Spatial Variability Of Soil Properties Research Articles

Trends in geographic and gender balance among authors

Trends in geographic and gender balance among authors

SPATIAL VARIABILITY STATUS OF SELECTED SOIL PROPERTIES IN NORTH-EAST AKWA IBOM STATE, NIGERIA

Spatial variability status of selected soil properties in North-east Akwa Ibom State, Nigeria was assessed. The aim was to establish baseline information on the spatial variability status of selected soil properties in North-east Akwa Ibom State for site -specific and sustainable soil management. A terrain attribute (plan curvature) that is capable of capturing the short-scale spatial variability of soil properties in the field was used to guide field sampling. Plan curvature map was generated from digital elevation model (DEM) of the study area acquired from United State Geological Survey (USGS) at 30m resolution. The plan curvature map was classified into straight, convex and concave surfaces. Modified conditioned Latin hypercube sampling method was used in selecting observation points. Each observation point was purposefully selected to fall within the three classes of the plan curvature map to give a good coverage of both feature space (terrain attributes) and geographical space (study area). Soil samples were collected from each observation point at depths of 0-30 cm and 30-60 cm using soil auger. A total number of 152 soil samples were collected in the study area for laboratory analysis. Data generated were subjected to analysis of variance (ANOVA) and means were separated using least significant difference (LSD) at 5% level of significance. The depth interval of 0-30cm and 30-60 cm were integrated to form depth interval of 0-60 cm and data with skewed distribution were log transformed for semivariogram analysis. Soil properties were then subjected to semi-variogram analysis. The results showed that plan curvature was able to capture short scale spatial variation in some soil properties under study. Soil texture was sand in the surface and subsurface soils of both convex and concave slope while in straight slope, the soil texture was sand in the surface and loamy sand in the subsurface. Soil pH was strongly acidic in both straight and concave slope and slightly acidic in convex slope in both surface and subsurface soil. Organic carbon was high in concave and convex slope and very high in straight topography in both surface and subsurface soils. Total N was low in the convex and concave slope but moderately low in straight terrain. The available P of straight slope was higher than that of concave and convex slope.From the semi-variogram analysis, all the selected soil properties exhibited spatial dependence within some distances. The strength of the spatial dependence varied from moderate for sand, silt, soil pH, organic carbon, total N and available P to weak for clay and strong for exchangeable K. The best fitted models were Exponential for sand and silt; Gaussian for available P and Spherical for clay, pH, organic carbon, total N and exchangeable K.The range of autocorrelation was 136.2 m for sand, 76.4m for silt, 1.6 m for clay, 1.69 m for soil pH, 9.4 m for organic C, 7.1m for total N, 39.2 m for available P and 7.8 m for exchangeable K. This shows that beyond these ranges, the selected soil properties should be managed differently

Probabilistic Assessment on 3D Stability and Failure Mechanism of Undrained Slopes Based on the Kinematic Approach of Limit Analysis

Most slope collapses have three-dimensional (3D) characteristics due to the uniform soil. The traditional plane-strain analyses neglect the 3D effects and could yield an overestimation or underestimation of the slope stability. The present study analyzes the stability of 3D slopes characterized by spatially variable undrained shear strength. A 3D slope reliability analysis approach that can fully consider the spatial variability of soil properties is proposed based on the plastic limit analysis upper-bound theorem. Within the framework of Monte Carlo simulation, the effects of strength gradient, the coefficient of variation, and the scale of fluctuation of soil undrained shear strength on the slope reliability index are examined. The length of the 3D failure surface is highlighted as an essential characteristic in 3D slope analysis. The slope failure mechanisms are classified, and the occurrence probability of each type is quantified. The obtained results indicate that the degree of nonstationarity and the coefficient of variation of undrained shear strength have significant impacts on the slope failure width, and the variation of failure mechanism highly depends on the combined effect of slope inclination and soil strength. The findings could guide slope reinforcement and facilitate a more efficient and accurate risk evaluation of slopes.

