onstrated under field conditions (e.g., Flury, 1996;Jarvis, 2002).We introduce an improved, one-dimensional, non-steady-state dual-In recent years, our knowledge of the mechanisms that permeability model (MACRO 5.1). The model simulates water flow generate and sustain preferential movement of water and solute transport in the vadose zone of structured soils by coupling and solutes has been incorporated into several simulaa high-conductivity-low porosity macropore domain to a low-conductivity-high porosity domain representing the soil matrix. Mass exchange tion models (Feyen et al., 1998; Jarvis, 1998; Š imů nek between the domains is approximated by first-order expressions. The et al., 2003). Dual-permeability models divide the total numerical solutions are briefly described, focusing on the dual-permesoil pore space into one part (e.g., soil matrix) characterability formulation. The solution method for water flow in macropores ized by a large storage capacity and small flow capacity was verified by comparing simulation results with analytical solutions and another part (e.g., macropores) with a small storage for a "kinematic wave". The model was tested against high timecapacity and a large flow capacity. One example of this resolution measurements of water flow and nonreactive (Cl Ϫ ) solute type of model is MACRO (Jarvis, 1994), which couples transport in transient microlysimeter experiments. The objective was to classical treatments of flow and transport processes in test the identifiability of four key model parameters determining the the matrix (Richards' equation, convection-dispersion degree of preferential flow using the generalized likelihood uncertainty equation) to a macropore region where flow is assumed estimation (GLUE) procedure. The parameters were chosen either beto be gravity-driven. MACRO has been widely used, cause they are difficult or impossible to measure directly or because both as a research tool (e.g., Larsson and Jarvis, 1999; they were considered sensitive on the basis of earlier experience with Kä tterer et al., 2001) and in management (e.g., in pestithe model. The measurements, indicating strong preferential flow, were adequately reproduced by the model simulations (overall model ef-cide regulation in the EU, Forum for the Coordination ficiency ϭ 0.62). The GLUE procedure conditioned the saturated of Pesticide Fate Models and Their Use, 1995), because matrix hydraulic conductivity, the macroporosity, and the mass exit is physically based, numerically robust for all soil change coefficient (diffusion pathlength), indicating that these paramhydrological types (even for long-term simulations, i.e., eters would be identifiable in inverse modeling approaches based decades), and is relatively parsimonious with respect to on microlysimeter experiments. The conditioning of the kinematic parameter requirements (Š imů nek et al., 2003). Despite exponent was poor, which was attributed primarily to correlation with these advantages, a number of limitations of the model the macroporosity.

This paper describes an updated version of a screening tool for groundwater vulnerability assessment to evaluate pesticide leaching to groundwater, based on a revised version of the attenuation factor. The tool has been implemented in a geographical information system (GIS) covering the major islands of the state of Hawaii, USA. The Hawaii Department of Agriculture currently uses the tool in their pesticide evaluation process as a first-tier screening tool. The basic soil properties and pesticide properties necessary to compute the index, and estimates of their uncertainty, are included in the GIS. Uncertainties in soil and pesticide properties are accounted for using first-order uncertainty analysis. Classifications of pesticides as 'likely', 'uncertain' or 'unlikely' to leach are made on the basis of the uncertainty and a comparison of the revised attenuation factor with values and uncertainties of two reference chemicals. The reference chemicals represent what are considered to be a 'leachable' and a 'non-leachable' pesticide under Hawaii conditions. It is concluded that the tool is suitable for screening new and already used pesticides for the islands of Hawaii. However, the tool is associated with uncertainties that are not accounted for, so a conservative approach with respect to interpretation of the results and selection of pesticide parameters used in the tool is recommended.

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A simulation tool for site-specific vulnerability assessments of pesticide leaching to groundwater was developed, based on the pesticide fate and transport model MACRO, parameterized using pedotransfer functions and reasonable worst-case parameter values. The effects of uncertainty in the pedotransfer functions on simulation results were examined for 48 combinations of soils, pesticides and application timings, by sampling pedotransfer function regression errors and propagating them through the simulation model in a Monte Carlo analysis. An uncertainty factor, f(u), was derived, defined as the ratio between the concentration simulated with no errors, c(sim), and the 80th percentile concentration for the scenario. The pedotransfer function errors caused a large variation in simulation results, with f(u) ranging from 1.14 to 1440, with a median of 2.8. A non-linear relationship was found between f(u) and c(sim), which can be used to account for parameter uncertainty by correcting the simulated concentration, c(sim), to an estimated 80th percentile value. For fine-textured soils, the predictions were most sensitive to errors in the pedotransfer functions for two parameters regulating macropore flow (the saturated matrix hydraulic conductivity, K(b), and the effective diffusion pathlength, d) and two water retention function parameters (van Genuchten's N and alpha parameters). For coarse-textured soils, the model was also sensitive to errors in the exponent in the degradation water response function and the dispersivity, in addition to K(b), but showed little sensitivity to d. To reduce uncertainty in model predictions, improved pedotransfer functions for K(b), d, N and alpha would therefore be most useful.

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