Testing MACRO (version 5.1) for pesticide leaching in a Dutch clay soil

Abstract

Testing of pesticide leaching models against comprehensive field-scale measurements is necessary to increase confidence in their predictive ability when used as regulatory tools. Version 5.1 of the MACRO model was tested against measurements of water flow and the behaviour of bromide, bentazone [3-isopropyl-1H-2,1,3-benzothiadiazin-4(3H)-one-2,2-dioxide] and imidacloprid [1-(6-chloro-3-pyridylmethyl)-N-nitroimidazolidin-2-ylideneamine] in a cracked clay soil. In keeping with EU (FOCUS) procedures, the model was first calibrated against the measured moisture profiles and bromide concentrations in soil and in drain water. Uncalibrated pesticide simulations based on laboratory measurements of sorption and degradation were then compared with field data on the leaching of bentazone and imidacloprid. Calibrated parameter values indicated that a high degree of physical non-equilibrium (i.e. strong macropore flow) was necessary to describe solute transport in this soil. Comparison of measured and simulated bentazone concentration profiles revealed that the bulk of the bentazone movement in this soil was underestimated by MACRO. Nevertheless, the model simulated the dynamics of the bentazone breakthrough in drain water rather well and, in particular, accurately simulated the timing and the concentration level of the early bentazone breakthrough in drain water. The imidacloprid concentration profiles and its persistence in soil were simulated well. Moreover, the timing of the early imidacloprid breakthrough in the drain water was simulated well, although the simulated concentrations were about 2-3 times larger than measured. Deep groundwater concentrations for all substances were underestimated by MACRO, although it simulated concentrations in the shallow groundwater reasonably well. It is concluded that, in the context of ecotoxicological risk assessments for surface water, MACRO can give reasonably good simulations of pesticide concentrations in water draining from cracking clay soils, but that prior calibration against hydrologic and tracer data is desirable to reduce uncertainty and improve accuracy.

Simulation of movement of pesticides towards drains with a preferential flow version of PEARL

“…A parameter that may affect the leaching of sorbing substances is the fraction of solid phase in contact with the bypass domain [ f s, byp in equation (17)]. As noticed before, it was decided not to calibrate this parameter because Scorza Júnior et al 44 found a very small response of the simulated drainage concentration to this parameter using the MACRO model. To check whether this finding also applies to the PEARL model, f s, byp was varied between 0.01 and 0.04 (the lower and higher limits of the range given by Dubus et al ,43).…”

“…This value is within the range reported by Dubus et al ,43 and is also the default value for the MACRO model 9. Although this parameter is considered to be highly uncertain,43 it was decided not to calibrate this parameter, because Scorza Júnior et al 44 showed that the sensitivity of the drainage concentration to this parameter as simulated with MACRO was very small. The half‐life under reference conditions for imidacloprid was obtained in the laboratory and was 91 days.…”

Predicting rapid herbicide leaching to surface waters from an artificially drained headwater catchment using a one dimensional two-domain model coupled with a simple groundwater model

“…It was selected for this study, primarily because it has been used extensively, and with some success, in earlier work to represent water and pesticide dynamics in drained soils exhibiting preferential flow (e.g. Brown et al, 2004;Jarvis, 1995;Jarvis, 1999, 2000;Scorza Júnior et al, 2007;Villholth et al, 2000). It computes a complete water balance from precipitation inputs, including variably saturated flow, evapotranspiration, root water uptake for different depths (which may be assigned different properties) and losses to field drainage.…”

Parameterization Guidelines and Considerations for Hydrologic Models

“…MACRO, SWAT, SHAW, EPIC/APEX, ADAPT, RZWQM, MIKE SHE Yan and Haan (1991), Engel et al (2007), Chen (2012) Santhi et al (2001), Scorza et al (2007) 3. Minimize the number of optimized parameters.…”

“…Use multiple criteria to optimize parameter values (more than one model output or target is compared with observed data). Boyle et al (2000), Doherty and Johnston (2003), Raat et al (2004), Dai et al (2010) Santhi et al 2001, Scorza et al (2007), Thorp et al (2007) 6. Use hard and soft data to optimize parameters (soft data are qualitative knowledge from experimentalists, such as estimated ET).…”