Spatial variability of soil properties and its effect on meadow productivity

The spatial heterogeneity of the yield of perennial grasses was studied to identify soil factors that most affect the productivity of sown meadows in South Karelia. Soil variability was studied using various statistical and geostatistical methods. In the course of research, it was found that the best conditions for the growth of perennial grasses are created on soils with a carbon content of about 8% and a slightly acidic or close to neutral reaction environment. It is noted that the type of soil affects the organic carbon content and change in the yield of perennial grasses. In the diagnostic range of peat soils, as the transition from peat-mineral soils to peat soils the organic carbon content in the soil increases and the yield of grasses decreases. The acidity distribution is rather anthropogenic in nature.

Machine Learning Models for Prediction of Soil Properties in the Riparian Forests

Spatial variability of soil properties is a critical factor for the planning, management, and exploitation of soil resources. Thus, the use of different digital soil mapping models to provide accuracy plays a crucial role in providing soil physicochemical properties maps. Soil spatial variability in forest stands is not well-known in Iran. Meanwhile, riparian buffers are important for several services such as providing high water quality, nutrient recycling, and buffering agricultural production. Accordingly, in this research, 103 soil samples were taken using the Latin hypercubic method in the Maroon riparian forest of Behbahan and agricultural lands in the vicinity of the forest to evaluate the spatial variability of soil nitrogen, potassium, organic carbon, C:N ratio, pH, calcium carbonate, sand, silt, clay, and bulk density. Different machine learning models, including artificial neural networks, random forest, cubist regression tree, and k-nearest neighbor were used to compare the estimation of soil properties. Moreover, three main sources of spatial information including remote sensing images, digital elevation model, and climate parameters were used as ancillary data. Our results indicated that the random forest model has the best results in estimating soil pH, nitrogen, potassium, and bulk density. In contrast, the cubist regression tree indicated the best estimation for organic carbon, C:N ratio, phosphorous, and clay. Further, artificial neural networks showed the best estimation for calcium carbonate, sand, and silt contents. Our results revealed that geospatial information such as terrain parameters, climate parameters, and satellite images could be well used as ancillary data for the spatial mapping of soil physiochemical properties in riparian forests and agricultural lands. In conclusion, a specific machine learning model needs to be used for each soil property to provide highly accurate maps with less error.

Sampling the spatial variability of grassland soil properties

The properties of grazed grassland soils are frequently degraded in tropical areas. The objective of this work was to review the literature on the theory and results of sampling and analysis of the spatial variability of grassland soil properties worldwide and in Ecuador. The methodology, results, and discussion of 15 articles were reviewed, available in the databases: Scopus, SciELO, ScienceDirect, Scimago Journal & Country Rank, Dialnet, and SpringerLink, and the respective theory of 5 books. It was found that worldwide there are soil sampling works that evaluate the spatial variability of their properties, while at the national level, the samples do not consider such variability. Therefore, it is crucial to conduct research in Ecuador on the spatial variability of soil properties with grasslands, allowing reliable decisions to be made for their management and conservation.

A coupling analysis method of foundation soil dynamic responses induced by metro train based on PDEM and stochastic field theory

Foundation soil is an important medium for vibration wave propagation in the running of metro train. The spatial variability of soil properties has an important influence on vibration response. The parameters of foundation soil are often determined. Based on the probability density evolution method (PDEM) and stochastic field theory, a coupling model of the metro train-track-shield tunnel-foundation soil is established. The stochastic field is expressed by the series of stochastic variables and deterministic space functions by the Karhunen-Loeve Expansion (KLE) method. The stochastic parameters of the cross-section of tunnel-foundation soil are specified to simulate the stochastic field of foundation soil. Based on PDEM, the environmental vibration caused by metro trains in shield tunnels is studied. The results of ground surface vibration displacement under different metro train speeds are analyzed, and the upper and lower limits and probability density information of ground surface vibration displacement are obtained. The results show that the probability density distribution of ground vibration displacement at each time is approximately normal due to the stochastic field of foundation soil. The peak and valley of the vibration displacement curve at the inverted arch of the tunnel are caused by the wheel distance of the metro train. The probability density distribution of velocity and acceleration is more sensitive to the stochastic field of velocity foundation soil and the change in metro train speed. The maximum Z vibration level results under different metro train speeds all meet the requirements of vibration allowable limits in the specification.

