by MACRO based on either daily or hourly rainfall (daily rainfall
totals also shown)
1.8
0
1.5
5
1.2
10
0.9
15
0.6
20
0.3
25
0.0
Rain (mm/d)
Drainage (mm/h)
Observed
30
0
5
10
15
20
25
30
Time from application (d)
0
1.8
5
1.5
1.2
10
0.9
15
0.6
20
0.3
25
Rain (mm/d)
Drainage (mm/h)
Daily rainfall
30
0.0
0
5
10
15
20
25
30
1.8
0
1.5
5
Hourly rainfall
1.2
10
0.9
15
0.6
20
0.3
25
0.0
Rain (mm/d)
Drainage (mm/h)
Time from application (d)
30
0
5
10
15
20
25
30
Time from application (d)
observed drainage
MACRO
rainfall
The major difference between the simulations with daily and hourly rainfall was in the timing
of events. Whereas the simulation with daily values missed the timing of events by up to one
day, the simulation with hourly rainfall matched the timing exactly. Using daily rainfall, it
was possible to simulate the first event 20 days after application, whereas that was not the
case with hourly data. For both simulations, there were small events predicted 24 and 26 days
after application which were not observed. Figure 33 demonstrates that actual rainfall
71
intensities were seldom greater than the default intensity of 2 mm used by MACRO with daily
rainfall data. For this reason, peak flow rates were generally smaller using hourly data. There
was also greater flow between events with hourly data as the assumed intensity of 2 mm/h
used with daily data reduces the number of hours over which rain is received relative to that
observed. However, all of these differences are relatively small and it can be concluded that
the use of daily rainfall data and an appropriate intensity value does not greatly change the
simulation of flow.
Figure 33
Comparison between hourly rainfall at Wytham for the events following
application and the default intensity used by MACRO with daily rainfall data
3.5
3
Rainfall (mm/h)
2.5
2
1.5
1
0.5
30
29
28
27
26
25
24
23
22
21
20
19
18
0
Tim e from application (d)
Hourly intensity
Intensity used by MACRO
Figure 34 shows the two simulations of isoproturon concentrations. There was relatively little
difference between model runs using daily or hourly rainfall, although the latter gave slightly
smaller peak concentrations. Again it can be concluded that there is no significant adverse
effect from using daily rainfall data which is more widely available. For both simulations,
concentrations of isoproturon observed in the first event after application were greatly underestimated, whereas those in the third event were very well matched. This follows the same
pattern as demonstrated for CRACK-NP. There was no monitoring of isoproturon during the
second event after application.
72
Figure 34
Comparison between observed concentrations of isoproturon in drainflow from
the Wytham site (first and third events after application only) and those
simulated by MACRO using either daily or hourly rainfall (FRACMAC =0.01)
300
Isoproturon ( µg/l)
250
200
150
100
50
0
0
5
10
15
20
25
30
Time from application (d)
observed
MACRO (daily rainfall)
MACRO (hourly rainfall)
One of the input parameters to which MACRO simulations of pesticide transport are
particularly sensitive is the proportion of sorption sites in the macropore region
(FRACMAC). The default value is 0.1 implying that 10% of sorption sites are within the
macropore region and 90% in the micropores, but this value should be changed for a specific
soil. As the value of FRACMAC decreases, retention of pesticide in the macropores
decreases and any leaching through the macropores will increase. Smaller values for this
sensitive parameter will generally increase pesticide transport in soils where macropore flow
is a dominant process (see Figure 35). Conversely, smaller values will decrease pesticide
transport in soils where macropore flow is generally precluded as the amount of sorption in
the micropore domain is increased. Logically, FRACMAC might be set to the macroporosity
as a fraction of the total porosity so that sorption is set equal in each domain. In practice, this
results in values of FRACMAC which are rather large (generally 0.02-0.30) and which
artificially restrict movement of pesticide in the macropores. This observation might result
from the fact that transport in the macropore region is rather fast; as the model assumption of
instantaneous sorption cannot be expected to hold within the macropores, a reduction in
sorption capacity in this region might be used to compensate. A value of 0.01-0.04 (1-4% of
sorption sites in the macropores) is considered more realistic for many soils. In the absence
of a validated method for selecting FRACMAC, caution is advised and it should be noted that
the sensitivity of output to this parameter may limit the predictive capability of the model.
73
Figure 35
Effect of variation in the proportion of sorption sites in the macropore region
(FRACMAC) on the maximum concentration of isoproturon simulated by
MACRO for the third event after application at Wytham
Maximum predicted concentration ( µg/l)
200
180
160
140
120
100
80
60
40
20
0
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
Fraction of sorption sites in the macropore region
4.4.4
MACRO_DB - Wytham
As at Brimstone Farm, the simulation of drainflow at Wytham by MACRO_DB was less
influenced by macropore flow than that with MACRO. Again, the boundary between
micropores and macropores was shifted from field capacity towards saturation (1.9 and
2.7 kPa in the topsoil and subsoil, respectively). In addition, the proportion of sorption sites
in the macropores (2%) was double that estimated using expert judgement for the stand-alone
version of macro (1%). The hydrograph simulated by MACRO_DB (Figure 36) was similar
to that from MACRO using daily rainfall although less peaks in flow were simulated and
there was greater flow between events. As with the other models, total drainflow was greatly
over-estimated (13.2 mm compared to 1.8 mm). Because MACRO_DB did not simulate any
flow in the first event after application of isoproturon, the model did not simulate any
pesticide leaving the site (Figure 37). Patterns of concentrations in the third event after
application were well simulated by the model although the maximum concentration was
under-estimated by a factor of 1.8. In this respect, the stand-alone version of MACRO was
more accurate than the database version.
74
Figure 36
Comparison between observed drainflow from the Wytham site and that
simulated by MACRO_DB together with daily rainfall
1.8
0
1.5
5
1.2
10
0.9
15
0.6
20
0.3
25
0.0
Rain (mm/d)
Drainage (mm/h)
Observed
30
0
5
10
15
20
25
30
1.8
0
1.5
5
1.2
10
0.9
15
0.6
20
0.3
25
Rain (mm/d)
Drainage (mm/h)
Time from application (d)
30
0.0
0
5
10
15
20
25
30
Time from application (d)
observed drainage
Comparison between observed concentrations of isoproturon in drainflow from
the Wytham site (first and third events after application only) and those
simulated by MACRO_DB
300
250
Isoproturon ( µg/l)
Figure 37
rainfall
MACRO_DB
200
150
100
50
0
0
5
10
15
20
25
Time from application (d)
observed
75
MACRO_DB
30
4.4.5
PLM - Wytham
The percentage of fast mobile phase within PLM was set to 95% according to expert
judgement. The measured half-life of 18.2 days at 15oC and 33% gravimetric water content
was used for the top horizon (0-30 cm). Koc was set to the experimental value of 77 ml/g.
