Abstract This paper presents a new piecewise model for simulating one‐dimensional infiltration curves in two‐layer finite columns consisting of a hydrophobic layer overlaying wettable soil. The model combines a physics‐based equation for infiltration in water‐repellent substrates with an analytical framework for ponded conditions. While the underlying wettable soil layer is defined by hydraulic conductivity, K , and sorptivity, S , the hydrophobic substrate incorporates additional parameters: the average time required for water to traverse the layer, t 1 , the mean hydraulic conductivity under dry conditions, K dh , the infiltration rate of all activated pathways, Q s , and a factor accounting the curvature of the infiltration curve, D . The model was used to estimate the hydraulic parameters from the inverse analysis of different infiltration curves followed by gravity flow. These curves were measured in different hydrophobic substrates and in two‐layer soil columns consisting of two hydrophobic materials placed on sand and loam soil, respectively. The model was subsequently validated using undisturbed cores collected from different shrubland soils. The results demonstrated that the shape of the infiltration rate curve is a key indicator for distinguishing between hydrophobic and layered column setups. In addition, the model made it possible to differentiate between the different hydrophobic behaviors and also to assess the impact of the wettable soil layer on the infiltration curve. Robust fits ( R 2 > 0.99) were achieved between experimental and optimized infiltration curves. Finally, realistic optimizations of the hydraulic parameters defining the degree of hydrophobicity and the hydraulic characteristics of the wettable soil layer were successfully obtained.
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D. Moret‐Fernández
B. Latorre
D. Yilmaz
Soil Science Society of America Journal
Centre National de la Recherche Scientifique
Université Claude Bernard Lyon 1
Consejo Superior de Investigaciones Científicas
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Moret‐Fernández et al. (Fri,) studied this question.
www.synapsesocial.com/papers/69fd7fb8bfa21ec5bbf08392 — DOI: https://doi.org/10.1002/saj2.70255