Probabilistic Analysis of Strip Footings Using the Subset Simulation Approach and the Influence of Spatial Variability of Two Clay Layers

Abstract This article studies the probabilistic analysis at the ultimate limit state (ULS) of a strip footing resting on a purely cohesive soil composed of two layers with spatial variability of the upper or lower layer and treats the simultaneous effect of the variability of the two layers on the probability of failure Pf value using the subset simulation approach. The results are examined by comparing the Pf values obtained with the Pf value corresponding to the reference case of two homogenous clay layers. In general, the subset simulation approach is used in cases of uncertain parameters modeled by random variables. But in this paper, it is employed with uncertain parameters modeled by random fields because the spatial variability of soil properties strongly affects the behavior of geotechnical structures and causes a significant change in the variability of their responses. The soil cohesion parameter is modelled as a non-Gaussian (log-normal) anisotropic random field using a square exponential autocorrelation function by the optimal linear expansion estimation (EOLE) and the deterministic model is based on numerical simulations using the finite difference software FLAC3D.

Assessment of the spatial variability of selected soil chemical properties using geostatistical analysis in the north-western highlands of Ethiopia

ABSTRACT The aim of this research was to evaluate the status of selected soil chemical properties and to explore their spatial variability in various agroecosystems (AEs) of Choke Mountain, northwestern highlands of Ethiopia. A total of 74 geo-referenced composite surface (0-30 cm) soil samples were collected from representative farm lands. Soil chemical properties were analyzed following standard procedures. In addition, semi-variogram model was applied to estimate the spatial variability of soil properties. The results showed that soil chemical properties are below their threshold values in all agroecosystems and soil groups. The value of soil pH ranged from 4.3 to 6.8; Exchangeable acidity ranged from 0.124 to 0.180 cmol (+) kg−1; EC ranged from 0.02 to 0.19 dS m−1; OC ranged from 0.7 to 3.8%; TN ranged from 0.11 to 0.27%; Exchangeable potassium from 0.29 to 2.0 cmol (+) kg−1and CEC from 22 to 56 cmol (+)kg−1. The results also revealed that soil pH, exchangeable acidity, electrical conductivity, and exchangeable potassium showed strong spatial dependence whereas SOC and TN had moderate spatial dependence. The results indicated that soil quality is deteriorating. Hence, crop, soil and site-specific soil management strategies, primarily focusing on improving the content of SOM and addressing the problems of soil acidity are suggested to improve the soil conditions.

Using Geostatistics for Spatial Analysis of Soil Moisture Content, Electrical Conductivity, and pH at Paddy Fields

Soil is dynamic due to various internal and external processes exerted on the soil, resulting in unique soil characteristics in space in short and long distances. Geostatistics (kriging) is the method of quantifying the spatial variation of soil properties. This research was mainly aimed at applying geostatistics to quantify and interpolate the spatial dependence and structure of three soil properties, namely pH, EC, and Soil Moisture Content (SMC) in a small area. This research was conducted on paddy fields in Mlandingan Kulon Village, Situbondo Regency. Sampling was conducted on an area of 9.2 ha with 31 sample points. Normal data distribution was found for pH and EC, whereas this was not the case for SMC. The results of the analysis showed that most of the pH values were alkaline (>8), EC values were non-saline (<2 mm/cm), and SMC was in the low category (<20%). The results show that for three soil properties, weak dependencies were observed. The values of Root Mean Square Error (RMSE) confirmed that kriging with exponential was better compared to the spherical model, resulting in the RMSE of 0.546 (pH), 0.041 (EC), and 1.512 (SMC).