Water contents at the time of application were stated in the dataset to be 54% at 10 cm depth
and 51% at 30 cm depth. These were above total porosity and hence were not used for model
evaluation. Instead, the initial water content (i.e. the amount of water required to moisten the
profile to field capacity) was calibrated to achieve a simultaneous onset of simulated and
observed leaching. In Figure 38, results using the calibrated moisture deficit value of 10 mm
are shown.
In common with the other models, drainage was markedly over-estimated by PLM. The
simulated volume of drainage water over the period shown in Figure 38 was 14.5 mm whilst
the observed flow was 1.8 mm. Over the two events for which isoproturon concentrations in
drainflow were monitored, the simulated data matched the measured concentrations very well
(Figure 39). PLM was the only model to accurately simulate concentrations of isoproturon in
drainflow over the first event. In terms of maximum pesticide concentrations, it can be
concluded that PLM performed better for Wytham than any of the other models, although it
was necessary to calibrate the initial moisture deficit.
Comparison between measured drainflow from the Wytham site and that
simulated by PLM
7
PLM
6
Drainage (mm/day)
Figure 38
observed
5
4
3
2
1
0
0
4
8
12
16
20
Time from application (d)
76
24
28
Figure 39
Comparison between measured concentrations of isoproturon in drainflow
(first and third events after application only) from the Wytham site and those
simulated by PLM
400
Isoproturon (µg/l)
350
300
250
200
150
100
50
0
0
5
10
15
20
25
30
Time from application (d)
observed
4.4.6
PLM
SWAT - Wytham
All input parameters for SWAT including pesticide Koc and half-life were taken from
measured values apart from conductivity at field capacity which was derived from a pedotransfer function (Hollis & Woods, 1989). The measured half-life was a laboratory value at
15oC, so this was converted to a field value using the mean topsoil temperature over the
period after application and an approximation of the Arrhenius equation with a mean
literature value for the exponent (0.08). In common with the other models, SWAT overestimated the total flow over the main events after application (10.3 mm simulated by SWAT
compared to 1.8 mm observed). However, SWAT under-estimated the maximum
concentration of isoproturon in both of the events monitored. Observed maxima were 290
and 129 µg/l in the first and third event after application, respectively, whereas SWAT
predicted maxima of 18 and 11 µg/l, respectively. At this site, the more sophisticated models
clearly performed better for the third event after application, although results for the first
event were comparable.
4.4.7
Overview - Wytham
As set out in Section 4.4, there appears to be a loss of water from the Wytham site which is
not accounted for in the water balance. All of the models tested (including LEACHP) greatly
over-estimated the amount of drainflow over the monitoring period by factors of between five
and ten. Thus comparison of the models from a hydrological viewpoint is essentially
meaningless.
In terms of the maximum concentration of isoproturon observed in drainflow (Table 15),
PLM gave the best simulation over the two events monitored. This model was the only one to
predict the large concentrations observed during the first event after application, but the
maximum concentration during the third event was over-estimated by a factor of two. The
three deterministic models (CRACK-NP, MACRO and MACRO_DB) gave excellent
77
simulations of isoproturon losses for the third event after application, but under-estimated
concentrations during the first event by factors of thirty or greater. The simple model SWAT
was unable to simulate the observed maximum concentrations of isoproturon, with simulated
values under-estimating actual values by more than an order of magnitude for both events.
The discrepancy between observed and simulated drainflow totals means that comparisons of
total loadings of isoproturon over the two events are meaningless. Overall, the evaluation of
the models against data for Wytham again points to the difficulties in reliably simulating such
a highly-structured and heterogeneous clay soil (c.f. Section 4.1.7).
Table 15
Event
Comparison between maximum concentrations of isoproturon (µg/l) in the two
events monitored at Wytham and those simulated in uncalibrated runs with the
models
Observed*
CRACK-NP
MACRO
MACRO_DB
PLM
SWAT
First event
290
7
9
0
403
18
Third event
129
94
135
73
205
11
4.5
Overall evaluation
A common aspect of results for all of the models tested is their failure to accurately simulate
the water balance without calibration. The current exercise aimed to evaluate the regulatory
use of the models where site-specific calibration is not often possible. The temptation to
correct the simulation of the water balance before simulating pesticide behaviour was thus
ignored. As pointed out by Armstrong et al. (1996) amongst others, a correct simulation of
the water balance is a fundamental requirement for accurate simulation of pesticide transport.
Further work into the application of methods to estimate potential evapotranspiration (often
with only sparse weather data) is required. However, the models often gave very different
water balances starting from the same data for potential evapotranspiration and it is clear that
work on how the models manipulate input to simulate actual evapotranspiration is also
required.
4.5.1
Non-preferential flow benchmark (LEACHP)
Three of the four datasets used were for clay-rich soils where preferential flow in the form of
bypass flow has been shown to be dominant. As expected, LEACHP could not describe the
observed behaviour for these soils and all of the preferential flow models can be considered a
considerable improvement despite the discrepancies demonstrated between observed and
simulated results. The SSLRC lysimeter dataset monitored movement of bromide and
isoproturon through a range of representative intermediate arable soils. LEACHP generally
gave a better simulation of the observed leaching of bromide than the preferential flow
models. Preferential flow was not a dominant process for the transport of bromide and results
probably also reflect the greater ease of selecting input parameters where preferential flow is
not described. However, LEACHP was unable to reproduce the observed leaching of
isoproturon without calibration. This was surprisingly the case even for the sandy Cuckney
78
soil. Results call into question the predictive application for regulatory purposes of a
non-preferential flow model to a wide range of intermediate soils where preferential
flow may be a relatively minor component, but appears to still be a dominant process
for pesticide transport. The predictive ability of the preferential flow models for these
intermediate soils was variable, but results suggest that there may still be significant benefits
from using a model which includes preferential flow to simulate pesticide transport through
such soils.
