Variability Of Soil Properties Research Articles

Novel method to estimate horizontal variability of shear wave velocity through multichannel analysis of surface waves

Scale of fluctuations (SOFs) of spatially variable soil properties have been regarded as one of the important parameters for performing reliability-based design in geotechnical engineering. However, the information required to estimate the SOFs in practice is limited, especially in the horizontal direction. In this study, the potential use of Multichannel Analysis of Surface Waves (MASW) to estimate the SOFs of shear wave velocity (VS) in horizontal direction is investigated through a series of numerical investigations. 2D random field models with a vertically increasing trend of VS were first simulated with different levels of variability controlled by the coefficient of variation (COV) and SOF. Afterwards, a large number of numerical MASW tests were performed using a 2D finite difference method, where the survey lines were progressively shifted. Results show that the COV of VS can be determined from the scatter of the dispersion curves, whereas the horizontal SOF of VS can be appropriately estimated from the phase velocity profile presented in the wavelength form. Additionally, in-situ MASW tests were conducted to estimate the horizontal SOF, and the obtained results align with those estimated by different methods. It is highlighted that the accuracy of the estimation depends on survey length. The interpretation using a long array reflects an averaged site condition of the survey area, thus losing the variability information. Favorable predictions are produced when survey length is shorter than 1.0 SOF. However, it should be noted that use of short survey length may limit the depth of investigation.

Torsional Vibration of Rock-Socketed Piles Considering the Bottom Sediment and Construction Disturbance

Based on the virtual soil pile model, this study investigates the torsional vibration of rock-socketed piles while considering the influence of bottom sediment. First, the bottom sediment is perceived as a virtual soil pile, however, its parameters are still determined according to the characteristics of the sediment. The pile, along with the virtual soil pile, is partitioned vertically into numerous segments in accordance to the stratified characteristics of the rock and soil around the pile. To account for the radial inhomogeneity arising from construction disturbance, the surrounding soil is divided into multiple annular zones characterized by progressively varied soil properties in the radial direction. The governing equations which delineate the soil-pile system under torsional dynamic loading are subsequently formulated and solved to yield an analytical solution for pile complex impedance. After ensuring reliability, the established analytical solution is employed to examine the coupling effect of bottom sediment parameters, pile parameters, and construction disturbance on the torsional vibration characteristics of rock-socketed piles.

Influence of Spatially Varied Soil Property on Train-Induced Soil-to-Column Vibration Transmission

The proximity of residential areas to traffic corridors has raised concerns among researchers regarding train-induced vibrations. Experimental studies have consistently shown variations in train-induced vibrations, which can be attributed to uncertainties and randomness in both the vibration source and propagation path. This study aims to investigate the influence factors and quantify their contributions to train-induced vibration variations through a comprehensive field measurement campaign and numerical simulations using random finite element models. During the field measurements, vibrations were recorded at the ground surface and the column base for a total of 53 passbys of the same type of metro train on parallel rail lines. Variations in train-induced vibrations caused by the source-to-receiver distance and spatially varied soil properties were analyzed and compared. Specifically, the investigation focused on the spatial variation of soil properties including elastic modulus, density and damping ratio in the topsoil and the second layer of clay, while simultaneously considering the influence of spatial variations of all three soil properties on the transmission of train-induced vibrations from the surface soil to the structural columns. Understanding these variations is crucial for the probabilistic assessment of building serviceability during train passbys. The findings of this research provide valuable insights for probabilistic prediction and evaluation of building vibration comfort.

Characterizing the spatial variability of marine soil properties with site-specific sparse data using a Bayesian data fusion approach

Characterizing the spatial variability of marine soil properties with site-specific sparse data using a Bayesian data fusion approach

Validation of a new road and contact model for vehicle–soft soil terrain interaction through an elaborate modelling of the FED-Alpha vehicle

Employing multibody dynamic simulations with semi-empirical tyre models is widely recognised as a cost-effective approach. A recent development introduces a novel road and tyre–soil contact model that is not only swift and memory-efficient but also addresses limitations in classical semi-empirical models. This study conducts a thorough validation of the new road and contact model by creating a detailed multibody model of the four-wheeled vehicle, Fuel Efficiency Demonstrator – Alpha, integral to NATO’s Next-Generation reference mobility model. The comprehensive model encompasses the chassis, suspension, tyres, engine, transmission and various other components. Through simulations of various driving scenarios, accounting for complex terrain geometries, spatially varying soil properties and multi-pass phenomena, the model’s performance is evaluated. The simulation results are compared with physical measurements, providing a detailed assessment of the tyre–soil model’s predictive accuracy for wheeled vehicle mobility on deformable terrains.