County level calibration strategy to evaluate peanut irrigation water use under different climate change scenarios

Regional scale simulation of crop yield is challenging due to the spatial variability of soil properties, crop varieties, management practices and weather conditions. Point-based crop models are commonly used for spatial simulation with increased availability of high-resolution spatial datasets. However, it is still difficult to calibrate crop models well due to the spatial variability of model inputs. The focus of this work was to determine if a single set of cultivar and soil parameters could be calibrated to simulate county level peanut yield and to evaluate the effects of irrigation to mitigate potential climate change impacts on peanut yield. Model input data for fourteen seasons and five major peanut producing counties was assembled and used for model calibration. Three seasons of data were withheld and used for an independent evaluation. Overall, peanut growth duration and county level yields were simulated well with a set of optimum cultivar and soil parameters for each county. The model calibration showed that simulated maturity dates and yields were in good agreement with the observed county level values reported by NASS, giving an overall R2 of 0.71 and 0.73 and RMSE values of 6 days and 333 kg/ha, respectively. The model also generated good simulations of maturity dates and yield for the three evaluation seasons, with an overall R2 of 0.83 and 0.76 and RMSE values of 5 days and 429 kg/ha, respectively. The results from future climate simulations indicated that the rainfed yields will suffer from increasing daytime temperature and an irrigation strategy could potentially offset the heat and water stress to maintain higher peanut production in the Southeastern USA. This study can enhance the accuracy of simulating the impact of climate change on crop production by providing a calibration and evaluation strategy that aggregates spatial heterogeneity of model inputs at the regional scale. Further research should test this method for other crops and more model applications, such as nitrogen leaching, groundwater use and fertilizer management.

Long-term changes in soil chemical properties with cropland-to-orchard conversion on the Loess Plateau, China: Regulatory factors and relations with apple yield

CONTEXTFarmland-to-apple orchard conversion prevails on the Loess Plateau in China and in other developing countries, but simultaneously, apple orchards have been severely overfertilized in the past two decades. OBJECTIVEWe quantified the changes in soil chemical properties between the 1980s and 2010s, and identified environmental factors that controlled spatial variability of soil properties and relations with apple yields. In addition, early warnings were provided by forecasting the changes in soil properties to 2100 under various fertilization scenarios. METHODSGeostatistics was performed using soil data obtained from the 1980s and 2010s to evaluate spatial-temporal variations in soil chemical properties. Drivers of the variation were identified by boosted regression tree analysis. Variance partitioning analysis and regression analysis were conducted to quantify the threshold values and then identify the potential factors limiting apple yield. Scenario analysis was conducted to forecast soil degradation, and thus, help to determine optimum fertilization. RESULTS AND CONCLUSIONSSoil properties, except pH, varied considerably in the 1980s and 2010s, with coefficients of variation between 22.55% and 60.62%. Compared with the 1980s, pH decreased in the 2010s, whereas concentrations of soil organic matter (SOM), total nitrogen (TN), available N (AN), available phosphorus (AP), and available potassium (AK) increased. Fertilization was the dominant factor affecting soil properties (except AP), followed by climate, terrain, and soil physical factors. Apple yield was significantly related to all properties, and critical thresholds were identified for pH, SOM, AN, AP, and AK to obtain maximum apple yield. In most apple orchards, pH, SOM, AP, and AK were much lower than corresponding thresholds, whereas AN exceeded the threshold. VPA identified soil as the most important factor explaining variation in apple yield, followed by anthropogenic, climatic, and topographic factors. Compared with an optimal fertilization scenario, continuing with conventional fertilization was predicted to lead to severe soil acidification and excessive accumulation of AN, AP, and AK in 2100. Thus, overfertilization must be controlled to improve apple orchard soil fertility and productivity. To summarize, soil chemical properties in apple orchards on the Loess Plateau changed substantially following long-term overfertilization, and soil acidification and low levels of SOM, AP, and AK were key variables limiting apple production. SIGNIFICANCEThe results provide new insights into spatial variability of soil properties and relations of soil properties to apple yields at a regional scale, and thus, contribute to optimizing orchard management in China and other countries with similar dilemmas in apple production systems.