4.5.2
CRACK-NP
CRACK-NP was found to be extremely unstable with even minor changes to the input file
provided by the authors causing the model to crash due to numerical instability. In some
cases, even changing parameters from those supplied to those suggested in the user manual
(e.g. canopy interception capacity, time interval) resulted in these problems. Particular
difficulties were encountered with parameters defining macroporosity, initial water contents,
water table and crop growth. The model crashed only occasionally when a single value was
changed. Instead, the instability was caused by interactions of several parameters. This
instability placed restrictions on the evaluation of the model as the input values selected by
the modeller did not always coincide with values with which the model would run. It should
be noted that the model authors are actively working to solve these problems with numerical
instability. For the two heavy clay datasets (Brimstone Farm and Wytham), CRACK-NP
performed very similarly to the stand-alone version of MACRO. This is not surprising given
the common ancestry of the two models. For Cockle Park, CRACK-NP greatly overestimated the observed movement of isoproturon to drains (more than two orders of
magnitude). It was concluded that either the assumptions of zero movement of water and
solute in the soil matrix or the assumption that preferential flow is generated at the soil
surface made the model unsuitable for application to soils where clay content is less than 5060%. Of most concern are results for trifluralin (Koc 4000 ml/g) at Cockle Park where
maximum simulated concentrations in drainflow (499 µg/l) were only slightly less than those
for isoproturon (Koc 100 ml/g). Total losses of trifluralin to drains were predicted to be
larger than those for isoproturon, presumably because of the former’s greater persistence.
These results were checked by simulating a hypothetical application of trifluralin at
Brimstone Farm and comparing to simulated behaviour of isoproturon. The very large overestimate of transport of the more strongly-sorbed compound was confirmed and appears to
relate to the assumption that pesticide sorption is limited to the soil aggregates and negligible
with the cracks. The model is not recommended for regulatory use.
4.5.3
MACRO
MACRO is the only one of the models evaluated which is known to have been used for
regulatory purposes. MACRO was applied to all of the datasets and showed considerable
variability in predictive ability. At Brimstone Farm, MACRO generally gave reasonable
simulations of total drainflow, but under-estimated maximum concentrations in 1993/94 and
over-estimated them in the remaining two seasons. On the other heavy clay soil at Wytham,
an excellent simulation was obtained for the third event after application although both flow
and pesticide concentrations for the first event were under-estimated. The simulation for
Cockle Park gave a good match to initial concentrations of isoproturon, but enhanced
79
movement late in the season relative to that observed suggests a problem with over-prediction
of pesticide movement in matrix flow over longer periods of time. Results for the
intermediate soils in the SSLRC lysimeter dataset are relatively encouraging with
uncalibrated simulations giving reasonable estimates (within a factor of four or better) of total
isoproturon losses from all five soils.
On balance, the findings of this evaluation suggest that MACRO should continue to be
the preferred preferential flow model for regulatory purposes. This is reinforced by the
user-friendliness of the model, the good documentation and the relatively large number of
model applications reported in the literature. Results for the relatively sandy soils in the
SSLRC lysimeters suggest that MACRO was equally or more accurate than the nonpreferential flow benchmark (LEACHP) for such soils. Robust parameter selection for
MACRO is still very difficult and output is particularly sensitive to changes in some of the
more problematic parameters (e.g. aggregate half-width, position of the boundary between
micropore and macropore domains, proportion of sorption sites within each). A useful
development for the stand-alone version of MACRO would be some general guidance on
realistic values for these parameters in a range of representative soils. Such guidance is only
likely to be developed as the number of applications of MACRO to field data increases. At
the present time, MACRO cannot be considered broadly validated and it should only be used
for regulatory purposes with great caution (it is worth noting that, in the opinion of the
authors, the same can be said for the various non-preferential flow models used for regulatory
purposes). Considerable previous experience with MACRO is required and a comprehensive
calibration step should be included wherever possible. However, results for Brimstone Farm
demonstrate that a set of input parameters giving acceptable simulations of pesticide fate in
one season may fail to do so in subsequent seasons.
4.5.4
MACRO_DB
Relative to the soils parameters used with the stand-alone version of MACRO, those
automatically selected within MACRO_DB reduced the emphasis on preferential flow for all
of the simulations. This was because the boundary between micropores and macropores was
set closer to saturation using pedotransfer functions than using expert judgement, even though
site-specific soils data were used within MACRO_DB rather than series average values from
SEISMIC. In addition, the proportion of sorption sites in the macropore region (FRACMAC)
was set to larger values by MACRO_DB than using expert judgement. As a result,
MACRO_DB simulated smaller concentrations of pesticide than MACRO for all model runs.
Generally, this decreased the accuracy of the pesticide simulation, although there were
exceptions where the smaller concentrations were closer to those observed (Brimstone Farm
in 1995/96). Comparison of observed and simulated hydrographs suggested that
MACRO_DB was placing too great an emphasis on matrix flow relative to preferential flow.
In a number of cases MACRO_DB failed to predict any loss of pesticide to drains from the
clay soils (1993/94 at Brimstone and first event at Wytham) when large concentrations were
actually observed, whilst losses from the clay loam soil at Cockle Park were greatly underestimated. For the intermediate soils studied in the SSLRC lysimeters, MACRO_DB gave no
improvement on the simulation of isoproturon leaching to 1.05-m depth relative to the nonpreferential flow benchmark, LEACHP. Whilst the philosophy behind MACRO_DB is
commendable, the consistent under-estimation of preferential flow relative to matrix
flow in a broad range of soils suggests that further work on parameter selection and
extensive testing against field data are required before the system can be considered
80
valid as a regulatory tool. Taken as a block, results from this study suggest that the best use
of preferential flow models to address any regulatory concerns over the potential impact of
preferential flow, may be through the development of standard modelling scenarios. The
MACRO_DB system provides an excellent framework to support any such development.
4.5.5
PLM
PLM can be considered a semi-empirical model as parameters describing the proportion of
fast mobile phase and the depth leached per time interval in the fast and slow regions cannot
be linked to soil properties. Calibration for at least the percentage of fast mobile phase is
required in a wide range of soils and this greatly limits any regulatory use of the model.
Additionally, results for the SSLRC lysimeters show that parameters calibrated to bromide
leaching were not transferable to simulations for isoproturon. However, in heavy clay soils
where almost all of the flow can be considered to be “fast”, there is the possibility to run PLM
without calibration. Using this approach, the model gave the best overall simulation of total
flow at Brimstone Farm, but considerably over-estimated concentrations of isoproturon in
flow. At Wytham, PLM gave the best simulation of maximum concentrations of isoproturon
over the two events monitored. For the clay loam soil at Cockle Park, simulations with
acceptable water flow over-estimated maximum concentrations of isoproturon and trifluralin
by three orders of magnitude and predictive work for all but the heaviest clays cannot be
recommended. Simulations for the intermediate soils in the SSLRC lysimeter dataset
revealed a serious weakness in the model. For such soils, PLM is extremely sensitive to
changes in the percentage of fast mobile phase over a very small range (Figures 27-28). The
breakpoint at which this sensitivity occurs is a function of the air capacity of the soil (total
porosity - water held at field capacity) with calibration giving larger values for percentage of
fast mobile phase in sandy and loamy soils where air capacity is large than in a clay or loam
soil where air capacity is smaller. The extreme sensitivity of PLM and the relationship of the
percentage of fast mobile phase to air capacity make selection of this parameter extremely
difficult in intermediate soils even where a calibration step is possible. The use of PLM in
such soils is not recommended. Model evaluation suggests that there may be potential for
the regulatory use of PLM without calibration in the heaviest clays where matrix flow is
insignificant, but use for intermediate soils is not recommended even after calibration.
