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Featured Application: An alternative interpretation of Borehole Resistivity Logging surveys when conventional methods do not provide a satisfactory interpretation. In this paper, we present the application of a special soil resistivity model, the Exponentially Graded Multilayered Soil model, to the interpretation of Borehole Resistivity Logging surveys. The model, which was recently introduced by the authors, offers an alternative to the interpretation provided by constant resistivity layer models. The model is applied to synthetic soil surveys as well as to a real survey that is not satisfactorily interpreted using conventional models. This model aids in the design and calculation of grounding systems in electrical engineering, providing an alternative to calculations performed by traditional layered models. [ABSTRACT FROM AUTHOR]

In this paper, we present the application of a special soil resistivity model, the Exponentially Graded Multilayered Soil model, to the interpretation of Borehole Resistivity Logging surveys. The model, which was recently introduced by the authors, offers an alternative to the interpretation provided by constant resistivity layer models. The model is applied to synthetic soil surveys as well as to a real survey that is not satisfactorily interpreted using conventional models. This model aids in the design and calculation of grounding systems in electrical engineering, providing an alternative to calculations performed by traditional layered models.

Managed grasslands have the potential to store carbon (C) and partially mitigate climate change. However, it remains difficult to predict potential C storage under a given soil or management practice. To study C storage dynamics due to long-term (1952-2009) phosphorus (P) fertilizer and irrigation treatments in New Zealand grasslands, we measured radiocarbon (14C) in archived soil along with observed changes in C stocks to constrain a compartmental soil model. Productivity increases from P application and irrigation in these trials resulted in very similar C accumulation rates between 1959 and 2009. The ∆14C changes over the same time period were similar in plots that were both irrigated and fertilized, and only differed in a non-irrigated fertilized plot. Model results indicated that decomposition rates of fast cycling C (0.1 to 0.2 year-1) increased to nearly offset increases in inputs. With increasing P fertilization, decomposition rates also increased in the slow pool (0.005 to 0.008 year-1). Our findings show sustained, significant (i.e. greater than 4 per mille) increases in C stocks regardless of treatment or inputs. As the majority of fresh inputs remain in the soil for less than 10 years, these long term increases reflect dynamics of the slow pool. Additionally, frequent irrigation was associated with reduced stocks and increased decomposition of fresh plant material. Rates of C gain and decay highlight trade-offs between productivity, nutrient availability, and soil C sequestration as a climate change mitigation strategy.Supplementary informationThe online version contains supplementary material available at 10.1007/s10533-021-00838-z.

Several researchers have developed formulations to represent the soil electrical parameters (σg, εr) to compute ground-return parameters correctly and to obtain more accurate transient responses in power systems. This paper investigates the impact of the physical factors (frequency effect, water content, and porosity level) on σg, εr on transient responses in an overhead transmission line (TL) exposed to lightning strikes. For this purpose, four soil models are available in the literature to calculate σg, εr dependent on these physical factors. Transient voltages are calculated considering two approaches: Carson’s (C), which assumes a constant σg and disregards ground displacement currents, and Nakagawa’s (N), which includes the displacement currents with the σg, εr dependent on these physical factors. Results demonstrated that responses assessed with N differ significantly from those computed with C, being more pronounced for poorly-conducting soils. Further, including these physical factors is necessary as transient response waveforms are notably influenced when a realistic ground is assumed. These differences in the transient voltages may overestimate the insulation level of electrical components and equipment in electrical power systems.

A continuous loading experiment of a two-dimensional granular system was carried out and the experimental data were obtained by digital image correlation (DIC). The deformation field of the granular system and the changing laws of the deflection angle and coordination number of the granules on force chains with time were obtained. Based on the granule element method, the quantitative calculation of contact force was realized, and the internal force chains of the granular system were identified. The effects of contact force between granules and mechanical parameters on the evolution of force chains in a two-dimensional granular system under line loads were analyzed. The formation, evolution, and reconstruction of force chains in a granular system during loading, as well as the influence of the force chain network evolution on the macroscopic mechanical properties of granules were discussed. The experimental results indicated that the evolution of force chains was directly related to the number, geometric properties, and permutation distribution of granules in direct contact with the external load. [ABSTRACT FROM AUTHOR]

