Simulation of pesticide leaching at Vredepeel and
Brimstone farm using the macropore model PLM
P.H. Nicholls
a,*, G.L. Harris
b, D. Brockie
a aIACR-Rothamsted, Harpenden, Herts AL5 2JQ, UKbADAS Land Research Centre, Gleadthorpe, Meden Vale, Mans®eld, Notts NG90 9PF, UK
Abstract
The distributions of bromide, bentazone and ethoprophos in the light-textured and unstructured soil at Vredepeel was simulated using the Pesticide Leaching Model (PLM). Distributions of all compounds were satisfactorily simulated using the data provided and without calibration. Since bromide is used as a tracer of water movement, this agreement indicates that PLM was able to model the movement and dispersion of water in the soil pro®le. However traces of bromide that remained near the soil surface were not predicted. Although PLM was designed to simulate concentrations of pesticide that reach drainage waters by preferential ¯ow in structured soils, the model was still able to predict the distribution of the large proportion of bentazone and ethoprophos that remained in the topsoil at Vredepeel. The soil at Brimstone farm is a very highly structured, heavy, cracking-clay soil. PLM was used to simulate concentrations of isoproturon measured in samples of drainage water taken from the site. Simulated concentrations similar to those measured were obtained but only after extensive calibration of the model.#2000 Elsevier Science B.V. All rights reserved.
Keywords:Pesticide leaching; Model validation; Preferential ¯ow; Macropores
1. Introduction
There is much interest in the simulation of the trace concentrations of pesticides that reach surface and ground waters. More knowledge is needed in Europe to assist in conforming to the EU drinking water directive that sets an upper concentration limit of 0.1mg lÿ1for a single pesticide in drinking water. Some models such as PRZM (Carsel
et al., 1985) were designed primarily to simulate concentrations of pesticides in the upper
*Corresponding author.
part of the soil profile and not the trace concentrations of compounds leaching to drainage channels through structured soils. Simulation of preferential (macropore or bypass flow) is necessary for such situations (Nicholls and Hall, 1995). Nevertheless macropore models, such as PLM, should also predict well distributions of compounds in the soil profile even of unstructured soils.
2. Description of the model PLM
PLM is fully described and documented by Hall (1994) and Nicholls and Hall (1995). Version 3 of the model was used. PLM is a layer model with the layer thickness set at 5 cm, and calculations are done for intervals of one day. Hence, the hourly data provided for Brimstone farm could not be simulated. Within each layer, soil solution is divided into mobile and immobile categories with the division set atÿ5 kPa (field capacity) and with only mobile water being displaced during drainage. One layer can be specified to contain drains and a percentage of the water reaching this layer can pass into the drains with the remainder continuing to seep downwards. PLM does not simulate a water table and so some of the effects caused by a water table rising and falling are not predicted. The rapid flow of some water and solute that occurs in structured soils is modelled by subdividing the mobile water into `slow' and `fast' categories. Solute in the top layer equilibrates with any `slow', `fast' and immobile water present and with the soil-solid phase for sorption. In lower layers, lateral equilibration of solute is only among `slow' mobile and immobile water and with soil-solid phase for sorption. Below the top layer, solute in `fast' mobile water only interacts with other categories if lateral flow of water occurs. Thus, solute can penetrate deeply into the soil profile by moving with the `fast' mobile water. Lateral flow occurs when water reaches lower layers in which immobile or `slow' mobile water categories are unfilled. Then, water moves into immobile pores before mobile and into `slow' before `fast' pores. Preferential flow will only occur when rainfall intensity is sufficient to allow water into `fast' mobile pores.
Processes such as evaporation of water and transpiration by crops are described by Hall (1994). Sorption is calculated from linear isotherms as a function of soil depth and time. Degradation of parent compound is calculated as a function of soil depth, temperature and soil-water content. A number of soil parameters, i.e. a (fraction of mobile water moving from one layer to the next during flow), b (hold-back factor restricting equalisation of solute concentrations during diffusion), ns and nf (numbers of layers
passed through during `slow' and `fast' drainage), can be given default values (a0.9, b0.1, ns7,nf15). Thus the model can often be calibrated by adjusting only one sensitive soil parameter, i.e.Pf(% `fast' pores in the mobile phase).