4.5.6
SWAT
SWAT is an empirical model which predicts concentrations of pesticides moving to surface
waters and is thus not applicable to the SSLRC lysimeter dataset. A previous evaluation gave
promising results for movement of three pesticides in overland flow from a sandy loam soil
(Brown & Hollis, 1995). Of the three remaining datasets, SWAT gave the best overall
simulation of maximum concentrations for Brimstone Farm and Cockle Park, but the worst
for Wytham. The simplicity of the model makes it easy to apply predictively, but there is no
potential to improve simulations via a calibration step where data are available to permit this.
The output from the model is limited and only losses in fast flow immediately after rainfall
are considered, so the model is not suitable for very detailed simulations or higher tier risk
assessment. However, results suggest that the model may be suitable for regulatory
modelling at broad scales or screening levels. The conceptualisation of SWAT is rather
different from the other models which simulate movement of water and solute within the soil
profile. Preferential flow together with overland flow is described using the response of a
81
given soil type to rainfall events and the model is the only one which can be considered three
dimensional. Perhaps the most important conclusion from the results obtained with SWAT is
that modelling approaches which aggregate the spatial and temporal variability associated
with preferential flow up to broader scales appear promising. Whilst it is likely that most
models will continue to try to describe preferential flow at the profile scale, the development
of alternative approaches should also be considered.
4.5.7
Levels of predictive accuracy
It is rather dangerous to assign generalised levels of accuracy to a given model as these are
likely to vary widely for different simulations. Nevertheless, this is a key requirement for
regulators who have to evaluate modelling submissions and is an important component in any
attempt to build confidence in the credibility of modelling. Levels of predictive accuracy can
be derived for any of the models from the data contained in this report, but they are
summarised in Table 16 for MACRO as this is recommended as the preferred model for
regulatory use. A number of factors should be considered:
• the number of datasets was limited and concentrated almost exclusively on one
compound (isoproturon);
• the datasets were of a generally high quality with much information available for
parameter selection;
• the modellers involved were relatively experienced with MACRO.
Table 16
Levels of predictive accuracy for uncalibrated simulations of the four datasets
with MACRO (all values are predicted values as a factor of the observed;
maximum concentrations are for the whole simulation and take no account of
timing of maximum)
Dataset
Total flow
Maximum pesticide
concentration
Total loss of
pesticide
SSLRC lysimeters
0.95-1.10
0.26-1.84
0.34-3.57
Cockle Park*
0.81
3.1
10.9
Brimstone Farm
0.20-1.12
0.15-91.4
-
Wytham
-
0.47
-
* Isoproturon only - no losses of trifluralin were simulated although consistent small losses were observed
Predictive ability was greatest for the longer simulations and the coarser-textured soils. Thus
for simulations of 1-2 seasons, simulated flow was within 20% of that observed, whereas
there was far greater inaccuracy for single events at Brimstone where antecedent moisture
status was critical. For all five of the soils in the SSLRC lysimeter experiment, simulated
values for both maximum pesticide concentration and total loss of pesticide in leachate were
within a factor of four of those observed. On the drained clay loam at Cockle Park, the total
loss of isoproturon was over-estimated by an order of magnitude although the maximum
82
simulated concentration only over-estimated that observed by a factor of three. As has
already been noted, variability and inaccuracy was greatest for the short-term simulations on
the two clay sites. Maximum concentrations of isoproturon at Brimstone in successive
seasons were under-estimated by a factor of seven and then over-estimated by factors of nine
and ninety-one. Although the maximum concentration of isoproturon observed at Wytham
was only under-predicted by a factor of two, MACRO simulated that it would occur in the
third event after application rather than the first, the importance of which was greatly underestimated.
5
REGULATORY IMPLICATIONS
a) There is evidence that preferential flow may be an important process for pesticide
transport through a wide range of soils. Comparisons with the benchmark model
(LEACHP) show that, if correctly applied, preferential flow models improve our ability to
simulate pesticide fate in both clays and a range of intermediate soils. However, results
of this evaluation suggest that the predictive ability of preferential flow models is still
patchy with inaccuracy generally increasing for more clay-rich soils.
b) Accurate simulation of pesticide fate (by any model) depends upon a reliable simulation
of the water balance which is in turn hindered by weakness in estimating
evapotranspiration. Further work is required on the application of methods to estimate
potential evapotranspiration from limited weather data and on the methods used by
models to manipulate input to simulate actual evapotranspiration. Applications of models
to datasets with high quality weather data and a water balance (i.e. either lysimeters or
impermeable drained soils) would allow methods for estimating potential
evapotranspiration to be compared.
c) Conclusions from the evaluation for each model are given in detail in Section 4.5. They
can be summarised as:
CRACK-NP
Not recommended for regulatory use
MACRO
The preferred model for regulatory use, but see points d and e below.
MACRO_DB
Not recommended for regulatory use
PLM
Potential for use predictively on heavy clays with negligible matrix
flow. Not recommended for lighter soils.
SWAT
May have regulatory applications at broad scales or screening levels.
d) Robust parameter selection for preferential flow models is still very difficult with output
often particularly sensitive to changes in the more problematic parameters. Considerable
previous experience with the model of choice is required and a comprehensive calibration
step should be included wherever possible. However, results for Brimstone Farm
demonstrate that a set of input parameters giving acceptable simulations of pesticide fate
in one season may fail to do so in subsequent seasons.
e) Where genuine regulatory concerns exist over the potential impact of preferential flow on
pesticide transport through soil, these may be best addressed through the development of
standard modelling scenarios which could be incorporated into systems such as
83
MACRO_DB. This would overcome current difficulties with selection of input
parameters for preferential flow models. The current programme of work within FOCUS
will deliver standard modelling scenarios for leaching and movement to surface waters
during 1998 and preferential flow may be an important mechanism for at least some of
these scenarios.
f) The philosophy behind MACRO_DB is excellent in putting forward automatic
procedures for parameter selection from readily-available data and restricting the
potential for subjectivity in the modelling process. However, further work on parameter
selection and extensive testing against field data are required before the system can be
considered valid as a regulatory tool.
g) For the datasets based on clay soils, the detailed mechanistic models did not significantly
out-perform two simple models (PLM and SWAT) which adopt semi-empirical
approaches to describing the aggregated effects of preferential flow. Given the scarcity
of European data for regulatory modelling, further development of such simple
approaches seems desirable.
