Hydraulic properties are required for modeling water and solute transport in unsaturated soils. The bottleneck for the successful application of numerical simulation models lays usually in their parameter estimation requirements. Methods to determine hydraulic properties can be classified into indirect and direct approaches. Indirect methods encompass the estimation of hydraulic properties by pedotransfer functions from more easily measured soil properties, and the prediction of the unsaturated hydraulic conductivity function from the
water retention curve
(
WRC
). In direct methods, observations of flow attributes from laboratory or field experiments are evaluated. This article reviews common methods to estimate the hydraulic conductivity function from the water retention characteristic and various direct measurement techniques in the laboratory and the field. We conclude with an outlook on contemporary developments in measurement techniques, stressing the key role of inverse modeling of experiments to derive optimum hydraulic properties and the importance of a future combination of noninvasive measurement techniques with inverse modeling by stochastic data fusion.
Modeling pollen dispersal to predict cross-pollination is of great importance for the ongoing discussion of adventitious presence of genetically modified material in food and feed. Two different modeling approaches for pollen dispersal were used to simulate two years of data for the rate of cross-pollination of non-GM maize (Zea mays (L.)) fields by pollen from a central 1 ha transgenic field. The models combine the processes of wind pollen dispersal (transport) and pollen competition. Both models used for the simulation of pollen dispersal were Lagrangian approaches: a stochastic particle Lagrange model and a Lagrangian transfer function model. Both modeling approaches proved to be appropriate for the simulation of the cross-pollination rates. However, model performance differed significantly between years. We considered different complexity in meteorological input data. Predictions compare well with experimental results for all simplification steps, except that systematic deviations occurred when only main wind direction was used. Concluding, it can be pointed out that both models might be adapted to other pollen dispersal experiments of different crops and plot sizes, when wind direction statistics are available. However, calibration of certain model parameters is necessary.
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