Climate change and unsustainable land management practices have resulted in extensive soil degradation, including alteration of soil structure (i.e., aggregate and pore size distributions), loss of soil organic carbon, and reduction of water and nutrient holding capacities. Although soil structure, hydrologic processes, and biogeochemical fluxes are tightly linked, their interaction is often unaccounted for in current ecohydrological, hydrological and terrestrial biosphere models. For more holistic predictions of soil hydrological and biogeochemical cycles, models need to incorporate soil structure and macroporosity dynamics, whether in a natural or agricultural ecosystem. Here, we present a theoretical framework that couples soil hydrologic processes and soil microbial activity to soil organic carbon dynamics through the dynamics of soil structure. In particular, we link the Millennial model for soil carbon dynamics, which explicitly models the formation and breakdown of soil aggregates, to a recent parameterization of the soil water retention and hydraulic conductivity curves and to solute and O2 diffusivities to soil microsites based on soil macroporosity. To illustrate the significance of incorporating the dynamics of soil structure, we apply the framework to a case study in which soil and vegetation recover over time from agricultural practices. The new framework enables more holistic predictions of the effects of climate change and land management practices on coupled soil hydrological and biogeochemical cycles. Plain Language Summary: Soil degradation due to climate change and unsustainable land management practices is a global phenomenon that threatens food security and Earth livability at large. While soil degradation involves modifications of both physical and biological properties of soils, mathematical models to predict these changes have focused independently on these two aspects, limiting our ability to holistically assess climate and human drivers of soil degradation. Here, we connected recent advances in modeling physical and biological soil processes to develop a unified framework that can account more holistically for potential changes in soil properties over time. The potential of this framework to predict soil changes is illustrated through an analysis of a case study of soil and vegetation recovery from agricultural practices. This work may represent an important step toward predicting the effects of land use and climate changes on soil degradation, hence enabling the design of more sustainable land management strategies. Key Points: A framework linking soil structure, carbon, and hydrology is needed for holistic predictions under environmental and land use changesThe novel Millennial model for soil carbon cycling is linked to a recent soil hydraulic parameterizationAggregated carbon is used as a proxy for soil macroporosity to simultaneously model the changes in soil properties and microbial activity [ABSTRACT FROM AUTHOR]

Developing and testing decadal-scale predictions of soil response to climate change is difficult because there are few long-term warming experiments or other direct observations of temperature response. As a result, spatial variation in temperature is often used to characterize the influence of temperature on soil organic carbon (SOC) stocks under current and warmer temperatures. This approach assumes that the decadal-scale response of SOC to warming is similar to the relationship between temperature and SOC stocks across sites that are at quasi steady state; however, this assumption is poorly tested. We developed four variants of a Reaction-network-based model of soil organic matter and microbes using measured SOC stocks from a 4,000-km latitudinal transect. Each variant reflects different assumptions about the temperature sensitivities of microbial activity and mineral sorption. All four model variants predicted the same response of SOC to temperature at steady state, but different projections of transient warming responses. The relative importance of Qmax, mean annual temperature, and net primary production, assessed using a machine-learning algorithm, changed depending on warming duration. When mineral sorption was temperature sensitive, the predicted average change in SOC after 100 years of 5 °C warming was −18% if warming decreased sorption or +9% if warming increased sorption. When microbial activity was temperature sensitive but mineral sorption was not, average site-level SOC loss was 5%. We conclude that spatial climate gradients of SOC stocks are insufficient to constrain the transient response; measurements that distinguish process controls and/or observations from long-term warming experiments, especially mineral fractions, are needed.

Adequate soil modeling is fundamental to assessing the grounding impedance of the grounding system and the transient responses in power systems. This paper aims to investigate three soil models with electrical parameters varying with frequency and water content, namely Smith-Longmire, Scott, and Messier models, and their impact on the ground potential rise (GPR) waveforms of vertical grounding rods. Besides that, the transient induced voltages across the string of insulators induced are also investigated for lightning currents representative first, and subsequent return strokes are employed. The harmonic grounding impedance (HGI) is calculated for rods of 3, 15, and 30 m buried in frequency-dependent (FD) soil model using commercial electromagnetic software (FEKO) with the Method of Moments (MoM) for a range of 100 Hz to 10 MHz. Simulation results show a significant modification of the calculated HGI beyond a particular frequency. The induced transient GPR waveforms show a notable reduction in their voltage peaks when the FD soil model is compared with those computed using the frequency-independent (FI) soil model. Finally, a backflashover analysis is carried out that demonstrates its dependence on the rod length, water content, and the specific soil model employed.

Many numerical simulation methods have been developed in the current research field. The authenticity of the simulation-based research depends on the accuracy of the model prepared. The modeling of the soil becomes challenging when there is the presence of water and the soil starts behaving like a fluid. This study focuses on the application of the MPS (moving particle semi-implicit) - CAE (computer-aided engineering) method to design the setup for the unconfined compression test to determine the correct parameters of the soil model. The soil material is assumed to be Bingham fluid and will be employed as a biviscosity model, where the soil particle exists at both rigid and fluid states depending upon the condition. The best-fitting soil model was designed for the primary parameters and is compared with the benchmark soil data to justify the claim. Furthermore, the authors endeavored to find the secondary governing factors that will affect the accuracy of the soil modeling. The results show that the values of the primary parameters are influenced by the values of the secondary parameters, resulting in a different model. The influence of the secondary parameters was studied, and the best-fitting soil model was designed by incorporating both primary and secondary parameters. The resultant soil model is expected to provide more accurate results if used as a reference in various construction simulations. [ABSTRACT FROM AUTHOR]