An optimal global biochar application strategy based on matching biochar and soil properties to reduce global cropland greenhouse gas emissions: findings from a global meta-analysis and density functional theory calculation

Biochar has been extensively utilized to amend soil and mitigate greenhouse gas (GHG) emissions from croplands. However, the effectiveness of biochar application in reducing cropland GHG emissions remains uncertain due to variations in soil properties and environmental conditions across regions. In this study, the impact of biochar surface functional groups on soil GHG emissions was investigated using molecular model calculation. Machine learning (ML) technology was applied to predict the responses of soil GHG emissions and crop yields under different biochar feedstocks and application rates, aiming to determine the optimum biochar application strategies based on specific soil properties and environmental conditions on a global scale. The findings suggest that the functional groups play an essential role in determining biochar surface activity and the soil’s capacity for adsorbing GHGs. ML was an effective method in predicting the changes in soil GHG emissions and crop yield following biochar application. Moreover, poor-fertility soils exhibited greater changes in GHG emissions compared to fertile soil. Implementing an optimized global strategy for biochar application may result in a substantial reduction of 684.25 Tg year−1 CO2 equivalent (equivalent to 7.87% of global cropland GHG emissions) while simultaneously improving crop yields. This study improves our understanding of the interaction between biochar surface properties and soil GHG, confirming the potential of global biochar application strategies in mitigating cropland GHG emissions and addressing global climate degradation. Further research efforts are required to optimize such strategies.Graphical

Stability and failure probability analysis of super-large irregularly shaped deep excavation in coastal area considering spatial variability in soil properties

ABSTRACT The objective of this paper is to investigate the effects of spatial variability in coastal silty soil properties on the stability and failure probability of deep excavation. A super-large irregularly shaped deep excavation in the coastal area in Wenzhou, China, is considered. A numerical model is established based on the random field theory, finite difference method and Monte-Carlo simulation method. Focusing on the friction angle and cohesion, the effects of horizontal and vertical correlation distances and variation coefficients on excavation stability are systematically studied. Meanwhile, the reliability evaluation approach is also applied to systematically examine the impact of the correlation distance on the surface settlement and horizontal displacement of pile. The results show that the greater correlation distance leads to a larger dispersion degree of the ground settlement curve and the displacement curve of the diaphragm wall around. The influence of the horizontal correlation distance on land settlement is more significant. The horizontal and vertical displacements of the pile top are less affected by the correlation distance. Compared with the deterministic analysis, the random analysis results considering the soil spatial variability produce larger displacement.

Topography‐driven variability in soil greenhouse gas emissions during potato growth season

AbstractTopographical variations strongly influence the spatial variability of soil physicochemical properties by affecting water retention, nutrient distribution and biochemical activity. These topography‐driven differences in soil dynamics can significantly impact greenhouse gas (GHG) emissions. Understanding the variation in GHG emissions over the growing season across topographic changes can facilitate the development of targeted precision agriculture strategies to mitigate GHG emissions. The objectives of this study were to evaluate the influence of topographical variations on soil properties and to assess the spatiotemporal variations of CO2 and N2O emissions throughout the various crop‐growing stages (CGS) of the potato growing season. Moreover, the impact of topography on potato yield was also examined. The experiment was conducted at Victoria Potato Farm, Prince Edward Island, Canada. A substantial N2O flux (80 g ha−1 day−1) was emitted after fertilizer application over the early CGS, and the upper positions had the highest cumulative N2O emissions (993 g ha−1), which aligned with the higher observed soil moisture in this zone. This finding highlights the critical importance of managing fertilizer application, as well as implementing mitigation strategies based on the spatial variability of soil properties to reduce emissions following fertilization. During the mid and late CGS, the depressional positions showed the highest cumulative N2O emissions (90 and 70 g ha−1, respectively). The highest cumulative CO2 emission was observed from the upper positions during the early CGS (1580 kg ha−1); however, the highest emissions were observed in the depressional areas during the mid and late CGS (1415 and 605 kg ha−1, respectively). Overall, the total N2O emission from the three zones accounting for both the differences in each zone's GHG fluxes and the length of each CGS indicated 43% emission in the upper areas, 32% and 25% for the depressional and mid‐slope positions, respectively. These values were 32%, 36% and 32% for CO2 in the upper, depressional and mid‐slope positions. This emission pattern aligns with the elevated soil‐activated carbon (AC), biological nitrogen availability (BNA) values and soil respiration rates in upper and depressional areas. In this study, significantly higher yields were also observed in depressional areas.