Spatial variability of soil properties, nitrogen balance and nitrate leaching using digital methods on heterogeneous arable fields in southern Germany

In this study, the spatial variability of nitrogen (N) balances and potential nitrate leaching were determined in heterogeneous arable fields in southern Germany using digital methods (tractor-mounted multispectral sensor, satellite data, vegetation indices and models) and measurements of nitrate stocks in deeper zones (1−2.5 m soil depth) with deep drilling. The aim of this study was to achieve precise localisation of N losses at high spatial resolution (sub field level; 10 × 10 m grid). The spatial variability of plant parameters within uniformly fertilised fields (crop yield, N uptake) was determined for the calculation of the N balances. The spatial variability of soil properties (e.g. soil organic carbon content, soil total nitrogen content) were determined to identify the causes of high or low N surpluses and nitrate leaching in sub-fields. N surplus determined using different digital methods and measured nitrate stocks showed similar spatial patterns. Site-specific N balancing identified zones with high N loss potential (N surplus up to 86 kg ha−1). Deep drilling showed zones with high nitrate loss (nitrate N up to 94 kg ha−1). N surplus and nitrate loss correlated with r = 0.49. This relationship could be impacted by many other soil and management factors. Soil properties showed considerable spatial variation within the fields. Soil organic carbon (SOC) and soil total nitrogen (TN) content were closely correlated in all fields (up to r = 0.96) and were most closely positively correlated with crop yield and N uptake (up to r = 0.74) and negatively correlated with N surplus (up to r = − 0.73). The sensor data and satellite data (crop yield, N uptake, N surplus) showed similar distribution patterns. Based on these results, digital technologies are suitable for the calculation of site-specific N surplus and estimation of nitrate leaching risk. Satellite or sensor based site-specific and yield-oriented fertilization is one approach to reduce N surplus on sub fields with low yield potential and high nitrate leaching risk.

A probabilistic framework for the stability analysis of slopes considering the coupling influence of random ground motion and conditional random field

The uncertainty of the input parameters can have a significant influence on the evaluation of the slope stability. A simple probabilistic framework is presented in this paper to quantify the influence of the uncertainties of the stochastic ground motion coupled with the spatial variability of soil parameters. The robust quasi–Monte Carlo simulation in the proposed framework is employed to estimate the failure probability of slopes. The conditional random field (CRF) is manipulated to simulate the spatial variability of soil properties with the sample points where the soil parameters are determined, and the spectral representation of stochastic process is applied to generate the horizontal and vertical stochastic ground motions. In each run of the Quasi–Monte Carlo simulation, a modified Newmark method based on an infinite slope failure model is applied to determine the permanent displacement of slopes under horizontal and vertical ground motions. The originality and significance of this proposed probabilistic framework are validated by a real slope case. The results of the case show that the uncertainty affecting the stability of the slope can decrease when CRF is used in a random field simulation to incorporate the real values of soil parameters at sample points, and the failure probability of slopes can be underestimated without considering stochastic vertical ground motion. Moreover, the correlated distance and critical permanent displacement are significant in predicting the failure probability of slopes.

Probabilistic Analysis of Slope against Uncertain Soil Parameters

The purpose of this article was to investigate the influence of uncertain soil parameters on slope stability problems using the probability method. The most important soil instability is probably the inherent spatial variability of soil properties and its influence on slope safety factors. The probability method is recognized for its validity both in practice and academic research. This method of geotechnical risk management involves uncertain parameters. In this study, randomly selected uncertain parameters were combined with traditional analysis using Bishop’s simple methodology and Monte Carlo simulation. The minimum safety factor and critical slip surface of the slope stability were analyzed by Bishop’s simple method. The influence of uncertain soil parameters was analyzed by the back analysis method with Monte Carlo simulation. The calculation was controlled by a critical slip surface, and the safety factor was equal to 1. The results of the analysis show the minimum safety factor and the significant slippery surface of slope stability with the original soil strength parameters. The new soil strength parameters were recommended by Monte Carlo simulation that was used for improving the soil strength parameters in construction. In addition, probability analysis can identify an insufficient understanding of groundwater level distribution and that the assumption of a uniform distribution increases the probability of failure. Governments can use the results of water level analysis in flood warnings and flood management projects to reduce risks to life and property.

