Spatial Variability Of Soil Properties Research Articles

Investigating the Number of Monte Carlo Simulations for Statistically Stationary Model Outputs

The number of random fields required to capture the spatial variability of soil properties and their impact on the performance of geotechnical systems is often varied. However, the number of random fields required to obtain higher-order statistical moments of model outputs has not yet been studied. This research aims to investigate the number of Monte Carlo simulations needed to achieve stationary higher-order statistics of a pore pressure head in an unsaturated soil slope under steady-state infiltration. The study recommends using at least 500 Monte Carlo samples for the probabilistic analysis of geotechnical engineering models. A more conservative choice for up to second-moment analysis is 1000 samples. The analysis reveals significant variations in skewness, which become stationary for all mesh grids when the number of samples exceeds 15,000. Kurtosis stabilizes only when the number of samples reaches 25,000. The pore pressure head in the unsaturated zone is less uncertain. Additionally, the probability density function of the pore pressure head follows a leptokurtic distribution.

Spatial Variation of Soil Properties: Foundation Design Challenges

The spatial variation of soil properties with specific reference to their coefficient of variation (COV), and their effect on deep foundation designs, is discussed. The difficulties in developing strength factors (Φ) for lateral load response governed by multiple resistance variables are examined. Classical and current analytical and experimental state of the art is described. Influence of amended topsoils on shear and cohesion is also examined. Finally, suggestions for determining nominal strength factors and future extension are made.

Spatial variability of soil properties and delineation of management zones for Suketi basin, Himachal Himalaya, India

Spatial variability of soil properties and delineation of management zones for Suketi basin, Himachal Himalaya, India

Regional characterization of vs30 with hybrid geotechnical and geological data

Regional characterization of soil properties requires not only geostatistical models considering the spatial variability of soil properties but also methods that account for various sources of heterogeneous information. This paper presents a multi-conditional random field approach to characterize the regional characteristics of the average shear-wave velocity in the top 30 meters of subsoil (vs30) based on hybrid geotechnical and geological data. The workflow for integrating multiple sources of soil properties information in a random field model for regional vs30 mapping is developed. A detailed two-dimensional (2D) synthetic digital soil field is generated to assess and verify this workflow. With the generated synthetic field, parametric studies on the investigation plans, the value of Markov Bayes coefficient B, element size, and a predefined grid of secondary data are performed. Practice on whether to incorporate secondary data for vs30 mapping and the determination of coefficient B are provided.

Digital mapping of selected soil properties using machine learning and geostatistical techniques in Mashhad plain, northeastern Iran

Understanding the spatial variation of soil properties is essential for monitoring land capabilities as well as the sustainable management of soil resources. The aim of this study was to predict digital soil properties mapping using 23 environmental variables, i.e., terrain attributes and remote sensing (RS) indices, across 1500 km2 of Mashhad plain lands. To achieve this purpose, a total of 180 soil samples (0–10 cm) were taken. The random forest (RF) model combined with ordinary kriging (OK), as well as regression kriging (RK), were applied to relate environmental variables and the studied soil properties. The results revealed that RF-OK was the best model with R2 and RMSE for silt (0.89% and 0.10%), followed by calcium carbonate equivalent (0.88% and 3.30%), clay (0.87% and 2.26%), soil organic carbon (0.86% and 0.24%), sand (0.84% and 4.21%), and pH (0.82% and 5.42%). The RS covariates, including band 5 (B5), modified soil-adjusted vegetation index (MSAVI), difference vegetation index (DVI), band 2 (B2), carbonate rock index (CRI2), gypsum index (GI), and enhanced vegetation index (EVI), and terrain attributes, including topographic wetness index (TWI) and elevation (EL), and topographic position index (TPI), were the most important variables in modeling different soil properties. RF-OK showed the prediction and uncertainty maps related to high precision and low standard deviation in most study areas, which indicate low overfitting and overtraining in modeling processes. In general, the RF-OK model, with low cost and high accuracy, can be applicable to use for predicting different soil properties, as well as spatial information acquired from an effort to maps to managing agriculture in areas at different conditions. Finally, this method can be applied to other regions of similar properties and for similar purposes.

