pl0516 - evaluation of preferentail flow models...report to the u.k. ministry of agriculture, food...
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Report to the U.K. Ministry of Agriculture, Food and Fisheries
MAFF project PL0516
Evaluation of the use ofpreferential flow models
to predict the movement ofpesticides 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, UKwww.cranfield.ac.uk/ecochemistry
MAFF Project PL0516
Evaluation of the use of preferential flow models topredict the movement of pesticides to water sources
under UK conditions
Final Report
Sabine Beulke, Colin Brown & Igor Dubus
Soil Survey and Land Research CentreCranfield University
Silsoe, Bedfordshire, MK45 4DT, UK
May 1998
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DISCLAIMER
The opinions expressed and conclusions drawn in this report are those of the authors, notnecessarily of the project’s sponsor.
DECLARATION OF INTEREST
In evaluating the various preferential flow models, the authors have attempted to maintainstrict 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 anddatabases within MACRO_DB which allow soil hydraulic parameters to be estimated frombasic data.
COMMENTS BY MODEL DEVELOPERS
A first draft of this report was circulated to model developers for comment in February 1998as 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 ofcomments for clarification, changes in emphasis and additional interpretation of the resultsobtained have been incorporated into this final report. We are indebted to the authors fortheir valuable contributions.
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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-NP2.3 MACRO2.4 MACRO_DB2.5 PLM2.6 SWAT
3 DATASETS FOR MODEL EVALUATION
3.1 Brimstone Farm3.2 Cockle Park3.3 SSLRC lysimeters3.4 Wytham
4 MODEL EVALUATION
4.1 Brimstone Farm4.1.1 LEACHP - Brimstone Farm4.1.2 CRACK-NP - Brimstone Farm4.1.3 MACRO - Brimstone Farm4.1.4 MACRO_DB - Brimstone Farm4.1.5 PLM - Brimstone Farm4.1.6 SWAT - Brimstone Farm4.1.7 Overview - Brimstone Farm
4.2 Cockle Park4.2.1 LEACHP - Cockle Park4.2.2 CRACK-NP - Cockle Park4.2.3 MACRO - Cockle Park4.2.4 MACRO_DB - Cockle Park4.2.5 PLM - Cockle Park4.2.6 SWAT - Cockle Park4.2.7 Overview - Cockle Park
4.3 SSLRC lysimeters4.3.1 LEACHP - SSLRC lysimeters4.3.2 CRACK-NP - SSLRC lysimeters4.3.3 MACRO - SSLRC lysimeters4.3.4 MACRO_DB - SSLRC lysimeters4.3.5 PLM - SSLRC lysimeters4.3.6 SWAT - SSLRC lysimeters4.3.7 Overview - SSLRC lysimeters
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4.4 Wytham4.4.1 LEACHP - Wytham4.4.2 CRACK-NP - Wytham4.4.3 MACRO - Wytham4.4.4 MACRO_DB - Wytham4.4.5 PLM - Wytham4.4.6 SWAT - Wytham4.4.7 Overview - Wytham
4.5 Overall evaluation4.5.1 Non preferential flow benchmark (LEACHP)4.5.2 CRACK-NP4.5.3 MACRO4.5.4 MACRO_DB4.5.5 PLM4.5.6 SWAT4.5.7 Levels of predictive accuracy
5 REGULATORY IMPLICATIONS
6 CONCLUSIONS
ACKNOWLEDGEMENTS
REFERENCES
APPENDICES
APPENDIX 1: Experimental details for the Brimstone siteAPPENDIX 2: Experimental details for the Cockle Park siteAPPENDIX 3: Experimental details for the SSLRC lysimetersAPPENDIX 4: Experimental details for the Wytham site
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SUMMARY
There has been rapid development in simulating the effects of preferential flow on pesticidetransport in soil. Applications for regulatory purposes appear desirable, but are limited by alack of information on the confidence which should be placed on results. Preferential flowmodels were evaluated against four datasets (see below for combinations) and results werecompared with those from a non-preferential flow benchmark (LEACHP). MACRO wasevaluated both as the stand-alone model (MACRO) and as the database version which allowsautomatic selection of soil hydraulic and crop parameters (MACRO_DB). Three of thedatasets 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 fiverepresentative soils.
LEACHP(benchmark)
CRACK-NP MACRO MACRO_DB PLM SWAT
Brimstone Farm X X X X X XCockle Park X X X X X XSSLRC lysimeters X - X X X -Wytham X X X X X X
Evaluation results for prediction of pesticide transport by the various models can besummarised as follows:
LEACHP The model failed to describe observed transport of pesticides in theintermediate lysimeter soils as well as in the heavier clay soils. Predictiveapplication of non-preferential flow models to a wide range of soils may becalled into question.
CRACK-NP The model was numerically unstable (the authors are actively working tosolve this problem) and a dramatic over-prediction was obtained for losses ofa 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 than50-60% clay. The model is not recommended for regulatory use.
MACRO MACRO could be applied to all of the soils tested. Its predictive ability wasgood in a range of intermediate soils, less good in the clay loam and veryvariable in the two heavy clay soils. The model is user-friendly, well-documented and there are many reports of model tests in the literature.MACRO should be the preferred preferential flow model for regulatorypurposes and also showed equal or better predictive ability than LEACHP insandier soils. Parameter selection for MACRO is still problematic and themodel should only be applied by an experienced user. A comprehensivecalibration step should be included wherever possible.
MACRO_DB Automatic parameter selection reduced the emphasis on preferential flowrelative to the stand-alone version of MACRO. In a range of intermediatesoils and the clay loam, simulations of leaching with MACRO_DB were littleor no better than those with LEACHP. Simulations for the clay soils wererelatively more accurate, but serious mis-matches to observed behaviour
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occurred in some circumstances. The philosophy behind MACRO_DB iscommendable, but the system is not recommended for regulatory use in itscurrent form.
PLM The model generally requires calibration for the percentage of fast mobilephase, but some predictive ability was demonstrated for heavy clay soilswhere matrix flow can be considered negligible. In soils with less than 50-60% clay, PLM is extremely sensitive to changes in the percentage of fastmobile phase over a very small range. This makes selection of this parameterextremely difficult even where calibration is possible and the application ofthe model to all but the heaviest clays is not recommended for regulatorypurposes.
SWAT The model gave good results for two of the three sites with drains, but was notapplicable to the lysimeter experiment with intermediate soils where waterimpacts upon groundwater rather than surface water. Model output is ratherrestricted, but as the model was not significantly out-performed by moredetailed models on the clay sites, this or similar approaches may haveapplications at broad scales or screening levels.
Although some of the models showed reasonable predictive ability for the range of soilscovered, regulatory concerns over preferential flow may be best addressed through thedevelopment of standard modelling scenarios. Further development of reliable methods toselect input parameters from basic information is required together with simple approaches todescribing preferential flow which are not data-intensive. A further report due in October1998 will investigate the sub-routines and inherent assumptions of the various models andconsider a number of generic issues for preferential flow modelling.
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1 INTRODUCTION
Mathematical modelling at a range of complexities has been given a prominent role in the fateand behaviour section of the registration process by Council Directive 91/414/EECconcerning the placing of plant protection products on the market. Although modellingstudies are frequently submitted as part of regulatory data packages, the weight which theseare afforded is restricted by a lack of information on the relative strengths and weaknesses ofcurrent models. In 1995, SSLRC reported on an evaluation of the use of pesticide leachingand runoff models available at the time (Brown & Hollis, 1995). The main models evaluatedwere LEACHP, PRZM-2 and VARLEACH and the project concluded that these models maybe used to predict residues of pesticides in topsoil, but are not able to adequately simulateleaching of pesticides to depth. The main reason for this failure was identified as theimportance of preferential flow in determining the extent of pesticide leaching and the lack ofany description of this process in the three models. The MACRO model which includes amechanistic description of flow through both micropores and macropores was brieflyevaluated and found to give improved predictions of pesticide leaching relative to non-preferential flow models. Parameter estimation was identified as a major problem in usingMACRO and the need for much wider validation was proposed.
There is now evidence to suggest that preferential flow may be an important process forpesticide 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 havenow been developed to simulate preferential flow and its influence on pesticide fate. Theincorporation of such models into the regulatory process appears desirable, but evidence oftheir predictive ability is required and concerns over difficulties with robust selection of anumber 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 beobtained by calibration. More advances are needed before predictions ofmacropore flow can be made using soil parameters in existing data bases.”
The FOCUS surface water group (Adriaanse et al., 1997) were only slightly more optimisticin their appraisal:
“Outputs from the macropore flow models MACRO and CRACK-NP are sensitiveto parameters related to the macropore region ... which are in turn difficult toestimate. This may lead to high levels of predictive uncertainty compared to theuse of models in non-structured sandy soils.”
The aim of this study was to evaluate the predictive ability of preferential flow models againstpesticide datasets for a range of UK conditions and to assess the extent to which the concernsraised above have been addressed by recent developments in this field such as the databasemanagement tool, MACRO_DB.
1.1 Objectives
The study had three objectives:
1. to review existing information on the validation of preferential flow models;
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2. to bring together recent UK datasets for pesticide experiments which are suitablefor evaluating preferential flow models;
3. to use the datasets to assess and, if possible quantify, the accuracy of the modelsunder UK conditions and to assess the implications of the findings for theregulatory 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 withinthe regulatory process for pesticides. Much of the regulatory use of these models is likely tobe predictive in order to estimate potential movement to ground and surface waters with littleor no potential for calibration of the model against experimental data. Accordingly, theevaluation has predominantly focused on comparing observed data with results from ‘blind’predictive simulations. The temptation to correct the simulation of the water balance beforesimulating pesticide behaviour was ignored, but it should be noted that a correct simulation ofthe 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 beenclearly identified and has been restricted to the more uncertain parameters. Simulationscalibrated to a given year or solute have then been re-run for a second year or solute to testhow transferable are the input parameters.
A particular area of uncertainty for modellers is in the selection of values describing pesticidesorption (organic carbon partition coefficient, Koc) and degradation (half-life), whether thesebe from site-specific laboratory or field measurements or from literature sources. These twoparameters 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 givencombination of soil and pesticide can be the overriding factor in obtaining an accuratesimulation. As the models are sensitive to changes in pesticide Koc and half-life, theuncertainty associated with these parameters offers an easy way to improve the fit betweenobserved and simulated data. Throughout this study, the temptation to do this has beenavoided, although occasionally results of simulations with site-specific values are comparedto those with literature values. The reader should be aware that the values used for modellingcannot be considered correct and that changes in Koc and half-life may have quite largeeffects upon simulation results. An increase in Koc for example would give smallerconcentrations leaving the soil profile in all soil types and a delay in breakthrough forcoarser-textured soils but not for clay soils. Half-life will have relatively little effect uponsimulations soon after application, but progressively more effect as time passes. With thislimitation in mind, the report tries to draw upon broad matches or discrepancies betweenobserved 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 experimentalinformation and using the experience of the modeller concerned to interpret that informationand fill any gaps. It should be remembered throughout that modelling is subjective and theparameter sets selected by any given modeller are interpretations of reality. As such, theycannot be considered either ‘correct’ or unique and will vary from modeller to modelleraccording to personal experience and prejudice. The exception to this is MACRO_DB whereonly a restricted amount of soils information is required to automatically select input values.
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The layout of the report has been designed with the intention that the reader can take as muchor as little as required from the document. Thus each model and each dataset is described indetail. This is followed by a section describing the simulations with each combination ofmodel and dataset. Once all simulations for a given dataset have been set out, an overviewcomparing 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 ofpreferential flow models are given. Inevitably, there is some repetition between layers in thehierarchy, but it is anticipated that not all readers will have the time or inclination to read thedocument in full.
It has not been possible to list the input parameters for a given simulation because of the sheernumber of model runs. However, all input and output has been archived at SSLRC and couldbe made available upon request. This would allow, for example, rapid re-evaluation of agiven model in the event of an update in either the model code or the methodology forselection of input parameters.
2 MODELS EVALUATED: OVERVIEW AND VALIDATION STATUS
There have been a number of pesticide models published over the last few years whichincorporate 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 availableand relevant to the UK. Descriptions of preferential flow in these models range from highlymechanistic 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 theMACRO_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 feltparticularly important to evaluate the performance of MACRO_DB. In addition to thepreferential flow models, LEACHP was used as a benchmark representative of models whichdo not describe preferential flow. A summary of the main processes which are incorporatedinto each of the models used in this study is given in Table 1 and the main input parametersrequired for a basic simulation are listed in Table 2. The important features of the models aredescribed below.
2.1 LEACHP (benchmark model)
LEACHP Version 3.1 (Hutson & Wagenet, 1992) does not simulate preferential flow and wasused in this study as a benchmark against which the influence of the various descriptions ofpreferential flow in the other models was assessed. In clay soils, it is clearly expected that theinclusion of preferential flow will improve the simulation of observed behaviour. However,this is less clear for coarser-textured soils where LEACHP and other models withoutpreferential flow will perform relatively better and there is the possibility that the inclusion ofpreferential flow would lead to a poorer simulation of reality.
LEACHP considers the soil to be a homogeneous medium through which water and solute aretransported according to Richards’ equation and the convection-dispersion equation,respectively. The model allows both chemical and biological degradation to be simulatedaccording to first-order kinetics and biological degradation to be corrected for temperatureand moisture effects. Two-site or instantaneous equilibrium sorption may be considered
11
together with a linear or Freundlich isotherm. The model has been extensively used andtested since its first publication in 1987. It is ideally suited to simulating data from leachingcolumns, but has also been found to simulate field behaviour in coarser-textured soils. In theUK, Brown et al. (1996) demonstrated a very close fit between LEACHP simulations and soiland soil water concentrations for a fungicide in a sandy loam soil. In broader evaluationprogrammes with a range of soils for the UK and Europe, Brown & Hollis (1995) and Walkeret 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 pesticideleaching to depth. LEACHP was considered to be more reliable overall than PRZM-2 orVARLEACH, although any of the three models could best match observed behaviour in agiven situation.
2.2 CRACK-NP
CRACK-NP is designed to simulate the movement of water, nitrate and pesticides in highly-structured, heavy clay soils where preferential flow in the form of bypass flow is thedominant hydrological pathway. The model is derived from the hydrological model CRACK(Jarvis & Leeds-Harrison, 1987; Jarvis, 1989) which divides the total soil porosity into thatwithin uniform aggregates and that in the cracks between. Water is assumed to move intoaggregates according to Philip’s infiltration theory and out of them in response to cropextraction and/or evaporation. Downwards movement of water is assumed to occur only inthe cracks based on Hagen-Poisseuille’s equation with a correction for path tortuosity andconnectivity. The assumption that there is no net flux of water within the soil matrix meansthat the model is only applicable for heavy clays where matrix flow can be considered anegligible component of total flow.
In CRACK-NP, the hydrological descriptions in the model have been left unchanged, whilstsolute transport is modelled assuming mass flow in the cracks and diffusion both within theaggregates and between cracks and aggregates. Pesticide sorption is described using a linearisotherm. 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 theinclusion of a direct physical description of the macropore structure which can be observed inthe field or derived from standard descriptions of soil structure. CRACK-NP Version 2.0 wasevaluated in this study.
CRACK-NP has only previously been evaluated against the Brimstone Farm data for which itwas developed. Armstrong et al. (1995a,b) describe excellent fits to observed data forBrimstone taken from 1985/86, 1989/90 and 1990/91 using only measured parameters. Themodel has been modified since these fits were obtained and the same input parameters usedwith 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 thetotal soil porosity divided into two flow domains (macropores and micropores), eachcharacterised by a flow rate and solute concentration. Soil water flow and solute transport inthe 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
12
approach with mass-flow. Exchange between the two domains is calculated according toapproximate, physically-based expressions using an effective aggregate half-width which is acrucial parameter. Whereas this parameter was empirical in early versions of the model, since1994 it has been physically-based and can be derived from field observation. In situationswhere preferential flow is unlikely to occur, the model reverts to the classical solution ofRichards’ equation and the convection-dispersion equation as in LEACHP. By varying theinput 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 anycombination of flow types between these two extremes. This means that the model isappropriate to describe preferential flow in a variety of soils, but the processes of finger flowand funnel flow in coarse-textured soils cannot be simulated. In MACRO, pesticidedegradation is modelled using first-order kinetics. Different half-lives can be specified for thesolid and the liquid phase of the macropores and micropores, respectively and thesedegradation rates may be adjusted for temperature and moisture effects. Sorption is assumedto be at instantaneous equilibrium and to be described by a linear isotherm. Strength ofsorption is the same in each pore domain, but the user must specify the distribution ofsorption sites between the two. Parameter values can be changed at any point during the runand this allows time-dependent sorption or changes in rate of degradation to be simulated ifrequired.
MACRO has been evaluated in a number of recent field and lysimeter studies. In earlierversions, MACRO could be run as both a one-domain model ignoring bypass flow and as atwo-domain model. In several studies these two options were compared to assess thesignificance of preferential flow and to validate the description of macroporosity implementedin the two-domain model. In sandy soils, the one-domain model was demonstrated to performwell by Saxena & Jarvis (1995) and Brown et al. (1997) suggesting that preferential flow isnot important in these soils. However, dichlorprop and bentazone leaching throughlysimeters with Swedish sand soils could not be reproduced by the model, probably due tofinger flow (Jarvis et al., 1994). Finger flow was also identified as a possible reason fordiscrepancies between simulated and observed leaching of alachlor through sandy loams byJarvis 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 aclear improvement over the assumption that soil porosity is homogeneous. Drainflow, heightof the water table and chloride concentrations in drainage from an irrigated heavy clay marshsoil 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 goodagreement between uncalibrated MACRO simulations and measured flow, bromide andisoproturon leaching through heavy clay lysimeters. Water flow through three lysimeterswith 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 alsowell reproduced. However, in common with other models, MACRO failed to describedichlorprop leaching through two Swedish soils unless the degradation rate was markedlydecreased from the laboratory value. The one-domain version was not applicable to theselysimeters. When preferential flow was ignored, MACRO also failed to describe leaching of36Cl through lysimeters with a clay soil, whilst the two-domain approach gave a reasonablematch to the observed data (Saxena et al., 1994). In a number of further studies, two-domainsimulations 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. Inwork by Brown et al. (1997) it failed to describe bromide and pesticide leaching through analluvial clay soil and the fits could not be markedly improved by calibrating parameters which
13
describe macroporosity. The model under-estimated the importance of preferential flow forwater and solute movement through two loamy soils which have been proposed to have a dualflow system with important contributions from both preferential and matrix flow.
In most of the above mentioned studies some of the model parameters were calibrated. Ifuncalibrated, MACRO performed less well, but still gave promising results. An uncalibratedsimulation with MACRO by Jarvis et al. (1995) reproduced the dissipation of alachlor in 0-10cm of a clay loam field soil, but gave discrepancies to the observed concentrations in suctioncup samples due to selection of inappropriate parameters describing macroporosity. Work byBrown et al. (1997) confirmed that these parameters are difficult to select. A modelevaluation against data for a clay loam soil reported by Brown (1996) showed that MACROperformed better than any of the non-preferential flow models tested. However, somesignificant discrepancies from observed data occurred late in the season.
In conclusion, validation studies with MACRO give promising results. However, markeddiscrepancies from measured data are occasionally observed. A drawback of the model is itscomplexity which leads to uncertainties in parameterisation. In particular, sensitiveparameters describing macroporosity are difficult to select. Calibrated simulations are often,but not always, able to reproduce observed leaching of pesticides. Accurate simulationswithout calibration are less frequent, indicating the continuing difficulties with selection ofappropriate input parameters. However, uncalibrated runs generally show improved match toobserved 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 datasources 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 thesoils databases which can be accessed by MACRO_DB is that contained in SEISMIC (Holliset al., 1993) and the system will automatically select input parameters for any soil series inEngland and Wales using a combination of simple rules and pedo-transfer functions. Thusthe sensitive parameters setting the boundary between micropores and macropores andgoverning the rate of exchange of water and solute between regions can be set independent ofany user subjectivity. MACRO_DB has been designed for management applications by thenon-specialist user in making exposure and risk assessments for pesticides. Given thecomplex nature of modelling preferential flow, it is likely that MACRO_DB will be used bysome companies to parameterise and run the model for selected scenarios. It is thus importantto evaluate the predictive ability of the complete system and this was done separately from theevaluation of the stand-alone version of MACRO. Measured soils data for each dataset wereentered into MACRO_DB which was then allowed to select input parameters. Standardparameter sets from within MACRO_DB were also selected for the crop of interest. Theseinput values were combined with site-specific data for pesticide properties, drainagecharacteristics and weather to evaluate the predictive ability of MACRO_DB.
MACRO_DB has only been released for approximately one year. At present, no evaluationsof the predictive ability of the system have been presented in the literature.
