Spatial Variability Of Soil Properties Research Articles

Reliability and parameter sensitivity analysis on geosynthetic-reinforced slope with considering spatially variability of soil properties

The Bishop method is often adopted to evaluate the stability of slope, but this method can’t effectively consider the inherent variation characteristics of soil. This study focuses on the reliability of geosynthetic-reinforced slope considering the spatial variability of soil properties. The improved Bishop method is adopted to evaluated stability of slope. The Karhunen-Loève expansion is adopted to generated 2-D random fields of spatial variability of soil properties. The Monte Carlo simulation combined with Latin hypercube sampling is adopted to analyze the reliability of geosynthetic-reinforced slope considering the spatial variability of soil properties. As a result, the spatial variability of soil shear strength has an important impact on the reliability of reinforced slope. The reliability of reinforced slope is more sensitive for variation of cohesion (COVc) compared with coefficient of variation of internal friction angle (COVφ). Considering slope’s spatial variability of cohesion (c) and internal friction angle (φ), the reinforced slope’s reliability is greater than that considering the spatial variability of φ or c. The reliability is sensitive for the autocorrelation distance especially the vertical direction. The correlation coefficient (r (c, φ)) has also a great influence on the reinforced slope’s reliability. The study may help provide more scientific help for the design of reinforced slope and make the construction of the project running safely and smoothly.

Using Bayesian compressed sensing and sparse dictionaries to interpolate soil properties

Capturing the spatial variations of soil properties through interpolation is an important aspect of soil mapping, usually conducted via geostatistics. Compressed sensing (CS) is an advanced signal processing theory that has been introduced in recent years for interpolating spatial data. Existing CS interpolation methods based on preconstructed bases require regularization parameters and can produce only smooth interpolation results. To avoid the influence of artificially regularization parameters and to obtain more realistic maps of soil properties, an interpolation method based on Bayesian compressed sensing and sparse dictionaries (BCS-D) is proposed. The results of applications to two examples confirm its feasibility for mapping soil properties and show that BCS-D can provide kriging-like maps with global and local variability, reducing the risk of over- or under-estimation of soil properties over large areas. The greater prediction accuracy of BCS-D over geostatistical simulation is another advantage. A strategy for employing small and multisource training datasets is also developed for dictionary learning. Generally, BCS-D can be adopted as an interpolation method to meet the demand for realistic and accurate soil maps.

Beyond prediction: methods for interpreting complex models of soil variation

• Describe a set of methods for the interpretation of complex mapping models. • Methods are not dependent on a specific type of model. • Reveal global model functioning and local drivers of soil variation. • Provide new insights into complex mapping models. • Might assist formulating hypotheses on the mechanisms driving soil variation. Understanding the spatial variation of soil properties is central to many sub-disciplines of soil science. Commonly in soil mapping studies, a soil map is constructed through prediction by a statistical or non-statistical model calibrated with measured values of the soil property and environmental covariates of which maps are available. In recent years, the field has gradually shifted attention towards more complex statistical and algorithmic tools from the field of machine learning. These models are particularly useful for their predictive capabilities and are often more accurate than classical models, but they lack interpretability and their functioning cannot be readily visualized. There is a need to understand how these models can be used for purposes other than making accurate prediction and whether it is possible to extract information on the relationships among variables found by the models. In this paper we describe and evaluate a set of methods for the interpretation of complex models of soil variation. An overview is presented of how model-independent methods can serve the purpose of interpreting and visualizing different aspects of the model. We illustrate the methods with the interpretation of two mapping models in a case study mapping topsoil organic carbon in France. We reveal the importance of each driver of soil variation, their interaction, as well as the functional form of the association between environmental covariate and the soil property. Interpretation is also conducted locally for an area and two spatial locations with distinct land use and climate. We show that in all cases important insights can be obtained, both into the overall model functioning and into the decision made by the model for a prediction at a location. This underpins the importance of going beyond accurate prediction in soil mapping studies. Interpretation of mapping models reveal how the predictions are made and can help us formulating hypotheses on the underlying soil processes and mechanisms driving soil variation.

