It uses a calibration curve to calculate the corresponding soil suction component (allowing to measure the \(\psi_{t}\) using a non-contact procedure and \(\psi_{m}\) via a contact procedure). Its calibration is based on the relationship between soil suction and relative humidity. However, this method's obtention of the SWRC is laborious and time consuming. Nevertheless, researchers have developed mathematical models such as the one proposed by [PERSON] [31]. A model like this requires simple parameters, such as the grain-size distribution, to estimate the van Genuchten parameters and, consequently, the obtention of the \(\psi_{m}\) of natural soils. Moreover, [PERSON] model provides a fast way to evaluate the SWRC of the soil. The van Genuchten model (Equation (1)) is also a frequently used model to predict the SWRC. Its parameters are obtained by performing a non-linear least-squares process:  
\[\frac{\theta-\theta_{r}}{\theta_{s}-\theta_{r}}=\left[1+\left(\alpha\psi_{m} \right)^{n}\right]^{-m} \tag{1}\]  
where \(\theta_{r}\) is the residual volumetric water content, \(\theta_{s}\) is the saturated volumetric water content, \(\psi_{m}\) is the matric suction, \(\alpha\) is calculated as \(1/\psi_{d}\) and indirectly describes the pore-size distribution (PSD), \(\psi_{d}\) is the inverse of the air entry value, and \(m\) and \(n\) are fitting parameters that affect the shape of the retention curve [32]. Moreover, the parameter \(n\) describes the dispersion of both the largest and smallest pores [33]. The most common fitting parameters used for the van Genuchten model are \(n=1/(1-m)\) and \(n=2/(1-m)\), which correspond to the Mualem and Burdine restrictions, respectively, and are obtained from a probabilistic approach. [PERSON] [34] considers a radius to evaluate the hydraulic conductivity; that is, the geometric mean of the radii of two capillary elements. In contrast, [PERSON] only considers the radius of the largest pore [35]. The SWRC induces a porosimetry curve because the water pressure in the soil is associated with an equivalent pore diameter, using the Laplace equation (Equation (2)):  
\[\psi_{m}=\frac{2 Ts}{R} \tag{2}\]  
where \(Ts\) is the superficial tension, and \(R\) corresponds to the radius of the pore. The model by [PERSON] [36] helps establish a relationship between the particle size distribution and porosimetry curve, where the physical volume of the soil is characterized by spheres of equivalent diameter, as indicated in Equation (3):  
\[F(D)=1/\left[1+\left(\frac{D_{g}}{D}\right)^{n}\right]^{m} \tag{3}\]  
where \(D_{g}\) is the geometrical diameter, \(D\) is the diameter of the particle size distribution, and \(n\) and \(m\) are the fitting parameters of the SWRC. It is possible to obtain the SWRC path by introducing the \(\alpha\) value into Equation (1) when the \(m\) and \(n\) values are obtained. However, an essential limitation of this model is that it only considers the porosimetry curve and, consequently, the \(\psi_{m}\) value. Soil structure relates to physical (i.e., solid density or porosity), chemical, and biological processes that are dependent either on the size of the soil core or the resolution of the measuring device. Given this context, it often causes a predicament when it comes to introducing measurable parameters of soil structure. In the case of the transport processes in soils, it occurs at two time scales: the first one is associated with macropore transport (packing of solid elements that generate pores), and the second is associated with micropore transport (network of connected pores surrounded by solid elements) [37]. To avoid working with different scales, a fractal dimension would be quite valuable for describing the heterogeneous soil structure on different scales, assuming that the properties are the same across the range of these scales [38]. To reduce the number of unknown functions in the aerated soil zone, [PERSON] [36] introduced the fractal relation to the common parameters.