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2.5 PLM
The Pesticide Leaching Model (PLM) described by Hall (1993) is a functional model, basedupon an approach of Addiscott (1977) which divides the soil profile into 5-cm layers and thesoil water into a mobile and an immobile phase. In PLM, the mobile water is defined as thewater 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 bothconvective flow of soil solution through water-filled pores and rapid transport throughmacropores or fissures. The empirical parameter which characterises the percentage of themobile phase (air capacity) characterised as ‘fast’ needs to be specified by the user and isconstant for all horizons, irrespective of their different characteristics. The depth leached pertime interval in the fast and slow regions also needs to be specified. Pesticide degradation isassumed to follow first-order kinetics with a bulked half-life for the solid and the three liquidphases. This half-life is adjusted for variations in soil temperature and moisture during therun. Sorption is restricted to the immobile soil water and the slow mobile water, whilst waterin the fast flow phase does not interact with soil surfaces. Instantaneous equilibrium betweenthe sorbed and solute phase is assumed together with a linear isotherm. The sorptioncoefficient in the upper 5 cm is increased daily.
PLM can be considered semi-empirical as parameters describing the proportion of fast flowand the depth leached per time interval in the fast and slow regions cannot be linked to soilproperties. The authors of PLM suggest that the model requires calibration for a givendataset, but that this can often be limited to only one sensitive parameter (the percentage offast 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 differentsoil types. Hall (1994) was able to calibrate PLM to describe dichlorprop leaching throughlysimeters with three Swedish soils but, in common with other models tested, it was necessaryto 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 ofagriculturally applied pesticides moving to surface waters via the combined pathways ofsurface 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 beenreported as the Hydrology of Soil Types (HOST) by Boorman et al. (1995). This systemgroups all UK soil series into twenty-nine classes based upon hydrological characteristics ofthe soil and the underlying substrate layer. Using the HOST system, soils have been groupedaccording to their potential for soil run-off into five classes which form the basis forprediction of the movement of water and associated pesticide to streams in response torainfall. Attenuation factors describe the decrease in concentrations of pesticide betweenevents. A modified version of SWAT has been incorporated into the Environment Agency’sPOPPIE programme to predict concentrations of pesticides in surface waters at the catchmentscale (Hollis & Brown, 1996).
SWAT has been evaluated against data from Cockle Park, Rosemaund and SSLRCexperiments 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 ofmagnitude the transient peak concentrations of a wide range of pesticides during rapid water
15
movement to streams in response to rainfall. Simulated concentrations were too great whenrainfall initiated water movement to streams very soon after application, particularly for themore mobile pesticides, and some predictions for pesticides sorbed very strongly to soil wererelatively poor.
Tab
le 1
: S
umm
ary
of p
roce
ss d
escr
ipti
on b
y C
RA
CK
-NP
, MA
CR
O, P
LM
, SW
AT
and
LE
AC
HP
(pa
rtly
ada
pted
fro
m B
oest
en e
t al.
(199
5)an
d A
dria
anse
et a
l. (1
997)
)
CR
AC
K-N
PM
AC
RO
PL
MS
WA
TL
EA
CH
PU
ser
frie
ndli
ness
Low
Hig
hH
igh
Low
Low
Ass
ista
nce
in d
eter
min
ing
mod
el p
aram
eter
sN
oS
ome
guid
ance
pro
vide
dfo
r a
few
par
amet
ers;
com
preh
ensi
ve h
elp
syst
em.
Som
e gu
idan
ce p
rovi
ded
for
a fe
w p
aram
eter
sN
o, b
ut p
aram
eter
s ar
ere
lati
vely
sim
ple
Lit
tle
guid
ance
ava
ilab
le
Ava
ilab
ilit
y of
nee
ded
data
Mos
t dat
a re
adil
yav
aila
ble,
som
e so
il d
ata
mus
t be
esti
mat
ed
Wea
ther
dat
a ob
tain
able
;so
me
soil
dat
a m
ust b
ees
tim
ated
fro
m p
edo-
tran
sfer
fun
ctio
ns o
rex
pert
judg
emen
t
Wea
ther
dat
a an
d so
ilpr
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ties
rea
dily
avai
labl
e; p
aram
eter
desc
ribi
ng m
acro
poro
sity
need
s to
be
cali
brat
edag
ains
t exp
erim
enta
l dat
a,ot
her
para
met
ers
need
tobe
est
imat
ed
Dat
a re
adil
y av
aila
ble
Wea
ther
dat
a ob
tain
able
,so
me
soil
dat
a m
ay n
eed
to b
e es
tim
ated
fro
mpe
do-t
rans
fer
func
tion
s
Sim
ulat
ion
prio
r to
fir
stap
plic
atio
n of
pes
tici
deY
esY
esN
oN
oY
esM
ulti
ple
pest
icid
eap
plic
atio
n po
ssib
leY
esY
esN
oN
oY
esS
oil m
odel
Soi
l col
umn
divi
ded
into
hom
ogen
eous
laye
rs o
fva
riab
le th
ickn
ess
Soi
l col
umn
divi
ded
into
hom
ogen
eous
laye
rs o
fva
riab
le th
ickn
ess
Soi
l col
umn
divi
ded
into
hom
ogen
eous
5-c
m la
yers
Top
soil
s on
ly c
onsi
dere
das
a s
impl
e m
ixin
g ce
llS
oil c
olum
n di
vide
d in
toho
mog
eneo
us la
yers
of
equa
l thi
ckne
ssH
ydro
logy
mod
elT
wo-
regi
on m
odel
wit
hag
greg
ates
and
cra
cks
betw
een
aggr
egat
es;
Phi
lips
’s in
filt
rati
oneq
uati
on f
or w
ater
ent
ryin
to a
ggre
gate
s, H
agen
-P
oiss
euil
le’s
equ
atio
n fo
rw
ater
mov
emen
t in
crac
ks
Tw
o-do
mai
n m
odel
wit
hto
tal p
ore
spac
e di
vide
din
to m
acro
pore
s an
dm
icro
pore
s; s
olut
ion
ofR
icha
rds’
equ
atio
n w
ithi
nm
icro
pore
s, c
apac
itan
ceap
proa
ch w
ithi
nm
acro
pore
s
Cap
acit
ance
mod
el o
ver
ati
me-
step
of
1 da
y ba
sed
upon
mob
ile
and
imm
obil
e w
ater
fra
ctio
nsw
ith
a di
visi
on a
t 5 k
Pa
and
a fu
rthe
r em
piri
cal
divi
sion
of
mob
ile
wat
erin
to a
slo
w a
nd f
ast f
low
dom
ain
Ver
tica
l mov
emen
tac
cord
ing
to a
mea
n da
ily
flux
Sol
utio
n of
Ric
hard
s’eq
uati
on
Tab
le 1
(co
ntin
ued)
CR
AC
K-N
PM
AC
RO
PL
MS
WA
TL
EA
CH
PP
refe
rent
ial f
low
Tw
o re
gion
mod
el w
ith
crac
ks; i
niti
atio
n of
cra
ckfl
ow if
rai
nfal
l int
ensi
tyex
ceed
s ag
greg
ate
sorp
tion
cap
acit
y
Tw
o do
mai
n m
odel
wit
hm
acro
pore
flo
w;
phys
ical
ly-b
ased
desc
ript
ion
(eff
ecti
veag
greg
ate
half
-wid
th,
boun
dary
wat
er te
nsio
n,w
ater
con
tent
and
hydr
auli
c co
nduc
tivi
ty)
Onl
y w
hen
fiel
d ca
paci
tyis
exc
eede
d an
d po
rosi
tyas
soci
ated
wit
h sl
ow f
low
is f
ille
d; e
mpi
rica
lde
scri
ptio
n of
mac
ropo
-ro
sity
(pe
rcen
tage
of
fast
flow
)
Rap
id f
low
des
crib
edus
ing
the
Sta
ndar
dP
erce
ntag
e R
unof
f va
lue
from
the
HO
ST
clas
sifi
cati
on
Not
con
side
red
Eva
potr
ansp
irat
ion
Inpu
t of
pote
ntia
lev
apot
rans
pira
tion
dat
aIn
put o
f po
tent
ial e
vapo
-tr
ansp
irat
ion
data
or
esti
mat
ion
usin
g P
enm
an-
Mon
teit
h’s
equa
tion
Est
imat
ion
of p
oten
tial
evap
otra
nspi
rati
on f
rom
mea
sure
d or
cal
cula
ted
pan
evap
orat
ion
data
Acc
ount
ed f
or in
mea
nda
ily
wat
er f
lux
duri
ng th
efi
eld
capa
city
per
iod
Wee
kly
pote
ntia
lev
apot
rans
pira
tion
dat
are
quir
ed, u
tili
ty f
ores
tim
atio
n pr
ovid
edO
verl
and
flow
Con
side
red,
but
not
reco
mm
ende
d fo
rpr
edic
tive
use
Con
side
red,
but
not
reco
mm
ende
d fo
rpr
edic
tive
use
Not
con
side
red
Con
side
red
as a
com
pone
nt o
f ra
pid
runo
ffN
ot c
onsi
dere
d al
thou
ghov
erfl
ow is
pos
sibl
e as
ach
eck
for
wat
er b
alan
ceD
rain
age
See
page
pot
enti
al th
eory
See
page
pot
enti
al th
eory
% o
f le
ache
d w
ater
whi
chm
oves
to d
rain
s us
er-
spec
ifie
d
Con
side
red
as a
com
pone
nt o
f ra
pid
runo
ffN
ot c
onsi
dere
d
Sub
late
ral f
low
Not
con
side
red
Not
con
side
red
Not
con
side
red
Con
side
red
as a
com
pone
nt o
f ra
pid
runo
ffN
ot c
onsi
dere
d
Pes
tici
de tr
ansp
ort
Dif
fusi
on w
ithi
n ag
gre-
gate
s, m
ass
flow
in c
rack
sC
onve
ctio
n-di
sper
sion
equa
tion
in m
icro
pore
s,m
ass
flow
in m
acro
pore
s
Mas
s fl
ow tr
ansp
ort
asso
ciat
ed w
ith
mob
ile
wat
er
Lin
ked
to w
ater
flu
x vi
a a
reta
rdat
ion
fact
orC
onve
ctio
n-di
sper
sion
equa
tion
Pes
tici
de s
orpt
ion
Lin
ear
in a
ggre
gate
s, n
oso
rpti
on in
cra
cks;
diff
eren
t Kd
for
each
laye
r
Lin
ear;
sor
ptio
n si
tes
part
itio
ned
betw
een
mic
ro-
and
mac
ropo
res;
diff
eren
t Kd
for
each
laye
r; K
d ca
n be
res
et to
ane
w v
alue
at a
ny ti
me
duri
ng th
e ru
n to
acc
ount
for
tim
e-de
pend
ent
sorp
tion
Lin
ear;
sor
ptio
nin
crea
sing
wit
h ti
me
in to
p5-
cm la
yer;
sor
ptio
n on
lyin
imm
obil
e an
d sl
owm
obil
e fl
ow d
omai
n; K
d
can
be s
et to
3 d
iffe
rent
valu
es d
own
the
prof
ile
Lin
ear
tim
e-de
pend
ent
sorp
tion
con
side
red
acco
rdin
g to
Wal
ker
(198
7)
Lin
ear
or F
reun
dlic
his
othe
rm; t
wo-
site
sorp
tion
pos
sibl
e; s
ingl
eK
oc c
orre
cted
acc
ordi
ngto
org
anic
car
bon
cont
ent
Tab
le 1
(co
ntin
ued)
CR
AC
K-N
PM
AC
RO
PL
MS
WA
TL
EA
CH
PP
esti
cide
deg
rada
tion
Fir
st-o
rder
; rat
e co
nsta
nts
corr
ecte
d fo
r te
mpe
ratu
rean
d m
oist
ure
effe
cts
(non
-op
tion
al, f
ixed
par
ame-
ters
); d
egra
dati
on a
ppli
esto
soi
l and
wat
er w
ithi
nag
greg
ates
, no
degr
adat
ion
in c
rack
s
Fir
st-o
rder
; tem
pera
ture
and
moi
stur
e ef
fect
s m
aybe
mod
elle
d; d
iffe
rent
rat
eco
nsta
nts
for
soli
d an
dli
quid
pha
ses
of m
icro
-an
d m
acro
pore
dom
ain
poss
ible
; dif
fere
nt r
ate
cons
tant
s fo
r ea
ch la
yer
Fir
st-o
rder
; rat
e co
nsta
nts
corr
ecte
d fo
r te
mpe
ratu
rean
d m
oist
ure
effe
cts
(non
-op
tion
al, f
ixed
par
ame-
ters
); d
egra
dati
on a
ppli
esto
bul
k so
il; h
alf-
live
s ca
nbe
set
to 3
dif
fere
nt v
alue
sdo
wn
the
prof
ile
Fir
st-o
rder
fie
ld h
alf-
life
requ
ired
Fir
st-o
rder
; tem
pera
ture
and
moi
stur
e ef
fect
s m
aybe
mod
elle
d; b
iolo
gica
lan
d ch
emic
al d
egra
dati
onpo
ssib
le; d
egra
dati
on in
bulk
soi
l or
in s
olut
ion
only
; dif
fere
nt r
ate
cons
tant
s fo
r ea
ch la
yer
Met
abol
ites
No
Yes
(1)
No
No
Yes
(m
axim
um o
f 3)
Pes
tici
de v
olat
ilis
atio
nN
ot c
onsi
dere
dN
ot c
onsi
dere
dN
ot c
onsi
dere
dC
onsi
dere
d w
ithi
n a
reta
rdat
ion
fact
orV
olat
ilit
y ac
ross
soi
lsu
rfac
eP
esti
cide
upt
ake
by p
lant
sN
oY
esN
oN
oY
esP
lant
sho
ot g
row
thL
inea
r in
terp
olat
ion
betw
een
zero
at
emer
genc
e an
d m
axim
umle
af a
rea
Lea
f ar
ea in
dice
s an
d fo
rmfa
ctor
s sp
ecif
ying
gro
wth
curv
e; s
tart
ing
date
of
regr
owth
of
win
ter-
sow
ncr
ops
in s
prin
g m
ay b
esp
ecif
ied
Not
con
side
red
Not
con
side
red
Em
piri
cal s
igm
oida
l cur
ve
Pla
nt r
oot g
row
thL
inea
r in
terp
olat
ion
betw
een
min
imum
at
emer
genc
e an
d m
axim
umw
hen
the
crop
has
its
max
imum
leaf
are
a; r
oot
volu
me
dist
ribu
ted
loga
rith
mic
ally
wit
h de
pth
Lin
ear
inte
rpol
atio
nbe
twee
n m
inim
um a
tem
erge
nce
and
max
imum
whe
n th
e cr
op h
as it
sm
axim
um le
af a
rea;
roo
tvo
lum
e di
stri
bute
dlo
gari
thm
ical
ly w
ith
dept
h
Gro
wth
25
mm
d-1
fro
mda
te o
f em
erge
nce
(or
regr
owth
in s
prin
g) to
acr
op-d
epen
dent
max
imum
dept
h; r
oot v
olum
edi
stri
bute
d lo
gari
thm
ical
lyw
ith
dept
h
Not
con
side
red
Bas
ed o
n D
avid
son
et a
l.(1
978)
plu
s a
scal
ing
fact
or
Tab
le 2
:In
put p
aram
eter
s re
quir
ed f
or b
asic
sim
ulat
ions
usi
ng C
RA
CK
-NP
, MA
CR
O, P
LM
, SW
AT
and
LE
AC
HP
(D
ata
in it
alic
s m
ay b
eei
ther
inpu
t or
calc
ulat
ed b
y th
e m
odel
)
Par
amet
er g
roup
ing
CR
AC
K-N
PM
AC
RO
PL
MS
WA
TL
EA
CH
PW
eath
erH
ourl
y m
ax/m
inte
mpe
ratu
res,
rai
nfal
l,po
tent
ial e
vapo
tran
s-pi
rati
on, u
tili
ty f
ores
tim
atio
n fr
om d
aily
valu
es p
rovi
ded
Dai
ly m
ax/m
inte
mpe
ratu
res,
rai
nfal
l,po
tent
ial e
vapo
tran
s-pi
rati
on o
r da
ily
max
/min
tem
pera
ture
s, r
ainf
all,
sola
r ra
diat
ion,
vap
our
pres
sure
, win
d sp
eed
and
heig
ht a
t whi
ch m
easu
red,
albe
do, a
tten
uati
on f
acto
rfo
r so
lar
radi
atio
n in
cro
p;an
nual
tem
pera
ture
am
pli-
tude
, ave
rage
ann
ual t
em-
pera
ture
, ave
rage
rai
nfal
lin
tens
ity,
lati
tude
Dai
ly m
ax/m
inte
mpe
ratu
res,
rai
nfal
l, pa
nev
apor
atio
n, p
an f
acto
r
Dai
ly r
ainf
all
Dai
ly m
ax/m
inte
mpe
ratu
res,
rai
nfal
l,w
eekl
y pa
n ev
apor
atio
nan
d pa
n fa
ctor
, or
pote
ntia
lev
apot
rans
pira
tion
Soi
lIn
itia
l wat
er c
onte
nts,
tota
lpo
rosi
ty, f
ield
cap
acit
y,w
ilti
ng p
oint
, sta
ble
crac
kpo
rosi
ty, c
rack
spa
cing
shri
nkag
e fa
ctor
, ini
tial
dept
h to
wat
er ta
ble,
tort
uosi
ty f
acto
r, p
edso
rpti
vity
at w
ilti
ng p
oint
,hy
drau
lic
cond
ucti
vity
,bu
lk d
ensi
ty to
psoi
l and
subs
oil
Init
ial t
empe
ratu
res,
init
ial
moi
stur
e, d
ispe
rsiv
ity,
effe
ctiv
e ag
greg
ate
half
-w
idth
, shr
inka
ge f
acto
r,hy
drau
lic
cond
ucti
vity
and
wat
er c
onte
nt a
nd w
ater
tens
ion
at b
ound
ary
betw
een
mic
ro-
and
mac
ropo
res,
sat
urat
edhy
drau
lic
cond
ucti
vity
,sa
tura
ted
wat
er c
onte
nt,
resi
dual
wat
er c
onte
nt,
pore
siz
e di
stri
buti
onin
dex,
wil
ting
poi
nt,
tort
uosi
ty f
acto
rs f
orm
icro
- an
d m
acro
pore
s,bu
lk d
ensi
ty
Fra
ctio
n of
wat
er m
ovin
gto
nex
t lay
er, %
of
fast
mob
ile
phas
e, r
ates
of
fast
and
slow
dra
inag
e; to
tal
poro
sity
, wat
er c
onte
nt a
t5,
200
and
150
0 kP
a, b
ulk
dens
ity
Min
imum
sta
ndar
dra
infa
ll v
olum
e, w
ater
cont
ent a
t 5, 2
00 a
nd 1
500
kPa,
org
anic
car
bon
cont
ent,
bulk
den
sity
, air
spac
e, in
tera
ctiv
e w
ater
,hy
drau
lic
cond
ucti
vity
at
5 kP
a
Par
ticl
e si
ze d
istr
ibut
ion,
orga
nic
carb
on c
onte
nt,
bulk
den
sity
, hyd
raul
icco
nduc
tivi
ty a
t ref
eren
cete
nsio
n, C
ampb
ell’
s w
ater
rete
ntio
n-co
nduc
tivi
tyfu
ncti
on p
aram
eter
s,di
sper
sivi
ty, i
niti
alm
oist
ure
and
tem
pera
ture
Tab
le 2
(co
ntin
ued)
Par
amet
er g
roup
ing
CR
AC
K-N
PM
AC
RO
PL
MS
WA
TL
EA
CH
PC
rop
Roo
t ada
ptab
ilit
y fa
ctor
,ca
nopy
inte
rcep
tion
capa
city
, cor
rect
ion
fact
orfo
r w
et c
anop
y ev
apo-
rati
on, c
riti
cal s
oil a
irco
nten
t and
wat
er c
onte
ntfo
r ro
ot w
ater
upt
ake,
dat
eof
cro
p em
erge
nce,
harv
est a
nd d
ate
at w
hich
max
imum
leaf
are
a is
achi
eved
, ini
tial
and
max
imum
roo
t dep
th, r
oot
dist
ribu
tion
fac
tor
Roo
t ada
ptab
ilit
y fa
ctor
,ca
nopy
inte
rcep
tion
capa
city
, cor
rect
ion
fact
orfo
r w
et c
anop
y ev
apo-
rati
on, c
riti
cal s
oil a
irco
nten
t and
wat
er te
nsio
nfo
r ro
ot w
ater
upt
ake,
dat
eof
cro
p em
erge
nce,
harv
est a
nd d
ate
at w
hich
max
imum
leaf
are
a is
achi
eved
, lea
f ar
ea in
dex
(LA
I), r
oot d
epth
and
cano
py h
eigh
t at a
spec
ifie
d da
te, L
AI
atha
rves
t, m
axim
um L
AI
and
root
dep
th, r
oot
dist
ribu
tion
fac
tor,
for
mfa
ctor
s fo
r gr
owth
cur
ve
Cro
p ty
pe, d
ate
of s
owin
gan
d ha
rves
tC
rop
not c
onsi
dere
dD
ate
of g
erm
inat
ion,
emer
genc
e, r
oot m
atur
ity,
shoo
t mat
urit
y an
dha
rves
t, re
lati
ve m
axim
umro
otin
g de
pth
and
crop
cove
r
Pes
tici
deD
iffu
sion
coe
ffic
ient
infr
ee w
ater
, hal
f-li
ves,
refe
renc
e te
mpe
ratu
re a
ndm
oist
ure,
Kd,
fra
ctio
n of
sorp
tion
sit
es in
mac
ro-
pore
s, c
anop
y w
ash-
off
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21
3 DATASETS FOR MODEL EVALUATION
Data have been collected from three field sites and one lysimeter experiment in the UK. Soilsat 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 movementthrough the profile. The lysimeter experiment (SSLRC) was conducted with five contrastingsoil types with varying texture and potential for preferential flow. Movement of isoproturonwas monitored in all of the datasets and was used for model evaluation. The fate of trifluralinat Cockle Park and the leaching of bromide through the SSLRC lysimeters was alsosimulated. The studies were supplemented with either experimental or literature data onpesticide sorption and degradation.