Modeling resilient modulus of subgrade soils using LSSVM optimized with swarm intelligence algorithms

Resilient modulus (Mr) of subgrade soils is one of the crucial inputs in pavement structural design methods. However, the spatial variability of soil properties and the nature of test protocols, the laboratory determination of Mr has become inexpedient. This paper aims to design an accurate soft computing technique for the prediction of Mr of subgrade soils using the hybrid least square support vector machine (LSSVM) approaches. Six swarm intelligence algorithms, namely particle swarm optimization (PSO), grey wolf optimizer (GWO), symbiotic organisms search (SOS), salp swarm algorithm (SSA), slime mould algorithm (SMA), and Harris hawks optimization (HHO) have been applied and compared to optimize the LSSVM parameters. For this purpose, a literature dataset (891 datasets) of different types of soils has been used to design and evaluate the proposed models. The input variables in all of the proposed models included confining stress, deviator stress, unconfined compressive strength, degree of soil saturation, soil moisture content, optimum moisture content, plasticity index, liquid limit, and percent of soil particles (P #200). The accuracy of the proposed models was assessed by comparing the predicted with the observed of Mr values with respect to different statistical analyses, i.e., root means square error (RMSE) and determination coefficient (R2). For modeling the Mr of subgrade soils, percent passing No. 200 sieve, optimum moisture content, and unconfined compressive strength were found to be the most significant variables. It is observed that the performance of LSSVM-GWO, LSSVM-SOS, and LSSVM-SSA outperforms other models in predicting accurate values of Mr. The (RMSE and R2) of the LSSVM-GWO, LSSVM-SSA, and LSSVM-SOS are (6.79 MPa and 0.940), (6.78 MPa and 0.940), and (6.72 MPa and 0.942), respectively, and hence, LSSVM-SOS can be used for high estimating accuracy of Mr of subgrade soils.

Soil properties resulting in superior maize yields upon climate warming

The impacts of global climate warming on maize yield vary regionally. However, less is known about how soil modulates regionally specific impacts and soil properties that are able to alleviate adverse impacts of climate warming on maize productivity. In this study, we investigated the impacts of multiple soil inherent properties on the sensitivity of maize yield (SY,T) to growing season temperature across China. Our results show that a 1°C warming resulted in the largest yield decline (11.2 ± 6.1%) in the mid-eastern region, but the moderate yield increase (1.5 ± 2.9%) in the north-eastern region. Spatial variability in soil properties explained around 72% of the variation in SY,T. Soil organic carbon (SOC) content positively contributed the greatest extent (28.9%) to spatial variation of SY,T, followed by field capacity (9.7%). Beneficial impacts of increasing SOC content were pronounced in the north-eastern region where SOC content (11.9 ± 4.3 g kg−1) was much higher than other regions. Other soil properties (e.g., plant wilting point, sand content, bulk density, and saturated water content) were generally negatively correlated with SY,T. This study is the first one to answer how soil inherent properties can modulate the negative impacts of climate warming on maize yield in China. Our findings highlight the importance of SOC in alleviating adverse global warming impacts on maize productivity. To ensure food security for a rapidly increasing population under a changing climate, appropriate farming management practices that improve SOC content could reduce risk of adverse effects of global climate warming through a gain in yield stability and more resilient production in China’s maize belt.

Representative slip surface identification and reliability analysis of slope systems in spatially variable soils

ABSTRACT A slope system is a series system with numerous potential slip surfaces (PSSs), and its failure probability is commonly evaluated by several significant failure surfaces, or representative slip surfaces (RSSs). Previous efforts have mainly identified the RSSs in spatially variable soils from the perspective of the correlations between different PSSs, the effects of the failure probabilities of the PSSs were rarely considered. With the goal of identifying RSSs from the perspective of the system failure probability, a method adopting the second-order reliability method (SORM) and the multimodal optimisation is proposed. In this method, the spatial variability of soil properties along the slip surface is characterised by local averaging to reduce the number of variables in SORM. Equations for calculating the correlation coefficient between different PSSs with correlated variables are derived. The task of RSS identification is transformed as a multimodal optimisation problem, and the PSSs that make great contributions to the system failure probability are determined as RSSs. The proposed method and the derived equations are demonstrated using two slope examples. The results show that the proposed method is capable of identifying RSSs with significant contributions, and it provides a proper estimate of the system failure probability.