Spatial variability of soil properties of intensively cultivated rice soils of selected command areas of Karnataka

The spatial distribution of soil properties and plant available Si was studied in three important rice producing command areas of Karnataka by collecting random georeferenced surface (0-15cm) soil samples using GPS and GIS techniques. The soil samples were analysed for different soil properties and plant available Si and the data along with GPS reading were used for the preparation of soil thematic maps using interpolation method and the interpolated maps were analysed with geostatistical parameters and the corresponding kriging maps for pH, electrical conductivity (EC), soil organic carbon (SOC), available nitrogen, phosphorus, potassium and plant available Si (CCSi and AASi) were prepared. These soil samples were categorized as low, medium and high as per criteria followed by soil testing laboratories. The content of soil pH, EC, OC, CEC, sand, silt and clay in surface soils varied from 5.2- 8.7, 0.05-0.49 dS m-1 , 2.2-17.5 g kg-1 , 3.6- 26.5 c mol (p+ ) kg-1 , 20.4-85.6, 0.9-59.7 and 1.9-47.8%, 15.7-65.4 mg g-1 and 19.2-106.7 mg g-1 , respectively. The available nitrogen (89.6- 313.6 kg ha-1 ) was low whereas, phosphorus (10.4-206.9 P2O5 kg ha-1 ) and available K2O (68.9-516.9 K2O kg ha-1 ) were low to medium in range. The CCSi and AASi content ranged from 15.7-65.4 mg g-1 and 20.0-106.7 mg g-1 , respectively

Spatial Variability of Soil Properties under Different Land Use Systems in Wokha District of Nagaland, India

The shifting cultivation practice is the predominant land use system of Nagaland, India, and Wokha district in particular. In general, there are four different forms of land use: forest, shifting cultivation, cultivated fallow, and wet terrace rice cultivation. In the Wokha district, a study was conducted in the year 2015 to evaluate the variability in soil characteristics among four different land use systems and to map their spatial distribution. A total of 381 soil samples were collected and tested for six soil fertility parameters; clay, pH, soil organic carbon, available nitrogen (N), phosphorus (P) and potassium (K). The results showed that all of the soils were very acidic in nature; however the mean value of soil pH was substantially higher in the shifting cultivation system. Soil organic carbon concentrations ranged from high to very high across all land use systems. Average soil organic carbon content was highest in the forest, followed by cultivated fallow, shifting, and wet terrace rice cultivation (WTRC) systems, whereas available N content was very low to low. The P concentration of the soil was very low in the WTRC system and low in the other systems. Soil K concentration was high in shifting (361.95 kg/ha), cultivated fallow (312.4 kg/ha), forest (309.73 kg/ha), and low in WTRC (166.975 kg/ha) land use systems. Available N, P and K deficiency was found in 93.8%, 72.2%, and 32.9% of soil samples, respectively. Soil organic carbon correlated positively with K and significantly positively with N. Soil pH correlated negatively with clay content and positively but non-significantly with P. The generated maps might be used to assist farmers in identifying the expected nutrient levels in their areas and encourage them to change their crop management practices to improve crop yield and profitability. Due to soil acidity, higher SOC, lower N and P, and higher potassium content, suitable acid loving crops, higher N and phosphorus fertilization, and some commercially advantageous potassium responsive crops may be encouraged in farming practices.

