A Family of Soil Water Retention Models Based on Sigmoid Functions

乙状窦函数 保水性 导水率 土壤水分 Pedotransfer函数 土壤科学 保水曲线 对数正态分布 大孔隙 数学 含水量 环境科学 地质学 岩土工程 统计 计算机科学 人工神经网络 化学 介孔材料 生物化学 机器学习 催化作用
作者
Peijun Li,Yuanyuan Zha,Bingxin Zuo,Yonggen Zhang
出处
期刊:Water Resources Research [Wiley]
卷期号:59 (3) 被引量:5
标识
DOI:10.1029/2022wr033160
摘要

Abstract The soil water retention curve is the fundamental soil hydraulic property to characterize soil water movement and solute transport. Many efforts have been devoted in the past decades to developing models to describe soil water retention curves. However, most of them are empirical equations or assume that soil pore size distributions conform to a lognormal distribution. Yet, few effects have been undertaken to systematically propose and compare a series of possible alternative probability density functions to describe the sigmoid retention curves with parameters physically explainable. Here, we proposed a family of five soil water retention models based on sigmoid functions with parameters of clear physical implications coinciding with the statistical measures of soil pore size distribution. Compared with the widely used models (i.e., Brooks & Corey, 1964; Kosugi, 1996; van Genuchten, 1980), the proposed models have somewhat improved performances to characterize water retention data for a wide range of soil textures without introducing additional model parameters. Two of the proposed models are capable of characterizing the observed two local extrema in the moisture capacity curves. The associated unsaturated hydraulic conductivity models of the proposed soil water retention models are also derived, which show superior performance in characterizing the observed hydraulic conductivities compared with competing models, especially in macropore regimes. Additionally, we analyzed the parameter‐equivalent conversion between the proposed and the existing models, and a simple linear regression equation can be used to derive the parameters of the proposed models from the existing and other alternative different proposed models.
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