Sub-basin scale spatial variability of soil properties in Central Iran

Aghasi, Bahareh; Jalalian, Ahmad; Khademi, Hossein; Toomanian, Norair

doi:10.1007/s12517-017-2921-4

Cited by 10 publications

(4 citation statements)

References 27 publications

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“…If p < 0.05, it was accepted as significant. The variability in SOM was divided into three levels: low variability (CV < 15%), medium variability (15% ≤ CV ≤ 35%), and high variability (CV > 35%) [52].…”

Section: Data Processing and Analysismentioning

confidence: 99%

Multi-Scale Integration and Distribution of Soil Organic Matter Spatial Variation in a Coal–Grain Compound Area

Zhang

Liu

et al. 2023

Sustainability

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Soil organic matter (SOM) scale effects are critical for crop growth and food security, especially in coal–grain complexes. However, few studies describe the spatial variation in SOM and its influencing factors at different sampling scales. Here, geostatistical theory and mathematical statistical methods were adopted to analyze the spatial variation characteristics of and structural differences in SOM in the coal mining subsidence area at Zhaogu No. 2 Mine at different sampling scales. The results showed that SOM varied spatially at large, medium, and small scales, and the coefficients of variation were 28.07%, 14.93%, and 14.31%, respectively, which are moderate values. The characteristic functions of the SOM content at different sampling scales differed, and the spatial structure scale effect was obvious. The spatial distribution of the SOM content fitted by the multiscale fitting model method was generally the same as the spatial distribution law of the SOM content fitted by the single scale kriging interpolation method; however, in terms of the detailed expression and spatial distribution of small-scale SOM content, the fitting model method was more accurate, and the accuracy increased by 36%. At the different sampling scales, sample size and soil type had specific effects on the SOM spatial distribution. These results provide research concepts and technical countermeasures for improving food security and the ecological environment in the coal–grain complex and help ensure sustainable agricultural lands.

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Section: Data Processing and Analysismentioning

confidence: 99%

Multi-Scale Integration and Distribution of Soil Organic Matter Spatial Variation in a Coal–Grain Compound Area

Zhang

Liu

et al. 2023

Sustainability

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“…The two indicators, skewness and kurtosis, revealed significant positive deviation values and kurtosis values of the SO 4 2− and Cl − contents in the samples from the three soil layers at 360 sampling sites in the study area. The coefficient of variation (CV) of the data reflects the degree of dispersion of the random variables, referring to relevant studies [27][28][29]. When CV ≤ 0.1, this represents weak variability; 0.1 < CV < 1 represents moderate variability; and CV ≥ 1 represents strong variability.…”

Section: Salinization Typesmentioning

confidence: 99%

Spatial Distribution Characteristics of Soil Salt Ions in Tumushuke City, Xinjiang

Jiang

et al. 2022

Sustainability

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Concrete durability in saline soil areas is a global problem. Both SO42− and Cl− in saline soil seriously affect the durability of concrete and restrict the sustainable development of engineering construction. Soil samples were collected from Tumushuke city, Xinjiang, and the concentrations of SO42− and Cl− in the soil were measured. Classical statistics and geostatistics methods were combined to analyze the distribution characteristics of the soil salts. Additionally, the kriging interpolation method was used to draw a salt distribution map. The results showed that the average contents of SO42− and Cl− in the soil of this area were 7037.08 and 6018.94 mg/kg, respectively. SO42− exhibited moderate variability at depths of 30–60 and 60–100 cm in the soil and strong variability in the surface layer; Cl− exhibited strong variability at depths of 0–30, 30–60, and 60–100 cm in the soil. The SO42− and Cl− concentrations exhibited high spatial correlation and showed regular changes. The horizontal distribution patterns of “low in the south and high in the north” and “high in the east and low in the west” were observed. Regarding the vertical distribution, the profiles of the SO42− and Cl− contents in the soils primarily exhibited “surface aggregation” distribution characteristics. Therefore, according to the spatial distribution characteristics of SO42− and Cl−, local concrete engineering construction can adopt different anti-erosion measures to enhance the safety and durability of concrete structures.

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“…The CV is the ratio of the standard deviation to the mean (Meng et al 2015), and it is performed to measure the variation extent of multi-fractal characteristics (Ussiri et al 2006). The variability of multi-fractal parameters of the soil PSD can be categorized into weak variation (CV < 10%), moderate variation (10% ≤ CV ≤ 100%), and strong variation (CV > 100%) (Aghasi et al 2017;Hu et al 2008).…”

Section: Descriptive Statistics Of Soil Psdmentioning

confidence: 99%

Using Multi-Fractal and Joint Multi-Fractal Theories to Characterise the Spatial Variability of Soil Particle Size Distribution in an Underground Coalmine Area

Wang

Zhang

et al. 2021

Preprint

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Underground coal mining leads to serious surface deformation, which negatively affects the physical properties of soils Soil particle size distribution (PSD) is one of the most basic soil physical characteristic that influences other important properties such as soil hydraulics and thermodynamics. Understanding the spatial variability of the soil PSD in subsided land can provide targeted guidance for land reclamation. In this study, we conducted a quantitative study on the spatial variability of the soil PSD in the Pingshuo mining area on the Loess plateau, Shanxi Province in China, and explored the effects of subsidence and reclamation on the soil PSD. A plot experiment, including one unmined plot (UMP), one subsided plot (SUP), and one reclaimed plot (RCP), was performed in Anjialing No.3 underground coal mine in the, Pingshuo mining area. Four multi-fractal parameters of the soil PSD—D(0), D(1), Δα(q), and Δf(α)—were analyzed at the three sample sites. The joint multi-fractal method was carried out to analyze the spatial correlation of the soil PSD to further reveal the impacts of coal mining subsidence and land reclamation on the soil PSD. The multi-fractal method can reflect the local non-uniformity and heterogeneity of the soil PSD, while the joint multi-fractal approach can illustrate the correlation of the soil PSD between different soil depths. The range and spatial variability of the soil PSD increased due to coal mining subsidence and the impact of subsidence on the spatial disturbance of the surface soil PSD was greater than that of the deeper layers. The spatial correlation of clay in subsided land was larger than those of unmined land and reclaimed land, whereas, for silt and sand, the correlation was smaller. Land reclamation decreased the spatial variability of the soil PSD, which was near that of the unmined land after reclamation.

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Sub-basin scale spatial variability of soil properties in Central Iran

Cited by 10 publications

References 27 publications

Multi-Scale Integration and Distribution of Soil Organic Matter Spatial Variation in a Coal–Grain Compound Area

Multi-Scale Integration and Distribution of Soil Organic Matter Spatial Variation in a Coal–Grain Compound Area

Spatial Distribution Characteristics of Soil Salt Ions in Tumushuke City, Xinjiang

Using Multi-Fractal and Joint Multi-Fractal Theories to Characterise the Spatial Variability of Soil Particle Size Distribution in an Underground Coalmine Area

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