Low sensitivity of three terrestrial biosphere models to soil texture over the South American tropics

Abstract. Drought stress is an increasing threat for vegetation in tropical regions, within the context of human-induced increase of drought frequency and severity observed over South American forests. Drought stress is induced when a plant's water demand is not met with its water supply through root water uptake. The latter depends on root and soil properties, including soil texture (i.e. the soil clay and sand fractions) that determines the soil water availability and its hydraulic properties. Hence, soil clay content is responsible for a significant fraction of the spatial variability in forest structure and productivity. Soil-textural properties largely vary at the spatial resolution used by Terrestrial Biosphere Models (TBMs) and it is currently unclear how this variability affects the outputs of these models used to predict the response of vegetation ecosystems to future climate change scenarios. In this study, we assessed the sensitivity of the carbon cycle of three state-of-the-art TBMs, i.e. ORganizing Carbon and Hydrology in Dynamic EcosystEms (ORCHIDEEv2.2), Ecosystem Demography model version 2 (ED2), and Lund–Potsdam–Jena General Ecosystem Simulator (LPJ-GUESS) to soil-textural properties at the regional level over the South American tropics using model default pedotransfer functions. For all three TBMs, the model outputs, including gross primary productivity (GPP), aboveground biomass (AGB), soil carbon content and drought stress, were shown to be mostly insensitive to soil-texture changes representative of the spatial variability in soil properties, except for a small region characterised by very low water availability in ORCHIDEEv2.2 and ED2. We argue that generic pedotransfer and simple drought stress functions, as currently implemented in TBMs, should be reconsidered to better capture the role of soil texture and its coupling to plant processes. Similarly, we suggest that better estimates of the soil-texture uncertainty resulting from soil-texture data aggregate should be considered in the future. Those steps forward are critical to properly account for future increasing drought stress conditions in tropical regions.

Efficient stochastic analysis of unsaturated slopes subjected to various rainfall intensities and patterns

Rainfall infiltration poses a disastrous threat to the slope stability in many regions around the world. This paper proposes an extreme gradient boosting (XGBoost)-based stochastic analysis framework to estimate the rainfall-induced slope failure probability. An unsaturated slope under rainfall infiltration in spatially varying soils is selected in this study to investigate the influences of the spatial variability of soil properties (including effective cohesion c′, effective friction angle φ′ and saturated hydraulic conductivity ks), as well as rainfall intensity and rainfall pattern on the slope failure probability. Results show that the proposed framework in this study is capable of computing the failure probability with accuracy and high efficiency. The spatial variability of ks cannot be overlooked in the reliability analysis. Otherwise, the rainfall-induced slope failure probability will be underestimated. It is found that the rainfall intensity and rainfall pattern have significant effect on the probability of failure. Moreover, the failure probabilities under various rainfall intensities and patterns can be easily obtained with the aid of the proposed framework, which can provide timely guidance for the landslide emergency management departments.

Pedo-transfer functions of the soil water characteristic curves of the vadose zone in a typical alluvial plain area in the lower reaches of the Yellow River using machine learning methods.

The soil water characteristic curve (SWCC) is of great significance for studying the hydrological cycle, agricultural water management, and unsaturated soil mechanics. However, it is difficult to effectively obtain a large number of SWCCs because of the cumbersome and expensive determination experiments for SWCCs. Pedo-transfer functions (PTFs) established using soil physicochemical properties have become an effective method for solving this problem. However, due to the limitations of the establishment methods and the wide spatial variability of soil properties, it is still difficult to establish PTFs in a specific region. In order to establish the PTFs of SWCCs for the alluvial plain area of the lower reaches of the Yellow River, 233 soil samples were collected from the vadose zone in a typical area. These data were used as the data sources, and eight variables including clay, silt content, fractal dimension, bulk density, total porosity, pH value, organic matter content, and electrical conductivity were used as the influencing factors. By applying and comparing three machine learning algorithms, the PTFs of the SWCCs based on the random forest algorithm were obtained. Based on the Gini index of the random forest, the insensitive factors were eliminated and the optimal variable input mode was constructed. Based on the verification, there was little difference between the predicted water content and the measured water content. The determination coefficient R2 is 0.9308; the root mean square error (RMSE) is 0.0447; and the mean relative error (MRE) is 22.40%.