3.1 Brimstone Farm
The Brimstone Farm data set was collected within a four-year collaborative government andindustry-funded research programme conducted at the Brimstone Farm facility developedjointly 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 wereavailable for the four control plots. Of these, two were conventionally mole drained (plots 5and 20). On one plot, the drainage system consisted of gravel-filled moles (plot 15), whilstthe remaining plot had close-spaced pipes (plot 9). Pesticides were applied to winter cerealsin three successive seasons (1993/94, 1994/95, 1995/96) and data for isoproturon weresupplied. Rates of drainflow and isoproturon concentrations in drainflow were monitored forthe first two key rainfall events of each season. Experimental set-up and results forBrimstone Farm are described by Nicholls et al. (1993), Harris et al. (1994, 1995) and Joneset 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 theDepartment of Agricultural and Environmental Science and Department of Agriculture,University of Newcastle upon Tyne and the former ADAS Soil & Water Research Centre. Apesticide experiment was carried out on an existing drainage trial on a clay loam soil of theDunkeswick series at the university farm at Cockle Park, Northumberland. Pesticidesincluding isoproturon and trifluralin were applied to winter wheat in two successive seasonsof which 1990/91 was chosen as the most comprehensively monitored. Losses of water andpesticides from a mole-drained plot in surface-layer flow through the top 30 cm of the soilprofile and mole drainflow were recorded between November 1990 and March 1991. Detailsof the Cockle Park experiment are given by Brown (1993) and Brown et al. (1995a, b). Asummary 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 theSoil Survey and Land Research Centre in Silsoe, Bedfordshire. Lysimeters were taken fromfive soil types representative of each of the three High and the two Intermediate leachingpotential classes identified in the National Rivers Authority policy and practice for the
22
protection of groundwater (NRA, 1992). These soils were in descending order ofvulnerability: a clay loam alluvial soil with a potential for direct by-pass flow to a shallowground water table (Enborne series), a deep structureless sandy soil with small organic mattercontent (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 peatsoil over structureless sand (Isleham series). Bromide and pesticides including isoproturonwere applied in two successive seasons (1994/95 and 1995/96) and their concentrations inleachate were monitored throughout the leaching periods. The SSLRC lysimeter dataset isdescribed in detail by Brown et al. (1997). Data relevant to this study are summarised inAppendix 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 whichwas funded by the Natural Environment Research Council, the Agriculture and FoodResearch Council and others. Isoproturon was applied in spring 1994 to a winter barley cropon 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 flowand occasionally also in overland flow were recorded together with the respective flow rates.In addition, tensiometer data, capacitance probe data and soil temperatures were monitoredover an extended period. Further information on the Wytham experiment is provided byHaria et al. (1994) and Johnson et al. (1994, 1995a,b, 1996). The data used in this study aresummarised 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 wasconsidered to be characterised by a linear adsorption isotherm and to be at instantaneousequilibrium. Half-lives and Koc values for the topsoil were set to measured values or toliterature data. Half-lives for deeper layers were derived from the top-layer value accordingto the relationship proposed by Jarvis et al. (1997). Half-lives determined in laboratorystudies were corrected for changes in temperature and moisture content during the model runaccording to literature relationships.
For LEACHP modelling, the Campbell’s parameters which characterise water releasecharacteristics and the relationship between hydraulic conductivity and water content wereestimated from the water retention data given in Appendices 1-4. The reference hydraulicconductivity 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 fieldcapacity (J.L. Hutson, personal communication). As drainage is not explicitly considered inLEACHP, observed rates of drainflow and associated pesticide concentrations were comparedto the simulated flow through the soil profiles at drain depth.
CRACK-NP simulations were based on an input file for Brimstone Farm which was providedwith the model. Attempts were made to change parameters to account for site-specificconditions, but in many cases the appropriate changes were not possible because they
23
destabilised the model which then crashed. For the same reason, simulations were usuallystarted 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 fromthe original values. Thereafter, the input parameters were adapted to the evaluated dataset asfar as possible. Full details are provided in the relevant results sections. As measured valuesfor parameters defining macroporosity (crack spacing, stable drainable porosity) were notavailable, these were selected according to expert judgement. For the heavy clay soil atWytham which is very similar to that at Brimstone, the default values were used (crackspacing in top layer = 0.05 m, stable drainable porosity = 5%). Crack spacing for the moremoderate clay loam at Cockle Park was set to a smaller value (0.02 m), whilst stable drainableporosity was set to a larger value (9%) to effectively reduce aggregate size. CRACK-NPenables the user to specify half-lives and Kd values for each soil layer. These were derivedfrom experimental values or literature data. If field half-lives were used, these had to becorrected for temperature and moisture effects as this subroutine cannot be switched off inCRACK-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-aloneversion of MACRO or default values were retained to avoid introducing unnecessary user-subjectivity. One exception is for the parameter describing the relative proportion of sorptionsites in the micropore and macropore regions (FRACMAC). The default value for thissensitive parameter is 0.1 (10% of sorption sites are in the macropores), but the value shouldbe adjusted for any given soil. Logically, FRACMAC should equate to the macroporosity asa fraction of the total porosity so that sorption is set equal in each domain. In practice, thisresults in values of FRACMAC which are rather large (generally 0.02-0.30) and whichartificially restrict movement of pesticide in the macropores. A value of 0.01-0.04 (1-4% ofsorption sites in the macropores) is considered more realistic for many soils. In the absenceof reliable guidance on the selection of this parameter, FRACMAC was empirically set to0.01 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 to0.04 for soils with an air capacity of 14% or greater (Cockle Park, Cuckney, Sonning andIsleham series from the SSLRC lysimeters). There were no soils with air capacities in therange 4-14%.
Other MACRO parameters which are difficult to select are those describing the soil hydraulicproperties. The pore size distribution index in the micropores (ZLAMB) was calculated byfitting the Brooks & Corey function (equation 11; Jarvis, 1994) to the measured water releasecurve. Expert judgement was used to establish the water tension at the boundary between thetwo flow domains (CTEN) as this cannot readily be independently estimated. Values rangedfrom 50 cm water tension (5 kPa) for heavy clay soils to 10 cm water tension (1 kPa) forcoarse sands. The water content equivalent to this tension (XMPOR) was then derived fromthe measured water release curve, whilst the conductivity at the boundary (KSM) wasestimated from the above values using the equation given by Laliberte et al. (1968). The poresize distribution index in the macropores (ZN) was calculated from CTEN using equationsbuilt into MACRO_DB . Saturated conductivity (KSATMIN) was derived using the pedo-transfer functions for soils in England and Wales described by Hollis & Woods (1989).Aggregate half-widths (ASCALE) control the movement of water and solute between themicropore and macropore domains. These were selected from basic descriptions of soilstructure using the rules built into MACRO_DB (see Section 2.4) so that an area forsubjectivity was eliminated as far as possible. Where field half-lives were used for a given
24
pesticide, functions correcting rate of degradation for effects of soil temperature and moisturecontent 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 stand-alone version of MACRO. A set of crop input parameters was selected from the validateddatabase 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. Soilhydraulic parameters were calculated within MACRO_DB using the automatic proceduresbased on pedo-transfer functions. The soils database was updated for each of the soils to bemodelled with basic soils information - content of sand, silt, clay and organic carbon, bulkdensity, pH and description of soil structure. The system then automatically calculates all thesoils properties required as input for MACRO and these were used without any changes at allto simulate observed behaviour. The parameter describing the relative proportion of sorptionsites in the micropore and macropore regions (FRACMAC) is automatically set within thesystem according to the soil properties and values are given in the results section for eachdataset.
For PLM modelling, the rate of slow and fast water movement through the soil has to bespecified. The former was set to 5 cm/day, whilst the fast flow rate was set to a value whichallowed 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 were70 cm, 75 cm, 105 cm and 100 cm, respectively. The percentage of fast mobile phase was setaccording to expert judgement for the clay soils based upon knowledge of the hydrology ofthe various soils. For the SSLRC lysimeter dataset, there was felt to be insufficient evidenceto support an independent estimate of the percentage of fast mobile phase in the fiveintermediate soils and the parameter was set by calibration to observed results. If a drainagesystem was present, all water leaching from the bottom of the soil profile was assumed to beintercepted by the drains.
SWAT has relatively simple input requirements. All values were taken from measured dataapart from hydraulic conductivity at field capacity which was derived from a pedo-transferfunction 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 consistedof point rates of drainflow and isoproturon concentrations (maximum nine per event) togetherwith total drainflow monitored on four plots at Brimstone Farm over the first two key eventsof three successive years. It should be noted that only two plots (5 and 20) were truereplicates (see Appendix 1). For simplicity, model simulations are initially compared toresults for just one plot (5). However, there was considerable variability between plots andthis 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 potentialevapotranspiration was estimated using Linacre’s equation (Linacre, 1977). If not otherwisestated, 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.
25
4.1.1 LEACHP - Brimstone Farm
Simulation by LEACHP of rates of drainflow at Brimstone for the two events in each of threeseasons is shown in Figure 1. Note that there is no experimental information between thestorm 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 thenext two seasons. The timing of the events was well simulated, but peaks in rate of flow wereunder-estimated apart from a single occasion in December 1995. Concentrations ofisoproturon simulated in drainflow were greatly below those observed (maximum simulatedin any of the three years 0.02 µg/l) confirming the expected poor performance of LEACHP onthis soil type where preferential flow through cracks and fissures is the dominant hydrologicalpathway.
Figure 1 Comparison between measured rates of drainflow from Plot 5 at BrimstoneFarm and those simulated by LEACHP
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26
4.1.2 CRACK-NP - Brimstone Farm
Water and solute movement observed at Brimstone Farm in previous studies were describedsuccessfully with CRACK-NP 1.0 (Armstrong et al., 1995a, b), although simulations withVersion 2.0 are not as good (see Section 2.2). It was expected, therefore, that theexperimental data from later seasons at Brimstone might be relatively well simulated by themodel. The input file set up by the model authors to simulate drainflow and isoproturonconcentrations during winter 1990/91 is provided with the model and was applied to datafrom 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 watercontents 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 (thesesimulations are not shown in Figure 2). Changing the initial depth of the water table from thedefault value (0.99 m) to drain depth (0.55 m) had no marked effect. The simulated drainflowis 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. Themodel simulated 55 and 74 mm of evaporation from the soil over the 1993/94 and 1994/95monitoring periods, respectively Given that the relevant periods were rather short (39 and 45days, respectively), the temperatures were rather low and the crop small at that time of theyear, these volumes appear to be too large. The actual predicted evaporation even exceededpotential 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 andonly water exceeding this amount enters the soil profile. This canopy interception capacity isconsidered to increase linearly from zero at emergence to its maximum at the date ofmaximum leaf area. The maximum was set to a rather large value (5 mm) and although theactual interception capacity was not greater than 1.5 mm at the relevant intervals, this meantthat the crop surface was wet over a considerable time. As the evaporation from a wet canopywas assumed to exceed potential evapotranspiration by a factor of 1.5, total water loss by thispathway amounted to unreasonably large volumes. The assumption of a linear increase ofcrop interception capacity from emergence onwards is unrealistic for winter sown crops. Inaddition, the default values for both crop interception capacity and the correction factor forwet canopy evaporation appeared to be too large. When these parameters were decreased tomore realistic values, the model destabilised and crashed. In an attempt to improve thesimulation in spite of this problem, water loss through the crop canopy was artificiallydecreased by assuming that the soil was bare over the simulation periods 1993/94 and1994/95. 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.
27
Figure 2 Comparison between rates of drainflow and isoproturon concentrations fromPlot 5 at Brimstone Farm and those simulated by CRACK-NP (application on 02/11/93,17/11/94 and 30/10/95)
simulated assuming bare soil
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28
In 1993/94, the onset of drainflow was simulated to occur earlier than that observed and thedrainflow intensities monitored from 13-14 November and 7-8 December 1993 were under-estimated. However, no drainage was simulated on 4 and 5 November 1993 if the run wasstarted two months before application. In 1994/95, the simulated maximum flow rate duringthe first leaching event (1.16 mm/hour) exceeded that observed from plot 5, but a betteragreement was achieved for plots 9, 15 and 20 (maximum flow rates 2.2, 1.9 and 1.4mm/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 isoproturonconcentrations in drainflow agreed relatively well with those observed in 1993/94 althoughthe simulation of hydrology was very poor over this period (Figure 2). In the second andthird season, for which the hydrograph was better matched by CRACK-NP, measuredisoproturon concentrations were markedly over-estimated.
In conclusion, the model could not predict water and solute movement at Brimstone Farmwith sufficient accuracy, even though the simulations were based on an input file set up forthe same site. In each season, the model was able to represent either drainflow or isoproturonleaching, but not both. Discrepancies between simulated and measured data showed nounderlying trends or consistency between years. Minor changes of input parametersdestabilised the model which then crashed. Parameterisation of the model according to expertjudgement was, thus, not possible even for a situation very similar to that for which thedefault file was provided.
The mis-match between CRACK-NP simulations and observed drainflow and pesticideconcentrations might to some extent be attributed to uncertainties involved in the estimationof hourly rainfall which is required as a model input. A utility (metconv.exe) is providedoutside the model to calculate hourly data from the daily values. These calculations are basedon 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 themanual (i.e. the peak rainfall intensity equals 30% of total daily rainfall) and this value wasused for the simulations reported in Figure 2. To test the sensitivity of the model output forchanges in this parameter, further simulations were carried out using a value for thisproportion of either 0.1 or 0.6. The results are demonstrated in Figure 3 for the first and thesecond event in 1993/94 together with the hourly rainfall calculated with the metconv.exeutility.
29
Figure 3 CRACK-NP simulations of drainflow and isoproturon concentrations indrainflow from the Brimstone site over the first and second event in 1993/94for different hourly rainfall patterns (maximum hourly rainfall = 0.1, 0.3 or0.6 x total daily rainfall)
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30
Changing the hourly rainfall pattern had a marked influence on the simulations by CRACK-NP (Figure 3). However, the results shown are extremely complex with no clear relationshipbetween the pattern of hourly rainfall generated and either rate of drainflow or concentrationsof pesticide simulated. Maximum isoproturon concentrations, which were simulated on thebasis 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 observedconcentrations at Brimstone Farm (factors up to 79) demonstrates that the uncertainty in thetested 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 experimentalvalues given by the model authors (0.199-0.467). The analysis suggests that 0.3 is probablythe 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 atBrimstone Farm. The boundary between micropores and macropores was set to 5 kPaaccording to expert judgement and then hydraulic parameters were selected around thisboundary according to measured properties. The same parameter set was used for each of thethree seasons with only weather data, time of application and crop growth varied. In theabsence of measured values, initial soil water content was set to establish drainageequilibrium (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 periodwas simulated before any of the measured events. Nevertheless, by varying the initial soilmoisture content, it might be possible to better simulate the first event of the season (but notthe 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 onlydaily rainfall data were available. The magnitude of drainflow was also well matched in twoof the three seasons (93/94 and 95/96), but there appears to be a considerable over-estimate ofpeak drainflow rates from plot 5 for 94/95. As for CRACK-NP (Section 4.1.2), it should benoted that simulated drainflow for the first event of the 94/95 season matched that from theother three plots much better (observed maxima 2.2, 1.9 and 1.4 mm/hour). Pointconcentrations of isoproturon in drainflow were not well simulated. Results for the 93/94season were best, but under-estimated peak concentrations by factors of 4 and 2 for the firstand second events, respectively. In both subsequent seasons, peak concentrations weregreatly over-estimated by MACRO. The model gave maximum values of 566 and 57 µg/l in94/95 and 95/96, respectively, whereas observed maxima from any of the four plots were 84and 1.2 µg/l, respectively.
31
Figure 4 Comparison between rates of drainflow and isoproturon concentrations fromPlot 5 at Brimstone Farm and those simulated by MACRO (application on02/11/93, 17/11/94 and 30/10/95)
Drainage
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20
30
40
50
60
17/12/95 27/12/95 06/01/96 16/01/96
Iso
pro
turo
n (
µg/l)
observed MACRO
32
The results given in Figure 4 suggest that the input parameters selected for MACRO were nottransferable between seasons. In part, this is accounted for by the absence of information oninitial water contents at the start of the simulation and the use of daily rainfall with an averageintensity rather than hourly data. However, even with this information, the enormousvariability in concentrations of isoproturon leaving the site in drainflow would not beaccurately simulated for all three seasons. The rate of application in the final season was onetenth of that in the first two, but even if concentrations are normalised to a single applicationrate, the range of maximum concentrations observed over the three seasons still spans twoorders of magnitude. Calibration of the model to reproduce results for any selected seasonwould be relatively simple, but it was not possible to produce a single parameter set toadequately simulate all three seasons. The four plots at Brimstone Farm for which results areavailable are not replicates in terms of drainage treatment and there is additional variability inresults 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 thebehaviour of isoproturon at Brimstone Farm are not being described by the model and/or arenot 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 parameterfile for the Denchworth soil at Brimstone Farm. The proportion of sorption sites within themacropore region (FRACMAC) was set by the system to 4%. The results of the simulationswith MACRO_DB are shown in Figure 5.
The principal difference between the input parameters used with the stand-alone version ofMACRO and those selected automatically by MACRO_DB was in the position of theboundary between micropores and macropores. In the former, this was set at 5 kPa watertension (field capacity), whereas the automatic procedure set the boundary at 2 kPa in thetopsoil and 3.2 kPa in the subsoil. The consequence of this difference for the MACRO_DBsimulation is that the soil would have to be relatively wetter before macropore flow would beinitiated, the conductivity of the micropore domain would be greater and the overallimportance of macropore flow would be reduced. Figure 5 demonstrates the effect of thischange with MACRO_DB simulating smoother drainflow hydrographs than MACRO (seeFigure 4). Peak rates of drainflow are smaller for MACRO_DB and a number of events areeither not simulated or greatly reduced in importance. Each event is followed by aconsiderable 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 thetwo methods, it is considered that the simulation of drainflow from MACRO is morerepresentative of the rapid responses to drainflow observed from this soil than the simulationusing MACRO_DB. MACRO_DB gave a better simulation of rates of drainflow thanMACRO for the first event of the 1994/95 season for plot 5 (and consequently a worsesimulation for the other three plots).
33
Figure 5 Comparison between rates of drainflow and isoproturon concentrations fromPlot 5 at Brimstone Farm and those simulated by MACRO_DB (application on02/11/93, 17/11/94 and 30/10/95)
Drainage
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
02/11/93 12/11/93 22/11/93 02/12/93 12/12/93
Dra
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(m
m/h
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r)
Isoproturon
0
100
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02/11/93 12/11/93 22/11/93 02/12/93 12/12/93
Iso
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0.00
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0.60
0.80
1.00
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07/12/94 12/12/94 17/12/94 22/12/94 27/12/94 01/01/95
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07/12/94 12/12/94 17/12/94 22/12/94 27/12/94 01/01/95
Iso
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µg/l)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
17/12/95 27/12/95 06/01/96 16/01/96
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m/h
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0
5
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35
40
17/12/95 27/12/95 06/01/96 16/01/96
Isopro
turo
n (
µg/l)
observed MACRO_DB
34
MACRO_DB simulated smaller concentrations of isoproturon in drainflow from BrimstoneFarm than MACRO. This was a direct result of the lesser influence of macropore flow in themodel run with MACRO_DB where hydraulic parameters were selected according to pedo-transfer functions . In addition, sorption within the macropores was four times as great forruns with MACRO_DB and losses to drains in rapid bypass flow would thus be furtherdecreased. Zero loss to drains was simulated in the first season when the largestconcentrations of isoproturon were observed. In 1994/95, the maximum concentration in thefirst event was under-estimated, whilst that in the second event was over-estimated by a factorof 20. In common with all other models, concentrations in the third season were over-estimated by more than an order of magnitude. It is not possible to say whether parameterselection 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 thetwo 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 withMACRO_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 wasset to 95% to reflect the domination of bypass flow at the site. This value was subsequentlydecreased to 80% in an attempt to improve the fit to observed concentrations of isoproturon indrainflow. Initial soil moisture deficits for the three seasons (i.e. the amounts of waterrequired to increase soil moisture to field capacity) were calibrated to values whereby thesimulated and observed drainflow started approximately on the same date. These were 20mm for 1993/94 and 1994/95 and 90 mm for 1995/96. The measured half-life (75 days at10oC and 80% field capacity) and Kd (2.9 ml/g) for isoproturon were used and the outputcompared to simulations based on the literature-derived data (half-life = 30 days at 10oC, fieldcapacity and Koc = 100 ml/g).