Spatiotemporal variability of soil physical properties and water, salt, nitrogen, and phosphorus contents for farm level

Soil water, salt, and nutrient variability are essential factors that impact crop productivity in agriculture systems. However, effective management of small farms requires access to fine-scale data on soil water, salt, and nutrients. Large-scale assessments of spatial variability using classical statistics and geostatistical methods can help identify nutrient-deficient zones. In Xinjiang, China, inadequate water and nutrient management has resulted in low crop productivity in agriculture systems. To address this issue, this study evaluated the mechanical composition, bulk density, and contents of water, salt, ammonium nitrogen ([Formula: see text]), nitrate nitrogen ([Formula: see text]), and available phosphorus (A-P) in soil at the farm level in the Xinjiang region. Results showed low variability in soil bulk density, medium variability in soil water content, mechanical composition, [Formula: see text], and A-P, and high variability in soil salt content and [Formula: see text]. Mechanical composition and A-P showed a small range of variation across different soil depths, while soil water content and [Formula: see text] in the surface layer varied significantly more than in other soil layers. [Formula: see text] variability increased with soil depth. Soil properties showed minimal differences over time. Multi-factor deficiencies, particularly in nitrogen, were observed throughout the study area. The generated maps offer a useful tool for farm managers and policymakers. In summary, this study highlights the significance of evaluating the spatial variability of soil properties for identifying zones deficient in water and nutrients, as well as those with salt accumulation. This information can be utilized to develop effective strategies for site-specific nutrient management.

Random finite element analysis of a clay-core-wall rockfill dam considering three-dimensional conditional random fields of soil parameters

Structural reliability and safety have been investigated using the random finite element method (RFEM) based on conditional random field (RF) by many researchers. However, the constraint RFEM has not been studied sufficiently to assess its influences on dam structural behaviours, especially in clay-core-wall rockfill dams. In this study, based on Kriging theory and in-situ measurements, a method is applied for constructing conditional RFs of dam material parameters. The results demonstrate that conditional RF has little effect on the mean values of maximum settlement, maximum deformation gradient and hydraulic fracture area ratio; relative to the unconditional simulations, the relative differences are 1%, 3%, and 7%, respectively. By establishing an effective Kriging estimation, the standard deviations of conditional maximum settlement, maximum deformation gradient and hydraulic fracture area ratio are relatively reduced by 16%, 4%, and 24%, respectively. Thus, the conditional simulation can provide more accurate security estimations for the cumulative probabilities of the structural responses. Measurement layout has a great effect on variation accuracy of random structural responses. The measurements should be taken near the locations of the structural behaviours of interest so that the conditional RFs can more accurately represent the spatial variability of soil properties and reduce simulation variation.

Deep learning methods for time-dependent reliability analysis of reservoir slopes in spatially variable soils

The Three Gorges Reservoir Area (TGRA) is one of the most important landslide-prone regions in China, and rational stability evaluation of reservoir slopes in it is of great significance to design mitigation measures and prevent landslide disasters. It is well recognized that seasonal rainfall and periodic reservoir water level fluctuation are the two major factors influencing the stability of reservoir slopes, and thus the reservoir slope stability may be varying with the external triggering factors. Although geotechnical reliability analysis offers a novel means to quantify slope stability in a probabilistic manner, the previous research focuses more on the time-independent slope reliability analysis, ignoring the effects of time-varying factors. How to evaluate the time-dependent reliability of reservoir slopes accurately and efficiently remains an open question. This study proposes deep learning (DL)-based time-dependent reliability analysis approach, and a practical case adapted from the Bazimen landslide in the TGRA is used for illustration. The predictive performances of the three DL algorithms, namely Convolutional Neural Network (CNN), Long short-term memory (LSTM), and Light gradient boosting machine (LightGBM) are systematically investigated. Results show that the proposed DL-based approach can reasonably portray the variation tendency of the Bazimen landslide time-dependent failure probability, which provides a promising way to rationally evaluate the time-dependent failure probability of reservoir slopes considering the spatial variability of soil properties. Among the three DL algorithms, the CNN performs the best in the Bazimen landslide example.