Brownfield Topsoil Vertical Heterogeneity: Implications for Germination and Soil Microbial Functioning.

Soil vertical heterogeneity refers to the variation in soil properties and composition with depth. In uncontaminated soils, properties including the organic matter content and nutrient concentrations typically change gradually with depth due to natural processes such as weathering, leaching, and organic matter decomposition. In contaminated soils, heavy metals and organic contaminants can migrate vertically through leaching or root uptake and translocation by plants and macrobiota, if present, leading to vertical heterogeneity in contaminant concentrations at different depths. Contaminants can alter soil properties, and we investigated the implications of soil vertical heterogeneity for germination and microbial functioning. We collected soil from an urban brownfield and created two conditions: structured soil samples collected with the soil core intact and mixed (unstructured) samples. When planted, the germination rate was significantly lower in the structured conditions (3.1 ± 1.7%) compared to mixed soils (17 ± 4.6%), suggesting that the vertical heterogeneity of contaminated soil influenced plant germination. To map the vertical distribution of contaminants and nutrient cycling rates in the structured soil samples, we collected 10 cm-deep soil cores from the barren site and a neighboring vegetated reference site and measured heavy metal concentrations, soil enzyme activities, and organic matter content in five 2 cm vertical layers. In the barren soil cores, metals were found concentrated in the top 2 cm layer, while in the vegetated soil cores, metals were uniformly distributed. No significant differences were observed for the organic matter content or moisture along depth. Published studies on vertical distribution of enzyme activities and metal concentrations have treated the top 10-20 cm as a single layer and thus would have not revealed the thin (<2 cm thick) metal cap on the surface of the barren soil core. Despite the metal cap, enzyme activities in the top layer were similar to those in the lower layers of the barren soil core, suggesting that high metal concentrations do not limit soil enzyme activity under all circumstances. Investigating vertical heterogeneity in postindustrial soils can inform efforts to convert industrial barrens to vegetated environments.

Examining intermediate soil properties variability through spatial interpolation methods in GIS

This study compares Kriging and Inverse Distance Weighting (IDW) spatial interpolation methods for estimating intermediate soil properties at a construction site in Astana, Kazakhstan. Using data from eight boreholes, seven engineering geological elements (EGE) were identified and analyzed at 6.5 m and 11.5 m depths. Kriging produced deformation modulus values ranging from -0.29 to 18.99 MPa at 6.5 m and -0.51 to 23.94 MPa at 11.5 m, capturing more spatial variability compared to IDW, which provided ranges of 3.3 to 18.99 MPa and 2.6 to 23.99 MPa, respectively. Kriging’s ability to account for spatial correlations resulted in more accurate predictions, particularly in areas with complex subsurface variability. Meanwhile, IDW offered reliable localized results, effective in more uniform geological conditions. The findings demonstrate that both methods are valuable for geotechnical applications, with the choice depending on data density and site variability.

Consideration of Different Soil Properties and Roughness in Shear Characteristics of Concrete–Soil Interface

To investigate the impact of diverse soil characteristics and surface irregularities on interfacial shear strength attributes, a large-scale straight shear apparatus and particle flow software were employed to conduct interfacial shear experiments with varying soil properties and surface irregularities. The results demonstrated that, under an identical R and normal stress conditions, the clay and silty clay shear stress–displacement curves exhibited strain softening, while the silt curve exhibited strain hardening. An increase in R can markedly enhance the peak shear strength at the interface, although a critical value exists beyond which this effect is no longer observed. The Rc is primarily contingent upon the soil properties. Numerical simulations demonstrate that the internal shear displacement and deformation resulting from the diverse soil properties are distinct. Clay particles are constituted of varying-sized particle aggregates that collectively resist shear. Silt particles resist shear through interfacial friction generated by shear. The practicality of Duncan and Clough’s constitutive model for interfacial shear with roughness influence is verified, and the constitutive model under strain hardening is modified.

