Pore-Based Modeling of Hydraulic Conductivity Function of Unsaturated Rooted Soils
Wang H. Chen R. Leung A.K. Garg A. Jiang Z.
May 2025John Wiley and Sons Ltd
International Journal for Numerical and Analytical Methods in Geomechanics
2025#49Issue 71790 - 1803 pp.
Mualems approach has been widely used to predict hydraulic conductivity functions (HCFs) of bare soils if a soil water retention curve (SWRC) model is available. The assumption that Mualems approach holds is that the distribution of soil pores is spatially completely random. Under this assumption, relative hydraulic conductivity (Kr) is determined by the continuance probability of water-filled pores. However, this assumption is not valid for rooted soils, as root growth causes soil particle rearrangement, and thus soil pore rearrangement, altering the probability of pore connectivity. After reconsidering Mualems assumption, this study attempts to develop a new approach for predicting HCF of rooted soils by modeling the root-induced pore rearrangement and the resultant change in the continuance probability of water-filled pores. Two approaches mentioned were incorporated with a root-dependent SWRC model to express HCF as a function of matric suction. The proposed model was validated against nine sets of measured HCFs from published studies. It was found that the proposed model reduced the root mean square error (RMSE) of Kr and lg Kr by 33% and 53%, respectively, as compared to traditional Mualems model. Physically, the models effectiveness depended on soil texture and root type. In fine-textured soils, roots were capable of displacing soil particles, thereby causing soil pore rearrangement. Also, coarse roots with high strength tend to alter pore distribution. After considering the effects of pore-level root-soil interaction on pore rearrangement, the proposed model provided a significant improvement in the prediction of HCF of unsaturated rooted soils.
hydraulic conductivity function , Mualems model , rooted soil , soil water retention curve , unsaturated soil
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School of Intelligent Civil and Ocean Engineering, Harbin Institute of Technology, Shenzhen, China
Guangdong Provincial Key Laboratory of Intelligent and Resilient Structures for Civil Engineering, Shenzhen, China
Department of Civil and Environmental Engineering, the Hong Kong University of Science and Technology, Hong Kong
Department of Civil and Smart Construction Engineering, Shantou University, Shantou, China
L.N. Gumilyov Eurasian National University, Astana, Kazakhstan
School of Intelligent Civil and Ocean Engineering
Guangdong Provincial Key Laboratory of Intelligent and Resilient Structures for Civil Engineering
Department of Civil and Environmental Engineering
Department of Civil and Smart Construction Engineering
L.N. Gumilyov Eurasian National University
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