Spatial heterogeneity of soil properties of recreational areas of Volgograd

The article presents the developed soil maps and maps of spatial variability of soil properties (pH, total salt and carbonate content, organic carbon, density) in some recreational areas of Volgograd. The anthropogenic soils in the studied recreational areas include various urbostratozems (Urbic Technosols (Transportic)), Ekranic Technosols (Transportic), as well as quasizems (Phaeozems (Tehnic)), among the agrogenic ones – accumulation-carbonate agrozems (Cambisols (Aric, Protocalcic). Natural soils are represented by light and dark humus soils, chestnut soils, solonetz, as well as stratozems and psammozems. According to the data of isolinear mapping, it was found that acid-alkaline properties of soils of green zones vary from slightly alkaline to alkaline depending on the soil horizon and anthropogenic load. The mapping did not reveal salinization of the upper 0–30 cm layer. Values of organic carbon vary greatly (1–10%) and are conditioned by the presence or absence of meliorative artificial layers, as well as by the character of territory use. The content of calcium carbonates in the upper 30 cm is small and varies from 0.7 to 5%. It is established that the physical properties (density) directly depend on the level of anthropogenic load, increasing in footpaths and lawns. Spatial assessment of the properties of urban soils is important for identifying areas with an excess or on the contrary – low content of certain chemical and physical indicators for the planning of landscaping and gardening of urban areas.

Spatial Variability in Soil Properties, Delineation Site-specific Management Division Based on Soil Fertility Using Fuzzy Clustering in Gwalior, Madhya Pradesh, India

Farmers who want to improve nitrogen usage efficiency (NUE) and crop yield must have access to information regarding the geographical variability and distribution of soil parameters. Fertilizer application relying on soil characteristic maps and fertilizers recommendations may help reduce fertilizer input without sacrificing crop production. The current research focused heavily on evaluating the variability of soil fertility status in the Madhya Pradesh Gwalior region using geostatistical techniques. To do this, 150 GPS-based surfaces (0–15 cm) soil samples were obtained from the Gwalior region's five districts (Gwalior, Shivpuri, Datia, Guna, & Ashok Nagar) during crop harvest during the rabi season of 2019–20. Statistics and geo-statistics were used to analyze the results of the laboratory analysis. The analysis revealed that the soil samples' pH, EC, SOC, and CaCO3 values, respectively, varied from 4.40 to 8.30, 0.09 to 1.03 dSm-1, 2.0 to 10.60 gkg-1, and 3.0 to 24.0 gkg-1. In contrast, the amounts of N, P, K, and S that are present in soil vary from 102.0 to 356.0 kg ha-1, 6.0 to 61.0 kg ha-1, 114.0 to 896.0 kg ha-1, and 5.90 to 49.20 mg kg-1, respectively.
 Through building semi-variograms and mapping the data utilizing standard kriging techniques, the information was studied using both traditional statistics and geostatistics. For soil properties, semi-variograms were created, and their geographical distributions were delineated. The Nugget/Sill (Co/Co+ C) ratio for the modeled variables revealed moderate to high spatial dependences. The best-fit models for the reported soil characteristics were exponential, spherical, and circular. The findings of this research demonstrated that soil fertility quality varied significantly across the Gwalior area. This knowledge may aid in making choices about crop succession and the usage of plant nutrients to increase farmers' financial returns.

An efficient variable selection-based Kriging model method for the reliability analysis of slopes with spatially variable soils

The random finite element method (RFEM) is an efficient tool to demonstrate the spatial variability of soil properties during reliability analysis of slopes, but it requires remarkable model evaluations and computational efforts. In this paper, an efficient variable selection-based Kriging model method is proposed to approximate the finite element analysis model in reliability analysis of slopes. The variable selection technique successfully remedies the “curse of dimensionality” within Kriging model induced by the numerous random variables in random field discretization. The implementation procedure of this method for the reliability analysis of slopes is introduced in detail. Two typical examples of soil slopes, as well as a real complex slope are subsequently analyzed to illustrate the validity of the proposed method. The results show that the local loss of variability of random field due to the variable selection method has little impact on the safety factor of slopes. The proposed method can significantly reduce the number of finite element analysis and obtain accurate results in reliability analysis of slopes considering the spatial variability of soil properties.