This study was conducted to examine the capability of topographic features and remote sensing data in combination with other auxiliary environmental variables (geology and geomorphology) to predict CEC by using different machine learning models ((random forest (RF), k-nearest neighbors (kNNs), Cubist model (Cu), and support vector machines (SVMs)) in the west of Iran. Accordingly, the collection of ninety-seven soil samples was performed from the surface layer (0–20 cm), and a number of soil properties and X-ray analyses, as well as CEC, were determined in the laboratory. The X-ray analysis showed that the clay types as the main dominant factor on CEC varied from illite to smectite. The results of modeling also displayed that in the training dataset based on 10-fold cross-validation, RF was identified as the best model for predicting CEC (R2 = 0.86; root mean square error: RMSE = 2.76; ratio of performance to deviation: RPD = 2.67), whereas the Cu model outperformed in the validation dataset (R2 = 0.49; RMSE = 4.51; RPD = 1.43)). RF, the best and most accurate model, was thus used to prepare the CEC map. The results confirm higher CEC in the early Quaternary deposits along with higher soil development and enrichment with smectite and vermiculite. On the other hand, lower CEC was observed in mountainous and coarse-textured soils (silt loam and sandy loam). The important variable analysis also showed that some topographic attributes (valley depth, elevation, slope, terrain ruggedness index—TRI) and remotely sensed data (ferric oxides, normalized difference moisture index—NDMI, and salinity index) could be considered as the most imperative variables explaining the variability of CEC by the best model in the study area. [ABSTRACT FROM AUTHOR]

Soil nitrogen (N) is an important driver of plant productivity and ecosystem functioning; consequently, it is critical to understand its spatial variability from local‐to‐global scales. Here, we provide a quantitative assessment of the three‐dimensional spatial distribution of soil N across the United States (CONUS) using a digital soil mapping approach. We used a random forest‐regression kriging algorithm to predict soil N concentrations and associated uncertainty across six soil depths (0–5, 5–15, 15–30, 30–60, 60–100, and 100–200 cm) at 5‐km spatial grids. Across CONUS, there is a strong spatial dependence of soil N, where soil N concentrations decrease but uncertainty increases with soil depth. Soil N was higher in Pacific Northwest, Northeast, and Great Lakes National Ecological Observatory Network (NEON) ecoclimatic domains. Model uncertainty was higher in Atlantic Neotropical, Southern Rockies/Colorado Plateau, and Southeast NEON domains. We also compared our soil N predictions with satellite‐derived gross primary production and forest biomass from the National Biomass and Carbon Dataset. Finally, we used uncertainty information to propose optimized locations for designing future soil surveys and found that the Atlantic Neotropical, Pacific Northwest, Pacific Southwest, and Appalachian/Cumberland Plateau NEON domains may require larger survey efforts. We highlight the need to increase knowledge of biophysical factors regulating soil processes at deeper depths to better characterize the three‐dimensional space of soils. Our results provide a national benchmark regarding the spatial variability and uncertainty of soil N and reveal areas in need of a better representation. [ABSTRACT FROM AUTHOR]

Abstract Soil nitrogen (N) is an important driver of plant productivity and ecosystem functioning; consequently, it is critical to understand its spatial variability from local‐to‐global scales. Here, we provide a quantitative assessment of the three‐dimensional spatial distribution of soil N across the United States (CONUS) using a digital soil mapping approach. We used a random forest‐regression kriging algorithm to predict soil N concentrations and associated uncertainty across six soil depths (0–5, 5–15, 15–30, 30–60, 60–100, and 100–200 cm) at 5‐km spatial grids. Across CONUS, there is a strong spatial dependence of soil N, where soil N concentrations decrease but uncertainty increases with soil depth. Soil N was higher in Pacific Northwest, Northeast, and Great Lakes National Ecological Observatory Network (NEON) ecoclimatic domains. Model uncertainty was higher in Atlantic Neotropical, Southern Rockies/Colorado Plateau, and Southeast NEON domains. We also compared our soil N predictions with satellite‐derived gross primary production and forest biomass from the National Biomass and Carbon Dataset. Finally, we used uncertainty information to propose optimized locations for designing future soil surveys and found that the Atlantic Neotropical, Pacific Northwest, Pacific Southwest, and Appalachian/Cumberland Plateau NEON domains may require larger survey efforts. We highlight the need to increase knowledge of biophysical factors regulating soil processes at deeper depths to better characterize the three‐dimensional space of soils. Our results provide a national benchmark regarding the spatial variability and uncertainty of soil N and reveal areas in need of a better representation.