Seismic responses of rectangular tunnels in liquefiable soil considering spatial variability of soil properties

The objective of this paper was to investigate the dynamic response of a tunnel-soil system in the soil with spatial variability of shear modulus with various coefficients of variation and scales of fluctuation. The soil shear modulus was highlighted and modeled as an isotropic random field using the spectral representation method, then mapped into the finite difference model. The dynamic stochastic responses of the excess pore water pressure ratio, the liquefied zone, the ground displacement, and the uplift displacement of the tunnel were discussed. It was observed that the effect of coefficient of variation was pronounced in which an increase of the coefficient of variation brought out a reduction in the corresponding dynamic stochastic responses. However, the scale of fluctuation was found to be insignificant in the tunnel soil system.

Hyperspectral Estimates of Soil Moisture Content Incorporating Harmonic Indicators and Machine Learning

Soil is one of the most significant natural resources in the world, and its health is closely related to food security, ecological security, and water security. It is the basic task of soil environmental quality assessment to monitor the temporal and spatial variation of soil properties scientifically and reasonably. Soil moisture content (SMC) is an important soil property, which plays an important role in agricultural practice, hydrological process, and ecological balance. In this paper, a hyperspectral SMC estimation method for mixed soil types was proposed combining some spectral processing technologies and principal component analysis (PCA). The original spectra were processed by wavelet packet transform (WPT), first-order differential (FOD), and harmonic decomposition (HD) successively, and then PCA dimensionality reduction was used to obtain two groups of characteristic variables: WPT-FOD-PCA (WFP) and WPT-FOD-HD-PCA (WFHP). On this basis, three regression models of principal component regression (PCR), partial least squares regression (PLSR), and back propagation (BP) neural network were applied to compare the SMC predictive ability of different parameters. Meanwhile, we also compared the results with the estimates of conventional spectral indices. The results indicate that the estimation results based on spectral indices have significant errors. Moreover, the BP models (WFP-BP and WFHP-BP) show more accurate results when the same variables are selected. For the same regression model, the choice of variables is more important. The three models based on WFHP (WFHP-PCR, WFHP-PLSR, and WFHP-BP) all show high accuracy and maintain good consistency in the prediction of high and low SMC values. The optimal model was determined to be WFHP-BP with an R2 of 0.932 and a prediction error below 2%. This study can provide information on farm entropy before planting crops on arable land as well as a technical reference for estimating SMC from hyperspectral images (satellite and UAV, etc.).

Evaluation of the scale of fluctuation based on variance reduction method

Many uncertainties exist in geotechnical engineering. Spatial variability is a type of uncertainty that has received more and more attention in recent years. Scale of fluctuation (SOF) is a parameter for measuring the spatial variability of soil properties. To investigate the proper method for computing the SOF of soil properties, two types of methods (auto-correlation function method (ACM), and variance reduction method (VRM)) are compared theoretically and numerically, and the influence factors, such as number of data points involved in curve fitting for the auto-correlation function (ACF) or variance reduction function (VRF) and sampling interval, on the SOFs are analyzed. To improve the accuracy of curve fitting for the ACF or VRF, a weighted least squares (WLS) method is proposed, in which the weight of each data point of sample function decreases with the increasing of lag distance between two points in a random field. Three types of soil parameters, including the CPT, the physical, and the hydraulic parameters, are tested by in-situ and laboratory tests, and the SOFs of these parameters are computed. Computation of the SOFs proves that the VRM combined with the suggested WLS is a good method for computing the SOFs of soil properties. Comparison of the SOFs shows that there are negligible differences among the three types of soil parameters of clay layers in Hefei, which is further proved by the Wilcoxon test from a statistical point of view. By using the VRM combine with WLS, the SOFs of clay are computed based on 509 sets of CPT data from 34 sites in Hefei. The statistics of SOFs can give a reference for the selection of SOF for reliability analysis or reliability-based design of geo-structures with spatial variability.