6
CONCLUSIONS
Preferential flow appears to be an important process for pesticide transport through a wide
range of soils including both clays (Harris et al., 1994; Johnson et al., 1994; Brown et al.,
1995a) and intermediate soils (Flury et al., 1995; Aderhold & Nordmeyer, 1995; Brown et al.,
1997). The development of preferential flow models over the last 5-10 years is an important
advance which improves our ability to simulate the fate of pesticides in soil. Results
presented in this report for a wide range of soils show considerable promise for some of the
preferential flow models, but there are still some significant problems with selection of input
parameters which raise questions over the predictive use of such models for regulatory
purposes. On the other hand, it is clear that use of models which do not simulate preferential
flow is also questionable for all but the coarsest soils.
The MACRO model described with a degree of accuracy leaching of pesticides through a
wide range of soils and this is proposed as the preferred preferential flow model for regulatory
use. Predictive ability was better for a clay loam and a range of intermediate soils than for
two heavy clays where there are inherent difficulties in predicting observed behaviour
because of the extreme spatial and temporal heterogeneity in their structure. The
MACRO_DB system is a useful conceptual development in allowing input parameters for this
complex model to be derived from basic soil properties and eliminating much of the
subjectivity from the modelling process. However, evaluation results suggest that more work
and perhaps changes to the system will be required before output from MACRO_DB can be
relied upon for regulatory purposes. The development of standard modelling scenarios may
be the best way to use preferential flow models to address any regulatory concerns over the
potential impact of preferential flow and the MACRO_DB system provides an excellent
framework to support any such development. The simpler approaches adopted by PLM and
SWAT gave results which were not significantly worse than those from the mechanistic
models for the clay soils. Further development of models which aggregate preferential flow
into broad descriptions rather than attempting to simulate the process in detail seems
desirable, particularly given the sparcity of European data for regulatory modelling.
84
FUTURE WORK
As an extension to this programme of work, SSLRC are undertaking a detailed investigation
into the sub-routines and inherent assumptions of the various models in relation to the
different simulations obtained in the evaluation. A number of generic issues associated with
preferential flow modelling are also being considered. The results of this work will be
available as a written report by the end of October 1998.
ACKNOWLEDGEMENTS
This work was funded within the Pesticides Research Programme of the Ministry of
Agriculture, Fisheries and Food. The co-operation of all the organisations who have allowed
their data to be used for this project is gratefully acknowledged as follows:
Brimstone Farm:
Cockle Park
Wytham
Brimstone Steering Group, ADAS, IACR-Rothamsted;
ADAS, University of Newcastle;
Institute of Hydrology, Horticulture Research International.
85
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89
APPENDIX 1: Experimental details for the Brimstone site
Exp. Title:
PESTICIDE RESIDUES IN WATER - HYDROLOGICAL STUDIES IN
BRIMSTONE FARM III
Summary:
Isoproturon was applied to winter cereals in three successive seasons (1993/94,
1994/95, 1995/96) at four drained plots at Brimstone Farm, Oxfordshire. The
soil is a heavy clay loam of the Denchworth series with a thick relatively
impermeable subsoil. Drainflow and isoproturon concentrations were
monitored during the first two key rainfall events of each season.
Duration:
1993-1996
Type of exp.: Plot lysimeter (1900 m2 plot)
Site:
Elevation:
Geographical location:
Pedological description:
Current land use:
Irrigation:
Drainage:
100-106 m
Brimstone Farm, Oxfordshire, Grid Reference 248 947
Pelo-stagnogley of the Denchworth series, 2% slope
Winter cereals
No irrigation
Pipe drains at 0.9 m depth with permeable backfill to
within 0.35 m, secondary drainage at 55 cm depth
consisting of conventional moles 2 m apart
Experiment:
Application:
02/11/93 isoproturon at 2.438 kg a.i./ha,
17/11/94 isoproturon at 2.5 kg a.i./ha,
30/10/95 isoproturon at 0.25 kg a.i./ha
Sampling:
Water samples taken from the first two key rainfall events
after pesticide application, isoproturon concentrations
(µg/l) and drainflow (mm/hr) available as point values
(maximum nine points per event) for:
plot 5
plot 9
plot 15
plot 20
13/11/93-14/11/93
07/12/93-08/12/93
13/11/93-15/11/93
07/12/93-08/12/93
13/11/93-14/11/93
08/12/93-09/12/93
13/11/93-14/11/93
08/12/93-09/12/93
08/12/94-09/12/94
26/12/94-29/12/94
08/12/94-09/12/94
26/12/94-29/12/94
08/12/94-09/12/94
26/12/94-29/12/94
08/12/94
27/12/94-29/12/94
19/12/95-23/12/95
06/01/96-10/01/96
19/12/95-23/12/95
07/01/96-09/01/96
20/12/95-23/12/95
07/01/96-09/01/96
19/12/95-23/12/95
08/01/96-09/01/96
Soil cultivations:
1993: site tined, power harrowed and rolled in late
September/early October, additional cultivation to prepare
seedbed
90
no information available for the other two seasons
Crop:
winter wheat sown on 21/10/93
winter cereal sown on 27/10/94, harvested on 07/08/95
winter oat sown on 19/10/95, harvested 19/08/96
Weather:
daily rainfall, minimum, maximum temperature
01/09/93-06/04/94
14/09/94-23/01/95
01/09/95-30/03/96
Soil:
0-24 cm
OC
%
3.6
pH (H2O)
7.6
Sand
%
10.5
Silt
%
29.5
Clay
%
60.0
3
bulk density
g/cm
1.00
total porosity
% vol.
60.8
water @ 0 kPa
% vol.
56.8
water @ 5 kPa
% vol.
55.2
water @ 10 k Pa
% vol.
54.6
water @ 40 kPa
% vol.
48.4
water @ 200 kPa
% vol.
44.3
water @ 1500 kPa
% vol.