Interpreting the spatial distribution of soil properties with a physically-based distributed hydrological model

Digital soil maps are commonly data-driven as the development of physically-based models for soil mapping is difficult due to the complexity of soils. However, physically-based hydrologic models have been successful in simulating water dynamics. Since water movement is a major driver of pedogenesis, the physical rules that govern water movement might help explain and predict the spatial variation of soil properties. Here, we demonstrate the novel use of a physically-based, distributed hydrologic model to inform the spatial distribution of soil properties. The Distributed Hydrology Soil Vegetation Model (DHSVM) was utilized to simulate soil moisture content (SM) and water table depth (WTD) in two hillslope catchments under pasture and forest management wherein hydrologic model outputs were then compared with soil properties measured in situ. SM sensors and wells were installed in both catchments to validate simulations of soil water movement via Nash-Sutcliffe Efficiency (E). In-situ observations were made at 87 sites within both catchments to study the connection between simulated water movement (SM and WTD) and observed soil properties, namely the depth and thickness of the argillic (Bt), fragic (Btx), and C horizons, and the depth of redoximorphic features. The simulated time series of SM and WTD were also clustered per season using Dynamic Time Warping (DTW), which identified similarity among time series at varying timescales. Model validation suggested that simulations of surficial SM (0–20 cm) were reasonable (E = 0.45), however, simulated subsurface SM (45–60 cm) and WTD were not sufficiently accurate. The thickness of Btx horizons were spatially grouped into different populations by SM clusters from every season except spring. For the other properties, only SM dynamics of specific seasons grouped into significantly different populations, suggesting that the explanatory power of simulated water movement varies seasonally and was greater during winter. Here, we show clusters of simulated SM separated soil properties into statistically different populations, showing that hydrologic models could inform areas that followed different water dynamics related to pedogenic trajectories and related biogeochemical processes not necessarily simulated by the model. As such, physically-based modeling of water dynamics can, therefore, inform and advance digital soil mapping by linking water movement patterns stemming from hydrologic model outputs to spatial patterns of soil properties and pedogenesis.

Intercropping in Coconut Plantations Regulate Soil Characteristics by Microbial Communities

Intercropping is a commonly employed agricultural technique that offers numerous advantages, such as increasing land productivity, enhancing soil health, and controlling soil-borne pathogens. In this study, Artemisia argyi, Dioscorea esculenta, and Arachis pintoi were intercropped with coconuts and compared with naturally growing weeds (Bidens pilosa), respectively. The regulatory mechanism of intercropping was examined by analyzing the variability in soil properties and microbial community structure across different intercropping modes and soil depths (0–20 cm, 20–40 cm, and 40–60 cm). The results indicate that intercropping can increase the diversity of soil bacteria and fungi. Moreover, as soil depth increases, the changes in microbial communities weaken. Intercropping reduced soil SOM and increased pH, which is directly related to the changes in the abundance of Acidobacteria in the soil. In various intercropping systems, the disparities resulting from intercropping with A. pintoi are particularly pronounced. Specifically, intercropping with A. pintoi leads to an increase in soil potassium and phosphorus levels, as well as an enhancement in the abundance of Bacillus sp., which plays a crucial role in the suppression of plant pathogenic fungi within the soil ecosystem. The results of the correlation analysis and structural equation modeling (SEM) suggest that the impacts of three intercropping systems on microbial composition and soil indicators exhibit considerable variation. However, a common critical factor influencing these effects is the soil phosphorus content. Furthermore, our findings indicate that intercropping resulted in lower soil nitrogen levels, exacerbating nitrogen deficiency and masking its impact on the microbial community composition.

Data Mining Virtual Contaminated Sites to Develop an Educational Learning Tool supported by a k-NN Machine Learning Predictive Algorithm for use in Environmental Engineering Education

