Mapping ground water vulnerability to pesticide leaching with a process-based metamodel of EuroPEARL

To support EU policy, indicators of pesticide leaching at the European level are required. For this reason, a metamodel of the spatially distributed European pesticide leaching model EuroPEARL was developed. EuroPEARL considers transient flow and solute transport and assumes Freundlich adsorption, first-order degradation and passive plant uptake of pesticides. Physical parameters are depth dependent while (bio)-chemical parameters are depth, temperature, and moisture dependent. The metamodel is based on an analytical expression that describes the mass fraction of pesticide leached. The metamodel ignores vertical parameter variations and assumes steady flow. The calibration dataset was generated with EuroPEARL and consisted of approximately 60,000 simulations done for 56 pesticides with different half-lives and partitioning coefficients. The target variable was the 80th percentile of the annual average leaching concentration at 1-m depth from a time series of 20 yr. The metamodel explains over 90% of the variation of the original model with only four independent spatial attributes. These parameters are available in European soil and climate databases, so that the calibrated metamodel could be applied to generate maps of the predicted leaching concentration in the European Union. Maps generated with the metamodel showed a good similarity with the maps obtained with EuroPEARL, which was confirmed by means of quantitative performance indicators.     

Modeling macropore flow effects on pesticide leaching: inverse parameter estimation using microlysimeters

Macropore flow is a key factor determining pesticide fate, but models accounting for this process need parameters that cannot be easily measured. This study was conducted to investigate the use of inverse techniques to estimate parameters controlling macropore flow and pesticide fate in the dual-permeability model MACRO. Undisturbed columns were sampled at three landscape positions (hilltop, slope, hollow) with contrasting texture and organic carbon content. Transient leaching experiments were performed for an anionic tracer and the herbicide MCPA (4-chloro-2methylphenoxy acetic acid) during a 4-mo period, first under natural rainfall, and then under controlled irrigation in the laboratory. The tracer breakthrough for the liner-textured soil from the hilltop showed strong evidence of macropore flow, resulting in a rapid leaching of MCPA, while leaching was minimal from the organic-rich hollow soil, since macropore flow was weaker and adsorption stronger. The MACRO model was linked to the inverse modeling program SUFI (Sequential Uncertainty Fitting) to enable calibration of nine key model parameters. Based on calculated model efficiencies, MACRO-SUFI gave generally good predictions of water movement and tracer and pesticide transport, although some errors were attributed to difficulties in simulating the effects of soil moisture on degradation and the timing of water outflows. Even after calibration, significant uncertainties remained for some key parameters controlling macropore flow. Nevertheless, the parameter estimates were significantly different between landscape positions and could also be related to basic soil properties. The posterior uncertainty ranges could probably be reduced with a more exhaustive sampling of the parameter space and improved experimental designs.     

Sensitivity and first-step uncertainty analyses for the preferential flow model MACRO

Sensitivity analyses for the preferential flow model MACRO were carried out using one-at-a-time and Monte Carlo sampling approaches. Four different scenarios were generated by simulating leaching to depth of two hypothetical pesticides in a sandy loam and a more structured clay loam soil. Sensitivity of the model was assessed using the predictions for accumulated water percolated at a 1-m depth and accumulated pesticide losses in percolation. Results for simulated percolation were similar for the two soils. Predictions of water volumes percolated were found to be only marginally affected by changes in input parameters and the most influential parameter was the water content defining the boundary between micropores and macropores in this dual-porosity model. In contrast, predictions of pesticide losses were found to be dependent on the scenarios considered and to be significantly affected by variations in input parameters. In most scenarios, predictions for pesticide losses by MACRO were most influenced by parameters related to sorption and degradation. Under specific circumstances, pesticide losses can be largely affected by changes in hydrological properties of the soil. Since parameters were varied within ranges that approximated their uncertainty, a first-step assessment of uncertainty for the predictions of pesticide losses was possible. Large uncertainties in the predictions were reported, although these are likely to have been overestimated by considering a large number of input parameters in the exercise. It appears desirable that a probabilistic framework accounting for uncertainty is integrated into the estimation of pesticide exposure for regulatory purposes.     