Spatial variation of soil properties and evaluation of the risk of soil erodibility on a river alluvial and marine sedimentary plain in eastern China

Spatial variation of soil properties and evaluation of the risk of soil erodibility on a river alluvial and marine sedimentary plain in eastern China

Spatial Analysis of Soil Texture using GIS based Geostatistics Models and Influence of Soil Texture on Soil Hydraulic Conductivity in Melur Block of Madurai District, Tamil Nadu

Background: Soil texture is one of the important property constitute ultimate size particles. Proportions of these ultimate soil particles depend on the pedogenesis and mineralogical composition of parent material and also decide all other soil properties. Due to the imprudent crop selection and imperfect agricultural methods, the very fertile soil is becoming denser and low productive. Geostatistics is a set of Models and tools developed for statistical analysis of continuous data. A critical aspect of agriculture ecosystem and environment modeling is the accurate prediction for the spatial variability of soil properties. Methods: In this study, Geostatistical modeling based Kriging techniques has been used to identify the textural variation of soil in the present scenario of Melur Block of Madurai district. Totally, 150 soil samples were collected across Melur block of Madurai district based on grid soil survey and geo coordinates. Result: Geostatistical soil textural maps have been generated based on Kriging tools and these would be utilized for Land Use Pattern, precision agriculture, conservation practices, as well as land management in the research region. The characterization of the spatial variability and influence of soil texture on hydraulic conductivity is essential to accomplish a better understanding of intricate relations between soil properties and environmental factors.

Potential of Precise Fertilization through Adoption of Management Zones Strategy to Enhance Wheat Production

The variability in soil properties and crop yield can be overcome by adoption of smart farming practices through interpolation and mapping of spatial variability patterns. Geospatial technologies can be utilized to determine the cause of spatial variability in fields for site-specific application of fertilizer. This study was designed to quantify and identify the spatial variation in soil properties and wheat (Triticum aestivum L.) yield and to delineate prescription maps for precise application of fertilizer in a semi-arid subtropical region of Pakistan. To examine the variability in soil properties on the production of the considered crop, this study comprised two different fields and each field was divided into (20 × 20 m) grids. The samples of soil were collected at 15 cm and 30 cm soil depths before the fertilization to analyze the different soil characteristics i.e., nitrogen (N), electrical conductivity (EC), potassium (K), soil organic matter (SOM), phosphorus (P), and pH. The boundaries of selected fields and grid points were established with a real-time kinematics-global positioning system (RTK-GPS). The soil data were acquired with a soil proximal sensor at a depth of 7 cm after fertilization. The statistical analysis coefficient of variation (CV), geostatistical-analysis-nugget-to-sill ratio (N:S), and the interpolated maps (ArcGIS pro 2.3) were used to characterize the least to moderate variability of soil parameters and yield, demanding site-specific management of fertilizer application. Cluster analysis was conducted using Minitab 21, which classified soil and yield characteristics into five categories: “very good”, “very low”, “good”, “poor”, and “medium”, with an external heterogeneity and internal homogeneity both more than 60%. Significant relationships (p < 0.05) between soil and crop properties were used to develop the management zones (MZs) for the precise application of fertilizer in wheat fields. Significant differences (p < 0.05) in soil nutrients were found in the very high and very low productivity zones at both sampling times, which suggest delineating the MZs for precise application of fertilizer according to the need of crop and soil properties. The results revealed that the optimum number of MZs for the wheat fields was five and there was heterogeneity in the soil nutrients in five MZs. The findings of this study also highlight the necessity of predicting the crop and soil factors by using precision technologies to develop the prescription maps, because sampling and analysis of soil are expensive and time-consuming. Based on the demand of the soil and crops, site-specific fertilization can increase economic and environmental efficiency.