Drainflow from the four plots at the Brimstone site during the first two key events in threesuccessive 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 thatsimulated with PLM
Year Event Observed SimulatedPlot 5 Plot 9 Plot 15 Plot 20
1993/94 1 4.3 3.9 5.5 6.6 3.82 8.8 13.4 8.7 10.5 8.6
1994/95 1 5.8 12.0 12.8 2.5 3.52 22.4 15.5 26.7 6.9 24.4
1995/96 1 57.8 59.9 45.1 43.0 44.32 9.1 12.2 2.7 3.0 10.8
35
PLM outputs are given at a daily resolution. Therefore, data measured at a higher resolutionsuch as those from Brimstone farm may not be represented in detail by the model. Takingthis into account, the observed drainflow is represented relatively well by the model. Incontrast, isoproturon concentrations were markedly over-estimated (Figure 6). Thus, whilstCRACK-NP and MACRO under-estimated concentrations in the first season and over-estimated them in the two subsequent seasons, PLM over-estimated concentrations by aconsiderable margin in all three seasons. Decreasing the half-life from 75 to 30 days andincreasing Kd in the topsoil from 2.9 to 3.6 ml/g gave slightly smaller concentrations, butthere is still a considerable over-estimation of observed data.
Figure 6 Isoproturon concentrations in drainflow from Plot 5 at Brimstone Farm andthose simulated by PLM (application on 02/11/93, 17/11/94 and 30/10/95)
0
200
400
600
800
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1200
02/11/93 12/11/93 22/11/93 02/12/93 12/12/93
Isopro
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n (
µg/l)
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600
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07/12/94 12/12/94 17/12/94 22/12/94 27/12/94 01/01/95
Iso
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µg/l)
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40
50
60
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80
17/12/95 27/12/95 06/01/96 16/01/96
Isopro
turo
n (
µg/l)
observed PLM (DT50 = 75 d, Kd = 2.9 ml/g) PLM (DT50 = 30 d, Kd = 3.6 ml/g)
36
Decreasing the percentage of fast mobile phase from 95 to 80% had no significant effect onPLM simulations for the 1993/94 season, but greatly reduced the amount of drainflow. Theassociated loss of isoproturon was also reduced between observed events in 1994/95 and1995/96. However, concentrations of isoproturon simulated during the events were almostunchanged 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 agiven 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 under-estimated 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 magnitudeof 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 thetime from application to the storm event. As this was shortest in 1993/94, the largestconcentrations were simulated in this year. Clearly, CRACK-NP and MACRO includeadditional effects which resulted in much larger concentrations simulated for the secondseason than for the first.
4.1.7 Overview - Brimstone Farm
Point rates of drainflow and associated concentrations of isoproturon were available for fourplots for two storm events in each of three successive seasons. In addition, total drainflow butnot loss of isoproturon was available for each event. There was considerable variationbetween the four plots which arose partly from the natural variability expected in this highly-structured soil and partly from the fact that they were not replicates in terms of drainagetreatment (see Section 3.1). There was no information between the two events so that it wasonly possible to evaluate model performance over the duration of the storm. The observedmaximum rate of flow and concentration may not have matched the actual maximum over thestorm event, but the graphs depicted naturally draw the reader to make the comparison withthe equivalent maxima simulated by the model.
Notwithstanding the above comments, the Brimstone Farm experiment provides a highquality dataset on a soil which is extremely vulnerable to preferential flow through cracks andfissures to the artificial drainage system. Table 4 summarises simulated flows for each of thesix events and compares them with the mean and standard deviation for the four plots atBrimstone. There is no consistent pattern of one model performing better than another. PLMand SWAT were best in the first season, all models performed reasonably in the second andMACRO 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 ofthe six events, MACRO and SWAT for four events and PLM for five events. It should benoted 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.
37
Table 4 Comparison between total flow (mm) observed for the six events at BrimstoneFarm 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)
11.613.5
10.015.1
5.911.7
3.524.4
9.816.4
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
44.310.8
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 leaveBrimstone in drainflow. Maximum values are given in Table 5, again with the mean observedmaximum from the four plots and the associated standard deviation. Of the 20 simulatedmaximum concentrations reported in Table 5, only one (CRACK-NP for the second event in1993/94) falls within one standard deviation of the mean observed, indicating the difficulty ofsimulating this site without calibration. Taking a broader measure of acceptability of withinone order of magnitude of the observed mean, CRACK-NP, MACRO and SWAT wereacceptable for both events in 1993/94 and the first event in 1994/95. For the same threeevents, PLM was acceptable for two events and MACRO_DB for one. None of the modelsgave 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) observedin the six events at Brimstone Farm and those simulated in uncalibrated runswith the models
Event Observed* CRACK-NP MACRO MACRO_DB PLM SWAT
93/94 First event Second event
465 (132)134 (47)
156141
69.155.7
00
967682
14043.4
94/95 First event Second event
65.1 (14.7)2.6 (2.3)
527206
566524
8.250.1
808613
80.237.3
95/96 First event Second event
0.64 (0.41)0.21 (0.28)
24.4 5.3
58.512.4
35.1 2.9
51.335.4
8.23.9
* 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 seasonswhich the models were not able to simulate. Only PLM and SWAT correctly ranked theseasons in terms of maximum concentrations (i.e. 1993/94 > 1994/95 > 1995/96) and SWATappeared to give the best simulation of maximum concentrations over all six events. All ofthe simulations reported above are vast improvements on those obtained with non-preferential
38
flow models such as LEACHP (Section 4.1.1). However there are clearly important processescontrolling pesticide losses at Brimstone Farm which are either not accurately treated by thepreferential flow models or are not evident from the dataset supplied (e.g. structuralvariations, shrink-swell status). It can be concluded that simulation of such a heavy clay soilwithout 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 atBrimstone Farm and Wytham. This is demonstrated by the significant component of flowbetween storm events and the smaller concentrations of isoproturon detected in drainflow.Daily rates of drainflow and point concentrations of isoproturon and trifluralin in drainflowthroughout the winter of 1990/91 were used for model evaluation. There were no site-specificmeasurements for the water release curve at Cockle Park so, where required, these were takenfrom the SEISMIC database for the representative profile of Dunkeswick series under arablecultivation (Hollis et al., 1993). Rainfall was supplied on a daily resolution together withmaximum and minimum temperature. Potential evapotranspiration was calculated usingLinacre’s equation (Linacre, 1977). If not otherwise stated, Kd values used as inputparameters for Cockle Park modelling were calculated from the soil organic carbon contentand 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, thefield 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 trifluralinat Cockle Park. Input values were taken from measured data wherever possible. Drainflowwas approximated by simulating leaching to drain depth (50 cm) and assuming all leachatewas intercepted by the drainage system. Field half-lives of 35 and 180 days for isoproturonand trifluralin, respectively, were set constant throughout the model run.
Although LEACHP does not include any description of preferential flow, simulated drainflowmatched reasonably well to that observed for this clay loam soil (Figure 7). Peak rates offlow were under-estimated, whereas flow between events was over-estimated. Totaldrainflow simulated for the period was 248 mm (90% of observed). Preferential flow isknown to be extremely important for pesticide transport in such soils and LEACHPcompletely failed to describe the movement of either compound to drains. The major eventsfor isoproturon loss were missed and only a small breakthrough at the end of the season wassimulated (Figure 8). This pattern of breakthrough is typical of models without a descriptionof preferential flow. No losses of trifluralin were simulated because of the strong sorption tosoil, whereas concentrations up to 0.06 µg/l were observed in drainflow over the winterperiod.
39
Figure 7 Comparison between measured rates of mole drainflow from Cockle Park andthose predicted by LEACHP
observed
0
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Time from application (d)
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Time from application (d)
Dra
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Figure 8 Comparison between observed concentrations of isoproturon in mole drainflowfrom Cockle Park and those predicted by LEACHP
Isoproturon
0
1
2
3
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5
6
0 20 40 60 80 100 120 140
Time from application (d)
Iso
pro
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g/l)
observed LEACHP
40
4.2.2 CRACK-NP - Cockle Park
The original hydrological model CRACK was developed to describe leaching throughcracking clay soils and was only applied to heavy clay soils of the Denchworth and Eveshamseries (≈ 60% clay). Following incorporation of nitrate and pesticide sub-routines, CRACK-NP has only previously been applied to the Denchworth soil at Brimstone. Therefore, it wasinteresting to check the ability of the model to describe water and solute movement in themore moderate Dunkeswick clay loam over clay soil at the Cockle Park site (38% clay at 60cm depth). The input file provided for the Denchworth soil at Brimstone Farm was modifiedto account for soil properties and the experimental design at Cockle Park. The simulation wasstarted on the day of application and the initial water contents were set to greater values thanthose measured, because the model destabilised and crashed if actual soil water values wereused or the simulation was started prior to application. The model requires information aboutthe initial depth of a water table which has to be smaller than or equal to the total depth of theprofile. As significant drainflow does not start before the water table has risen above thedrain depth, this is a sensitive parameter. At Cockle Park no water table was present and thisparameter was set to an artificial value (0.79 m). Smaller values were not possible due toinstability of the model. For the same reason, aggregate sorptivity had to be set to the defaultvalue.
The observed and simulated drainflow are given in Figure 9. The model gave a good matchto both the timing of drainflow and the peak flow rate. However, cumulative drainflow fromCRACK-NP was only 69% of that observed because flow between events was under-estimated. Isoproturon concentrations were over-estimated by more than two orders ofmagnitude (Figure 10). The assumptions inherent in CRACK-NP (i.e. negligible movementof water and solute in the micropores) are probably not valid for the clay loam soil at CocklePark (N.Jarvis, personal communication) and this appears to be confirmed by the resultspresented for isoproturon. An alternative explanation is that preferential flow in cracks is notinitiated at the soil surface as simulated by CRACK-NP, but at the base of the plough layerwhere a temporary perched water table is known to develop (A. Armstrong, author’scomments). Preferential flow generated at depth will clearly contain less pesticide thansimulated 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 simulatedresults, it can be concluded that the model in its current form should not be applied to soilswhere clay content decreases significantly below 50-60%.
Maximum concentrations of trifluralin simulated by CRACK-NP over-estimated observedvalues by four orders of magnitude and were only slightly smaller than those for isoproturondespite the very large difference in adsorption properties. Simulated total losses of trifluralinwere actually larger than those of isoproturon, presumably because the former is morepersistent. These results were checked by simulating a hypothetical application of trifluralinat 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 wasconfirmed. CRACK-NP assumes that sorption of pesticides is limited to the soil aggregateswith no sorption within the cracks (Adriaanse et al., 1997). Once pesticide enters preferentialflow in the cracks, there is thus little if any potential for further attenuation. As results withtrifluralin at Cockle Park were reproduced for Brimstone Farm, it seems likely that this is theprincipal reason for the large over-estimate of concentrations of this strongly-sorbedpesticide. If this is the case, then this assumption would appear not to be valid for CocklePark.
41
Figure 9 Comparison between measured rates of mole drainflow from Cockle Park andthose predicted by CRACK-NP
observed
0
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Time from application (d)
Dra
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Time from application (d)
Dra
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Figure 10 Comparison between observed concentrations of isoproturon and trifluralin inmole drainflow from Cockle Park and those predicted by CRACK-NP
Isoproturon
0
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400
500
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700
800
0 20 40 60 80 100 120 140
Time from application (d)
Isopro
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µ g/l)
Trifluralin
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500
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Time from application (d)
Trif
lura
lin (
µ g/l)
observed CRACK-NP
42
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 at1.5 kPa in the topsoil and 2.5 kPa in the subsoil. As there was no description of soil structureat 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 runfor 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 1990/91season and variation in rate according to soil temperature and moisture content wasminimised.
MACRO gave a reasonable simulation of the measured hydrograph, particularly early in theseason (Figure 11). An event 45-50 days after application was not simulated and peak flowsfrom 90 to 130 days after application were under-estimated. This suggests that simulatedevapotranspiration was greater than that observed and this is reinforced by the total simulateddrainflow (225 mm) which was only 81% of that observed. As the simulation covered thewinter months when actual evapotranspiration approximated to potential evapotranspiration,it is most likely that the values for potential evapotranspiration used as input to the modelwere an over-estimate.
Figure 11 Comparison between measured rates of mole drainflow from Cockle Park andthose predicted by MACRO
observed
0
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0 20 40 60 80 100 120 140
Time from application (d)
Dra
inag
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ay)
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Time from application (d)
Dra
inag
e (m
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ay)
43
Concentrations of isoproturon in drainflow for the first significant event after applicationwere well simulated by the model (Figure 12), although the maximum value was over-estimated by a factor of three. Subsequently, concentrations but not their pattern were wellmatched from 40 to 70 days after application. From 90 days after application, allconcentrations were greatly over-estimated (factors of between ten and twenty). As flow wasgreatest during this period, the total loss of isoproturon in drainflow was also over-estimatedby a factor of ten. Thus, although MACRO gave reasonably accurate simulation of the firstpulse of pesticide reaching drainflow, there was a considerable error associated with thesimulation of total loss of pesticide over the season. It is not clear why the model simulatessuch large concentrations of isoproturon late in the season. One possibility is that this resultsfrom slower leaching through the micropores implying that the input parameters place toomuch emphasis on matrix flow relative to conditions at the site. However, such largeconcentrations 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 throughthe micropores. A second explanation is that pesticide moved down the soil profile inpreferential flow early in the season resides in the soil micropores at depth before acting as asource for movement to drains later in the season (N. Jarvis, author’s comments). Changes toparameter values made to address the problem had little effect on the simulation, but thisaspect 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 compoundtrifluralin although very small concentrations of this pesticide were detected in drainflowthroughout much of the season. Further evaluation is recommended to investigate thepredictive capacity of MACRO for more strongly-sorbed pesticides - all of the validationstudies reported in Section 2.3 have been carried out with relatively mobile pesticides. It islikely 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 morestrongly-sorbed pesticides. This will be investigated in an extension to this programme ofwork.
Figure 12 Comparison between observed concentrations of isoproturon in mole drainflowfrom Cockle Park and those predicted by MACRO
Isoproturon
0
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14
0 20 40 60 80 100 120 140
Time from application (d)
Iso
pro
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n (
g/l)
observed MACRO
44
4.2.4 MACRO_DB - Cockle Park
The MACRO input file described in Section 4.2.3 was adjusted to include soil/hydraulic andcrop parameters derived from MACRO_DB. Soil/hydraulic parameters were derived usingthe pedo-transfer functions incorporated into MACRO_DB and based on data for bulkdensity, 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 byMACRO_DB (0.9 kPa in the topsoil and 1.4 kPa in the subsoil) than using expert judgementwith the stand-alone version of MACRO (see Section 4.2.3). This would reduce the effect ofpreferential flow in MACRO_DB relative to MACRO. In the absence of a full soil survey forthe site, information on structure sizes was taken from a standard description for theDunkeswick series contained within MACRO_DB. Crop parameters were taken from withinthe system for a winter wheat crop at a German site with the dates of emergence and harvestadjusted to those at Cockle Park. Pesticide parameters and weather data were the same asthose used with the stand-alone version of MACRO except that the proportion of sorptionsites in the macropores (FRACMAC) was set to 0.06 by MACRO_DB whereas a value of0.04 was used in the stand-alone version.
Figure 13 Comparison between measured rates of mole drainflow from Cockle Park andthose predicted by MACRO_DB
observed
0
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Time from application (d)
Dra
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Time from application (d)
Dra
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45
As with LEACHP, MACRO_DB gave a reasonable approximation to the observed pattern ofdrainflow, 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 ofthe pattern of isoproturon concentrations in drainflow was poor (Figure 14). Although timingof 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 ofisoproturon 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 topredict any losses of trifluralin to drains over the monitoring period. It can be concluded thatthe parameter set selected using MACRO_DB under-estimated the effect of macropore flowon pesticide transport at the site.
Figure 14 Comparison between observed concentrations of isoproturon in mole drainflowfrom Cockle Park and those predicted by MACRO_DB
Isoproturon
0
1
2
3
4
5
6
0 20 40 60 80 100 120 140
Time from application (d)
Iso
pro
turo
n (
g/l)
observed MACRO_DB
4.2.5 PLM - Cockle Park
To simulate pesticide movement at Cockle Park, the percentage of fast mobile phase was setto 60%. This was assumed to be a reasonable value for the Dunkeswick soil. Measureddrainflow was reasonably well simulated by the model although rates of flow between eventswere consistently over-estimated as the model simulated a slow leaching to the drains (Figure15). Concentrations of both isoproturon and trifluralin were markedly over-estimated (factorsof up to 950). In an attempt to improve simulation of pesticide concentrations, the percentageof fast mobile phase was reduced to 30%. With this value, simulation of drainflow patternsdeviated considerably from those observed (Figure 15). There was a significant reduction inpredicted concentrations of the two herbicides in drainflow, but these still greatly exceededmeasured values (Figure 16). Using a literature half-life of 60 days for trifluralin instead ofthe experimental value (180 days) gave no significant improvement of the fit to observedconcentrations. The reasons for the very poor simulation of pesticide losses at Cockle Parkare not clear, but may relate to an interaction between the parameter for the percentage of fastmobile phase and the air capacity of the soil (see Section 4.3.5). Topsoil air capacity was15.6% at Cockle Park, compared to 4.0 and 3.0% at Brimstone Farm and Wytham,
46
respectively. It is concluded that PLM is not suitable for application to the clay loam soil atCockle Park without calibration.
Figure 15 Comparison between measured rates of mole drainflow from Cockle Park andthose predicted by PLM
60% fast flow
0
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16
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Dra
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Figure 16 Comparison between observed concentrations of isoproturon and trifluralin inmole drainflow from Cockle Park and those predicted by PLM (30% fastmobile phase)
Isoproturon
0
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Time from application (d)
Isopro
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Trifluralin
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Time from application (d)
Triflu
ralin
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47
4.2.6 SWAT - Cockle Park
An evaluation of SWAT against data from Cockle Park has previously been reported byBrown & Hollis (1996) and only a brief reprise is given here. SWAT is only intended topredict rapid flow during storm events although a catchment version (SWATCATCH)describing the complete water balance is available and is incorporated into the EnvironmentAgency’s POPPIE system (Hollis & Brown, 1996). Figure 17 shows that SWAT was able tomatch 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 theground 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 andlosses of rapid throughflow over storm events predicted by SWAT
0
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Time f rom application (d)
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Observed SWAT rapid flow
Concentrations of isoproturon and trifluralin in drainflow were reasonably well simulated bySWAT (Figure 18). Peak concentrations were over-estimated by a factor of 2.5 forisoproturon and under-estimated by a factor of 2 for trifluralin. Concentrations more than 100days after application were very well matched for isoproturon, but less so for trifluralin wherea small increase in concentration with time was observed. Although the model only simulatedflow during storm events, larger observed concentrations generally coincided with times atwhich rapid flow was simulated. Results suggest that the simple approach adopted by SWATmay be appropriate for two contrasting pesticides at this site. In particular, SWAT was theonly one of the models evaluated which was able to simulate the small concentrations of thestrongly-sorbed compound trifluralin detected in drainflow.
48
Figure 18 Comparison between observed concentrations of isoproturon and trifluralin inmole drainflow from Cockle Park and those predicted by SWAT
Isoproturon
0
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Time from application (d)
Iso
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Time from application (d)
Tri
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4.2.7 Overview - Cockle Park
There was great variability in the accuracy with which the various models simulated results atCockle Park (see Table 6). Using the same inputs for potential evapotranspiration, all of themodels under-estimated total drainflow, but the discrepancy with the observed total wassmaller 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 andcomparison to observed results.
