Core Ideas: Increasing macropore volume may decrease the final cumulative outflow. Heterogeneous macroporous geometries can be represented explicitly or implicitly. HYDRUS‐1D and SWAP performed well in soils with heterogeneous macroporous geometries. Dead‐end macropores are included by changing lateral transfer parameters with depth. Heterogeneous macroporous geometries (HMGs) comprise unevenly distributed macropores with depth. A large variety of macropore distributions produce fast water flow and chemical transport that deviate from uniform flow. We analyzed the measured pressure head and outflow in column experiments with a uniform matrix (Exp. I), one central macropore (main bypass) (Exp. II), and HMG (Exp. III) and evaluated the performance of the models HYDRUS‐1D and SWAP under these conditions. Two replicate soil columns were prepared with a 62‐cm silty loam layer above a 5‐cm sandy loam layer. Well‐defined infiltration and drainage conditions were applied to top and bottom boundaries, respectively. Pressure head and outflow were measured at short time intervals, and calibration was performed by PEST. Experiment I was conducted to calibrate the matrix parameters and Exp. II to calibrate macropore parameters. In Exp. III, four dead‐end macropores were created around the main bypass, and the models were run using the previously calibrated parameters, updating only the macropore geometry parameters. The results indicated that HMGs increased total macropore influx, especially in the internal catchment domain. Interaction between the internal catchment, main bypass, and matrix domains was necessary for explaining the change in cumulative outflow and outflow onset observations. The simulations with both models were accurate for HMG regarding pressure head and outflow. The implicit representation of HMGs by HYDRUS‐1D improved outcomes for cumulative outflow, whereas the explicit representation by SWAP improved results for lateral mass transfer. The ability to model the effects of HMGs is essential for environmental and agricultural studies.

Pesticide transport simulation by SWAP–PEARL (Soil–Water–Atmosphere–Plant and Pesticide Emission Assessment at Regional and Local scales) models can help to predict pesticide leaching at regional scales. For reasons of economic and time efficiency, measurement efforts should be prioritized towards critical parameters. The objective of this research is to perform a Morris screening and Sobol–Jansen sensitivity analysis to SWAP–PEARL models, using a reasonable worst‐case scenario. Three pesticide compounds were analyzed:, bentazon (zero sorption), imidacloprid (moderately sorbed), and compound I (highly sorbed). Initial macropore and pesticide parameter values were varied by ±20% to generate parameter ranges. The outputs analyzed were the concentration in drainage water, the average concentration in groundwater between 1 and 2 m, and the concentration in the soil system at 100‐cm depth. Influential parameters found through the Morris method were analyzed using the Sobol–Jansen method. The results for bentazon indicate that the degradation half‐life (DT50), the bottom depth of the internal catchment ( z _ic ), and the proportion of the internal catchment at the soil surface ( p _{ic_0} ) are critical parameters in all the outputs analyzed. For imidacloprid and compound I, the most relevant parameters for drainage output are the Freundlich sorption exponent ( F _exp ) and z _ic ; for groundwater, the relevant parameters are F _exp , the bottom depth of static macropores ( z _st ), and p _{ic_0} ; and for soil concentrations at 100‐cm depth, the relevant parameters are F _exp , z _ic , and p _{ic_0} . The Morris and Sobol–Jansen methods produce the same results for the first position in the ranking. Measurement efforts should be performed to update national soil databases, including critical pesticide and macropore parameters.

The vertical change in the number of macropores causes a variation of the relative macroporosity ( w _f ) and the effective aggregate width ( d _ag ) over the soil profile. Both parameters are used in HYDRUS to represent this variation, increasing the number of parameters and making automated calibration challenging. The working hypothesis is that we can improve an analytical estimation of w _f and d _ag developed in previous research by inverse estimation with a meta‐model for HYDRUS 2D/3D, using disk infiltrometer data of infiltration at zero pressure head. We generate a meta‐model that describes the vertical heterogeneity of the macropore number with a general function using four parameters: the relative macroporosity at the soil surface ( w _fs ), the effective macropore radius ( r _m ), the maximum depth of macropores ( z _max ) and the shape parameter of the w _f curve ( m ). The meta‐model computes the variation of w _f and d _ag over depth, thus reducing the parameters for automated calibration with HYDRUS. We theoretically described how to directly obtain the meta‐model parameters with disk infiltrometer data, providing an example for field conditions. A complete parametrization of matrix and macropore parameters for HYDRUS 2D/3D was generated from these data and previous studies, which were updated by automated calibration. Only w _fs was calibrated, increasing by about ~3.5 times the initial measurement. We tied several macropore parameters to w _fs during calibration by their physical or mathematical relations. This methodology can be utilized to estimate HYDRUS parameters for risk assessment or detailed plot studies. Highlights: A meta‐model to estimate macropore parameters for HYDRUS 2D/3D is presented. The meta‐model reduces the number of macropore parameters for HYDRUS 2D/3D. Initial estimations of meta‐model parameters are obtained by disk infiltrometer. The parameters were updated through calibration with HYDRUS 2D/3D. Preferential flow is predicted by a two‐dimensional model.