Data-driven spatio-temporal analysis of consolidation for rapid reclamation

Spatial extension of lands through rapid reclamation is attractive for congested coastal megacities, although reclamations might suffer from project delay and budget overrun, often due to encountering unforeseen ground conditions. To accelerate reclamations, accurate prediction of soil consolidation over a construction duration of multiple years is needed for reclaimed lands, which often contain spatially varying subsurface stratigraphy and soil parameters. This calls for a spatio-temporal analysis of consolidation with a sound understanding of subsurface stratigraphic alternations of fine/coarse-grained soils and spatial variability of consolidation parameters (e.g. permeability). In this study, a unified framework, capable of simultaneously modelling stratigraphic variation and spatial variability of soil properties through machine learning of limited site investigation data, is combined with the finite-element method and Monte Carlo simulation for spatio-temporal consolidation analysis of reclaimed lands. The proposed method is applied to a real reclamation project in Hong Kong. Results indicate that the proposed method can accurately characterise subsurface geological cross-sections and spatially varying soil permeability with quantified uncertainty. Ignorance of spatial variability of soil permeability may result in an underestimation of consolidation time and an overestimation of undrained shear strength gain, and thus pose significant risks to reclamation projects.

Digital Mapping of Soil Properties Using Ensemble Machine Learning Approaches in an Agricultural Lowland Area of Lombardy, Italy

Sustainable agricultural landscape management needs reliable and accurate soil maps and updated geospatial soil information. Recently, machine learning (ML) models have commonly been used in digital soil mapping, together with limited data, for various types of landscapes. In this study, we tested linear and nonlinear ML models in predicting and mapping soil properties in an agricultural lowland landscape of Lombardy region, Italy. We further evaluated the ability of an ensemble learning model, based on a stacking approach, to predict the spatial variation of soil properties, such as sand, silt, and clay contents, soil organic carbon content, pH, and topsoil depth. Therefore, we combined the predictions of the base learners (ML models) with two meta-learners. Prediction accuracies were assessed using a nested cross-validation procedure. Nonetheless, the nonlinear single models generally performed well, with RF having the best results; the stacking models did not outperform all the individual base learners. The most important topographic predictors of the soil properties were vertical distance to channel network and channel network base level. The results yield valuable information for sustainable land use in an area with a particular soil water cycle, as well as for future climate and socioeconomic changes influencing water content, soil pollution dynamics, and food security.

Effect of spatial variability of soil properties and geostatistical conditional simulation on reliability characteristics and critical slip surfaces of soil slopes

Evaluation of the stability and determination of the Critical Slip Surface (CSS) of soil slopes are salient topics in geotechnical engineering. On the other hand, the stability and CSS are not only affected by soil heterogeneity but also by the boreholes’ location and method of predicting soil parameters in the domain of analysis. The unconditional simulation in which known data and its location are not incorporated may lead to results far from reality. Moreover, in some conditional simulations, the borehole data are directly mapped into the analysis section without taking the location of the known data into account, which can either overestimate or underestimate the stability of the slope. In the current study, the Finite Element Method (FEM) is coupled with the geostatistical method to evaluate the reliability characteristics and CSS distribution with consideration of the known data, location of boreholes, uncertainty of surcharge load, and soil heterogeneity. The results of a real case demonstrate that in comparison to the unconditional simulation, utilizing the conditional simulation improves the distribution of Factor of Safety (FS) by up to 14% while decreasing the related standard deviation by 4% to 40%. Moreover, conditional simulation offers a significant reduction in uncertainty of the slip surface and unsafe distance from the edge of the slope. Besides, it is concluded that soil heterogeneity has a major impact on CSS distribution and induces the local CSS, which cannot occur on a homogeneous slope.