Parameter Observed LEACHP CRACK-NP MACRO MACRO_DB PLM1 SWAT
Total drainflow (mm)
Loss of IPU (mg/m2)
Max conc of IPU (µg/l)
Loss of trifluralin (mg/m2)
Max conc of trifluralin (µg/l)
277
0.141
4.2
0.001
0.06
248
0.019
0.3
0
0
187
20.6
729
44.0
499
225
1.53
13.1
0
0
231
0.0004
0.14
0
0
233
26.2
506
1.21
10.2
83.1*
0.171
10.3
0.0001
0.02
1 results for 60% fast mobile phase,* Fast flow only
For losses of isoproturon, the best simulation was obtained with SWAT. Despite itssimplicity in simulating only fast flow (30% of the total observed), SWAT gave an excellentsimulation of the total loss of IPU and over-estimated the maximum concentration by a factorof 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 losseslate in the season than those observed (concentrations over-estimated by factors of ten totwenty) meant that the total loss of IPU was an order of magnitude larger than that observed.
49
Parameters selected using MACRO_DB reduced the effect of preferential flow on the loss ofisoproturon to drains which was greatly under-estimated. Both CRACK-NP and PLM greatlyover-estimated the observed loss of isoproturon. This suggests that CRACK-NP should notbe applied to such a clay loam soil with 30% clay in the topsoil and a significant componentof matrix flow. Results for PLM were more surprising, but the large proportion of fast mobilephase necessary to simulate patterns of drainflow resulted in maximum concentrations ofisoproturon which were two orders of magnitude larger than those observed. When theproportion of fast mobile phase was halved, flow was not acceptably simulated, whilstconcentrations of isoproturon were still over-estimated by a factor of 25. LEACHP whichwas used as a benchmark for non-preferential flow models did simulate a small breakthroughof isoproturon late in the season, but this did not match the pattern of losses which weredominated by movement soon after application.
Cockle Park was the only dataset where information on the movement of a strongly-sorbedpesticide was available. Results for trifluralin (Koc = 4000 ml/g) divide the models into threegroups. CRACK-NP and PLM again over-estimated concentrations. For CRACK-NP, thiswas particularly marked with maximum concentrations of trifluralin only slightly smaller thanthose of isoproturon and total losses of the more persistent trifluralin actually larger. Thisresult raises serious questions over the pesticide routines incorporated into CRACK-NP andwas confirmed by hypothetical simulations of trifluralin for the Brimstone dataset whichagain resulted in concentrations almost as large as those of isoproturon. Neither MACRO,MACRO_DB, nor LEACHP simulated any loss of trifluralin to drains. Althoughconcentrations were relatively small, the ability of the models to simulate leaching of more-strongly sorbed compounds requires further attention (almost all validation studies have beencarried out with relatively mobile pesticides). The only model to accurately simulate the traceconcentrations of trifluralin moving in drainflow throughout the season was SWAT. Coupledwith the accurate simulation of isoproturon, it can be concluded that this extremely simplemodel performed best for Cockle Park.
4.3 SSLRC Lysimeters
Replicate, undisturbed lysimeters from five contrasting soil types were monitored over twosuccessive seasons. Volumes of leachate and concentrations of bromide and isoproturon weremeasured at intervals of 1-4 weeks. Rainfall and air temperature were monitored on-site andpotential evapotranspiration was estimated using Linacre’s equation (Linacre, 1977). As nodegradation or sorption studies were carried out with these soil types, average literaturevalues 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 wherepossible.
50
4.3.1 LEACHP - SSLRC lysimeters
The simulated amounts of water draining from the lysimeters between 18/11/94 and 18/08/96agreed 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 matchbetween observed and simulated bromide concentrations was not as good for the first seasonin 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 lossesgreatly under-estimated for the Sonning, Ludford and Enborne soils and over-estimated bymore than a factor of ten in the Cuckney soil (sandiest soil for which LEACHP shouldperform best). The observed timing of isoproturon leaching through the Cuckney soil wasrepresented relatively well by the model (Figure 20). The model was rather sensitive tochanges in dispersivity and a decrease of this parameter from 100 to 50 mm decreasedisoproturon losses from the Cuckney soil by a factor of 3 to 1.6 mg/m2. Uncertainties aboutthe Kd value may have contributed to the discrepancies between simulated and observed datain the Cuckney soil. No sorption experiments were carried out for this soil and an averageliterature Koc value of 100 was used instead. Based on this value, the compound is verymobile and persistent once it has passed out of the topsoil due to the very low organic carboncontents in deeper layers. Increasing the Koc from 100 to 120 reduced the simulatedisoproturon loss from the Cuckney lysimeters from 5.05 to 2.9 mg/m2. There was no leachingof isoproturon from the Isleham soil and this was correctly simulated by LEACHP.
Table 7 Comparison between observed flows, bromide and isoproturon loads over twoseasons from each of the SSLRC lysimeters and those simulated by LEACHP
Soil Flow Bromide load Isoproturon loadobserved* simulated observed* simulated observed* simulated
(mm) (mm) (g/m2) (g/m2) (mg/m2) (mg/m2)
Cuckney 474; 477 416 11.36; 9.97 11.80 0.36; 0.20 5.05
Sonning 417 395 10.52 9.44 0.92 8.9 x 10-5
Ludford 412 399 6.01; 6.33 7.95 1.53; 4.94 4.4 x 10-2
Enborne 339 347 3.44; 2.97 3.78 2.77; 5.04 2.2 x 10-3
Isleham 335; 356 368 4.02; 3.94 5.77 0; 0 4.4 x 10-7
* where available, values are given for replicate lysimeters
51
Figure 19 Comparison between the observed leaching of bromide through the fiveSSLRC lysimeter soil types and LEACHP simulations
Cuckney
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Time from first application (d)
Bro
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/l)
Sonning
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Time from first application (d)B
rom
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Enborne
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Bro
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Ludford
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Isleham
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Bro
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Lysimeter 1 Lysimeter 2 LEACHP
52
Figure 20 Comparison between the observed leaching of isoproturon through theCuckney lysimeters and LEACHP simulations
Cuckney
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35
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Time from first application (d)
Isopro
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Lysimeter 1 Lysimeter 2 LEACHP
4.3.2 CRACK-NP - SSLRC lysimeters
The concepts built into CRACK-NP mean that the model is only applicable to very heavyclay soils where net water movement within the soil matrix can be considered negligible. Allof the soils tested in the lysimeter experiment were groundwater soils with intermediatetextures and thus fall outside the range of soils to which CRACK-NP applies. The model wastherefore not evaluated against this dataset.
4.3.3 MACRO - SSLRC lysimeters
MACRO was run uncalibrated for leaching of water, bromide and isoproturon. Detailed soildescriptions and physical/chemical analyses for each of the five soil types were available forselection of input parameters. The boundary between the two flow domains was set to 0.9-1.1kPa for Cuckney, Sonning, Ludford and Isleham soils), whilst for the clay loam Enborne soilit was set to 1.5 kPa in the topsoil and 3.5 kPa in the subsoil. As set out in Section 4, theproportion of sorption sites in the macropores was set to 0.01 for Ludford and Enbornelysimeters and to 0.04 for the remaining soils. There were no data for initial moisturecontents, 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 variouslysimeters. With the exception of Ludford soil, the soils were correctly ranked in terms ofrelative flow, although the difference between the soils with greatest and least flow wasunder-estimated by the model. Generally, the model gave a very good simulation of totalflow for the first season, but either under- or over-estimated leaching in the second. Thissuggests that the simulation of the drying and wetting cycle over the period between the twowinters was less reliable than the simulation of leaching over the winter.
53
Table 8 Comparison between observed flows, bromide and isoproturon loads over twoseasons from each of the SSLRC lysimeters and those simulated by MACRO
Soil Flow Bromide load Isoproturon loadobserved* simulated observed* simulated observed* simulated
(mm) (mm) (g/m2) (g/m2) (mg/m2) (mg/m2)
Cuckney 474; 477 451 11.36; 9.97 11.78 0.36; 0.20 1.00
Sonning 417 421 10.52 10.76 0.92 0.52
Ludford 412 454 6.01; 6.33 10.37 1.53; 4.94 4.91
Enborne 339 366 3.44; 2.97 7.40 2.77; 5.04 1.33
Isleham 335; 356 353 4.02; 3.94 8.26 0; 0 0
* where available, values are given for replicate lysimeters
Simulations of bromide leaching with MACRO are shown in Figure 21. Comparison withLEACHP simulations (Figure 19) allows the effect of incorporating preferential flow to beexamined for the five soils. For the two sandiest of the five soils (Cuckney and Sonning) andthe peat soil (Isleham), MACRO gave a worse simulation than LEACHP in the first seasonwhen the model over-estimated the observed concentrations of bromide. However, in thesecond season, MACRO was better able to simulate the larger concentrations of bromide inleachate than LEACHP. Without calibration, MACRO was not able to simulate the pattern ofbromide concentrations from the clay loam Enborne lysimeter, although the magnitude ofconcentrations were much better matched for each season than with LEACHP. Surprisingly,LEACHP gave the better simulation for both seasons for the other soil where extensivepreferential flow was observed (Ludford).
It is known that leaching of pesticides is much more influenced by preferential flow than thatof the non-interactive tracer bromide. Table 8 and Figure 22 show that MACRO gave muchbetter simulations of isoproturon leaching than LEACHP for all four of the soils from whichleaching occurred (neither model simulated leaching from the peaty Isleham soil). Mean totalloss of isoproturon was very well matched for the Sonning and Ludford lysimeters, whilst thatfrom Cuckney and Enborne soils was over- and under-estimated, respectively, each by afactor of approximately three. The pattern of isoproturon concentrations was also relativelywell simulated for the Ludford, Cuckney and Enborne soils, although magnitude ofconcentrations was consistently under-estimated for the latter. By contrast, pattern ofleaching was not well described for the Sonning lysimeter where breakthrough occurredearlier than simulated and observed concentrations were under-estimated in the first seasonand over-estimated in the second. Overall, results of simulations of isoproturon leaching withMACRO through the five soils are extremely encouraging, particularly given that nocalibration was undertaken apart from adjustment of initial water contents to match theobserved onset of flow.
54
Figure 21 Comparison between the observed leaching of bromide through the fiveSSLRC lysimeter soil types and MACRO simulations
Cuckney
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Bro
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Sonning
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Time from f irst application (d)
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Enborne
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Ludford
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Isleham
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Bro
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Lysimeter 1 Lysimeter 2 MACRO
55
Figure 22 Comparison between observed concentrations of isoproturon in leachate fromfour soils and those predicted by MACRO
Cuckney
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Iso
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Sonning
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Enborne
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Ludford
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Iso
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Lysimeter 1 Lysimeter 2 MACRO
4.3.4 MACRO_DB - SSLRC lysimeters
MACRO_DB was used to set up input files for the five soil types based on the parameterestimation techniques built into the system. All soil parameters required for the simulationwere automatically calculated within MACRO_DB from the base soils information (texture,organic carbon, bulk density and aggregate characterisation) obtained from soil descriptionsfor each pair of lysimeters. As with MACRO (Section 4.3.3), the soils parameters placedlittle emphasis on preferential flow in either Cuckney or Isleham soil. For the remaining threesoil types, the simulations resulted in preferential flow being less important than insimulations with the stand-alone version of MACRO based on measured hydraulic propertiesof the soils together with the conception of each soil’s behaviour held by the modeller. Theboundary between the two flow regions (CTEN) selected by MACRO_DB was almostidentical 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 and1.6 kPa in the subsoil) thus reducing the impact of preferential flow. The biggest differencebetween simulations with MACRO_DB and those with MACRO was in the proportion ofsorption sites in the macropore region. In MACRO this was set to 1-4% depending upon soilproperties, whereas MACRO_DB gave values as follows: Cuckney (27%), Sonning (22%),
56
Ludford (17%), Enborne (11%) and Isleham (22%). The result of these larger values wouldbe to increase sorption within the macropore region and thus reduce the impact of preferentialflow on pesticide transport under conditions where preferential flow is important. Wherematrix flow is more important, sorption within the micropores would be less and thus overalltransport of pesticide might be greater.
Apart from the Ludford soil, MACRO_DB ranked the soils correctly for total volume ofleachate (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 fivesoils.
Table 9 Comparison between observed flows, bromide and isoproturon loads over twoseasons from each of the SSLRC lysimeters and those simulated byMACRO_DB
Soil Flow Bromide load Isoproturon loadobserved* simulated observed* simulated observed* simulated
(mm) (mm) (g/m2) (g/m2) (mg/m2) (mg/m2)
Cuckney 474; 477 434 11.36; 9.97 10.45 0.36; 0.20 1.74
Sonning 417 424 10.52 8.81 0.92 0
Ludford 412 435 6.01; 6.33 10.14 1.53; 4.94 0
Enborne 339 384 3.44; 2.97 9.69 2.77; 5.04 0
Isleham 335; 356 397 4.02; 3.94 7.28 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 forLudford, Enborne and Isleham lysimeters. Although the general magnitude of concentrationsof 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 orMACRO. For Cuckney and Sonning lysimeters, timing of the peaks in breakthrough wasdelayed and this was accentuated in the second season. This is likely to have resulted fromthe smaller total volume of leachate simulated relative to that from MACRO and lessdispersivity from default parameters for MACRO_DB relative to those for LEACHP. Thetwo soils found to be most prone to preferential flow were not well simulated for bromidewith both seasons mis-matched for Enborne and an over-estimation of concentrations fromthe sandy clay loam Ludford soil during the first season. Patterns of bromide from theIsleham soil were reasonably well simulated.
57
Figure 23 Comparison between the observed leaching of bromide through the fiveSSLRC lysimeter soil types and MACRO_DB simulations
Cuckney
0
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Time from first application (d)
Bro
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/l)
Sonning
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Time from first application (d)
Bro
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Enborne
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Bro
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Ludford
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Time from first application (d)
Bro
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/l)
Isleham
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Time from first application (d)
Bro
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/l)
Lysimeter 1 Lysimeter 2 MACRO_DB
Total losses of isoproturon simulated by MACRO_DB (Table 9) demonstrated that the inputparameters selected made preferential flow relatively unimportant. The model simulated noloss of isoproturon from four of the five soils whereas only the peaty Isleham soil wasobserved to show no consistent leaching over the experimental period. However, for the
58
Cuckney soil where matrix flow can be expected to dominate, the total loss was over-estimated by a factor of six because sorption in the micropore region was decreased by thesystem giving a large value to FRACMAC. The pattern of concentrations of isoproturon inleachate was also not well matched for Cuckney soil (Figure 24), particularly in the secondseason when observed concentrations were greatly over-estimated. It can be concluded thatthe use of the automatic techniques for parameter selection built into MACRO_DB did notproduce reliable simulations for the three intermediate soils where preferential flow wasfound to have a significant impact upon pesticide leaching (Sonning, Ludford, Enborne). Forall 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 sandyCuckney soil and the organic Isleham soil, the use of the system in its current form for a rangeof intermediate soils cannot be recommended.
Figure 24 Comparison between observed concentrations of isoproturon from theCuckney lysimeter and those simulated by MACRO_DB
Cuckney
0
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Time from first application (d)
Iso
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Lysimeter 1 Lysimeter 2 MACRO_DB
4.3.5 PLM - SSLRC lysimeters
There was felt to be insufficient guidance available to support an independent selection of thepercentage of fast mobile phase for the various soils, so this parameter was calibrated. Themodel was first run against results for bromide leaching through the five soil types with thepercentage of fast mobile phase calibrated to optimise the fit to observed concentrations. Thecalibrated values were then used for simulations of isoproturon movement through thelysimeters. If necessary, a second calibration was carried out to improve the fit to observeddata for isoproturon. A relative weakness in PLM is that multiple solute applications cannotbe simulated and that a pre-run of the model to allow equilibration of soil hydrology is notpossible. Hence, PLM was re-started at the date of application in the second season whilst allother models were run for the entire monitoring period. This might have introduced someerrors concerning leachate concentrations simulated by PLM, especially for the first date afterthe second application (= 374 days).
Total flow through the five soils simulated is given in Table 10 on the basis of the percentageof fast flow calibrated for bromide. Surprisingly, the flows simulated for isoproturon differedfrom those for bromide by not more than 1.4 mm although the percentages of fast flow were
59
changed markedly for three of the soils (Table 11). Total flow through all soils was under-estimated by PLM. To some extent, this can be attributed to the model assumption thatpercolation of water does not occur before water contents for the whole soil profile are abovefield capacity. However, simulated evapotranspiration is the dominant factor controllingleaching through the lysimeters. The model should be used with pan evaporation data whichare 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, thisfactor was set to 0.8, effectively reducing PET by 80%, but the model still over-estimatedevapotranspiration and under-estimated leaching. It would be possible to calibrate the panfactor to better simulate total leaching, although the pan factor is included in the modelspecifically to adjust pan evaporation.
Table 10 Comparison between observed flows, bromide and isoproturon loads over twoseasons from each of the SSLRC lysimeters and those simulated by PLM(calibrated runs)
Soil Flow Bromide load Isoproturon loadobserved* simulated** observed* simulated observed* simulated
(mm) (mm) (g/m2) (g/m2) (mg/m2) (mg/m2)
Cuckney 474; 477 345 11.36; 9.97 10.51 0.36; 0.20 0.24
Sonning 417 329 10.52 6.03 0.92 1.09
Ludford 412 360 6.01; 6.33 4.96 1.53; 4.94 5.01
Enborne 339 273 3.44; 2.97 5.10 2.77; 5.04 2.99
Isleham 335; 356 281 4.02; 3.94 4.62 0; 0 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 wellrepresented by PLM (Figure 25, Table 10). For these soils, the calibrated percentage of fastmobile phase was zero, suggesting that preferential flow was not an important pathway forleaching of this solute. The ‘best-fit’ percentage of fast mobile phase for the Sonning soil wasalso zero, but in this soil, total bromide loss was under-estimated and the pattern of bromideleaching in the second season was not well represented. For the Enborne soil, the optimumvalue for the percentage of fast mobile phase was found to be 95% corresponding to the greatimportance of preferential flow in this soil. However, on the basis of this value, total bromideloads were still under-estimated and leaching over the second season was not well matched bythe model. For the Isleham soil, the fit to bromide results could only be improved by settingthe model to include an effect of anion exclusion.
60
Figure 25 Comparison between the observed leaching of bromide through the fiveSSLRC lysimeter soil types and PLM simulations
Cuckney
0
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30
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50
60
70
80
0 100 200 300 400 500
Time from first application (d)
Bro
mid
e (
mg
/l)
Sonning
0
10
20
30
40
50
60
70
80
0 100 200 300 400 500
Time from first application (d)B
rom
ide
(m
g/l)
Enborne
0
10
20
30
40
50
0 100 200 300 400 500
Time from first application (d)
Bro
mid
e (
mg
/l)
Ludford
0
10
20
30
40
50
0 100 200 300 400 500
Time from first application (d)
Bro
mid
e (
mg
/l)
Isleham
0
10
20
30
40
50
0 100 200 300 400 500
Time from first application (d)
Bro
mid
e (
mg
/l)
Lysimeter 1 Lysimeter 2 PLM
61
Table 11 Percentage of fast mobile phase used to model the leaching behaviour ofbromide and isoproturon through the five SSLRC lysimeter soil types withPLM calibrated to give the best fit to observed results
Soil type Calibrated percentage of fast mobilephase for bromide leaching
Calibrated percentage of fast mobilephase for isoproturon leaching
Enborne
Cuckney
Sonning
Ludford
Isleham
95
0
0
0
0*
40
0
72
57
-
* Anion exclusion set to 75% of the water below wilting point
The percentage of fast mobile phase calibrated for bromide was not transferable toisoproturon (Table 11). It was necessary to include fast flow into the isoproturon simulationsfor 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 of40% which was much less than that found for bromide (95%). The Cuckney soil was the onlyone for which the best fit to measured concentrations in leachate was achieved without anyfast flow for both bromide and isoproturon. Total isoproturon loads from this soil werematched closely by the model and patterns of leaching were reasonably well represented.Simulated isoproturon losses from the Sonning soil agreed closely with those measured, butthe observed pattern of concentrations was not well matched. For both Enborne and Ludfordsoils, PLM gave isoproturon losses similar to those from one of the replicate lysimeters(Table 10). Timing of breakthrough was well simulated and maximum concentrations werealso well simulated in the second season (Figure 26). The Isleham soil was not calibrated asthere was no leaching of isoproturon.