37.4
(Graham Beard, SSLRC, personal communication)
Chemical:
Isoproturon (IPU)
Kd 2.9 ml/g
half-life 75 days at 10oC, 80% FC
91
24-52 cm
1.1
8.0
10.5
25.0
64.5
1.18
55.4
48.5
46.1
45.6
43.8
41.3
33.5
52-68 cm
0.9
8.2
5.6
21.4
73.0
1.22
53.9
52.6
51.2
50.6
48.2
46.5
38.0
APPENDIX 2: Experimental details for the Cockle Park site
Exp Title:
DEGRADATION AND MOVEMENT OF TWO PESTICIDES IN PLOT
LYSIMETERS AT COCKLE PARK
Summary:
Isoproturon and trifluralin were applied to a winter wheat crop in two
successive seasons of which the second season was chosen for model
evaluation. Soil samples were taken to 90 cm depth with 15 cm intervals
approximately every 3-4 weeks. Surface layer flow (N.B. this is flow through
the top 30 cm of the soil profile and not exclusively overland flow) and mole
drainflow were monitored and subsamples of flow were collected and analysed
for pesticides in the aqueous phase and sorbed to sediment.
Duration:
01.10.89 - 30.09.91
Type of exp.: Plot lysimeter
Site:
Size: Three plots of 0.25 ha each (25 x 100 m)
Altitude: 80 m above sea
Latitude: 55.2o North
Geographical location: 6 km north of Morpeth, Northumberland.
Pedological/geological description: Clay loam of the Dunkeswick series, a
pelo-stagnogley in glacial till derived mainly from Carboniferous shale.
2% slope.
Depth of perched water table (cm): Variable, but within the top 0-100 cm;
water table can rise almost to the surface in absence of drainage, but generally
kept below 30-40 cm by mole-drainage system.
Current land-use: Taken out of permanent pasture in October 1989; currently
cereal rotation with wheat and barley.
Irrigation:
No irrigation
Drainage:
Mole channels at 50 cm depth and 1.8 m apart and collector drains with gravel
to within 30 cm of the surface laid across the slope at 40 m intervals.
Surface layer flow collected by interceptor drains to a depth of 30 cm at the
bottom of the slope.
Experiment: Application details:
13/11/90 isoproturon at 2.50 kg a.i./ha (Hytane 500 FW 500 g/l SC)
"
trifluralin at 0.96 kg a.i./ha (Treflan 480 g/l EC)
Application method/incorporation: surface spray with no incorporation using a
tractor-mounted sprayer. Approximately 300 l water/ha.
92
Sampling: Soil samples taken to 90 cm divided into 6 layers of 15 cm every 16 weeks according to time of year; subsamples of surface-layer flow and
drainflow taken according to rate of flow.
Soil cultivations:
11/10/90 - site ploughed to 30 cm depth
Crop:
Winter wheat:
12/10/90 winter wheat (var. Mercia) drilled at a rate of 200 kg/ha;
04/03/91 GS 15,22; 10/04/91 GS 16,23; 09/05/91 GS 31-32;
22/05/91 GS 33; 13/06/91 GS 47;
Crop harvested on 13/09/91;
17/09/91 stubble burnt;
Weather:
Daily minimum/maximum temperature and rainfall available in an ASCII file.
Soil:
Field capacity at 5 kPa: 0.396 g/g
Permanent wilting point: Not determined
Depth
0-15
15-30
30-45
45-60
60-75
75-90
Corg (%)
3.27
2.48
1.10
0.94
0.89
0.79
pH
5.80
6.39
7.08
7.22
7.03
7.29
At 60 cm depth: Sand = 28.3%;
Silt = 44.1%;
Bulk density: 1.12 kg/l at 10 cm depth;
Saturated conductivity:
0 cm depth
20 cm depth
40 cm depth
60 cm depth
80 cm depth
Clay = 37.55;
1.50 kg/l at 60 cm depth;
2.2 x 10-7 m/s
5.7 x 10-7 m/s
1.8 x 10-8 m/s
8.0 x 10-9 m/s
3.0 x 10-9 m/s
Biomass: 44.3 mg nitrogen/kg
depth
cm
0-27
27-46
49-77
Sand
%
47
44
26
Silt
%
31
32
36
Clay
%
22
24
37
(Graham Beard, SSLRC, personal communication)
93
77-129
31
37
32
depth
cm
20
water @ 5 kPa
% vol.
40.5
water @ 10 k Pa
% vol.
36.5
water @ 40 kPa
% vol.
29.3
water @ 200 kPa % vol.
23.1
water @ 1500 kPa % vol.
18.1
(SEISMIC data for Dunkeswick series)
Chemicals:
50
33.6
30.4
24.8
20.0
16.1
70
37.6
34.9
29.9
25.3
21.5
150
35.3
32.9
28.2
24.0
24.0
Water solubility:
Isoproturon
55 mg/l
Trifluralin
<1 mg/l
Vapour pressure:
Isoproturon 2.5 x 10-8 mm Hg
Trifluralin
1.1 x 10-4 mm Hg
Adsorption properties: Determined in topsoil (5-12 cm; Corg = 3.8%; pH =5.3)
and in subsoil (40-50 cm; Corg = 0.7%; pH = 6.4)
Soil:water = 1:2
Pesticide
Isoproturon
Trifluralin
Topsoil
Kfr
1/n
0.99
0.90
224
1.01
Subsoil
Kfr
1/n
0.34
0.80
20.3
0.94
Half-life (d):
1) Determined in laboratory at 24oC and 23.1% moisture content using topsoil
sample (5-12 cm; Corg = 3.8%; pH =5.3)
Isoproturon 31 d;
Trifluralin 289 d;
2) Determined from field residue data
Isoproturon 35 d
Trifluralin 180 d
94
APPENDIX 3: Experimental details for the SSLRC lysimeter experiment
Exp. Title:
PESTICIDE MOBILITY: LYSIMETER STUDY TO VALIDATE THE
RELATIVE LEACHING POTENTIAL OF UK SOILS
Summary:
Isoproturon and a bromide tracer were applied in autumn 1994 and 1995 to
replicate undisturbed soil cores buried in the ground at the SSLRC lysismeter
station at Silsoe, Bedfordshire and cropped with winter wheat. The lysimeters
were from five sites with soil types representative of one of the three High and
two Intermediate leaching potential classes identified in the Environment
Agency’s groundwater protection policy (NRA, 1992). Leaching was
monitored and flow subsamples were analysed for isoproturon and bromide
concentrations. At the end of the experiment, the cores were irrigated with a
dye to stain the major flow pathways, then excavated and residues of
isoproturon in soil subsamples were determined.
