Modelling soil hydraulic properties with an improved pore‐solid fractal (PSF) model through image analysis. (12th August 2021)

• Record Type: Journal Article
• Authors: Sun, Xiaoqin; She, Dongli; Wang, Hongde; Fei, Yuanhang; Gao, Lei
• Is Part Of: European journal of soil science. Volume 73:Number 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-08-12
• Subjects: changepoint -- fractal dimension -- goodness‐of‐fit; Soil science -- Periodicals; 631.4
• DOI: 10.1111/ejss.13156
• Languages: English
• ISSNs: 1351-0754

Abstract

Abstract: Soil hydraulic properties are important for studying Earth science. The pore‐solid fractal (PSF) model, combined with a critical path analysis from percolation theory, seems to be more promising in the modelling of soil hydraulic properties. The accuracy of the PSF model depends on the accurate acquisition of fractal dimensions, which requires the combination of micro‐CT scanning and image analysis technology. In addition, there is a changepoint in soil water movement due to the coexistence of soil micro‐ and macromorphology. Determining the changepoint and using different fractal dimensions to predict hydraulic properties on different sides of the changepoint can further improve the accuracy of the PSF model. Therefore, in this study, we tested the changepoint in soil water movement and adopted an improved PSF model to predict hydraulic parameters in saline soil based on image analysis. The results showed that the two‐sample t ‐test could identify the changepoint accurately. There was only one changepoint in coastal saline soil when predicting hydraulic properties. Micro‐CT scanning and image analysis can obtain fractal dimensions more accurately and quickly. The coefficients of determination of all treatments were above 0.9. The improved PSF model was more accurate than the previous model in predicting soil hydraulic properties. A comparison of goodness‐of‐fit criteria showed that it is better to adopt the geometrical mean error ratio ( GMER ) and geometrical standard deviation error ratio ( GSDER ) as the judgement standard. Due to the anisotropy of soil, the improved PSF model demonstrated a higher accuracy in predicting water content than hydraulic conductivity. The hydraulic conductivity prediction accuracy was negatively correlated with the degree of anisotropy ( DA ) parameter, and the improved model was more suitable for soils with weak anisotropy. Our research can provide a simple and accurate method for parameter calculation of the PSF model to predict soil hydraulic properties more accurately. Highlights: The two‐sample t‐test can find the changepoint accurately in the process of soil drying. Micro‐CT scanning and image analysis can calculate fractal dimension more accurately. The improved PSF model is more accurate when predicting soil hydraulic properties. Anisotropy is an important factor that restricts the prediction accuracy of PSF model.