It is known that, when dealing with 50–60 Hz electromagnetic interference problems between power/railway lines and metallic pipelines/telecommunication cables, an important role is played by the soil modeling. To this aim, this paper presents a novel algorithm devoted to the construction of an equivalent two-layer soil model starting from the combined use of data available from different kinds of earth conductivity maps: on one side, the ones related to the geological characteristics relevant to the area of interest and, on the other side, the ones based on radio electric propagation measurements. From these data and by means of a suitable algorithm, the parameters characterizing the equivalent two-layer soil model are obtained. [ABSTRACT FROM AUTHOR]

This study was conducted to examine the capability of topographic features and remote sensing data in combination with other auxiliary environmental variables (geology and geomorphology) to predict CEC by using different machine learning models ((random forest (RF), k-nearest neighbors (kNNs), Cubist model (Cu), and support vector machines (SVMs)) in the west of Iran. Accordingly, the collection of ninety-seven soil samples was performed from the surface layer (0–20 cm), and a number of soil properties and X-ray analyses, as well as CEC, were determined in the laboratory. The X-ray analysis showed that the clay types as the main dominant factor on CEC varied from illite to smectite. The results of modeling also displayed that in the training dataset based on 10-fold cross-validation, RF was identified as the best model for predicting CEC (R2 = 0.86; root mean square error: RMSE = 2.76; ratio of performance to deviation: RPD = 2.67), whereas the Cu model outperformed in the validation dataset (R2 = 0.49; RMSE = 4.51; RPD = 1.43)). RF, the best and most accurate model, was thus used to prepare the CEC map. The results confirm higher CEC in the early Quaternary deposits along with higher soil development and enrichment with smectite and vermiculite. On the other hand, lower CEC was observed in mountainous and coarse-textured soils (silt loam and sandy loam). The important variable analysis also showed that some topographic attributes (valley depth, elevation, slope, terrain ruggedness index—TRI) and remotely sensed data (ferric oxides, normalized difference moisture index—NDMI, and salinity index) could be considered as the most imperative variables explaining the variability of CEC by the best model in the study area.

Transdisciplinary approaches that provide holistic views are essential to properly understand soil processes and the importance of soil to society and will be crucial in the future to integrate distinct disciplines into soil studies. A myriad of challenges faces soil science at the beginning of the 2020s. The main aim of this overview is to assess past achievements and current challenges regarding soil threats such as erosion and soil contamination related to different United Nations sustainable development goals (SDGs) including (1) sustainable food production, (2) ensure healthy lives and reduce environmental risks (SDG3), (3) ensure water availability (SDG6), and (4) enhanced soil carbon sequestration because of climate change (SDG13). Twenty experts from different disciplines related to soil sciences offer perspectives on important research directions. Special attention must be paid to some concerns such as (1) effective soil conservation strategies; (2) new computational technologies, models, and in situ measurements that will bring new insights to in-soil process at spatiotemporal scales, their relationships, dynamics, and thresholds; (3) impacts of human activities, wildfires, and climate change on soil microorganisms and thereby on biogeochemical cycles and water relationships; (4) microplastics as a new potential pollutant; (5) the development of green technologies for soil rehabilitation; and (6) the reduction of greenhouse gas emissions by simultaneous soil carbon sequestration and reduction in nitrous oxide emission. Manuscripts on topics such as these are particularly welcomed in Air, Soil and Water Research. [ABSTRACT FROM AUTHOR]

The objective of this paper is to numerically study the behavior pipe pile under axial compression embedded in organic soil has been numerically predicted. The pipe pile used in the study has been produced by steel and it has outer and inner diameters of 20 mm and 15 mm, respectively. The pile embedded in organic soil, which has the pile length ratios of 10, 20 and 30 (L/D), has been exposed to the axial load for different diameter ratios (d/D = 0, 0.25, 0.50 and 0.75). Numerical analyses have been performed by using Plaxis 3D computer program which is based on finite element method. The capability of the numerical analysis in the prediction of the load capacity of pipe pile has been studied. It has been understood that the results obtained from numerical analysis and experiment are in a good agreement, and then it has been observed in the parametric study that the load capacity of single pipe pile increases with the increase of the pile length and the wall thickness. [ABSTRACT FROM AUTHOR]

Microbial‐derived nitrogen (N) is now recognized as an important source of soil organic N. However, the mechanisms that govern the production of microbial necromass N, its turnover, and stabilization in soil remain poorly understood. To assess the effects of elevated temperature on bacterial and fungal necromass N production, turnover, and stabilization, we incubated 15N‐labeled bacterial and fungal necromass under optimum moisture conditions at 10°C, 15°C, and 25°C. We developed a new 15N tracing model to calculate the production and mineralization rates of necromass N. Our results showed that bacterial and fungal necromass N had similar mineralization rates, despite their contrasting chemistry. Most bacterial and fungal necromass 15N was recovered in the mineral‐associated organic matter fraction through microbial anabolism, suggesting that mineral association plays an important role in stabilizing necromass N in soil, independently of necromass chemistry. Elevated temperature significantly increased the accumulation of necromass N in soil, due to the relatively higher microbial turnover and production of necromass N with increasing temperature than the increases in microbial necromass N mineralization. In conclusion, we found elevated temperature may increase the contribution of microbial necromass N to mineral‐stabilized soil organic N. [ABSTRACT FROM AUTHOR]