Duration:
April 1994-May 1996
Type of exp.: Lysimeter study (replicate lysimeters 105 cm in length and 80 cm in diameter)
Site:
Elevation:
Geographical location:
Latitude:
Irrigation:
Experiment:
Application:
60 m
Soil Survey and Land Research Centre, Silsoe,
Bedfordshire, Grid reference: TL 079352
51.9oN
No irrigation
18/11/94 isoproturon at 2.5 kg a.i. /ha
bromide at 67 kg/ha (100 kg KBr/ha)
30/10/95 isoproturon at 2.5 kg a.i. /ha
bromide at 67 kg/ha (100 kg KBr/ha)
Sampling:
Subsamples of leachate were taken every 1-4 weeks.
During the second season, flow was monitored at an
hourly resolution. At the end of the experiment, residues
of isoproturon in the soil profiles were determined.
Crop:
Winter wheat (var. Riband)
Sown on 07/11/94, harvested on 27/11/95
Sown on 11/10/95, harvested on 05/08/96
Weather:
Hourly rainfall, air temperature, wind speed, relative
humidity, air pressure and radiation
Daily rainfall, minimum, maximum temperature
95
Soils: Leaching potential
High 1
High 2
High 3
Intermediate 1
Intermediate 2
Soil Series
Enborne
Cuckney
Sonning
Ludford
Isleham
Description
cracking clay soil in alluvium
unstructured, free-draining sand
free-draining shallow loam over gravel
deep, weakly-structured loam
shallow peat over free-draining sand.
Topsoil
Cuckney
Sonning
Ludford
Enborne
Isleham
Layer depth (cm)
30
31
28
20
36
% Organic carbon
pH in CaCl2
CaCO3 eq (g/kg)
Bulk density (g/cm3)
0.7
6.6
2.7
1.51
1.0
6.4
2.1
1.57
1.0
6.9
1.79
4.0
7.0
2.0
1.12
28.9
7.3
0.69
Total porosity
Air-capacity
Water @ 5 kPa
Water @ 40 kPa
Water @ 200 kPa
Water @ 1500 kPa
40.72
28.42
14.62
8.66
5.80
4.38
38.25
14.51
23.74
15.14
11.20
-
29.98
2.29
27.69
21.76
20.11
18.47
56.19
3.88
52.31
41.64
40.59
21.49
72.91
22.60
50.31
39.64
32.10
26.85
Texture*
%Sand
%Silt
%Clay
%Stones
S
91.4
3.4
5.2
4.3
SL
66.6
22.7
10.7
15.6
SCL
58.5
23.0
18.5
-
CL
44.6
25.0
30.4
1.1
LP
39.4
46.6
14.0
-
96
Subsoil 1
Cuckney
Sonning
Ludford
Enborne
Isleham
Layer depth (cm)
26
32
33
26
7
% Organic carbon
pH in CaCl2
CaCO3 eq (g/kg)
Bulk density (g/cm3)
<0.05
6.3
1.53
1.4
6.3
1.51
0.3
5.3
1.66
1.6
7.2
1.18
21.5
6.5
0.55
Total porosity
Air-capacity
Water @ 5 kPa
Water @ 40 kPa
Water @ 200 kPa
Water @ 1500 kPa
42.28
38.20
5.78
3.21
1.60
1.26
43.14
25.59
17.55
13.40
8.48
8.76
37.42
12.78
24.64
16.05
15.16
14.00
55.66
7.32
48.34
38.26
36.65
26.25
79.07
27.73
51.34
40.37
38.26
27.09
Texture*
%Sand
%Silt
%Clay
%Stones
S
97.8
0.1
2.1
0.0
SL
81.9
6.6
11.5
31.4
SL
78.4
4.6
17.0
-
CL
42.4
25.7
31.9
1.5
SP
51.2
39.4
9.4
-
Subsoil 2
Cuckney
Sonning
Ludford
Enborne
Isleham
Layer depth (cm)
54
37
24
35
21
% Organic carbon
pH in CaCl2
CaCO3 eq (g/kg)
Bulk density (g/cm3)
0.1
5.6
1.60
1.1
6.3
1.42
0.3
4.8
1.59
0.4
7.2
1.60
0.5
6.0
1.50
Total porosity
Air-capacity
Water @ 5 kPa
Water @ 40 kPa
Water @ 200 kPa
Water @ 1500 kPa
39.78
31.65
11.44
6.64
3.92
3.40
46.28
29.39
18.99
13.88
7.16
6.03
40.03
21.89
18.14
10.73
10.13
8.12
39.73
12.77
26.96
15.72
9.93
6.96
43.45
31.50
16.11
7.16
3.51
2.38
Texture*
%Sand
%Silt
%Clay
%Stones
S
95.2
0.5
4.3
0.0
SL/LS
82.2
3.5
14.3
46.2
SL
82.0
3.1
14.9
-
LS
81.2
11.1
7.7
23.0
S
95.1
3.3
1.6
-
97
Subsoil 3
Cuckney
Sonning
Ludford
Enborne
Isleham
Layer depth (cm)
-
10
37
29
8
% Organic carbon
pH in CaCl2
CaCO3 eq (g/kg)
Bulk density (g/cm3)
-
0.3
7.7
166.8
1.33
0.2
5.0
1.54
3.3
6.1
1.4
-
0.6
4.0
1.54
Total porosity
Air-capacity
Water @ 5 kPa
Water @ 40 kPa
Water @ 200 kPa
Water @ 1500 kPa
-
49.84
37.59
13.22
9.29
4.43
3.81
42.02
28.93
15.42
9.50
7.82
7.43
-
42.05
24.73
21.63
10.33
7.40
5.41
Texture*
%Sand
%Silt
%Clay
%Stones
-
S
93.4
3.4
3.2
53.4
LS
84.4
2.9
12.7
-
ZCL
12.6
55.2
32.2
0.0
S
93.1
4.2
2.7
-
Subsoil 4
Cuckney
Sonning
Ludford
Enborne
Isleham
Layer depth (cm)
-
-
-
-
28
% Organic carbon
pH in CaCl2
CaCO3 eq (g/kg)
Bulk density (g/cm3)
-
-
-
-
0.3
4.4
1.52
Total porosity
Air-capacity
Water @ 5 kPa
Water @ 40 kPa
Water @ 200 kPa
Water @ 1500 kPa
-
-
-
-
42.49
28.77
19.84
7.51
4.75
3.27
Texture*
%Sand
%Silt
%Clay
%Stones
-
-
-
-
S
94.2
3.5
2.3
-
* Textural abbreviations: S = sand; LS = loamy sand; SL = sandy loam; SCL = sandy clay
loam; CL = clay loam; ZCL = silty clay loam; LP = loamy peat; SP = sandy peat.