Effect of soil physical properties on soil infiltration rates

The physical properties of the soil are studied to understand the influence of soil properties on infiltration rate. The effect of soil physical properties on infiltration rates on logged-over forests was measured with a mini-disk infiltrometer across various levels of soil disturbances. Results of soil analysis suggest are mostly loamy texture and the bulk density has varied from 0.74 - 1.02 g cm−3, respectively. The basic infiltration rate has varied from a minimum of 0.61 mmhr−1 to a maximum of 45.22 mmhr−1 with an average of 3.81 mmhr−1. The results of simple regression analyses showed that there was little association between the physical properties of the soil and the infiltration rate. This study suggests that the high variation of infiltration rate in this study site is attributed to the high spatial variability of soil properties.

Towards site specific management zones delineation in rotational cropping system: Application of multivariate spatial clustering model based on soil properties

Site|specific management for irrigation purposes remains a great challenge especially when dealing with mixed cropping systems due to spatial variability of soil properties. This variability generates crop yield heterogeneity within agricultural fields.In this study, rice-corn, cotton rotational cropping system was cultivated in Tolima region-Colombia. A total of 72 geo-referenced representative surface soil samples (0–20 cm depth) were collected covering a total area of 5 ha of Inceptisols. The aim was to delineate field management zones (MZs) using three methods M1, M2 and M3, and evaluate their usefulness for a further site-specific management. Georeferenced soil samples were analyzed for soil texture, bulk density (BD), field capacity (FC), soil available water (AW), mesopores percentage (Mes), soil penetration resistance (PR), organic matter (OM), available phosphorus (P), and exchangeable bases content (Ca + Mg/K). Spatial variability of soil properties was analyzed with geostatistics approach. Further, spatial principal component (sPC), were performed to delineate the MZs using: M1- Fisher-Jenks algorithm with the first sPC; M2- Fuzzy k-means cluster analysis with two sPC; and M3- Fuzzy k-means with soil variables. MZs delineation results were validated by comparing the zones to soil properties and yield data coming from the rice-corn, cotton system. Two MZs were recommended using M2 due to its lower fragmentation compared to other methodologies. This delineation could be suggested for fertilization management since Clay + Silt, OM, P, and Ca + Mg/K content differentiate when delineating one and two MZs. M2 provided differences for AW, Mes, and PR when considering three MZs which can be used as a basis for irrigation site-specific management in precision agriculture.

Field scale variability in soil properties and silage corn yield

Field scale spatial variability of soil properties, crop quality parameters and yield are needed to evaluate the efficiency of management practices in crop production. The purpose of this study was to determine the magnitude of field variability in soil properties, silage yield of corn (Zea mays L.) varieties, and to characterize their spatial structures, and map the stated attributes. The experiment was conducted in an alluvial flood plain of lower Kazova watershed in Tokat province of Turkey. Several physical and chemical soil properties and silage corn yield were determined. Coefficient of variation (CV%) varied from 1.0% (pH) to 38.1% (P2O5) in herbicide not applied plots and from 0.9% (pH) to exchangeable Na (55.1%) in herbicide applied plots. Calcium carbonate, organic matter and clay displayed well defined spatial structure. Sand, pH and electrical conductivity (EC) showed moderate spatial dependency. However, silt, moisture content, bulk density, plant available phosphorus and potassium had weak spatial structure. Silage corn yield distribution map successfully distinguished the three corn hybrids planted. The difference in vegetation period among three corn hybrids was effective in distinguishing the location of hybrids within the field. However, the variability in each of the hybrids blocks was assumed to occur due to the difference in short range soil properties. The longest range values were obtained for silage corn yield at both herbicide applied and herbicide unapplied plots.