Impact of data quality and model complexity on prediction of pesticide leaching

Accurate input data for leaching models are expensive and difficult to obtain which may lead to the use of "general" non-site-specific input data. This study investigated the effect of using different quality data on model outputs. Three models of varying complexity, GLEAMS, LEACHM, and HYDRUS-2D, were used to simulate pesticide leaching at a field trial near Hamilton, New Zealand, on an allophanic silt loam using input data of varying quality. Each model was run for four different pesticides (hexazinone, procymidone, picloram and triclopyr); three different sets of pesticide sorption and degradation parameters (i.e., site optimized, laboratory derived, and sourced from the USDA Pesticide Properties Database); and three different sets of soil physical data of varying quality (i.e., site specific, regional database, and particle size distribution data). We found that the selection of site-optimized pesticide sorption (Koc) and degradation parameters (half-life), compared to the use of more general database derived values, had significantly more impact than the quality of the soil input data used, but interestingly also more impact than the choice of the models. Models run with pesticide sorption and degradation parameters derived from observed solute concentrations data provided simulation outputs with goodness-of-fit values closest to optimum, followed by laboratory-derived parameters, with the USDA parameters providing the least accurate simulations. In general, when using pesticide sorption and degradation parameters optimized from site solute concentrations, the more complex models (LEACHM and HYDRUS-2D) were more accurate. However, when using USDA database derived parameters, all models performed about equally.     

Stochastic modeling of diffuse pesticide losses from a small agricultural catchment

The objective of this study was to identify the main sources of variation in pesticide losses at field and catchment scales using the dual permeability model MACRO. Stochastic simulations of the leaching of the herbicide MCPA (4-chloro-2-methylphenoxyacetic acid) were compared with seven years of measured concentrations in a stream draining a small agricultural catchment and one year of measured concentrations at the outlet of a field located within the catchment. MACRO was parameterized from measured probability distributions accounting for spatial variability of soil properties and local pedotransfer functions derived from information gathered in field- and catchment-scale soil surveys. At the field scale, a single deterministic simulation using the means of the input distributions was also performed. The deterministic run failed to reproduce the summer outflows when most leaching occurred, and greatly underestimated pesticide leaching. In contrast, the stochastic simulations successfully predicted the hydrologic response of the field and catchment and there was a good resemblance between the simulations and measured MCPA concentrations at the field outlet. At the catchment scale, the stochastic approach underestimated the concentrations of MCPA in the stream, probably mostly due to point sources, but perhaps also because the distributions used for the input variables did not accurately reflect conditions in the catchment. Sensitivity analyses showed that the most important factors affecting MACRO modeled diffuse MCPA losses from this catchment were soil properties controlling macropore flow, precipitation following application, and organic carbon content.     

Effects of soil variability and weather conditions on pesticide leaching--a farm-level evaluation

In line with European regulations, Dutch law imposes an environmental threshold of 0.1 microg L(-1) on pesticide concentrations in ground water. During registration, the risk of exceeding this threshold is assessed through simulations for one or a few standard scenarios that do not reflect spatial variability under field conditions. The introduction of precision agriculture, where soil variability is actively managed, can increase control over pesticide leaching. This study presents a step-wise evaluation of the effects of soil variability and weather conditions on pesticide leaching. The evaluation was conducted on a 100-ha arable farm and aimed at identifying opportunities for precision management. As a first step, a relative risk assessment identified pesticides presenting a relatively high risk to the environment. Second, the effect of weather conditions was analyzed through 20 years of simulations for three distinct soil profiles. Results were summarized in cumulative probability plots to provide a probabilistic characterization of historical weather data. The year matching 90% probability (1981) served as a reference to simulate pesticide leaching from 612 soil profiles. After interpolation, areas where concentrations exceeded the environmental threshold were identified. Out of a total of 19 pesticides, isoproturon [N-dimethyl-N'-(4-(1-methylethyl)phenyl)urea], metribuzin [4-amino-6-tert-butyl-3-(methylthio)-as-triazin-5(4H)-one], and bentazon [2,1,3-benzothiadiazin-4(3H)-one, 3-isopropyl-, 2,2-dioxide] showed the highest risk for leaching. Leaching was strongly affected by soil variability at the within-field, field, and farm levels. Opportunities for precision management were apparent, but depended on the scale level at which environmental thresholds were implemented. When legislation is formulated in this issue, the presented step-wise evaluation can serve as a basis for identification and precision management of high-risk pesticides.     