62
Figure 26 PLM simulations of isoproturon leaching through all soils except Isleham calibrated to the observed behaviour of isoproturon (no calibration required forCuckney)
Cuckney
0
1
2
3
4
5
6
7
8
9
10
0 100 200 300 400 500
Time from first application (d)
Isopro
turo
n (
µ g/l)
Sonning
0
1
2
3
4
5
6
7
8
9
10
0 100 200 300 400 500
Time from first application (d)
Isopro
turo
n (
µ g/l)
Enborne
0
10
20
30
40
50
60
70
80
90
100
110
0 100 200 300 400 500
Time from first application (d)
Isopro
turo
n (
µg/l)
Ludford
0
10
20
30
40
50
60
70
80
90
100
110
0 100 200 300 400 500
Time from first application (d)
Isopro
turo
n (
µg/l)
Lysimeter 1 Lysimeter 2 PLM
The best-fit percentage of fast mobile phase for modelling of isoproturon leaching increasedin the order Cuckney<Enborne<Ludford<Sonning. This order is slightly surprising aspatterns of leaching suggested that preferential flow was a more dominant process for theEnborne and Ludford soils than for Sonning. This can partly be attributed to the fact thatwithin PLM the relative predominance of preferential flow is determined for any given soil bya combination of the empirical parameter setting the percentage of fast mobile phase and themaximum amount of mobile water (i.e. the air capacity). Thus, the larger value forpercentage of fast mobile water reported in Table 11 for the Sonning soil relative to theLudford soil is a function of the large air capacity of the Sonning soil and should not beconsidered to indicate that preferential flow is more important in this soil than in the Ludfordsoil. However, even if the relationship to air-capacity is considered, the small proportion offast mobile water for the Enborne lysimeter suggests that preferential flow is less dominant inthis than in the Sonning soil. This does not correspond to our findings on the importance ofpreferential 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 largepotential for transmitting water via matrix flow and this fraction must be filled beforepreferential flow is initiated. Thus, increasing the percentage of fast mobile phase will have
63
virtually no effect on simulations until a critical value is exceeded. At this point, a dramaticincrease in pesticide leaching is observed. The extreme sensitivity of isoproturonconcentrations 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 somewhatreduced and sensitivity occurs at a lower percentage of fast mobile phase. Figure 28 contrastsfor the Sonning and Enborne soils the effect of variation in the percentage of fast mobilephase on the maximum concentration of isoproturon simulated by PLM at any time over thetwo seasons of monitoring. The interaction between the proportion of fast mobile water andthe air capacity makes parameter estimation very difficult. The extreme sensitivity of modeloutput to changes in the percentage of fast mobile phase makes it impossible to establish anappropriate value for this parameter and the use of PLM for intermediate soils is notrecommended. The finding, that parameters calibrated for bromide were not transferable tosimulations of isoproturon behaviour, suggests that use of PLM for such soils should not berecommended even where a calibration step is possible.
Figure 27 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
0
5
10
15
20
25
0 100 200 300 400 500
Time from first application (d)
Iso
pro
turo
n (µ
g/l)
70%
72%
73%
64
Figure 28 Sensitivity analyses contrasting the effect of variations in the percentage of fastmobile phase on the maximum concentration of isoproturon simulated by PLMfor the Sonning and Enborne lysimeters in each of the two seasons ofmonitoring
Sonning
0
100
200
300
400
500
600
700
800
900
0 10 20 30 40 50 60 70 80 90 100
Percentage of fast mobile phase
Ma
xim
um
co
nce
ntr
atio
n (
µg/l)
first year
second year
Enborne
0
100
200
300
400
500
600
700
800
900
0 10 20 30 40 50 60 70 80 90 100
Percentage of fast mobile phase
Ma
xim
um
co
nce
ntr
atio
n (
µg/l)
first year
second year
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 notapplied 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 begenerally stated that a description of preferential flow is not a prerequisite for simulatingsandy soils, particularly as current preferential flow models cannot describe either finger orfunnel flow. Furthermore, models without preferential flow cannot describe movement ofpesticides in heavy clay soils whereas preferential flow models offer improved simulationwith appropriate selection of input parameters and/or calibration. The SSLRC dataset gavethe chance to investigate the need for a description of preferential flow to accurately simulatea range of intermediate soils overlying aquifers which are typical of arable use in England and
65
Wales. As such intermediate soils make up the biggest part of our arable resource, resultswill have important implications, although it should be noted that regulatory modelling mostfrequently concentrates on either very sandy or clay-rich soils. The assumptions built intotwo of the models (CRACK-NP and SWAT) meant that they were not suitable for simulatingthis dataset. As there was no guidance available on selecting the percentage of fast mobilephase for PLM simulations in the various soil types, the model was evaluated followingcalibration for this parameter. Values obtained with PLM are shown in parentheses in Tables12-14 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 fourmodels (Table 12), even though all simulations used the same data for potentialevapotranspiration. Relative amounts of flow simulated for the different soil types also variedfrom model to model, although all models showed greater leaching from the Cuckney,Sonning and Ludford lysimeters than from the Enborne and Isleham cores. Overall, MACRObest simulated the range between maximum and minimum total flow from the various soiltypes (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 thehydrological differences between this range of soils. PLM under-estimated total flow from alllysimeters due to an over-estimation of evapotranspiration caused by the use of panevaporation data adjusted using an empirical pan factor (see Section 4.3.5). As previouslydiscussed, it would be possible to calibrate the pan factor to better simulate total leaching.
Table 12 Comparison between observed total flow from the five soil types and thatsimulated by the four models tested
Soil type Total flow over two winter wheat seasons (mm)Mean observed LEACHP MACRO MACRO_DB PLM
CuckneySonningLudfordEnborneIsleham
476417412339346
416395399347368
451421454366353
434424435384397
(345)(329)(360)(273)(281)
LEACHP gave the best simulation of losses of bromide from the five soils although totalswere generally over-estimated (Table 13). This suggests that preferential flow was not animportant process for the transport of bromide and this was confirmed by calibratedsimulations with PLM where four of the five best-fit simulations had the percentage of fastmobile phase set to zero. MACRO and MACRO_DB gave reasonable simulations of bromidetransport through the coarse-textured Cuckney and Sonning soils, but over-estimated totallosses from the remaining three soils by factors of up to two. This might have implicationsfor the use of MACRO to simulate leaching of exceptionally mobile pesticides.
66
Table 13 Comparison between observed total loss of bromide from the five soil typesand 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
CuckneySonningLudfordEnborneIsleham
10.710.5 6.2 3.2 4.0
11.8 9.4 8.0 3.8 5.8
11.810.810.4 7.4 8.3
10.5 8.810.1 9.7 7.3
(10.5) (6.0) (5.0) (5.1) (4.6)
Despite the poor simulation of total flow, it was possible to calibrate PLM to accuratelysimulate total losses of isoproturon from all five soils (Table 14) although patterns ofconcentrations were not always well matched. Parameters calibrated for bromide leachingwere not transferable to simulations of isoproturon transport. A proportion of fast flow had tobe included in simulations for Sonning, Ludford and Enborne soils, demonstrating thatpreferential flow was important for the transport of isoproturon. It is thus not surprising thatLEACHP was unable to simulate leaching of isoproturon through the Sonning, Enborne andLudford lysimeters (LEACHP did simulate a very small loss for the latter), although the largeover-estimation in the total loss from the sandy Cuckney soil was not expected. Thesimulations generated using MACRO_DB were rather moderate with respect to preferentialflow and there was no improvement in results relative to those from LEACHP except for theCuckney soil where the loss was within a factor of six of that observed. Of the models testedwithout calibration, MACRO was best able to simulate the observed leaching of isoproturonthrough the five intermediate soils. The model gave reasonable estimates of the total loss ofisoproturon from all five soils with estimates within a factor of two for three of the soils andwithin a factor of four for the remaining two soils. Although the model failed to simulate theactual pattern of concentrations of isoproturon from one of the soils (Sonning), results suggesta relatively high predictive ability for MACRO in a range of intermediate soils. It shouldhowever be considered that the modellers involved were relatively experienced in selectingparameters for MACRO.
Table 14 Comparison between observed total loss of isoproturon from the five soil typesand that simulated by the four models tested
Soil type Total loss of isoproturon over two winter wheat seasons (mg/m2)Mean observed LEACHP MACRO MACRO_DB PLM
CuckneySonningLudfordEnborneIsleham
0.280.923.243.91
0
5.050
0.0400
1.000.524.911.33
0
1.740000
(0.24)(1.09)(5.01)(2.99)
(0)
67
4.4 Wytham
Drainflow (three events) and isoproturon concentrations and loads (two events) were used formodel evaluation. Soils information for the site was taken from a description made bySSLRC in autumn 1993. Comprehensive weather data including potential evapotranspirationestimated according to the Penman equation (Penman, 1963) was provided for March 1993 toOctober 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 hourlyresolution and comparisons were made between modelling with daily and hourly rainfall forMACRO (the only one of the models evaluated which can use both). In general, isoproturonhalf-life was set to the measured value (18.2 days at 15oC and 33% w/w) in topsoil. A Koc of77 ml/g was calculated from the mean of two measured Kd values and the organic carboncontent in the topsoil (Appendix 4). With some models, additional simulations were carriedout 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 14December 1993 - 30 April 1994, total rainfall at the site was 306 mm, surface runoff was 1.7mm and drainflow was 76.4 mm. The estimated potential evapotranspiration over this periodwas 133.4 mm, whilst MACRO estimated direct evaporation of intercepted rainfall from thecrop canopy to be 25.2 mm. Even assuming that actual evapotranspiration meets the potentialamount (MACRO predicted a reduction of 13 mm), the total water lost from the soil and cropis 159 mm and the total accounted for at the site is 237 mm, leaving 69 mm of rainfallunaccounted for. Assuming that all flow gauges at the site were functioning correctly, themost likely explanation for this missing water is that it was leaving the site via a secondarydrainage system which was not monitored. The alternative explanation of slow seepage togroundwater is not a possibility at this site. All six of the models tested support the secondarydrainage system explanation with a significant over-estimate of the volume of drainflow bothover 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 beconsidered 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 temperatureand moisture effects. Koc was set to the experimental value of 77 ml/g. The simulation wasstarted 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 theprofile 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 roughlyproportional to rainfall intensity. In contrast, drainflow from the Wytham site was initiatedonly by the most intense rainfall and occurred over very short periods. This discrepancybetween observed and simulated drainflow was seen to varying degrees for all of the modelsused 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/land were far below those observed (maximum 290 µg/l). As expected, the model was notapplicable to this very heavy clay soil where bypass flow is dominant.
68
Figure 29 Comparison between observed rates of drainflow from the Wytham site andthose simulated by LEACHP along with daily rainfall
observed
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 5 10 15 20 25 30
Time from application (d)
Dra
inage (
mm
/hour)
0
5
10
15
20
25
30
Rain (m
m/day)
predicted
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
0 5 10 15 20 25 30
Time from application (d)
Dra
inage (
mm
/hour)
0
5
10
15
20
25
30
Rain (m
m/day)
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 asituation very similar to that for which the model was demonstrated to be valid (Armstrong etal., 1995a, b) In a first run, the input file for Brimstone Farm simulations provided with themodel was only slightly changed (application rate and date, pesticide half-life and Kd, cropdates, drain depth and spacing) to calculate drainage and isoproturon concentrations atWytham.
With the input file from Brimstone Farm, no drainage was predicted to occur from theWytham site. As for Brimstone, this could be attributed to an over-estimation ofevapotranspiration which was estimated to be 63 mm between 12/03/94 and 10/04/94although potential evapotranspiration was only 48.5 mm. For this dataset, the discrepancybetween actual and potential evapotranspiration was even greater than for Brimstone, becausethe simulation period was several months after crop emergence. The assumption of a linearincrease of crop interception capacity from emergence (early November 1993) onwards leadto a maximum possible storage of 2.7-3.4 mm rainfall in March/April 1994. This meant, thatthe crop surface was wet over a considerable time within the simulation interval. Asevaporation from a wet canopy was 1.5 greater than potential evapotranspiration,
69
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 correctionfactor for wet canopy evaporation. However, these changes destabilised the model.Therefore, CRACK-NP was subsequently run assuming a bare soil to artificially decrease theamount of evapotranspiration. In contrast to the previous run, the simulation was started 5½months before application to allow equilibration of water contents (initial water contents wereset to default values) and depth of water table. These changes decreased evapotranspirationto 44 mm (12/03-10/04/94).
Peaks in simulated drainflow for the run with bare soil agreed relatively well with thatobserved (Figure 30). However, measured total drainage from 0 to 30 days after applicationwas over-estimated by a factor of eight due to flow simulated between events when none wasobserved. In addition, CRACK-NP failed to predict significant drainflow during the firstevent. Isoproturon concentrations measured over the first event were greatly under-estimatedbecause of the failure to simulate drainflow accurately, whereas those observed during thethird event after application (day 27/28) were very well matched (Figure 31). If the half-lifein topsoil was set to a literature value of 30 days at 8oC and field capacity together with aliterature 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).
Figure 30 Comparison between observed drainflow from the Wytham site and thatsimulated by CRACK-NP (bare soil run) together with daily rainfall
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0 5 10 15 20 25 30
Time from application (d)
Dra
inage (
mm
/hour)
0
5
10
15
20
25
30
Rain (m
m/day)
observed drainage CRACK-NP rainfall
70
Figure 31 Comparison between observed concentrations of isoproturon in drainflow fromthe Wytham site (first and third events after application only) and thosesimulated by CRACK-NP (bare soil run ; DT50 = 18.2 days, Koc = 77 ml/g)
0
50
100
150
200
250
300
0 5 10 15 20 25 30
Time from application (d)
Isopro
turo
n (
µg/l)
observed CRACK-NP
4.4.3 MACRO - Wytham
Hydraulic input parameters for MACRO were selected as set out in Section 4. The boundarybetween micropores and macropores was set using expert judgement to 5 kPa. Initial watercontent was set to establish drainage equilibrium (i.e. fully wetted but without initiatingdrainflow). The simulation was started several months before application. Pesticide Koc andhalf-life were set to measured values of 77 ml/g and 18.2 d at 15oC, respectively. Theproportion 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 madebetween a simulation with hourly rainfall where intensity will vary each hour and one withdaily 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 togreater peak flows than those observed and flow simulated between events when none wasactually observed. The measured flow over the period shown was 1.8 mm, whereas thatpredicted using daily and hourly rainfall was 18.4 and 16.0 mm, respectively. As discussedabove, a significant proportion of rainfall is not accounted for by the water balance measuredat Wytham and this makes evaluation of simulated drainflow impossible. MACRO doesallow water to seep from a saturated bottom boundary. This was used to approximate asecondary drainage system at the site and an extremely good simulation of drainflow was thenachieved through calibration.
71
Figure 32 Comparison between observed drainflow from the Wytham site and thatsimulated by MACRO based on either daily or hourly rainfall (daily rainfalltotals also shown)
Observed
0.0
0.3
0.6
0.9
1.2
1.5
1.8
0 5 10 15 20 25 30
Time from application (d)
Dra
inage (
mm
/h)
0
5
10
15
20
25
30
Ra
in (m
m/d
)
Daily rainfall
0.0
0.3
0.6
0.9
1.2
1.5
1.8
0 5 10 15 20 25 30
Time from application (d)
Dra
inage (
mm
/h)
0
5
10
15
20
25
30
Ra
in (m
m/d
)
Hourly rainfall
0.0
0.3
0.6
0.9
1.2
1.5
1.8
0 5 10 15 20 25 30
Time from application (d)
Dra
inage (
mm
/h)
0
5
10
15
20
25
30
Ra
in (m
m/d
)
observed drainage MACRO rainfall
The major difference between the simulations with daily and hourly rainfall was in the timingof events. Whereas the simulation with daily values missed the timing of events by up to oneday, the simulation with hourly rainfall matched the timing exactly. Using daily rainfall, itwas possible to simulate the first event 20 days after application, whereas that was not thecase with hourly data. For both simulations, there were small events predicted 24 and 26 daysafter application which were not observed. Figure 33 demonstrates that actual rainfall
72
intensities were seldom greater than the default intensity of 2 mm used by MACRO with dailyrainfall data. For this reason, peak flow rates were generally smaller using hourly data. Therewas also greater flow between events with hourly data as the assumed intensity of 2 mm/hused with daily data reduces the number of hours over which rain is received relative to thatobserved. However, all of these differences are relatively small and it can be concluded thatthe use of daily rainfall data and an appropriate intensity value does not greatly change thesimulation of flow.
Figure 33 Comparison between hourly rainfall at Wytham for the events followingapplication and the default intensity used by MACRO with daily rainfall data
0
0.5
1
1.5
2
2.5
3
3.5
18
19
20
21
22
23
24
25
26
27
28
29
30
Time from application (d)
Rai
nfa
ll (m
m/h
)
Hourly intensity Intensity used by MACRO
Figure 34 shows the two simulations of isoproturon concentrations. There was relatively littledifference between model runs using daily or hourly rainfall, although the latter gave slightlysmaller peak concentrations. Again it can be concluded that there is no significant adverseeffect 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 under-estimated, whereas those in the third event were very well matched. This follows the samepattern as demonstrated for CRACK-NP. There was no monitoring of isoproturon during thesecond event after application.
73
Figure 34 Comparison between observed concentrations of isoproturon in drainflow fromthe Wytham site (first and third events after application only) and thosesimulated by MACRO using either daily or hourly rainfall (FRACMAC =0.01)
0
50
100
150
200
250
300
0 5 10 15 20 25 30
Time from application (d)
Isopro
turo
n (
µg/l)
observed MACRO (daily rainfall) MACRO (hourly rainfall)
One of the input parameters to which MACRO simulations of pesticide transport areparticularly 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 themacropore region and 90% in the micropores, but this value should be changed for a specificsoil. As the value of FRACMAC decreases, retention of pesticide in the macroporesdecreases and any leaching through the macropores will increase. Smaller values for thissensitive parameter will generally increase pesticide transport in soils where macropore flowis a dominant process (see Figure 35). Conversely, smaller values will decrease pesticidetransport in soils where macropore flow is generally precluded as the amount of sorption inthe micropore domain is increased. Logically, FRACMAC might be set to the macroporosityas a fraction of the total porosity so that sorption is set equal in each domain. In practice, thisresults in values of FRACMAC which are rather large (generally 0.02-0.30) and whichartificially restrict movement of pesticide in the macropores. This observation might resultfrom the fact that transport in the macropore region is rather fast; as the model assumption ofinstantaneous sorption cannot be expected to hold within the macropores, a reduction insorption capacity in this region might be used to compensate. A value of 0.01-0.04 (1-4% ofsorption sites in the macropores) is considered more realistic for many soils. In the absenceof a validated method for selecting FRACMAC, caution is advised and it should be noted thatthe sensitivity of output to this parameter may limit the predictive capability of the model.
74
Figure 35 Effect of variation in the proportion of sorption sites in the macropore region(FRACMAC) on the maximum concentration of isoproturon simulated byMACRO for the third event after application at Wytham
0
20
40
60
80
100
120
140
160
180
200
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
Maxi
mum
pre
dic
ted c
once
ntr
atio
n (
µg/l)
4.4.4 MACRO_DB - Wytham
As at Brimstone Farm, the simulation of drainflow at Wytham by MACRO_DB was lessinfluenced by macropore flow than that with MACRO. Again, the boundary betweenmicropores and macropores was shifted from field capacity towards saturation (1.9 and2.7 kPa in the topsoil and subsoil, respectively). In addition, the proportion of sorption sitesin the macropores (2%) was double that estimated using expert judgement for the stand-aloneversion of macro (1%). The hydrograph simulated by MACRO_DB (Figure 36) was similarto that from MACRO using daily rainfall although less peaks in flow were simulated andthere was greater flow between events. As with the other models, total drainflow was greatlyover-estimated (13.2 mm compared to 1.8 mm). Because MACRO_DB did not simulate anyflow in the first event after application of isoproturon, the model did not simulate anypesticide leaving the site (Figure 37). Patterns of concentrations in the third event afterapplication were well simulated by the model although the maximum concentration wasunder-estimated by a factor of 1.8. In this respect, the stand-alone version of MACRO wasmore accurate than the database version.
75
Figure 36 Comparison between observed drainflow from the Wytham site and thatsimulated by MACRO_DB together with daily rainfall
Observed
0.0
0.3
0.6
0.9
1.2
1.5
1.8
0 5 10 15 20 25 30
Time from application (d)
Dra
inage (
mm
/h)
0
5
10
15
20
25
30
Ra
in (m
m/d
)
0.0
0.3
0.6
0.9
1.2
1.5
1.8
0 5 10 15 20 25 30
Time from application (d)
Dra
inage (
mm
/h)
0
5
10
15
20
25
30
Ra
in (m
m/d
)
observed drainage MACRO_DB rainfall
Figure 37 Comparison between observed concentrations of isoproturon in drainflow fromthe Wytham site (first and third events after application only) and thosesimulated by MACRO_DB
0
50
100
150
200
250
300
0 5 10 15 20 25 30
Time from application (d)
Isopro
turo
n (
µg/l)
observed MACRO_DB
76
4.4.5 PLM - Wytham
The percentage of fast mobile phase within PLM was set to 95% according to expertjudgement. The measured half-life of 18.2 days at 15oC and 33% gravimetric water contentwas 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 depthand 51% at 30 cm depth. These were above total porosity and hence were not used for modelevaluation. Instead, the initial water content (i.e. the amount of water required to moisten theprofile to field capacity) was calibrated to achieve a simultaneous onset of simulated andobserved leaching. In Figure 38, results using the calibrated moisture deficit value of 10 mmare shown.