Since 2003, the regional long-term soil monitoring network (SMN) Schleswig–Holstein (SH) includes an intensive monitoring program (I-BDF) with (sub-)annual measurements at four sites. This is the first study investigating the benefits of this SMN where study sites are no experiments but managed by independent farmers. The main objective of this study was to investigate whether, and under which circumstances, annual soil carbon and nutrient measurements are more beneficial within a soil monitoring network than common five- to ten-year measurements using modeling and nutrient balances. Soil measurements (stocks of soil organic carbon (SOC), Ntot, P and Mg), weekly leachate-NO3–N and management data were used for comparison. C and N changes were modeled with DNDC (DeNitrification–DeComposition); P and Mg were calculated as full nutrient balances and compared to the observations using performance metrics. The results show that DNDC could reproduce the long-term trend of SOC and Ntot well, but this could also be by coincidence as the type of trendline depended on the starting year. The model results could not depict measured short-term variations in soil which were due to field heterogeneities caused by farm management. NO3-N leaching was strongly overestimated when organic fertilization and stronger rainfall occurred. Comparing stock changes with nutrient balances revealed that, in several cases, long-term trends could be shown to a limited extent and reproduced only very few short-term changes and variations. The results suggest that only annual soil property measurements can depict the soil's variability and contribute to the identification of the true long-term trend. [ABSTRACT FROM AUTHOR]

For food security issues or global climate change, there is a growing need for large-scale knowledge of soil organic carbon (SOC) contents in agricultural soils. To capture and quantify SOC contents at a field scale, Earth Observation (EO) can be a valuable data source for area-wide mapping. The extraction of exposed soils from EO data is challenging due to temporal or permanent vegetation cover, the influence of soil moisture or the condition of the soil surface. Compositing techniques of multitemporal satellite images provide an alternative to retrieve exposed soils and to produce a data source. The repeatable soil composites, containing averaged exposed soil areas over several years, are relatively independent from seasonal soil moisture and surface conditions and provide a new EO-based data source that can be used to estimate SOC contents over large geographical areas with a high spatial resolution. Here, we applied the Soil Composite Mapping Processor (SCMaP) to the Landsat archive between 1984 and 2014 of images covering Bavaria, Germany. Compared to existing SOC modeling approaches based on single scenes, the 30-year SCMaP soil reflectance composite (SRC) with a spatial resolution of 30 m is used. The SRC spectral information is correlated with point soil data using different machine learning algorithms to estimate the SOC contents in cropland topsoils of Bavaria. We developed a pre-processing technique to address the issue of combining point information with EO pixels for the purpose of modeling. We applied different modeling methods often used in EO soil studies to choose the best SOC prediction model. Based on the model accuracies and performances, the Random Forest (RF) showed the best capabilities to predict the SOC contents in Bavaria (R² = 0.67, RMSE = 1.24%, RPD = 1.77, CCC = 0.78). We further validated the model results with an independent dataset. The comparison between the measured and predicted SOC contents showed a mean difference of 0.11% SOC using the best RF model. The SCMaP SRC is a promising approach to predict the spatial SOC distribution over large geographical extents with a high spatial resolution (30 m).

Land application of domestic and food processing wastewater is used due to its low cost, energy use, and maintenance. Design procedures are generally based on empirical relationships that may not account for critical site and waste-specific conditions. A mathematical model was utilized to simulate the complexity of wastewater land application. Multiple scenarios were run to determine system performance as measured by chemical oxygen demand (COD) and the nitrification/denitrification process. The modeling results showed that COD and nitrification occurred within the first 15.4 cm of a sandy loam soil. Increasing the dosing frequency slightly reduced the COD effluent concentration. Complete denitrification does not occur in a typical land application wastewater treatment system. In a domestic wastewater land application system, up to 32% of nitrate can be removed by increasing the dosing frequency and providing more organic carbon. In a food processing wastewater land application system, up to 56% of nitrate can be removed by increasing the dosing frequency and hydraulic and organic loadings. HYDRUS CW2D modeling is a valuable design tool to simulate multiple operation strategies and predict carbon degradation, nitrification, and denitrification. The model result can provide operational strategies to maximize the treatment while minimizing environmental impacts. [ABSTRACT FROM AUTHOR]

The soil models are crucial for calculating the performances of the grounding systems in electrical engineering. Usually, some approximations and simplified calculation procedures are used in engineering practice. These procedures are based on the assumption of the uniform soil model. However, real soil consists of more layers with different resistivities/conductances and thickness. The procedure of obtaining equivalent uniform and two-layer soil models using an optimization approach and numerical simulation is presented here. The grounding system used in the simulations is the single grounding rod as one of the simplest grounding structures. The proposed procedure does not require approximations and neglecting during calculation of the grounding system performances due to using the simulation tool based on numerical method. The equivalent soil model determination through the optimization is performed by using the co-simulation setup between the metaheuristic optimization and numerical simulation tools. [ABSTRACT FROM AUTHOR]

Highlights • Soil parameters required for empirical off-road vehicle mobility models are documented. • Review of pressure-sinkage models for zero slip. • Review of pressure-sinkage models for non-zero slip. • Review of shear stress-displacement models. • Review of vibrational models to characterize soil compaction. Abstract ISTVS embarked on a project in 2016 that aims at updating the current ISTVS standards related to nomenclature, definitions, and measurement techniques for modelling, parameterizing, and, respectively, testing and validation of soft soil parameters and vehicle running gear-terrain interaction. As part of this project, a comprehensive literature review was conducted on the parameterization of fundamental terramechanics models. Soil parameters of the empirical models to assess off-road vehicle mobility, and parameters of the models to characterize the response of the terrain interacting with running gears or plates from the existing terramechanics literature and other researchers' reports were identified. This review documents and summarizes the modelling approaches that may be applicable to real-time applications of terramechanics in simulation, as well as in controller design. [ABSTRACT FROM AUTHOR]