Extraction of the stable component of electrical surveys of soils and the consequence on the mapping of their thicknesses

Knowledge of the spatial variability of soil thickness (ST) in agricultural fields is useful for improving crop management. Spatial patterns of ST can be characterized using temporal stability analysis of high spatial density and temporal frequency data, associated with sparse and difficult to obtain data on the target variable. Apparent electrical resistivity measurements of soil at high spatial density have been widely used to infer the spatial variability of soil properties. However, electrical resistivity measurements are sensitive to many factors (e.g. soil water content). Thus, it remains difficult to interpret them according to a characteristic of the soil such as its thickness. The objective of this study was to present a geostatistical approach called automatic factorial cokriging (AFACK), which allows capturing the time-invariant part between the spatio-temporal measurements of a signal, called stable component in the sequel. The stable component achieved by AFACK was then used as a covariate in a multivariate estimation algorithm to map a static soil property pattern. The underlying idea was to highlight that the spatial estimates of a static soil property pattern is well achieved by integrating, in a multivariate estimation algorithm, the stable component over the time of a covariate instead of its single acquisitions. The latter generally show weak and inconsistent relationships with the static properties of the soil such as its thickness. These inconsistent relationships are mainly generated by the temporal variability of the signal measurements carried out under various conditions. Through three surveys of soil resistivity measurements carried out on three dates on the soil of an agricultural plot, results revealed that AFACK allows: (i) reproducing an initial signal deliberately corrupted by a noise, (ii) extracting the time-stable component of two or more soil electrical resistivity surveys. Cluster analysis results also revealed that the time-stable component of the signal exhibits well homogeneous areas in space conversely to surveys considered separately. Results also showed that the time-stable component of the electrical signal is better linked to the thickness of soil (ST). In addition, the mapping of ST by collocated cokriging (CC), using any stable component between two or more electrical resistivity surveys carried out by AFACK, leaded to an invariable map of ST, unlike the maps achieved by CC using, as covariate, soil electrical surveys considered individually. Statistical indicators resulting from leave-one-out cross-validation (LOOCV) showed that CC using any invariant part of the signal outperforms CC using the single acquisitions. Correlation coefficient (r), mean absolute error (MAE) and root mean square error (RMSE) values range between 0.93 and 0.94; 4.33 and 5.03; 0.75 and 0.88, respectively, for estimates using as a covariate an invariant part of the signal. While values of these statistical indicators range between 0.64 and 0.74; 7.20 and 8.69; 1.25 and 1.52, respectively, for estimates by CC using single acquisitions. Values of these indicators are almost invariant regardless of the invariant part used in CC, unlike the scenario using the surveys individually.

Statistical analysis and prediction of spatial resilient modulus of coarse-grained soils for pavement subbase and base layers using MLR, ANN and Ensemble techniques

Prediction methods for the estimation of the resilient modulus({M}_{R}) of unbound granular materials continues to be fraught with challenges associated with the in situ dependency of the existing methods, which makes them unreliable when considering the spatial variability of soil properties. Moreover, the artificial intelligence methods, which prove to be superior for {M}_{R} prediction, indicate that more recent and hybrid machine learning methods are more apt. Consequently, a comparative analysis was executed in the present study using advanced statistical approaches and a simple routine method for the prediction of the {M}_{R} of coarse-grained soils used as unbound granular materials in highway pavements. This study considered also the spatial variability of the routine soil properties based on the long-term pavement performance database. The result of the descriptive analysis conducted using principal component analysis revealed that the variation in {M}_{R} is due to three principal factors, which include the effect of moisture on the fines content, effect of coarse particles on the compaction characteristics and the effect of the soil stress state. Subsequently, prediction of the {M}_{R} using multiple linear regression was unreliable based on residual analysis and multi-collinearity problems. Thus, predictive modeling of {M}_{R} was then executed using artificial neural network (ANN) and ensemble techniques (random forest and gradient boosting machine), which resulted in very high R2 values above 0.9, considered apt for MEPD. The ANN model was found to provide a superior prediction performance than the ensemble techniques.