In common with the other models, drainage was markedly over-estimated by PLM. Thesimulated volume of drainage water over the period shown in Figure 38 was 14.5 mm whilstthe observed flow was 1.8 mm. Over the two events for which isoproturon concentrations indrainflow were monitored, the simulated data matched the measured concentrations very well(Figure 39). PLM was the only model to accurately simulate concentrations of isoproturon indrainflow over the first event. In terms of maximum pesticide concentrations, it can beconcluded that PLM performed better for Wytham than any of the other models, although itwas necessary to calibrate the initial moisture deficit.
Figure 38 Comparison between measured drainflow from the Wytham site and thatsimulated by PLM
0
1
2
3
4
5
6
7
0 4 8 12 16 20 24 28
Time from application (d)
Dra
inag
e (m
m/d
ay)
PLM
observed
77
Figure 39 Comparison between measured concentrations of isoproturon in drainflow(first and third events after application only) from the Wytham site and thosesimulated by PLM
0
50
100
150
200
250
300
350
400
0 5 10 15 20 25 30
Time from application (d)
Isopro
turo
n (
µg/l)
observed PLM
4.4.6 SWAT - Wytham
All input parameters for SWAT including pesticide Koc and half-life were taken frommeasured values apart from conductivity at field capacity which was derived from a pedo-transfer function (Hollis & Woods, 1989). The measured half-life was a laboratory value at15oC, so this was converted to a field value using the mean topsoil temperature over theperiod after application and an approximation of the Arrhenius equation with a meanliterature value for the exponent (0.08). In common with the other models, SWAT over-estimated the total flow over the main events after application (10.3 mm simulated by SWATcompared to 1.8 mm observed). However, SWAT under-estimated the maximumconcentration of isoproturon in both of the events monitored. Observed maxima were 290and 129 µg/l in the first and third event after application, respectively, whereas SWATpredicted maxima of 18 and 11 µg/l, respectively. At this site, the more sophisticated modelsclearly performed better for the third event after application, although results for the firstevent 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 isnot accounted for in the water balance. All of the models tested (including LEACHP) greatlyover-estimated the amount of drainflow over the monitoring period by factors of between fiveand ten. Thus comparison of the models from a hydrological viewpoint is essentiallymeaningless.
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 topredict the large concentrations observed during the first event after application, but themaximum concentration during the third event was over-estimated by a factor of two. Thethree deterministic models (CRACK-NP, MACRO and MACRO_DB) gave excellent
78
simulations of isoproturon losses for the third event after application, but under-estimatedconcentrations during the first event by factors of thirty or greater. The simple model SWATwas unable to simulate the observed maximum concentrations of isoproturon, with simulatedvalues 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 oftotal loadings of isoproturon over the two events are meaningless. Overall, the evaluation ofthe models against data for Wytham again points to the difficulties in reliably simulating sucha highly-structured and heterogeneous clay soil (c.f. Section 4.1.7).
Table 15 Comparison between maximum concentrations of isoproturon (µg/l) in the twoevents monitored at Wytham and those simulated in uncalibrated runs with themodels
Event Observed* CRACK-NP MACRO MACRO_DB PLM SWAT
First event
Third event
290
129
7
94
9
135
0
73
403
205
18
11
4.5 Overall evaluation
A common aspect of results for all of the models tested is their failure to accurately simulatethe water balance without calibration. The current exercise aimed to evaluate the regulatoryuse of the models where site-specific calibration is not often possible. The temptation tocorrect the simulation of the water balance before simulating pesticide behaviour was thusignored. As pointed out by Armstrong et al. (1996) amongst others, a correct simulation ofthe water balance is a fundamental requirement for accurate simulation of pesticide transport.Further work into the application of methods to estimate potential evapotranspiration (oftenwith only sparse weather data) is required. However, the models often gave very differentwater balances starting from the same data for potential evapotranspiration and it is clear thatwork on how the models manipulate input to simulate actual evapotranspiration is alsorequired.
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 ofbypass flow has been shown to be dominant. As expected, LEACHP could not describe theobserved behaviour for these soils and all of the preferential flow models can be considered aconsiderable improvement despite the discrepancies demonstrated between observed andsimulated results. The SSLRC lysimeter dataset monitored movement of bromide andisoproturon through a range of representative intermediate arable soils. LEACHP generallygave a better simulation of the observed leaching of bromide than the preferential flowmodels. Preferential flow was not a dominant process for the transport of bromide and resultsprobably also reflect the greater ease of selecting input parameters where preferential flow isnot described. However, LEACHP was unable to reproduce the observed leaching ofisoproturon without calibration. This was surprisingly the case even for the sandy Cuckney
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soil. Results call into question the predictive application for regulatory purposes of anon-preferential flow model to a wide range of intermediate soils where preferentialflow may be a relatively minor component, but appears to still be a dominant processfor pesticide transport. The predictive ability of the preferential flow models for theseintermediate soils was variable, but results suggest that there may still be significant benefitsfrom using a model which includes preferential flow to simulate pesticide transport throughsuch soils.
4.5.2 CRACK-NP
CRACK-NP was found to be extremely unstable with even minor changes to the input fileprovided by the authors causing the model to crash due to numerical instability. In somecases, even changing parameters from those supplied to those suggested in the user manual(e.g. canopy interception capacity, time interval) resulted in these problems. Particulardifficulties were encountered with parameters defining macroporosity, initial water contents,water table and crop growth. The model crashed only occasionally when a single value waschanged. Instead, the instability was caused by interactions of several parameters. Thisinstability placed restrictions on the evaluation of the model as the input values selected bythe modeller did not always coincide with values with which the model would run. It shouldbe noted that the model authors are actively working to solve these problems with numericalinstability. For the two heavy clay datasets (Brimstone Farm and Wytham), CRACK-NPperformed very similarly to the stand-alone version of MACRO. This is not surprising giventhe common ancestry of the two models. For Cockle Park, CRACK-NP greatly over-estimated the observed movement of isoproturon to drains (more than two orders ofmagnitude). It was concluded that either the assumptions of zero movement of water andsolute in the soil matrix or the assumption that preferential flow is generated at the soilsurface made the model unsuitable for application to soils where clay content is less than 50-60%. Of most concern are results for trifluralin (Koc 4000 ml/g) at Cockle Park wheremaximum simulated concentrations in drainflow (499 µg/l) were only slightly less than thosefor isoproturon (Koc 100 ml/g). Total losses of trifluralin to drains were predicted to belarger than those for isoproturon, presumably because of the former’s greater persistence.These results were checked by simulating a hypothetical application of trifluralin atBrimstone Farm and comparing to simulated behaviour of isoproturon. The very large over-estimate of transport of the more strongly-sorbed compound was confirmed and appears torelate to the assumption that pesticide sorption is limited to the soil aggregates and negligiblewith 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 forregulatory purposes. MACRO was applied to all of the datasets and showed considerablevariability in predictive ability. At Brimstone Farm, MACRO generally gave reasonablesimulations of total drainflow, but under-estimated maximum concentrations in 1993/94 andover-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 flowand pesticide concentrations for the first event were under-estimated. The simulation forCockle Park gave a good match to initial concentrations of isoproturon, but enhanced
80
movement late in the season relative to that observed suggests a problem with over-predictionof pesticide movement in matrix flow over longer periods of time. Results for theintermediate soils in the SSLRC lysimeter dataset are relatively encouraging withuncalibrated simulations giving reasonable estimates (within a factor of four or better) of totalisoproturon losses from all five soils.
On balance, the findings of this evaluation suggest that MACRO should continue to bethe preferred preferential flow model for regulatory purposes. This is reinforced by theuser-friendliness of the model, the good documentation and the relatively large number ofmodel applications reported in the literature. Results for the relatively sandy soils in theSSLRC lysimeters suggest that MACRO was equally or more accurate than the non-preferential flow benchmark (LEACHP) for such soils. Robust parameter selection forMACRO is still very difficult and output is particularly sensitive to changes in some of themore problematic parameters (e.g. aggregate half-width, position of the boundary betweenmicropore and macropore domains, proportion of sorption sites within each). A usefuldevelopment for the stand-alone version of MACRO would be some general guidance onrealistic values for these parameters in a range of representative soils. Such guidance is onlylikely to be developed as the number of applications of MACRO to field data increases. Atthe present time, MACRO cannot be considered broadly validated and it should only be usedfor regulatory purposes with great caution (it is worth noting that, in the opinion of theauthors, the same can be said for the various non-preferential flow models used for regulatorypurposes). Considerable previous experience with MACRO is required and a comprehensivecalibration step should be included wherever possible. However, results for Brimstone Farmdemonstrate that a set of input parameters giving acceptable simulations of pesticide fate inone 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, thoseautomatically selected within MACRO_DB reduced the emphasis on preferential flow for allof the simulations. This was because the boundary between micropores and macropores wasset closer to saturation using pedotransfer functions than using expert judgement, even thoughsite-specific soils data were used within MACRO_DB rather than series average values fromSEISMIC. 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 wereexceptions where the smaller concentrations were closer to those observed (Brimstone Farmin 1995/96). Comparison of observed and simulated hydrographs suggested thatMACRO_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 theclay soils (1993/94 at Brimstone and first event at Wytham) when large concentrations wereactually observed, whilst losses from the clay loam soil at Cockle Park were greatly under-estimated. For the intermediate soils studied in the SSLRC lysimeters, MACRO_DB gave noimprovement on the simulation of isoproturon leaching to 1.05-m depth relative to the non-preferential flow benchmark, LEACHP. Whilst the philosophy behind MACRO_DB iscommendable, the consistent under-estimation of preferential flow relative to matrixflow in a broad range of soils suggests that further work on parameter selection andextensive testing against field data are required before the system can be considered
81
valid as a regulatory tool. Taken as a block, results from this study suggest that the best useof preferential flow models to address any regulatory concerns over the potential impact ofpreferential flow, may be through the development of standard modelling scenarios. TheMACRO_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 offast mobile phase and the depth leached per time interval in the fast and slow regions cannotbe linked to soil properties. Calibration for at least the percentage of fast mobile phase isrequired 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 bromideleaching were not transferable to simulations for isoproturon. However, in heavy clay soilswhere almost all of the flow can be considered to be “fast”, there is the possibility to run PLMwithout calibration. Using this approach, the model gave the best overall simulation of totalflow at Brimstone Farm, but considerably over-estimated concentrations of isoproturon inflow. At Wytham, PLM gave the best simulation of maximum concentrations of isoproturonover the two events monitored. For the clay loam soil at Cockle Park, simulations withacceptable water flow over-estimated maximum concentrations of isoproturon and trifluralinby three orders of magnitude and predictive work for all but the heaviest clays cannot berecommended. Simulations for the intermediate soils in the SSLRC lysimeter datasetrevealed a serious weakness in the model. For such soils, PLM is extremely sensitive tochanges in the percentage of fast mobile phase over a very small range (Figures 27-28). Thebreakpoint at which this sensitivity occurs is a function of the air capacity of the soil (totalporosity - water held at field capacity) with calibration giving larger values for percentage offast mobile phase in sandy and loamy soils where air capacity is large than in a clay or loamsoil where air capacity is smaller. The extreme sensitivity of PLM and the relationship of thepercentage of fast mobile phase to air capacity make selection of this parameter extremelydifficult in intermediate soils even where a calibration step is possible. The use of PLM insuch soils is not recommended. Model evaluation suggests that there may be potential forthe regulatory use of PLM without calibration in the heaviest clays where matrix flow isinsignificant, 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 surfacewaters and is thus not applicable to the SSLRC lysimeter dataset. A previous evaluation gavepromising 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 overallsimulation of maximum concentrations for Brimstone Farm and Cockle Park, but the worstfor Wytham. The simplicity of the model makes it easy to apply predictively, but there is nopotential 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 rainfallare considered, so the model is not suitable for very detailed simulations or higher tier riskassessment. However, results suggest that the model may be suitable for regulatorymodelling at broad scales or screening levels. The conceptualisation of SWAT is ratherdifferent from the other models which simulate movement of water and solute within the soilprofile. Preferential flow together with overland flow is described using the response of a
82
given soil type to rainfall events and the model is the only one which can be considered threedimensional. Perhaps the most important conclusion from the results obtained with SWAT isthat modelling approaches which aggregate the spatial and temporal variability associatedwith preferential flow up to broader scales appear promising. Whilst it is likely that mostmodels will continue to try to describe preferential flow at the profile scale, the developmentof 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 arelikely to vary widely for different simulations. Nevertheless, this is a key requirement forregulators who have to evaluate modelling submissions and is an important component in anyattempt to build confidence in the credibility of modelling. Levels of predictive accuracy canbe derived for any of the models from the data contained in this report, but they aresummarised in Table 16 for MACRO as this is recommended as the preferred model forregulatory use. A number of factors should be considered:
• the number of datasets was limited and concentrated almost exclusively on onecompound (isoproturon);
• the datasets were of a generally high quality with much information available forparameter selection;
• the modellers involved were relatively experienced with MACRO.
Table 16 Levels of predictive accuracy for uncalibrated simulations of the four datasetswith MACRO (all values are predicted values as a factor of the observed;maximum concentrations are for the whole simulation and take no account oftiming of maximum)
Dataset Total flow Maximum pesticideconcentration
Total loss ofpesticide
SSLRC lysimeters
Cockle Park*
Brimstone Farm
Wytham
0.95-1.10
0.81
0.20-1.12
-
0.26-1.84
3.1
0.15-91.4
0.47
0.34-3.57
10.9
-
-
* 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. Thusfor simulations of 1-2 seasons, simulated flow was within 20% of that observed, whereasthere was far greater inaccuracy for single events at Brimstone where antecedent moisturestatus was critical. For all five of the soils in the SSLRC lysimeter experiment, simulatedvalues for both maximum pesticide concentration and total loss of pesticide in leachate werewithin a factor of four of those observed. On the drained clay loam at Cockle Park, the totalloss of isoproturon was over-estimated by an order of magnitude although the maximum
83
simulated concentration only over-estimated that observed by a factor of three. As hasalready been noted, variability and inaccuracy was greatest for the short-term simulations onthe two clay sites. Maximum concentrations of isoproturon at Brimstone in successiveseasons were under-estimated by a factor of seven and then over-estimated by factors of nineand ninety-one. Although the maximum concentration of isoproturon observed at Wythamwas only under-predicted by a factor of two, MACRO simulated that it would occur in thethird event after application rather than the first, the importance of which was greatly under-estimated.
5 REGULATORY IMPLICATIONS
a) There is evidence that preferential flow may be an important process for pesticidetransport through a wide range of soils. Comparisons with the benchmark model(LEACHP) show that, if correctly applied, preferential flow models improve our ability tosimulate pesticide fate in both clays and a range of intermediate soils. However, resultsof this evaluation suggest that the predictive ability of preferential flow models is stillpatchy with inaccuracy generally increasing for more clay-rich soils.
b) Accurate simulation of pesticide fate (by any model) depends upon a reliable simulationof the water balance which is in turn hindered by weakness in estimatingevapotranspiration. Further work is required on the application of methods to estimatepotential evapotranspiration from limited weather data and on the methods used bymodels to manipulate input to simulate actual evapotranspiration. Applications of modelsto datasets with high quality weather data and a water balance (i.e. either lysimeters orimpermeable drained soils) would allow methods for estimating potentialevapotranspiration to be compared.
c) Conclusions from the evaluation for each model are given in detail in Section 4.5. Theycan 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 outputoften particularly sensitive to changes in the more problematic parameters. Considerableprevious experience with the model of choice is required and a comprehensive calibrationstep should be included wherever possible. However, results for Brimstone Farmdemonstrate that a set of input parameters giving acceptable simulations of pesticide fatein one season may fail to do so in subsequent seasons.
e) Where genuine regulatory concerns exist over the potential impact of preferential flow onpesticide transport through soil, these may be best addressed through the development ofstandard modelling scenarios which could be incorporated into systems such as
84
MACRO_DB. This would overcome current difficulties with selection of inputparameters for preferential flow models. The current programme of work within FOCUSwill deliver standard modelling scenarios for leaching and movement to surface watersduring 1998 and preferential flow may be an important mechanism for at least some ofthese scenarios.
f) The philosophy behind MACRO_DB is excellent in putting forward automaticprocedures for parameter selection from readily-available data and restricting thepotential for subjectivity in the modelling process. However, further work on parameterselection and extensive testing against field data are required before the system can beconsidered valid as a regulatory tool.
g) For the datasets based on clay soils, the detailed mechanistic models did not significantlyout-perform two simple models (PLM and SWAT) which adopt semi-empiricalapproaches to describing the aggregated effects of preferential flow. Given the scarcityof European data for regulatory modelling, further development of such simpleapproaches seems desirable.
6 CONCLUSIONS
Preferential flow appears to be an important process for pesticide transport through a widerange 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 importantadvance which improves our ability to simulate the fate of pesticides in soil. Resultspresented in this report for a wide range of soils show considerable promise for some of thepreferential flow models, but there are still some significant problems with selection of inputparameters which raise questions over the predictive use of such models for regulatorypurposes. On the other hand, it is clear that use of models which do not simulate preferentialflow is also questionable for all but the coarsest soils.
The MACRO model described with a degree of accuracy leaching of pesticides through awide range of soils and this is proposed as the preferred preferential flow model for regulatoryuse. Predictive ability was better for a clay loam and a range of intermediate soils than fortwo heavy clays where there are inherent difficulties in predicting observed behaviourbecause of the extreme spatial and temporal heterogeneity in their structure. TheMACRO_DB system is a useful conceptual development in allowing input parameters for thiscomplex model to be derived from basic soil properties and eliminating much of thesubjectivity from the modelling process. However, evaluation results suggest that more workand perhaps changes to the system will be required before output from MACRO_DB can berelied upon for regulatory purposes. The development of standard modelling scenarios maybe the best way to use preferential flow models to address any regulatory concerns over thepotential impact of preferential flow and the MACRO_DB system provides an excellentframework to support any such development. The simpler approaches adopted by PLM andSWAT gave results which were not significantly worse than those from the mechanisticmodels for the clay soils. Further development of models which aggregate preferential flowinto broad descriptions rather than attempting to simulate the process in detail seemsdesirable, particularly given the sparcity of European data for regulatory modelling.
85
FUTURE WORK
As an extension to this programme of work, SSLRC are undertaking a detailed investigationinto the sub-routines and inherent assumptions of the various models in relation to thedifferent simulations obtained in the evaluation. A number of generic issues associated withpreferential flow modelling are also being considered. The results of this work will beavailable as a written report by the end of October 1998.
ACKNOWLEDGEMENTS
This work was funded within the Pesticides Research Programme of the Ministry ofAgriculture, Fisheries and Food. The co-operation of all the organisations who have allowedtheir data to be used for this project is gratefully acknowledged as follows:
Brimstone Farm: Brimstone Steering Group, ADAS, IACR-Rothamsted;Cockle Park ADAS, University of Newcastle;Wytham Institute of Hydrology, Horticulture Research International.
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HOLLIS, J.M., HALLETT, S.H. & KEAY, C.A. 1993. The development and application of anintegrated database for modelling the environmental fate of herbicides. ProceedingsBrighton Crop Protection Conference - Weeds - 1993, 3, 1355-1364.
HOLLIS, J.M. & WOODS, S.M. 1989. The measurement and estimation of saturated soilhydraulic conductivity. SSLRC Research Report, Silsoe, Beds.
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HUTSON J.L. & WAGENET R.J. 1992. Leaching Estimation and CHemistry Model, Version 3.Dept. of soil, crop and atmospheric sciences, Research series No. 92-3. Cornell University,New York.
JABRO, J.D., JEMISON, J.M., FOX, R.H. & FRITTON, D.D. 1994. Predicting bromide leachingunder field conditions using SLIM and MACRO. Soil Science, 157, 215-223.
JARVIS, N.J. 1989. CRACK - a model of water and solute movement in cracking soils.Department of Soil Sciences Report 159, Swedish University of Agricultural Sciences,Uppsala, Sweden, pp. 38.
JARVIS, N.J. 1994. The MACRO model (Version 3.1). Technical description and samplesimulations. Reports & Dissertations 19, Department of Soil Science, Swedish Universityof Agricultural Sciences, Uppsala.
JARVIS, N.J. 1995. Simulation of soil water dynamics and herbicide persistence in a silt loamsoil using the MACRO model. Ecological Modelling 81, 97-109.
JARVIS, N.J., HOLLIS, J.M., NICHOLLS, P.H., MAYR, T. & EVANS, S.P. 1997. MACRO_DB: adecision support tool for assessing pesticide fate and mobility in soils. EnvironmentalSoftware, 12, 251-265.
