Abstract
Larger pores allowing macropore flow can rapidly transport contaminants as well as nutrients from the soil surface to the subsoil and groundwater. Modeling of the involved macropore flow is complex due to large number of required parameters. One way to deal with this difficulty is the precise evaluation of the soil hydraulic conductivity function using robust pedotransfer functions for the near-saturated part. Aiming to develop pedotransfer functions to estimate saturated and unsaturated hydraulic conductivities [Ks and K(h)] as well as water contents at specific pressure heads, we used Gaussian Process Regression (GPR), a non-parametric machine learning model, to obtain van Genuchten (1980) retention and conductivity parameters for Danish soils based on soil texture and organic matter. We defined K10 as K(h) at − 10 cm pressure head. The difference between the logarithm of Ks and K10 [log(Ks) − log(K10)] was denominated log(Kjump) and was assumed to convey the potential degree of macropore flow considering only soil hydraulic properties. Macropore flow in Denmark was evaluated through the developed PTFs for log(Ks) and log(K10) with an average RMSE of 0.635 and 0.594 (for K in cm d−1) under a confidence interval obtained by bootstrapping. Dynamics of pressure head in an initially saturated soil column with a zero-flux top boundary condition and a 2 m deep groundwater level were simulated using the Hydrus-1D model. The result of this modeling showed that soils in the eastern part of Denmark had a higher likelihood to experience macropore flow. Finally, using the spatial distribution of meteorological data, a macropore flow map of Denmark was produced. Precipitation was a dominating factor on macropore flow in sandy clay loam and sandy loam soils when compared to sandy and loamy sand soils. Finally, maps of relative risk classes of macropore flow qualitatively differentiated land areas in Denmark for vulnerability of nutrient loss and groundwater contamination.
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