Three-Dimensional Probabilistic Analysis of the Surface Settlement Based on Spatial Variability of Soil Properties: Case Study Zarbalizadeh NATM Tunnel

The design and construction of a tunnel in urban areas need special considerations. Usually, the excessive settlement caused by tunnelling during the construction process damages the adjacent infrastructures and utilities, especially if the tunnel is excavated by the new Austrian tunnelling method (NATM). Thus, it is important to make accurate predictions and effective control on tunnelling-induced settlement. In this study, the soil’s Young’s modulus was modelled using a three-dimensional random field and coupled with a finite difference method (FDM) analysis to reveal the scale of fluctuations (SOF) effects on the maximum surface settlement (Smax). The Fourier series method is employed to generate the field of Young’s modulus for soil, and sensitivity studies are further performed via Monte-Carlo simulations (MCS). The results demonstrate that the mean value of Smax increased from 28 to 31 mm and its coefficient of variation (COV) increased from 0.02 to 0.35, with an increasing horizontal SOF but both stabilize at higher values of SOF. Furthermore, the probability of failure increases as the COV increases for each allowable limit greater than the verification FDM of Smax. It is observed that ignoring the spatial variability of the soil properties leads to an underestimate of excessive surface settlement risk.

Efficient reliability‐based design of slope angles in spatially variable soils with field data

AbstractDeterministic single factor of safety method cannot explicitly account for the influences of various sources of uncertainties (e.g., spatial variability of geomaterials, measurement and transformation uncertainties) in stability design of slopes. Many probabilistic methods have been applied to the reliability‐based design (RBD) of slopes, but they typically require performing numerous deterministic slope stability analyses. In this paper, an efficient reliability‐based design method for spatially varying slopes based on field data is proposed. Here, the RBD of a slope angle is concerned. Reliability‐based design is implemented using an inverse First Order Reliability Method (inverse FORM ‐ IFORM). A sandy slope and a cohesive slope are investigated as examples, respectively, to illustrate the proposed method. The results indicate that the proposed method can quickly obtain rational design schemes of slope angles accounting for the spatial variability of soil properties, measurement and transformation uncertainties based on the field data. It can act as a practical and effective tool for the RBD of slopes in two‐dimensional spatially variable soils. Additionally, it is found that the random field mesh size affects the RBD results significantly, while the probability distribution and horizontal autocorrelation of soil parameters have slight influences on the RBD results.

Effects of spatial variability of soil properties and ground motion characteristics on permanent displacements of slopes

The seismic performance of earth slopes is a problem of great importance in geotechnical earthquake engineering associated with various sources of uncertainty. This research investigates the effects of two important causes of such uncertainty: (a) the spatial variability of soil properties and (b) the variability of the temporal and frequency characteristics of the earthquake excitation. The former is investigated based on a series of detailed dynamic numerical simulations using stationary random fields of both cross-correlated and spatially autocorrelated soil properties. The latter is explored by conducting incremental dynamic analysis at various levels of excitation intensity using (a) a total of forty historic earthquake and synthetic records, modified to match the Eurocode 8 design spectra for rock sites, and (b) thirty original historic earthquake records. The results demonstrate the significant effect of the spatial variability of soil properties on the permanent slope displacements. Moreover, they show that the effect of the uncertainty associated with the variability in the temporal and frequency characteristics of excitations is more important compared to the effect of the spatial variability of soil properties.

Soil database development with the application of machine learning methods in soil properties prediction

Excessive rainwater infiltration can be an important causal agent of both slope and whole tree uprooting failures. Early warnings or stabilization measures on high-risk slopes or trees are critically important. To identify the high-risk areas, it is necessary to conduct seepage, slope and tree stability analyses over a large region. Given the spatial variability of soil properties, a soil database is therefore required before performing distributed or Geographical Information System (GIS) -based water balance and stability analyses. Considering that the unsaturated soil properties could be very different from saturated soil properties, in this study, a soil database containing both saturated and unsaturated hydraulic and mechanical soil properties was developed for the first time. Machine learning methods were used to predict the unknown soil properties. Based on the predicted soil properties, spatial distributions of different saturated and unsaturated soil properties were generated using the ordinary kriging method. Then the soil database was developed with Singapore island being divided into 97 zones, with each zone having similar soil properties. In this study, the importance of different input variables in soil properties prediction was also investigated. In addition to soil plasticity (i.e., Liquid Limit (LL), Plastic Limit (PL) and Plasticity Index (PI)) and grain size distribution (i.e., gravel, sand, and fines fractions), location (i.e., longitude and latitude) was found to be of high importance as well and are recommended to be used as input variables to predict soil properties, especially when data volume is relatively limited. For those soil properties that cover a large range of values, model performance is better when logarithm values were used as the outputs. Moreover, given the possible correlation between some output parameters, the prediction of the Soil-water Characteristic Curve (SWCC) from a multi-output model is recommended after comparing its performance with a single output model. Furthermore, the performance of two commonly used machine learning methods (i.e., random forest regression and artificial neural network) in soil properties prediction were compared and the prediction error resulting from the random forest regression method was generally smaller. The developed database includes the mean values of saturated permeability, saturated and unsaturated shear strength parameters, and SWCC in each zone. The database can be applied in regional GIS-based water balance and slope stability analyses to account for the spatial heterogeneity instead of assuming constant soil properties.