Recent work by Mumford et al. used high-resolution numerical simulations of contaminated sites to evaluate the state of practice for contaminated site investigation1. These simulations were originally developed in collaboration with academic and industry partners, including the U.S. Department of Defense's Environmental Security Technology Certification Program (ESTCP), as a training tool for environmental monitoring and performance optimization. This project focuses on the development of an algorithm to determine available borehole information in those simulations based on user-input, to help leverage this work to create an educational tool. The algorithm begins by validating user-specified coordinates to ensure they fall within an acceptable range. Leveraging principles from linear algebra and a grid-based mapping system, it identifies the nearest existing borehole on a predefined coordinate grid. To optimize efficiency, the algorithm utilizes a 2-D array to store and retrieve coordinate data. It calculates distances from the input coordinates to all available borehole locations on the grid, to identify the shortest distance and select the closest borehole, enabling students to map contamination within virtual sites. In addition to boreholes, the application incorporates data for Membrane Interface Probes (MIPs), monitoring wells, groundwater samples, and soil samples that are used to investigate contaminated sites, providing a comprehensive tool for training environmental professionals. The k-nearest neighbors (k-NN) algorithm predicts Membrane Interface Probe (MIP) Photoionization Detector (ECD) readings at future locations, which indicate chlorinated contaminant levels. Two models were evaluated: one using spatial data and ECD values, and another incorporating electrical conductivity (EC) and hydraulic conductivity (K), which are also measured by MIP. By making use of additional parameters, variations in soil properties were better represented, leading to more accurate predictions. Through iterative testing and refinement, the tool’s accuracy and user-interface have been enhanced, ensuring robust reliability for classroom applications. These tools are slated for integration into 4th-year subsurface contamination courses at Queen's University, Carleton, and the University of Iowa. By automating borehole selection, instructors can interact more with students and dedicate less time to data processing tasks. The k-NN application will enable the use of machine learning for site assessment to optimize MIP instrument placement to reduce bias. Assistance from Cole Van De Ven (Carleton University) and Jessica Meyer (University of Iowa) in collecting virtual contaminated site data is gratefully acknowledged.

Effects of different soil and water conservation measures on plant functional traits in the Loess Plateau.

Soil and water conservation measures (SWCM) have wide-ranging effects on vegetation and soil, and their effects on the ecosystem are multifaceted, with complex mechanisms. While numerous studies have focused on the impact of such measures on soil, the improvement of plant functional traits is a major factor in the ecological recovery of the Loess Plateau. This survey extensively investigated no measure plots, vegetation measure plots, and engineering measure plots in the Loess Plateau. The impact of SWCM on plant functional traits was investigated using structural equation modeling. We examined six plant functional traits-leaf dry weight (LD), specific leaf area (SLA), leaf tissue density (LTD), leaf total phosphorus (LTP), leaf total nitrogen (LTN), and leaf volume (LV)-correlated with resource acquisition and allocation. In 122 plots, we explored the effects of measures, soil, diversity, and community structure on the weighted average of plant functional traits. The findings showed substantial positive correlations between LD and SLA, LD and LV, SLA and LV, SLA and LTP, and LTP and LTN. LTD has a substantial negative correlation with LD, LTD with SLA, and LTD with LV. SWCM limits diversity, and the mechanisms by which it affects plant functional traits vary. In the structural equation model (SEM) of vegetation measures, improving community structure enhances plant functional traits, but soil factors have the greatest influence on plant functional traits in SEM engineering measures. Plant functional trait differences on the Loess Plateau result are due to differential plant responses to diverse soil properties and community structure. Vegetation measures enhance the chemical properties of plant functional traits, while engineering measures improve physical properties. The study provides a theoretical foundation for vegetation restoration and management following the implementation of diverse SWCM.

Soil Physical and Chemical Properties under Shea Tree (Vitellaria paradoxa) at Different Stages of Growth

Shea tree (Vitellaria paradoxa), is one of the dominant agro-forestry species in Otuke district of Northern Uganda. Due to its economic importance and, in line with the numerous threats the tree is faced with, there is an urgent need for measures to conserve this species, for example, through incorporating annual food crops in the Shea tree parkland. This, however, requires a better understanding of tree-soil-food crop interactions. A number of studies of this aspect either considered only the mature Shea tree gardens or did not provide a clear distinction between the physiological states of the Shea tree. This was the motivation for this study where we compare variation in soil properties under mature and young Shea tree gardens with sites not having trees in Okwang sub-county, Otuke district. Five soil samples (up to 15 cm deep for top soil and 15-30 cm for sub-soil) were obtained per treatment using a soil auger. Our results show that in the top soil, only percent sand varied among the treatments, while, in the sub-soil, only percentage nitrogen and average phosphorus varied among the treatments. We also found that percentage top soil organic matter and percentage of sub-soil sand had negative strong correlations with maize and soybean yields, while percentage sub-soil clay had a strong positive correlation with maize and soybean yield. We conclude that variations in soil physical and chemical properties under Mature and Young Shea gardens only occur for those properties that have a direct link to tree residues