Turfgrass thatch effects on pesticide leaching: a laboratory and modeling study

Process-based models are frequently used to assess the water quality impacts of turfgrass management emanating from proposed or existing golf courses. Thatch complicates the prediction of pesticide transport because surface-applied pesticides must pass through an organic-rich layer before entering the soil. This study was conducted to (i) compare the use of a linear equilibrium model (LEM) and two-site nonequilibrium (2SNE) model to predict pesticide transport through soil and thatch + soil columns, and (ii) evaluate thatch effects on pesticide transport through soil columns with a volume-averaging approach. Pesticide breakthrough curves were obtained for soil and thatch + soil columns from a 1 cm h(-1) flux applied one day after applying triclopyr (3,5,6-trichloro-2-pyridinyloxyacetic acid) and carbaryl (1-napthyl-methyl carbamate). Pesticide and bromide transport parameters indicated that nonequilibrium processes were affecting pesticide transport. Columns containing zoysiagrass (Zoysia japonica Steud.) thatch had lower triclopyr and carbaryl leaching losses than did soil-only columns, although total reductions attributable to thatch did not exceed 15% of the applied pesticide. When laboratory-based retardation factors were used, the 2SNE model explained 88 to 93% of the variability for triclopyr and 70 to 94% of the variability for carbaryl. Laboratory-based retardation factors performed well in a 2SNE model to predict the peak concentration and tailing behavior of triclopyr and carbaryl with a volume-averaging approach. These results suggest that separate representation of the thatch layer in process-based models is not a prerequisite to obtain reasonable estimates of pesticide transport under steady state flow conditions.     

Prediction and uncertainty source analysis of the spatial and temporal disturbance from off-road vehicular traffic in a complex ecosystem

Vehicle use during military training activities results in soil disturbance and vegetation loss. The capacity of lands to sustain training is a function of the sensitivity of lands to vehicle use and the pattern of land use. The sensitivity of land to vehicle use has been extensively studied. Less well understood are the spatial patterns of vehicle disturbance. Since disturbance from off-road vehicular traffic moving through complex landscapes varies spatially, a spatially explicit nonlinear regression model (disturbance model) was used to predict the pattern of vehicle disturbance across a training facility. An uncertainty analysis of the model predictions assessed the spatial distribution of prediction uncertainty and the contribution of different error sources to that uncertainty.For the most part, this analysis showed that mapping and modeling process errors contributed more than 95% of the total uncertainty of predicted disturbance, while satellite imagery error contributed less than 5% of the uncertainty. When the total uncertainty was larger than a threshold, modeling error contributed 60% to 90% of the prediction uncertainty. Otherwise, mapping error contributed about 10% to 50% of the total uncertainty. These uncertainty sources were further partitioned spatially based on other sources of uncertainties associated with vehicle moment, landscape characterization, satellite imagery, etc.     

Field-scale variation in microbial activity and soil properties in relation to mineralization and sorption of pesticides in a sandy soil

Pesticides applied to agricultural soils are subject to environmental concerns because leaching to groundwater reservoirs and aquatic habitats may occur. Knowledge of field variation of pesticide-related parameters is required to evaluate the vulnerability of pesticide leaching. The mineralization and sorption of the pesticides glyphosate and metribuzin and the pesticide degradation product triazinamin in a field were measured and compared with the field-scale variation of geochemical and microbiological parameters. We focused on the soil parameters clay and organic carbon (C) content and on soil respiratory and enzymatic processes and microbial biomass. These parameters were measured in soil samples taken at two depths (Ap and Bs horizon) in 51 sampling points from a 4-ha agricultural fine sandy soil field. The results indicated that the spatial variation of the soil parameters, and in particular the content of organic C, had a major influence on the variability of the microbial parameters and on sorption and pesticide mineralization in the soil. For glyphosate, with a co-metabolic pathway for degradation, the mineralization was increased in soils with high microbial activity. The spatial variability, expressed as the CV, was about five times higher in the Bs horizon than in the Ap horizon, and the local-scale variation within 100 m(2) areas were two to three times lower than the field-scale variation within the entire field of about 4 ha.     