This paper studies the voltages developed on a wind turbine (WT) and a medium-voltage distribution line (MVDL) connected to a wind farm subjected to lightning strikes and located on frequency-dependent (FD) soils. The ground potential rise (GPR), voltages at the blade tip and on phase conductors of the MVDL are calculated using the full-wave electromagnetic software XGSLab® employing the rigorous Partial Element Equivalent Circuit (PEEC) method. The wind farm comprises four wind turbines with interconnected grounding systems using cables buried in resistivity soils of 1,000 and 5,000 Ω m. The voltages are computed for the first positive impulse (FPI) of 100-kA 10/ 350 μ s and for the subsequent negative impulse (SNI) of 50-kA, 1/ 200 μ s. Results have shown that voltage peaks increase notably as the soil resistivity increases. When the WTs are assumed, oscillations in the GPR waveforms for the SNI occur due to the multiple reflections between the blade and the turbine's base. However, the voltages for the FPI present smooth time-domain responses. Furthermore, the overvoltages developed at the MVDL are significantly dependent on the soil resistivity and lightning current waveform. [Display omitted] • Tall wind turbines, due to their height, are very vulnerable to lightning strikes. • Full-wave analysis of lightning strikes to wind farm is carried out in this paper. • A wind farm connected to a distribution line is investigated using full-wave XGSLab. • Oscillations occur in the GPR due to the several reflections along the wind turbine. • Overvoltages on distribution lines are dependent on ground and lightning current. [ABSTRACT FROM AUTHOR]

• Delta rice paddies are threatened by subsidence and rising sea levels. • Past sediment input into the Ebro Delta rice fields facilitated vertical accretion of the soil. • Soil accretion and SOC profile of paddy soils in the Ebro Delta have been modelled. • Sediment and crop residue input promotes vertical accretion and SOC accumulation. • Scenarios of sediment input to rice fields are simulated. Rice cultivation is popular in low-lying areas such as deltas, but climate change threatens the viability of the crop. In recent decades, the resilience of deltas to sea level rise (SLR) has been influenced by the reduction of sediment load from rivers due to the construction of dams, disrupting natural deposition in deltaic plains. Sediment and organic matter accumulation in wetlands are key to vertical accretion in the face of SLR and soil organic carbon (SOC) sequestration. In this sense, deltaic rice fields can retain sediments as well as wetlands and promote SOC sequestration, which is effective in adapting to SLR. In the Ebro Delta, the sediments that reached the fields through irrigation channels were used to build up and form rice fields in the wetlands of the area. We hypothesize that this sedimentation has been key to vertical accretion and SOC sequestration in rice fields. These processes were simulated by developing a process-based cohort model inspired by accretion in marsh equilibrium models (MEM). The model was able to simulate the soil carbon profile of rice fields in the Ebro Delta, based on the soil-accretion concept and considering the spatial heterogeneity of the area. Its predictions of vertical accretion and carbon content were more accurate for mineral and clay-like soils than for organic and sandy soils. Topsoil decomposition rate and organic matter content were the parameters that most influenced predictions of total vertical accretion and final soil organic carbon stock. Simulations were carried out according to future climate change scenarios, considering restoration of river sediment flux, to evaluate effects on SOC sequestration and vertical accretion in rice fields. Results showed that only with significant river sediment restoration did rice fields show positive vertical accretion, which facilitates SOC sequestration. [ABSTRACT FROM AUTHOR]

Recent modeling and comparison with field results showed that soil formation by chemical weathering, either from bedrock or unconsolidated material, is limited largely by solute transport. Chemical weathering rates are proportional to solute velocities. Nonreactive solute transport described by non-Gaussian transport theory appears compatible with soil formation rates. This change in understanding opens new possibilities for predicting soil production and depth across orders of magnitude of time scales. Percolation theory for modeling the evolution of soil depth and production was applied to new and published data for alpine and Mediterranean soils. The first goal was to check whether the empirical data conform to the theory. Secondly we analyzed discrepancies between theory and observation to find out if the theory is incomplete, if modifications of existing experimental procedures are needed and what parameters might be estimated improperly. Not all input parameters required for current theoretical formulations (particle size, erosion, and infiltration rates) are collected routinely in the field; thus, theory must address how to find these quantities from existing climate and soil data repositories, which implicitly introduces some uncertainties. Existing results for soil texture, typically reported at relevant field sites, had to be transformed to results for a median particle size, d50, a specific theoretical input parameter. The modeling tracked reasonably well the evolution of the alpine and Mediterranean soils. For the Alpine sites we found, however, that we consistently overestimated soil depths by ~45%. Particularly during early soil formation, chemical weathering is more severely limited by reaction kinetics than by solute transport. The kinetic limitation of mineral weathering can affect the system until 1 kyr to a maximum of 10 kyr of soil evolution. Thereafter, solute transport seems dominant. The trend and scatter of soil depth evolution is well captured, particularly for Mediterranean soils. We assume that some neglected processes, such as bioturbation, tree throw, and land use change contributed to local reorganization of the soil and thus to some differences to the model. Nonetheless, the model is able to generate soil depth and confirms decreasing production rates with age. A steady state for soils is not reached before about 100 kyr to 1 Myr