Variability of soil properties and their implications for agricultural management in the central plain of Manipur, India

Variability of soil properties and their implications for agricultural management in the central plain of Manipur, India

Optimized irrigation and fertilization can mitigate negative CO2 impacts on seed yield and vigor of hybrid maize

Seed yield and vigor of hybrid maize determine the planting, yield, and quality of maize, and consequently affect food, nutrition, and livelihood security; however, the response of seed yield and vigor to climate change is still unclear. We established an optimization-simulation framework consisting of a water‑nitrogen crop production function, a seed vigor and a gridded process-based model to optimize irrigation and nitrogen fertilization management, and used it to evaluate seed yield and vigor in major seed production locations of China, the USA, and Mexico. This framework could reflect the influence of water and nitrogen inputs at different stages on seed yield and vigor considering the spatio-temporal variability of climate and soil properties. Projected seed yield and vigor decreased by 5.8–9.0 % without adaptation by the 2050s, due to the 1.3–5.8 % decrease in seed number and seed protein concentration. Seed yield was positively correlated with CO2 and negatively correlated with temperature, while seed vigor depended on the response of components of seed vigor to climatic factors. Under optimized management, the direct positive effects of temperature on seed protein concentration and CO2 on seed number were strengthened, and the direct negative effects of temperature on seed number and CO2 on seed protein concentration were weakened, which mitigated the reductions in both seed yield and vigor. Elevated CO2 was projected to exacerbate the 2.6 % seed vigor reduction and mitigate the 2.9 % seed yield loss without adaptation, while optimized management could increase seed yield by 4.1 % and mitigate the 2.2 % seed vigor reduction in the Hexi Corridor of China, and decrease the seed yield and vigor reduction by 2.4–5.8 % in the USA and Mexico. Optimized management can strengthen the positive and mitigate the negative effects of climate change on irrigated hybrid maize and inform high-yield and high-quality seed production globally.

Post-fire recovery of nematode communities along a slope gradient in a pine forest.

We investigated the enduring consequences of a wildfire on nematode diversity and abundance in a pine forest, employing a slope gradient approach. Our primary objective was to determine the extent of post-fire alterations in the nematode community 3years after the incident, to understand if the ecosystem has returned to its pre-fire state or has transitioned to a distinctive ecological environment. Three distinct burned pine forest sites at varying elevations were sampled to capture short-scale soil property variations due to slope gradients, while unburned forest sites served as controls. A consistent pattern emerged where the lowest altitude sites exhibited the highest nematode abundances, although still lower than unburned sites. Fire-induced changes were profound, shifting from fungivore dominance in unburned sites to bacterivore and herbivore dominance in burned sites. Alterations in soil properties post-fire, particularly reduced organic matter and nitrogen content, were closely associated with nematode community shifts. Water availability played a crucial role with lower moisture levels at higher elevations impacting nematode populations. Structural differences in the nematode community primarily resulted from fire disturbance rather than altitude. This study emphasizes the persistent and transformative impact of wildfire on nematode communities, highlighting the intricate interplay between ecological disturbances, soil properties, and nematode trophic dynamics.

Impacts of vertical variations in soil properties on H*(10) simulations for 137Cs deposition

Photon transport simulations based on the Monte Carlo method have played a crucial role in assessing and estimating the ambient dose equivalent rates H*(10), resulting from the deposition of 137Cs in soil following the nuclear power plant accident in Fukushima. However, a comprehensive examination of the effect of vertical variations in soil properties on the simulation outcomes has not yet been performed. Disregarding the vertical distribution of soil properties not only leads to potential inaccuracies in the shielding responses of soil layers but also in the determination of the radioactive source inventory, particularly when using the concentration data in Bq/kg. These oversights diminish the reliability of the simulation results. This study addresses several soil property factors that could potentially influence the simulation results, including variations in chemical composition induced by water content, bulk density profile, and estimated inventory profile, all evaluated through an examined simulation model. The results show that inappropriate assignment of the soil density profile can cause considerable errors in the H*(10) simulation outcomes. Furthermore, the sensitivity of H*(10) to variations in soil vertical density is analyzed, with the results indicating that H*(10) can be highly sensitive to changes in the bulk density of the top 0–5 cm soil layers. These results should facilitate the establishment of appropriate simulation strategies and support the reassessment of past simulation results.