Simulating pesticide leaching and runoff in rice paddies with the RICEWQ-VADOFT model

There is a current need to simulate leaching and runoff of pesticide from rice (Oryza sativa L.) paddies for assessing environmental impacts on a valuable agricultural system. The objective of this study was to develop a model for determining predicted environmental concentration (PEC) in soil, runoff, and ground water through the linkage of two models, rice water quality model (RICEWQ) and vadose zone transport model (VADOFT), to simulate pesticide fate and transport within a rice paddy and underlying soil profile. Model performance was evaluated with a field data set obtained from a 2-yr field experiment in 1997 and 1998 in northern Italy. The predictions of amount of pesticide running off from the paddy field and accumulating in the paddy sediment were in agreement with measured values. Leaching into the vadose zone accounted for approximately 19% of the applied dose, but only a small amount of chemical (<0.1%) was predicted to reach ground water at a 5-m depth due to sorption and transformation in the soil. The permeability of the soil and the water management practices in the paddy field were shown to have a strong influence on pesticide fate. These factors need to be well characterized in the field if model predictions are to be successful. The combined model developed in this work is an effective tool for exposure assessments for soil, surface water, and ground water, in the particular conditions of rice cultivation.     

Hydrogeological relationships of sandy deposits: modeling of two-dimensional unsaturated water and pesticide transport

Prediction of the movement of water and solutes in the vadose zone requires information on the distribution of spatial trends and heterogeneities in porous media. The present study describes different lithofacies origination mainly from glaciofluvial deposits. Among different lithofacies, hydrological relationships were investigated. By means of a two-dimensional hydrological model it was evaluated how the flow of water and leaching of metribuzin (4-amino-6-tert-butyl-4,5-dihydro-3-methylthio-1,2,4-triazin-5-one) was affected. Two selected large outcrop sections consisting of glacial outwash deposits were used in the modeling study. Eleven different lithofacies were distinguished and described in terms of texture distribution, sorting, bedding style, and external boundaries based on excavated soil profiles from 27 locations representing seven predominantly sandy landforms in Denmark. Undisturbed soil columns were sampled from each of the lithofacies and brought to the laboratory to be analyzed. With respect to their soil hydraulic properties, the different lithofacies formed four different hydrofacies having relatively homogeneous, hydrogeological properties. Two large outcrop sections from one of the locations (a gravel pit) located near the terminal moraine of the former Weichsel glacier were used for the HYDRUS-2D modeling. Modeling results revealed that the spatial distribution of sedimentary bodies affected water flow and the leaching of metribuzin.     

Variations in pesticide leaching related to land use, pesticide properties, and unsaturated zone thickness

Pesticide leaching through variably thick soils beneath agricultural fields in Morgan Creek, Maryland was simulated for water years 1995 to 2004 using LEACHM (Leaching Estimation and Chemistry Model). Fifteen individual models were constructed to simulate five depths and three crop rotations with associated pesticide applications. Unsaturated zone thickness averaged 4.7 m but reached a maximum of 18.7 m. Average annual recharge to ground water decreased from 15.9 to 11.1 cm as the unsaturated zone increased in thickness from 1 to 10 m. These point estimates of recharge are at the lower end of previously published values, which used methods that integrate over larger areas capturing focused recharge in the numerous detention ponds in the watershed. The total amount of applied and leached masses for five parent pesticide compounds and seven metabolites were estimated for the 32-km2 Morgan Creek watershed by associating each hectare to the closest one-dimensional model analog of model depth and crop rotation scenario as determined from land-use surveys. LEACHM parameters were set such that branched, serial, first-order decay of pesticides and metabolites was realistically simulated. Leaching is predicted to be greatest for shallow soils and for persistent compounds with low sorptivity. Based on simulation results, percent parent compounds leached within the watershed can be described by a regression model of the form e(-depth) (a ln t1/2-b ln K OC) where t1/2 is the degradation half-life in aerobic soils, K OC is the organic carbon normalized sorption coefficient, and a and b are fitted coefficients (R2 = 0.86, p value = 7 x 10(-9)).     