98
APPENDIX 4: Experimental details for the Wytham site
Exp. Title:
WYTHAM EXPERIMENT: FATE AND BEHAVIOUR OF PESTICIDES IN
STRUCTURED CLAY SOILS
Summary:
Isoproturon was applied to a winter barley crop at a mole-drained clay site in
spring 1994. The site is characterised by marked differences between the A
horizon (0-30 cm) and the B horizon (30-120 cm) with hydrological response
to rainfall and drying being resticted to the A horizon and negligible in the B
horizon. A seasonal perched water table is found in the A horizon.
Soil samples were taken to 2 cm depth. Over two events, isoproturon
concentration in drainflow, interlayer flow and occasionally in overland flow
together with the respective flow rates were monitored at a 5-min to 30-min
resolution. In addition, hourly drainflow, tensiometer, capacitance probe and
soil temperature data were recorded for an extended period.
Duration:
26/08/93-29/07/94
Type of exp.: Field plot (600 m2 plot)
Site:
Elevation:
Geographical location:
Latitude:
Pedological description:
Current land use:
Irrigation:
Water collecting constructions:
Experiment:
Application:
Sampling:
76 m
Oxford University Farm, Wytham, Oxforshire
Grid Reference SP46660931
51.7oN
Clay of the Denchworth series, calcareous variant, 2°
convex slope
Arable (3 years of winter cereals, 1 year of oilseed rape)
No irrigation
Field drains at 80 cm depth with mole drains at 50 cm
depth and 3 m apart.
Gulley containing aggregate and backfilled with soil at
30 cm depth to collect lateral interlayer flow
Gulley to 5 cm depth to collect overland flow
12/03/94 isoproturon at 0.9 kg a.i./ha (Arelon)
Soil samples every week, 2 cm depth
Flow subsamples, triggered by flows > 0.054 l/s for drain
flow and 0.023 l/s for lateral interlayer flow
99
variable
soil temperature (°C)
water content (%)
water content (%)
water tension (kPa)
drainflow (mm/h)
IPU soil (mg/kg)
interval
22/11/93-23/06/94
22/11/93-25/05/94
22/11/93-23/06/94
22/11/93-23/06/94
18/11/93-23/06/94
12/03/94-23/06/94
depth (cm)
0, 10, 30
10
30
10, 30, 50, 75, 100
1st event after application:
IPU drain (µg/l)
drain flow rate (mm/h)
IPU lateral interflow (µg/l)
lateral interfl. flow rate (mm/h)
31/03/94-01/04/94
31/03/94-01/04/94
31/03/94-01/04/94
31/03/94-01/04/94
30 min
5 min
30 min
10 min
3rd event after application:
IPU drain (µg/l)
drain flow rate (mm/h)
IPU lateral interflow (µg/l)
lateral interfl. flow rate (mm/h)
IPU overland flow (µg/l)
overland flow flow rate (mm/h)
08/04/94-09/04/94
08/04/94
08/04/94-09/04/94
08/04/94-09/04/94
08/04/94
08/04/94
30 min
5 min
30 min
10 min
5/10 min
5 min
2
resolution
1h
1h
1h
1h
1h
1 week
Soil cultivations:
Straw from previous winter wheat crop chopped and
incorporated by ploughing and power harrow (Roterra) in
September 1993
Crop:
Barley var. ‘Fighter’, sown on 19/10/93, emergence from
04/11/93 onwards
Date
max. shoot length (cm)
max. root length (cm)
09/11/93
4.60
7.5
22/11/93
7.78
9.48
07/12/93
8.54
9.58
27/01/94
13.36
11.0
17/03/94
21.49
15.21
25/04/94
30.20
22.0
05/05/94
38.0
29.0
16/05/94
58.0
34.0
27/05/94
70.0
36.0
16/06/94
95.0
37.0
26/06/94
95.0
37.0
07/07/94
95.0
33.0
25/07/94
90.0
30.0
*=System devised by Slater & Goode, 1967
Weather:
100
growth stage*
1
2
2
3
3
4
5
6
7
7-8.1
8.1
8.2-8.3
8.4
variable
dry bulb temperature (°C)
max temperature (°C)
min temperature (°C)
solar radiation
net radiation
wet bulb temperature (°C)
dry bulb temperature (°C)
wind speed
wind direction
rainfall (mm)
albedo sky
albedo ground
soil temperature 1 cm (°C)
soil temperature 30 cm (°C)
water potential
heat budget
aero term
potential evaporation
interval
04/03/93-04/10/94
04/03/93-04/10/94
04/03/93-04/10/94
03/03/93-04/10/94
03/03/93-04/10/94
03/03/93-04/10/94
03/03/93-04/10/94
03/03/93-04/10/94
03/03/93-04/10/94
03/03/93-04/10/94
03/03/93-04/10/94
03/03/93-04/10/94
03/03/93-04/10/94
03/03/93-04/10/94
03/03/93-04/10/94
03/03/93-04/10/94
03/03/93-04/10/94
03/03/93-04/10/94
resolution
1h
1h
1h
1d
1d
1d
1d
1d
1d
1d
1d
1d
1d
1d
1d
1d
1d
1d
Soil:
0-26cm
26-54cm
OC
%
3.1
0.9
pH (H2O)
7.8
8.2
Sand
%
14.46
7.62
Silt
%
28.75
29.15
Clay
%
57.29
63.22
3
bulk density
g/cm
1.24
1.52
moisture 105°C
%
4.3
3.8
water at 0 kPa
% vol.
53.4
43.3
water at 5 kPa
% vol.
50.4
41.9
water at 10 kPa
% vol.
49.9
41.7
water at 40 kPa
% vol.
44.6
37
water at 200 kPa
% vol.
41.3
33.2
water at 1500 kPa % vol.
31.6
28.9
(Graham Beard, SSLRC, personal communication)
54-101cm
0.4
8.3
3.28
39.58
57.13
1.54
3.1
44
43.0
42.7
37.3
35.4
30.8
101-130cm
0.4
7.8
14.35
0.74
84.91
1.55
5.6
45.3
43.5
43.1
39.3
36.4
31.5
Initial water content: 54 % weight at 10 cm depth, 51% weight at 30 cm depth
Chemical:
Isoproturon (IPU)
Kd (topsoil) 2.3, 2.5 ml/g
half-life 18.2 days at 15°C and 33% gravimetric water content
101