Spatial Variability of Soil Properties in Palm Groves of the Central Algerian Sahara (Case of Zelfana)

Monitoring soil quality in irrigated areas is essential for assessing the sustainability of production systems. In this respect, the spatial variability of the properties of irrigated soils is a mean to know the evolution of the latter. This study aims to determine the spatial variability of soil organic matter, salinity, pH and active limestone using a geostatistical approach. The present study was carried out in the region of Zelfana located in the Algerian central Sahara, the samples were collected from a depth of 0 to 30 cm and analysed for organic matter, salinity, pH and active limestone. The analytical results show that the soil is very poor in organic matter, very salty, alkaline to very alkaline and moderately calcareous. The geostatistical analysis revealed various patterns and levels of spatial distribution of the studied properties. The results showed a weak spatial dependence for organic matter, moderate for pH and salinity and strong for active limestone. The variographic analysis showed that the nugget effect is weak for organic matter and pH, moderate for salinity, while active limestone does not show a nugget effect. The range varies from 75 meters for salinity to 299 meters for organic matter, confirming the validity of the adopted sampling and allowing the optimization of future sampling plans. The Arcgis autoKriging function was used to select the best theoretical variogram model from those most commonly used in geostatistics (Gaussian, spherical, exponential and circular). This model was used to produce the spatial variability maps using ordinary kriging. Spatial variability of soil properties is influenced by agricultural intensification, something that must be taken into consideration for integrated and sustainable land management in similar regions.

Spatial variation of soil temperature fields in a urban park

Soil temperature is the most important factor that regulates the rate of physical, chemical and biological processes in the soil. A peculiarity of the urban environment is the occurrence of “heat islands”. The increased temperature of urban environment significantly changes environmental conditions and contributes to the activation of phenomena that lead to the acceleration of global climate change. The aim of the work is to reveal the patterns of spatial variation of soil temperature in a city park at the different scale levels. Soil temperature was measured on a regular grid with different lags between measurement points. The measurement results were processed using geostatistical methods to quantify the spatial process at different scales. The results obtained allowed to quantify the patterns of spatial variability of temperature fields at different hierarchical levels. Scale-dependent effects of soil temperature variation were identified. The role of stand density, litter depth, and soil moisture on soil temperature variation was found. The results of the study are the basis for developing an optimal soil temperature measurement plan for environmental monitoring purposes. Suggestions were also made for the management of park stands in order to reduce the temperature load. The spatial variation in soil temperature demonstrates the occurrence of scale-dependent patterns. The spatial organization of temperature fields must be taken into account for optimal environmental monitoring and urban environmental management strategies. The soil temperature regime is characterised by a significant level of stability compared to air temperature. The soil temperature fields in an artificial park plantation are characterized by spatial patterns of a complex nature. The temperature field presents a spatial component that is invariant to time. It is most likely that the spatial variability of soil properties induced by natural factors and recreation are the cause of the generation of this pattern. Also in the soil temperature field there is a spatial pattern, which reflects the different sensitivity of the soil to the seasonal trend of temperature change. The generation of this pattern is due to the different insulating capacity of the forest litter in the park plantation. The results obtained point to the important role of leaf litter as a factor in the dynamics of the soil temperature regime. It is hypothesized that leaf litter in the park contributes to the enhancement of carbon sequestration during winter time.