JARVIS, N.J., LARSSON, M., FOGG, P.E. & CARTER, A.D. 1995. Validation of the dual-porosity model MACRO for assessing pesticide fate and mobility in soils. In Pesticidemovement to water, (ed. A. Walker et al.), pp. 161-170, British Crop Protection CouncilMonograph No. 47, The Lavenham Press Limited, Lavenham, Surrey.
JARVIS, N.J. & LEEDS-HARRISON, P.B. 1987. Modelling water movement in a drained claysoil. I. Description of the model, sample output and sensitivity analysis. Journal of SoilScience, 38, 487-498.
JARVIS, N.J., NICHOLLS, P.H., HOLLIS, J.M., MAYR, T. & EVANS, S.P. 1996. Pesticideexposure assessment for surface waters and groundwater using the decision-support toolMACRO_DB. In The environmental fate of xenobiotics (eds. A.A.M. Del Re, E. Capri,S.P.Evans & M. Trevisan). Proceedings of the X Symposium of Pesticide Chemistry,Piacenza, Italy, September 30 - October 2 1996, pp. 381-388.
JARVIS, N.J., STÄHLI, M., BERGSTRÖM, L. & JOHNSSON, H. 1994. Simulation of dichlorpropand bentazon leaching in soils of contrasting texture using the MACRO model. Journal ofEnvironmental Science and Health, A29, 1255-1277.
JOHNSON, A.C., HARIA, A.H., BATCHELOR, C.H. & WILLIAMS, R.J. 1995b. Fate andBehaviour of Pesticides in Structured Clay Soils. Institute of Hydrology, Second InterimReport.
JOHNSON, A.C., HARIA, A.H., BHARDWAJ, C.L., VÖLKNER, C., BATCHELOR, C.H. & WALKER
A. 1994. Water movement and isoproturon behaviour in a drained heavy clay soil: 2.Persistence and transport. Journal of Hydrology, 163, 217-231.
JOHNSON, A.C., HARIA, A.H., BHARDWAJ, C.L., WILLIAMS, R.J. & WALKER A. 1996.Preferential flow pathways and their capacity to transport isoproturon in a structured claysoil. Pesticide Science, 48, 225-237.
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JONES, R.J., HARRIS, G.L., CATT, J.A., BROMILOW, R.H., MASON, D.J. & ARNOLD, D.J. 1995.Management practices for reducing movement of pesticides to surface waters in crackingclay soils. Proceedings Brighton Crop Protection Conference - Weeds - 1995, 2, 489-498.
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LALIBERTE, G.E., BROOKS, R.H. & COREY, A.T. 1968. Permeability calculated fromdesaturation data. Journal of the Irrigation and Drainage Division, Proceedings of theAmerican Society of Civil Engineers, 94, 57-69.
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APPENDIX 1: Experimental details for the Brimstone site
Exp. Title: PESTICIDE RESIDUES IN WATER - HYDROLOGICAL STUDIES INBRIMSTONE 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. Thesoil is a heavy clay loam of the Denchworth series with a thick relativelyimpermeable subsoil. Drainflow and isoproturon concentrations weremonitored during the first two key rainfall events of each season.
Duration: 1993-1996
Type of exp.: Plot lysimeter (1900 m2 plot)
Site:Elevation: 100-106 mGeographical location: Brimstone Farm, Oxfordshire, Grid Reference 248 947Pedological description: Pelo-stagnogley of the Denchworth series, 2% slopeCurrent land use: Winter cerealsIrrigation: No irrigationDrainage: Pipe drains at 0.9 m depth with permeable backfill to
within 0.35 m, secondary drainage at 55 cm depthconsisting 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 eventsafter 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 13/11/93-15/11/93 13/11/93-14/11/93 13/11/93-14/11/9307/12/93-08/12/93 07/12/93-08/12/93 08/12/93-09/12/93 08/12/93-09/12/93
08/12/94-09/12/94 08/12/94-09/12/94 08/12/94-09/12/94 08/12/9426/12/94-29/12/94 26/12/94-29/12/94 26/12/94-29/12/94 27/12/94-29/12/94
19/12/95-23/12/95 19/12/95-23/12/95 20/12/95-23/12/95 19/12/95-23/12/9506/01/96-10/01/96 07/01/96-09/01/96 07/01/96-09/01/96 08/01/96-09/01/96
Soil cultivations: 1993: site tined, power harrowed and rolled in lateSeptember/early October, additional cultivation to prepareseedbed
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no information available for the other two seasons
Crop: winter wheat sown on 21/10/93winter cereal sown on 27/10/94, harvested on 07/08/95winter oat sown on 19/10/95, harvested 19/08/96
Weather: daily rainfall, minimum, maximum temperature01/09/93-06/04/9414/09/94-23/01/9501/09/95-30/03/96
Soil:
0-24 cm 24-52 cm 52-68 cmOC % 3.6 1.1 0.9pH (H2O) 7.6 8.0 8.2Sand % 10.5 10.5 5.6Silt % 29.5 25.0 21.4Clay % 60.0 64.5 73.0bulk density g/cm3 1.00 1.18 1.22total porosity % vol. 60.8 55.4 53.9water @ 0 kPa % vol. 56.8 48.5 52.6water @ 5 kPa % vol. 55.2 46.1 51.2water @ 10 k Pa % vol. 54.6 45.6 50.6water @ 40 kPa % vol. 48.4 43.8 48.2water @ 200 kPa % vol. 44.3 41.3 46.5water @ 1500 kPa % vol. 37.4 33.5 38.0(Graham Beard, SSLRC, personal communication)
Chemical: Isoproturon (IPU)Kd 2.9 ml/ghalf-life 75 days at 10oC, 80% FC
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APPENDIX 2: Experimental details for the Cockle Park site
Exp Title: DEGRADATION AND MOVEMENT OF TWO PESTICIDES IN PLOTLYSIMETERS AT COCKLE PARK
Summary: Isoproturon and trifluralin were applied to a winter wheat crop in twosuccessive seasons of which the second season was chosen for modelevaluation. Soil samples were taken to 90 cm depth with 15 cm intervalsapproximately every 3-4 weeks. Surface layer flow (N.B. this is flow throughthe top 30 cm of the soil profile and not exclusively overland flow) and moledrainflow were monitored and subsamples of flow were collected and analysedfor 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 generallykept 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 gravelto 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 thebottom 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 atractor-mounted sprayer. Approximately 300 l water/ha.
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Sampling: Soil samples taken to 90 cm divided into 6 layers of 15 cm every 1-6 weeks according to time of year; subsamples of surface-layer flow anddrainflow 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/gPermanent wilting point: Not determined
Depth Corg (%) pH 0-15 3.27 5.8015-30 2.48 6.3930-45 1.10 7.0845-60 0.94 7.2260-75 0.89 7.0375-90 0.79 7.29
At 60 cm depth: Sand = 28.3%; Silt = 44.1%; Clay = 37.55;
Bulk density: 1.12 kg/l at 10 cm depth; 1.50 kg/l at 60 cm depth;
Saturated conductivity: 0 cm depth 2.2 x 10-7 m/s 20 cm depth 5.7 x 10-7 m/s 40 cm depth 1.8 x 10-8 m/s 60 cm depth 8.0 x 10-9 m/s 80 cm depth 3.0 x 10-9 m/s
Biomass: 44.3 mg nitrogen/kg
depth cm 0-27 27-46 49-77 77-129 Sand % 47 44 26 31Silt % 31 32 36 37Clay % 22 24 37 32 (Graham Beard, SSLRC, personal communication)
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depth cm 20 50 70 150 water @ 5 kPa % vol. 40.5 33.6 37.6 35.3water @ 10 k Pa % vol. 36.5 30.4 34.9 32.9water @ 40 kPa % vol. 29.3 24.8 29.9 28.2water @ 200 kPa % vol. 23.1 20.0 25.3 24.0water @ 1500 kPa % vol. 18.1 16.1 21.5 24.0 (SEISMIC data for Dunkeswick series)
Chemicals: Water solubility:Isoproturon 55 mg/lTrifluralin <1 mg/l
Vapour pressure:Isoproturon 2.5 x 10-8 mm HgTrifluralin 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 Topsoil SubsoilKfr 1/n Kfr 1/n
Isoproturon 0.99 0.90 0.34 0.80Trifluralin 224 1.01 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 dataIsoproturon 35 dTrifluralin 180 d
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APPENDIX 3: Experimental details for the SSLRC lysimeter experiment
Exp. Title: PESTICIDE MOBILITY: LYSIMETER STUDY TO VALIDATE THERELATIVE LEACHING POTENTIAL OF UK SOILS
Summary: Isoproturon and a bromide tracer were applied in autumn 1994 and 1995 toreplicate undisturbed soil cores buried in the ground at the SSLRC lysismeterstation at Silsoe, Bedfordshire and cropped with winter wheat. The lysimeterswere from five sites with soil types representative of one of the three High andtwo Intermediate leaching potential classes identified in the EnvironmentAgency’s groundwater protection policy (NRA, 1992). Leaching wasmonitored and flow subsamples were analysed for isoproturon and bromideconcentrations. At the end of the experiment, the cores were irrigated with adye to stain the major flow pathways, then excavated and residues ofisoproturon 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: 60 mGeographical location: Soil Survey and Land Research Centre, Silsoe,
Bedfordshire, Grid reference: TL 079352Latitude: 51.9oNIrrigation: No irrigation
Experiment:Application: 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 anhourly resolution. At the end of the experiment, residuesof isoproturon in the soil profiles were determined.
Crop: Winter wheat (var. Riband)Sown on 07/11/94, harvested on 27/11/95Sown on 11/10/95, harvested on 05/08/96
Weather: Hourly rainfall, air temperature, wind speed, relativehumidity, air pressure and radiationDaily rainfall, minimum, maximum temperature
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Soils: Leaching potential Soil Series DescriptionHigh 1 Enborne cracking clay soil in alluviumHigh 2 Cuckney unstructured, free-draining sandHigh 3 Sonning free-draining shallow loam over gravelIntermediate 1 Ludford deep, weakly-structured loamIntermediate 2 Isleham shallow peat over free-draining sand.
Topsoil Cuckney Sonning Ludford Enborne Isleham
Layer depth (cm) 30 31 28 20 36
% Organic carbon 0.7 1.0 1.0 4.0 28.9pH in CaCl2 6.6 6.4 6.9 7.0 7.3CaCO3 eq (g/kg) 2.7 2.1 - 2.0 -Bulk density (g/cm3) 1.51 1.57 1.79 1.12 0.69
Total porosity 40.72 38.25 29.98 56.19 72.91Air-capacity 28.42 14.51 2.29 3.88 22.60Water @ 5 kPa 14.62 23.74 27.69 52.31 50.31Water @ 40 kPa 8.66 15.14 21.76 41.64 39.64Water @ 200 kPa 5.80 11.20 20.11 40.59 32.10Water @ 1500 kPa 4.38 - 18.47 21.49 26.85
Texture* S SL SCL CL LP%Sand 91.4 66.6 58.5 44.6 39.4%Silt 3.4 22.7 23.0 25.0 46.6%Clay 5.2 10.7 18.5 30.4 14.0%Stones 4.3 15.6 - 1.1 -
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Subsoil 1 Cuckney Sonning Ludford Enborne Isleham
Layer depth (cm) 26 32 33 26 7
% Organic carbon <0.05 1.4 0.3 1.6 21.5pH in CaCl2 6.3 6.3 5.3 7.2 6.5CaCO3 eq (g/kg) - - - - -Bulk density (g/cm3) 1.53 1.51 1.66 1.18 0.55
Total porosity 42.28 43.14 37.42 55.66 79.07Air-capacity 38.20 25.59 12.78 7.32 27.73Water @ 5 kPa 5.78 17.55 24.64 48.34 51.34Water @ 40 kPa 3.21 13.40 16.05 38.26 40.37Water @ 200 kPa 1.60 8.48 15.16 36.65 38.26Water @ 1500 kPa 1.26 8.76 14.00 26.25 27.09
Texture* S SL SL CL SP%Sand 97.8 81.9 78.4 42.4 51.2%Silt 0.1 6.6 4.6 25.7 39.4%Clay 2.1 11.5 17.0 31.9 9.4%Stones 0.0 31.4 - 1.5 -
Subsoil 2 Cuckney Sonning Ludford Enborne Isleham
Layer depth (cm) 54 37 24 35 21
% Organic carbon 0.1 1.1 0.3 0.4 0.5pH in CaCl2 5.6 6.3 4.8 7.2 6.0CaCO3 eq (g/kg) - - - - -Bulk density (g/cm3) 1.60 1.42 1.59 1.60 1.50
Total porosity 39.78 46.28 40.03 39.73 43.45Air-capacity 31.65 29.39 21.89 12.77 31.50Water @ 5 kPa 11.44 18.99 18.14 26.96 16.11Water @ 40 kPa 6.64 13.88 10.73 15.72 7.16Water @ 200 kPa 3.92 7.16 10.13 9.93 3.51Water @ 1500 kPa 3.40 6.03 8.12 6.96 2.38
Texture* S SL/LS SL LS S%Sand 95.2 82.2 82.0 81.2 95.1%Silt 0.5 3.5 3.1 11.1 3.3%Clay 4.3 14.3 14.9 7.7 1.6%Stones 0.0 46.2 - 23.0 -
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Subsoil 3 Cuckney Sonning Ludford Enborne Isleham
Layer depth (cm) - 10 37 29 8
% Organic carbon - 0.3 0.2 3.3 0.6pH in CaCl2 - 7.7 5.0 6.1 4.0CaCO3 eq (g/kg) - 166.8 - 1.4 -Bulk density (g/cm3) - 1.33 1.54 - 1.54
Total porosity - 49.84 42.02 - 42.05Air-capacity - 37.59 28.93 - 24.73Water @ 5 kPa - 13.22 15.42 - 21.63Water @ 40 kPa - 9.29 9.50 - 10.33Water @ 200 kPa - 4.43 7.82 - 7.40Water @ 1500 kPa - 3.81 7.43 - 5.41
Texture* - S LS ZCL S%Sand - 93.4 84.4 12.6 93.1%Silt - 3.4 2.9 55.2 4.2%Clay - 3.2 12.7 32.2 2.7%Stones - 53.4 - 0.0 -
Subsoil 4 Cuckney Sonning Ludford Enborne Isleham
Layer depth (cm) - - - - 28
% Organic carbon - - - - 0.3pH in CaCl2 - - - - 4.4CaCO3 eq (g/kg) - - - - -Bulk density (g/cm3) - - - - 1.52
Total porosity - - - - 42.49Air-capacity - - - - 28.77Water @ 5 kPa - - - - 19.84Water @ 40 kPa - - - - 7.51Water @ 200 kPa - - - - 4.75Water @ 1500 kPa - - - - 3.27
Texture* - - - - S%Sand - - - - 94.2%Silt - - - - 3.5%Clay - - - - 2.3%Stones - - - - -
* Textural abbreviations: S = sand; LS = loamy sand; SL = sandy loam; SCL = sandy clayloam; CL = clay loam; ZCL = silty clay loam; LP = loamy peat; SP = sandy peat.
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APPENDIX 4: Experimental details for the Wytham site
Exp. Title: WYTHAM EXPERIMENT: FATE AND BEHAVIOUR OF PESTICIDES INSTRUCTURED CLAY SOILS
Summary: Isoproturon was applied to a winter barley crop at a mole-drained clay site inspring 1994. The site is characterised by marked differences between the Ahorizon (0-30 cm) and the B horizon (30-120 cm) with hydrological responseto rainfall and drying being resticted to the A horizon and negligible in the Bhorizon. A seasonal perched water table is found in the A horizon.Soil samples were taken to 2 cm depth. Over two events, isoproturonconcentration in drainflow, interlayer flow and occasionally in overland flowtogether with the respective flow rates were monitored at a 5-min to 30-minresolution. In addition, hourly drainflow, tensiometer, capacitance probe andsoil 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: 76 mGeographical location: Oxford University Farm, Wytham, Oxforshire
Grid Reference SP46660931Latitude: 51.7oNPedological description: Clay of the Denchworth series, calcareous variant, 2°
convex slopeCurrent land use: Arable (3 years of winter cereals, 1 year of oilseed rape)Irrigation: No irrigationWater collecting constructions: Field drains at 80 cm depth with mole drains at 50 cm
depth and 3 m apart.Gulley containing aggregate and backfilled with soil at30 cm depth to collect lateral interlayer flowGulley to 5 cm depth to collect overland flow
Experiment:Application: 12/03/94 isoproturon at 0.9 kg a.i./ha (Arelon)
Sampling: Soil samples every week, 2 cm depthFlow subsamples, triggered by flows > 0.054 l/s for drainflow and 0.023 l/s for lateral interlayer flow
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variable interval depth (cm) resolutionsoil temperature (°C) 22/11/93-23/06/94 0, 10, 30 1 hwater content (%) 22/11/93-25/05/94 10 1 hwater content (%) 22/11/93-23/06/94 30 1 hwater tension (kPa) 22/11/93-23/06/94 10, 30, 50, 75, 100 1 hdrainflow (mm/h) 18/11/93-23/06/94 1 hIPU soil (mg/kg) 12/03/94-23/06/94 2 1 week
1st event after application:IPU drain (µg/l) 31/03/94-01/04/94 30 mindrain flow rate (mm/h) 31/03/94-01/04/94 5 minIPU lateral interflow (µg/l) 31/03/94-01/04/94 30 minlateral interfl. flow rate (mm/h) 31/03/94-01/04/94 10 min
3rd event after application:IPU drain (µg/l) 08/04/94-09/04/94 30 mindrain flow rate (mm/h) 08/04/94 5 minIPU lateral interflow (µg/l) 08/04/94-09/04/94 30 minlateral interfl. flow rate (mm/h) 08/04/94-09/04/94 10 minIPU overland flow (µg/l) 08/04/94 5/10 minoverland flow flow rate (mm/h) 08/04/94 5 min
Soil cultivations: Straw from previous winter wheat crop chopped andincorporated by ploughing and power harrow (Roterra) inSeptember 1993
Crop: Barley var. ‘Fighter’, sown on 19/10/93, emergence from04/11/93 onwards
Date max. shoot length (cm) max. root length (cm) growth stage*09/11/93 4.60 7.5 122/11/93 7.78 9.48 207/12/93 8.54 9.58 227/01/94 13.36 11.0 317/03/94 21.49 15.21 325/04/94 30.20 22.0 405/05/94 38.0 29.0 516/05/94 58.0 34.0 627/05/94 70.0 36.0 716/06/94 95.0 37.0 7-8.126/06/94 95.0 37.0 8.107/07/94 95.0 33.0 8.2-8.325/07/94 90.0 30.0 8.4*=System devised by Slater & Goode, 1967
Weather:
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variable interval resolutiondry bulb temperature (°C) 04/03/93-04/10/94 1 hmax temperature (°C) 04/03/93-04/10/94 1 hmin temperature (°C) 04/03/93-04/10/94 1 hsolar radiation 03/03/93-04/10/94 1 dnet radiation 03/03/93-04/10/94 1 dwet bulb temperature (°C) 03/03/93-04/10/94 1 ddry bulb temperature (°C) 03/03/93-04/10/94 1 dwind speed 03/03/93-04/10/94 1 dwind direction 03/03/93-04/10/94 1 drainfall (mm) 03/03/93-04/10/94 1 dalbedo sky 03/03/93-04/10/94 1 dalbedo ground 03/03/93-04/10/94 1 dsoil temperature 1 cm (°C) 03/03/93-04/10/94 1 dsoil temperature 30 cm (°C) 03/03/93-04/10/94 1 dwater potential 03/03/93-04/10/94 1 dheat budget 03/03/93-04/10/94 1 daero term 03/03/93-04/10/94 1 dpotential evaporation 03/03/93-04/10/94 1 d
Soil:
0-26cm 26-54cm 54-101cm 101-130cmOC % 3.1 0.9 0.4 0.4pH (H2O) 7.8 8.2 8.3 7.8Sand % 14.46 7.62 3.28 14.35Silt % 28.75 29.15 39.58 0.74Clay % 57.29 63.22 57.13 84.91bulk density g/cm3 1.24 1.52 1.54 1.55moisture 105°C % 4.3 3.8 3.1 5.6water at 0 kPa % vol. 53.4 43.3 44 45.3water at 5 kPa % vol. 50.4 41.9 43.0 43.5water at 10 kPa % vol. 49.9 41.7 42.7 43.1water at 40 kPa % vol. 44.6 37 37.3 39.3water at 200 kPa % vol. 41.3 33.2 35.4 36.4water at 1500 kPa % vol. 31.6 28.9 30.8 31.5(Graham Beard, SSLRC, personal communication)
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/ghalf-life 18.2 days at 15°C and 33% gravimetric water content