3. Simulation of the distribution of bromide bentazone and ethoprophos in the soil pro®le at the Vredepeel site
Input data used in the simulations are given in Tables 1 and 2. Vredepeel is an unstructured soil and no preferential flow was expected, so the percentage of `fast' pores
was set to zero. If possible, measured data were used directly from those provided (Boesten and van der Pas, 2000). Values of sorption coefficients and rates of degradation were determined by interpolation done manually from the graphical material supplied. Mathematical and statistical methods of interpolation were not used. Rates of degradation were taken from the laboratory-measured data done at higher temperatures and less weight was given to the measurements made at 58C. Less weight was given because of previous experience of other experiments using different soils and pesticides where it was difficult to determine accurately the slow rates of degradation that occur at low temperature. Secondly, the microbial activity that often governs degradation in the field is very attenuated at low temperature. Other parameters were set to the default values often used with PLM as described above. Where measured data on sorption or degradation in deeper soil layers was not available, expert judgement was used to estimate values from topsoil data. In order to gain most benefit from the simplicity of the
Table 1
Solute properties used for Vredepeel simulations
Compound Depth
Bromide 0±30 0.0 999 20.0 10
30±60 0.0 999 20.3 10
60±120 0.0 999 20.3 10
Bentazone 0±30 0.1 44 9.0 15
30±60 0.1 100 9.6 10
60±120 0.1 999 7.6 10
Ethoprophos 0±30 3.6 92 9.0 15
30±60 1.8 151 9.6 10
60±120 0.2 211 7.9 10
Table 2
Soil properties used for Vredepeel simulations
Total pore space Soil water content (l/l) Bulk density
(l/l) (kg/l)
5 kPa 200 kPa 1500 kPa
Depth (cm)
0±30 0.37 0.21 0.12 0.05 1.4
30±50 0.39 0.24 0.15 0.03 1.5
50±120 0.30 0.22 0.04 0.01 1.7
Initial soil water de®cit (mm) 0
Crop winter oats/barley
model, initial attempts at simulation are usually done using measured and default values as input data.
Only one simulation was done for each compound and no calibration was required.
3.1. Bromide
Sorption of bromide was set at zero and the half-life was arbitrarily set at 999 days at 108C and a soil water content of 20.3% because the model will not accept a value of infinity. Initial concentrations of bromide in the surface layer were 361 and 295 mg dmÿ3
for measured and simulated respectively. Distributions of bromide at later sampling dates are given in Fig. 1. The match between measured and simulated distributions is satisfactory but perhaps because of a fortuitous initial choice of input data. However, the small residues of bromide that remained rather firmly in the top 20 cm at day 474 were not simulated by PLM and in this sense too much leaching was predicted. However, it is just possible that the bromide retained at the surface may have been taken up by the roots of the crop and released after harvest.
Fig. 1. Distribution of bromide with depth in Vredepeel soil.
3.2. Bentazone
Initial concentrations of bentazone in the top layer were 1.58 and 1.54 mg kgÿ1for
measured and simulated, respectively. Results are given in Fig. 2. At day 103, movement is well simulated but in the top 40 cm too little degradation is calculated. This suggests that the laboratory half lives chosen for the input data were too long and hence gave poor simulations of degradation in the field, despite correction for temperature and soil water content. It is not known why degradation measured in the laboratory was slower than that in the field unless the microbial activity in the laboratory incubations became attenuated with time. At day 278 the distribution of bentazone was well simulated.
3.3. Ethoprophos
Initial concentrations of ethoprophos in the top layer were 6.8 and 6.7 mg kgÿ1for
measured and simulated, respectively and no allowance was made for possible volatilisation of the compound. Results are given in Fig. 3. There was little leaching of the more strongly sorbed ethoprophos and greatest concentrations stayed in the top layers. PLM largely predicted this but still calculated too much penetration into the soil. At day 474 only minute traces of ethoprophos were predicted to remain.
Simulation of the distributions of bromide tracer, indicates that PLM predicted the movement and dispersion of leaching water satisfactorily. The chosen input data led to predictions of too little degradation of bentazone. Residual amounts of compounds that remained near the soil surface were not predicted.
4. Simulation of concentrations of isoproturon in drainage waters from Brimstone farm
Brimstone data had been modelled before (Nicholls et al., 1993) and so no strictly uncalibrated run could be done. Input data used in the simulations are given in Tables 3±5. If possible, measured data were used directly from those provided (Harris et al., 2000). Sorption data were given by Nicholls et al. (1993). The half-life for isoproturon was obtained from the measured amounts remaining in topsoil provided. The time of onset of the first drainage event was calibrated by adjusting the initial soil-water deficit. The amount of water drained was calibrated by adjusting the proportion (%) `water to drains' parameter and the large differences between the amounts of water drained by the different plots should be noted.