Spatial relationship between peach tree productivity and soil properties in a newly reclaimed orchard

ABSTRACT The construction of banks and surface soil cutting involved in newly reclaiming orchards may cause spatial variability in soil properties, which could cause poor fruit tree productivity. This study examined spatial relationships between variations in peach tree properties and soil properties around each tree and assessed the possibility of site-specific field management for individual trees in a newly reclaimed orchard. Three and four years after reclamation in 2016, the 0.1-ha field located in Kyoto, Japan was divided into 32 grid cells at 5-m intervals for all individual peach trees and 128 grid cells at 2.5-m intervals for measuring soil properties around each tree (field scale). The 128 grid cells were each further divided into five positions at 0.71-m intervals for assessing selected soil properties (grid-cell scale). The soil data were geostatistically analyzed by calculating the nugget/sill ratios and anisotropy ratios (degree of variation in relation to direction) and drawing kriged maps. Coefficients of variation of soil properties at the grid-cell scale were much higher than those at the field scale. Pearson’s correlation analysis at the field scale indicated that the tree productivity was most strongly correlated with relative altitude, followed by the degree of soil reduction, time-domain reflectometry electrical conductivity (TDR-EC), subsoil pH (H2O), and TDR-volumetric water content. Tree productivity tended to be lower at lower elevations, where the degree of soil reduction, TDR-EC, subsoil pH (H2O), and soil water content tended to be high. Poor productivity appeared to be related to wet injury. Semivariograms of the soil properties related to soil water showed strong spatial dependence and had nugget/sill ratios of ≤24.0%. Kriged maps and anisotropy ratios further indicated that most tree property values decreased from southeast to northwest and showed variations similar to those of the soil properties related to soil water. Fine-scale mapping of soil properties in the orchard could reveal spatial soil-property variations around individual peach trees, and the result would imply the importance of soil management practices on a per-tree basis. Our findings could help to improve tree productivity in the northwestern area by increasing soil drainage for individual trees.

Effects of the number of simulation iterations and meshing accuracy in monte-carlo random finite-difference analysis

Monte-Carlo random finite-difference analysis (MCRFDA) can incorporate the spatial variability of soil properties into the analysis of geotechnical structures. However, two factors, namely, the fineness of the generated elements (reflected by the number of elements, Ne) and the number of MC simulation iterations, NMC, considerably affect the computational efficiency of this method, creating a barrier to its broad use in real-world engineering problems. Hence, an MCRFDA model of a circular underground cavern is developed in this study. The convergent deformation of the cavern is analyzed while considering the spatial variability distribution of the elastic modulus. Moreover, the effects of NMC and Ne on random FD calculations are investigated. The results show the following. An NMC greater than 500 is desirable for the FD analysis of a conventional structure. For a specific structure, Ne does not have a significant impact on the mean of the simulated values but appreciably affects the standard deviation (SD) of the simulated values, where reducing Ne increases the SD of the simulated values.

A stochastic approach on modeling the long-term unevenness of pavements considering spatially varying soil properties/Ein stochastischer Ansatz zur Modellierung der langfristigen Entwicklung von Fahrbahnunebenheiten unter Berücksichtigung räumlich variierender Bodeneigenschaften

Even within nominally homogenous soil layers, spatial variations of soil properties occur. Current design methods simplify varying soil properties by applying constant values. In order to achieve a reliable geotechnical design, the local variance of soil properties needs to be considered with respect to the long-term development of road unevenness. Hence, a variation of soil stiffness leads to spatially varying settlements and increasing dynamic forces on passing vehicles as time increases. This process continues steadily resulting in a noticeable reduction of driving comfort until a repair of the pavement is required. In this paper, a numerical model is applied to determine the long-term unevenness of pavements considering the spatial variation of soil properties. Thereby, the asphalt pavement, the subbase, and the subgrade are modeled by a set of coupled spring elements representing the soil behaviour of the respective layer. A stochastic distribution of soil properties based on a random walk is assumed to account for the spatial variation of the subsoil stiffness. The long-term unevenness development due to passing vehicles is extensively evaluated with respect to resulting dynamic forces and the impact of a deviating soil stiffness in individual soil layers. Finally, conclusions with respect to driving comfort are drawn.