Abstract In this paper a new approach is presented for modeling the behavior of frozen soils. A data-mining technique, Evolutionary Polynomial Regression (EPR), is used for modeling the thermo-mechanical behavior of frozen soils including the effects of confining pressure, strain rate and temperature. EPR enables to create explicit and well-structured equations representing the mechanical and thermal behavior of frozen soil using experimental data. A comprehensive set of triaxial tests were carried out on samples of a frozen soil and the data were used for training and verification of the EPR model. The developed EPR model was also used to simulate the entire stress-strain curve of triaxial tests, the data for which were not used during the training of the EPR model. The results of the EPR model predictions were compared with the actual data and it was shown that the proposed methodology can extract and reproduce the behavior of the frozen soil with a very high accuracy. It was also shown that the EPR model is able to accurately generalize the predictions to unseen cases. A sensitivity analysis revealed that the model developed from raw experimental data is able to extract and effectively represent the underlying mechanics of the behavior of frozen soils. The proposed methodology presents a unified approach to modeling of materials that can also help the user gain a deeper insight into the behavior of the materials. The main advantages of the proposed technique in modeling the complex behavior of frozen soil have been highlighted. Highlights • A programme of triaxial experiments is conducted on a frozen soil. • A data mining technique (EPR) is used for modeling the behavior of frozen soil. • The new approach can accurately predict the behavior of frozen soils. [ABSTRACT FROM AUTHOR]

Summary: This article focuses on modeling the strain hardening‐softening response of statically compacted silty sand as observed from a comprehensive series of suction‐controlled, consolidated‐drained triaxial tests accomplished in a fully automated, double‐walled triaxial test system via the axis‐translation technique. The constitutive model used in this work is based on the theory of Bounding Surface (BS) plasticity and is formulated within a critical state framework. The essential BS model parameters are calibrated using the full set of triaxial test results and then used for predictions of compacted silty sand response at matric suction states varying from 50 to 750 kPa. Complementary simulations using the Barcelona Basic Model have also been included, alongside BS model predictions, in order to get further enlightening insights into some of the main limitations and challenges facing both frameworks within the context of the experimental evidence resulting from the present research effort. In general, irrespective of the value of matric suction applied, the Barcelona Basic Model performs relatively well in predicting response at peak and critical state failure under low net confining pressure while the Bounding Surface Model performs relatively well under high net confining pressures. [ABSTRACT FROM AUTHOR]

This study aims to present accurate soil modeling and validation of a single roadside guardrail post as well as a single concrete pile installed near cut slopes or compacted sloping embankment. The conventional Winkler's elastic spring model and p-y curve approach for horizontal ground cannot directly be applied to sloping ground where ultimate soil resistance is significantly dependent on ground inclination. In this study, both grid-based 3-D FE model and particle-based SPH (smoothed particle hydrodynamics) model available in LS-DYNA have been adopted to predict the static behavior of a laterally loaded guardrail post. The SPH model has potential to eliminate any artificial soil stiffness due to the deterioration of the nodeconnected Lagrangian soil mesh. For this purpose, this study comprises two parts. Firstly, only 3-D FE modeling has been tested to show the numerical validity for a single concrete pile in sloping ground using Mohr-Coulomb material. However, this material option cannot be implemented for SPH elements. Nevertheless, Mohr-Coulomb model has been used since this material model requires six input soil data that can be obtained from the comparative papers in literatures. Secondly, this work is extended to compute the lateral resistance of a guardrail post located near the slope using the hybrid approach that combines Lagrange FE elements and SPH elements by the suitable node-merging option provided by LS-DYNA. For this analysis, the FHWA soil material developed for application to road-base soils has been used and also allows the application of SPH element. [ABSTRACT FROM AUTHOR]

