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TECHNICAL REPORTS

Vadose Zone Processes and Chemical Transport

Modeling Macropore Flow Effects on Pesticide Leaching

Inverse Parameter Estimation Using Microlysimeters

Department of Soil Sciences, SLU, Box 7014, 750 07 Uppsala, Sweden

^* Corresponding author (stephanie.roulier{at}mv.slu.se).

Received for publication October 16, 2002. 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 finer-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.

Abbreviations: EF, model efficiency • SUFI, Sequential Uncertainty Fitting

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