Fig. 3. Distribution of ethoprophos with depth in Vredepeel soil.
The analysis of the initial runs revealed that the measured concentrations of isoproturon in drainage water did not vary much with flow rate of water during the event and only declined gradually with time. Initial simulations were made with the percentage of `fast' pores parameter set to the high value of 80% in a specific attempt to simulate this independence from flow rate. Such a high value was also thought necessary for the heavy cracking-clay soil at Brimstone farm. The simulations gave concentrations in drainage water typically 10±15 times greater than those measured (Nicholls et al., 1996). Even so, the simulated concentrations were still sensitive to the different rates of flow of drainage water that occurred on different days. Predicted concentrations increased with rate of flow. This effect is probably greater than that observed because herbicide in the top layer is assumed to equilibrate with incoming water before being transported to depth via macropores. Complete equilibration in the topsoil is probably not attained in practice.
Table 3
Solute properties used for Brimstone farm simulations
Compound Depth
Isoproturon 0±30 2.9 80 20.3 10
30±60 2.9 80 20.3 10
60±100 2.9 80 20.3 10
Table 4
Soil properties used for Brimstone farm simulations
Depth (cm) Total pore space (l/l) Soil water content (l/l) Bulk density (kg/l)
5 kPa 200 kPa 1500 kPa
0±20 0.60 0.40 0.31 0.22 1.05
20±60 0.47 0.36 0.28 0.22 1.29
60±100 0.50 0.33 0.12 0.05 1.30
Table 5
Soil properties used for Brimstone farm simulations
Depth to mole drains (cm) 55
% water to drains Plot 6 40
% water to drains plot 9 90
Initial soil water de®cit plot 6 (mm) 160
Initial soil water de®cit Plot 9 (mm) 110
Crop winter oats/barley
Evaporation reduction factor 0.9
% `fast' pores in mobile phase 60
Rate of `slow' drainage (cm/day) 60
Simulations shown in Fig. 4 were done using a lower value of 60% for the `fast' pores parameter as given in Tables 3±5. Behaviour on the two different plots were simulated using this same value. Maximum concentrations in drainage water were simulated to within about two fold of those measured. Even though the amount of water leaving the two plots in the drains was very different, concentrations of isoproturon were of similar magnitude for both plots. The most sensitive parameters that affected the amount of drainage from the plots were the evaporation reduction factor and the `%' water to drains value. The time of the first drainage event was most affected by the initial soil water deficit (see Tables 4 and 5).
Concentrations of isoproturon in drainage water can be extremely sensitive to the `fast' pores parameter and can change by orders of magnitude. The low value calibrated for the `fast' pores parameter may be because the macropores are more tortuous or less
Fig. 4. Movement to drainage on plots 6 and 9 at Brimstone farm of isoproturon applied 8 October 1990 at 2.5 kg/ha.
continuous than expected for this soil and hence more interaction of the solute with soil solids occurs.
Changes in the tortuosity and continuity of the macropores may change markedly with time and location and simulations will thus require different values for the parameters that determine macropore flow for different experiments even at the same site.
5. Conclusions
Since bromide behaves as a tracer of water movement, the predictions indicate that PLM was able to model the movement and dispersion of water in the Vredepeel soil profile. However, traces of bromide that remained near the soil surface were not predicted. Although PLM was designed to simulate concentrations of pesticide that reach drainage waters by preferential flow in structured soils, the model was still able to predict the distribution of the large proportion of bentazone and ethoprophos in the profile of the light-textured soil at Vredepeel. However, the heterogeneous nature of field soils mean that these simulations are crude and inaccurate compared with physical experiments done under well defined conditions in other disciplines. Hence, a detailed mathematical analysis of deviations of simulated values from measured ones would be premature. Simulated concentrations were similar to those measured in drainage water at Brimstone farm but were only obtained after extensive calibration of the model. Even so, there were several fold discrepancies between some simulated concentrations and measured ones. The concentrations of pesticide in drainage waters are so sensitive to the input parameters that govern macropore flow that the predictive ability of PLM is limited at present.
Acknowledgements
The financial support of the UK Ministry of Agriculture Fisheries and Food and the COST 66 Action `Pesticides in the soil environment' of DGXII-EU is gratefully acknowledged.
References
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