Seven boreholes are drilled in the coastal region of Gujarat; three in Kachchh (at Jangi, Mandvi and Mundra), one in Saurashtra (Jodiya) and three in mainland Gujarat(Dahej, Kamboi and Dholera) to depth of 27–80 m for estimating the surface strong ground motion parameters. These parameters are required for seismic resistant design of structures in the areas. The adopted methodology comprises of three parts: (i) soil modeling and estimation of depth to Engineering Bed Layer (EBL) (a prominent subsurface soil layer with shear wave velocity of 450 m/s to 760 m/s with SPT N value of more than 80) (ii) Estimation of the ground motion at EBL (iii) estimation of ground motion at the surface by 1D ground response analysis using SHAKE program. The soil models are prepared from borehole data and shear wave velocity/ N-values. The ground motion at EBL is estimated using Stochastic Finite Fault source Modeling Technique by considering scenario earthquakes along two major faults of Kachchh (Kachchh Mainland Fault (Mw7.6) and Katrol Hill Fault (Mw7.5)), one of Saurashtra (North Kathiawar Fault (Mw 6.5)) and one of Mainland Gujarat (West Cambay Fault (Mw 6.0)). The surface ground motion is estimated by passing ground motion simulated at EBL through prepared soil models of each site using SHAKE program. The surface peak ground acceleration from 0.076g to 0.396 g and peak spectral acceleration from 0.24g to 1.4 g are estimated at seven coastal sites. Spectral accelerations are found higher than Bureau of Indian Standard (BIS) suggested values at Jangi (between 0.1 and 0.3 s & 0.8–1.4 s), Jodiya (0.1–0.3 s), Mundra (0.3–0.6 s) and Dholera (0.1–0.4 s) sites, where the importance factor of 1.5 needs to be considered. The study suggests taking BIS values at Dahej, Kamboi and Mandvi coastal sites. [ABSTRACT FROM AUTHOR]

Accurately estimating soil moisture at fine resolutions – scales approaching a few meters – has been a target of a broad community involved in soil moisture research and application development, from agriculture support, improving land-atmosphere interaction in weather and climate prediction, and hydrologic modeling, to a variety of military applications. The US Army interest in high spatial-resolution soil moisture products is based on the need to better understand soil impacts on broad range of applications, including but not limited to movement and maneuver and logistics. The Geospatial Weather-Affected Terrain Conditions and Hazards (GeoWATCH) application was developed to support Army needs for high resolution soil moisture products. This paper describes the GeoWATCH application, development of the high-resolution soil moisture analysis and prediction capability, and linkages to downstream Army applications that deliver weather-informed content to support decision making. This paper also describes product evaluation and validation efforts to understand application performance. • GeoWATCH was developed to provide real-time, downscaled, globally available soil moisture products. • GeoWATCH calculates high resolution soil moisture from elevation, land use and soils data with a water redistribution model. • The GeoWATCH web user interface provides easy access to view and download model results. • Model evaluation suggests the downscaling improves accuracy of soil moisture products from coarser resolution inputs. [ABSTRACT FROM AUTHOR]

In this work, we employ both the state-based peridynamics and the smoothed particle hydrodynamics (SPH) to simulate soil fragmentation/ejection induced by the blast of buried explosives. We use peridynamics representation to model soil medium, and we use SPH representation to model explosive gas. The key of the simulation is the coupling of the two. In the peridynamics–SPH computational domain, there is an interphase zone, in which a peridynamic particle can have SPH particles within its own horizon, while an SPH particle can have peridynamic particles within its supporting domain. The interactions of the peridynamic and SPH particles in this interphase zone are discussed. By assuming the equivalence of the two methods in the current configuration, we study how to choose simulation parameters that can seamlessly couple the two methods. A Drucker–Prager plasticity soil model at finite strain is used for soil medium and a charge model of equation of state is used to model the buried explosive. Numerical examples are carried out to simulate soil fragmentation/ejection induced by the shock waves from the buried explosive. It is shown that the numerical results are in general agreement with that of the experiment. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Geotechnical Journal is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Afyon Kocatepe University Journal of Science & Engineering / Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi is the property of Afyon Kocatepe University, Faculty of Science & Literature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: Understanding the soil–structure interaction (SSI) mechanism is crucial in the seismic design of nuclear power plant (NPP) containment systems. Although the numerical analysis method is generally used in seismic design, there is a need for experimental verification for the reliable estimation of SSI behavior. In this study a dynamic centrifuge test was performed to simulate the SSI behavior of a Hualien large-scale seismic test (LSST) during the Chi-Chi earthquake. To simulate the soil profile and dynamic soil properties of the Hualien site, a series of resonant column (RC) tests was performed to determine the model soil preparation conditions, such as the compaction density and the ratio of soil–gravel contents. The variations in the shear wave velocity (V S) profiles of the sand, gravel, and backfill layers in the model were estimated using the RC test results. During the centrifuge test, the V S profiles of the model were evaluated using in-flight bender element tests and compared with the in-situ V S profile at Hualien. The containment building model was modeled using aluminum and the proper scaling laws. A series of dynamic centrifuge tests was performed with a 1/50 scale model using the base motion recorded during the Chi-Chi-earthquake. In the soil layer and foundation level, the centrifuge test results were similar to the LSST data in both the time and frequency domains, but there were differences in the structure owing to the complex structural response as well as the material damping difference between the concrete in the prototype and aluminum in the model. In addition, as the input base motion amplitude was increased to a maximum value of 0.4g (prototype scale), the responses of the soil and containment model were measured. This study shows the potential of utilizing dynamic centrifuge tests as an experimental modeling tool for site specific SSI analyses of soil–foundation–NPP containment system. [Copyright &y& Elsevier]