Uncertainty assessment of the model RICEWQ in northern Italy

Model predictions are often seriously affected by uncertainties arising from many sources. Ignoring the uncertainty associated with model predictions may result in misleading interpretations when the model is used by a decision-maker for risk assessment. In this paper, an analysis of uncertainty was performed to estimate the uncertainty of model predictions and to screen out crucial variables using a Monte Carlo stochastic approach and a number of statistical methods, including ANOVA and stepwise multiple regression. The model studied was RICEWQ (Version 1.6.1), which was used to forecast pesticide fate in paddy fields. The results demonstrated that the paddy runoff concentration predicted by RICEWQ was in agreement with field measurements and the model can be applied to simulate pesticide fate at field scale. Model uncertainty was acceptable, runoff predictions conformed to a log-normal distribution with a short right tail, and predictions were reliable at field scale due to the narrow spread of uncertainty distribution. The main contribution of input variables to model uncertainty resulted from spatial (sediment-water partition coefficient and mixing depth to allow direct partitioning to bed) and management (time and rate of application) parameters, and weather conditions. Therefore, these crucial parameters should be carefully parameterized or precisely determined in each site-specific paddy field before the application of the model, since small errors of these parameters may induce large uncertainty of model outputs.     

Modeling pesticide volatilization from turf

Pesticide volatilization models are typically based on equilibrium partitioning of the chemical into solid, liquid, and gaseous phases in the soil environment. In turf systems direct vaporization from vegetation surfaces is a more likely source, and it is difficult to apply equilibrium methods to plant material due to the uncertainties of solid-liquid-gas partitioning. An alternative approach is to assume that pesticide volatilization is governed by the same processes that affect water evaporation. A model was developed in which evapotranspiration values, as determined by the Penman equation, were adjusted to chemical vaporization using ratios of water and chemical saturated vapor pressures and latent heats of vaporization. The model also assumes first-order degradation of pesticide on turf vegetation over time. The model was tested by comparisons of predictions with measurements of volatilization for eight pesticides measured during 3 to 7 d in 11 field experiments. Measured volatilization fluxes ranged from 0.1 to 22% of applied chemical. Pesticides were divided into two groups based on saturated vapor pressures and organic C partition coefficients. One pesticide was selected from each group to calibrate the model's volatilization constant for the group, and the remaining pesticides were used for model testing. Testing results indicated that the model provides relatively conservative estimates of pesticide volatilization. Predicted mean losses exceeded observations by 20%, and the model explained 67% of the observed variation in volatilization fluxes. The model was most accurate for those chemicals that exhibited the largest volatilization losses.     

The value of site-specific information and the environment: technology adoption and pesticide use under uncertainty

Remote sensing technology offers an opportunity to significantly increase the amount of site-specific information about field characteristics such as pest populations. Coupled with variable rate application technologies, this added information has the potential to provide environmental benefits through reduced pesticide applications. However, producers face a complicated adoption decision because output prices and crop yields are uncertain. A model is developed to examine the potential value of remote sensing information to pesticide applications in an option-value framework under uncertainty. Simulations suggest that remote sensing information could decrease pesticide use, but uncertainty and irreversibility are likely to limit technological adoption by farmers. Potential cost-share subsidies are discussed.     

Preferential flow of bromide, bentazon, and imidacloprid in a Dutch clay soil

Leaching to ground water and tile drains are important parts of the environmental assessment of pesticides. The aims of the present study were to (i) assess the significance of preferential flow for pesticide leaching under realistic worst-case conditions for Dutch agriculture (soil profile with thick clay layer and high rainfall) and (ii) collect a high-quality data set that is suitable for testing pesticide leaching models. The movement of water, bromide, and the pesticides bentazon [3-isopropyl-1H-2, 1,3-benzothiadiazine-4(3H)-one-2,2-dioxide] and imidacloprid [1-[(6-chloro-3-pyridinyl)-methyl]-N-nitro-2-imidazolidinimine] was monitored in a clay soil for about 1 yr. The 1.2-ha field was located in the central part of the Netherlands (51 degrees 53' N, 5 degrees 43' E). The soil was a Eutric Fluvisol cropped with winter wheat (Triticum aestivum L.). Tile drains were present at a 0.8- to 0.9-m depth and the ground water level fluctuated between a 0.5- and 2-m depth. All chemicals were applied in spring. None of the soil concentration profiles showed bimodal concentration distributions. However, for each substance the highest concentration in drain water was found in the first drainage event after its application, which indicates preferential flow. This preferential flow is probably caused by permanent macropores that were present in the 0.3- to 1.0-m layer. At the time of the first drainage event, the drain water concentration of each substance was about an order of magnitude higher than its ground water concentration. Thus, the flux concentrations in drain water proved to be a more sensitive detector of preferential flow than the resident concentrations in the soil profile and the ground water.