Soil Model Research Articles

Analysis of r - Adaptive coupling adaptive time integration strategy in FEM of seismic liquefaction

In seismic finite element analysis of saturated soils, the challenges primarily reside in liquefaction and the large deformation of soil induced by strong earthquakes. Traditional updated Lagrangian methods may result in severe mesh distortion after updating the spatial configuration of nodes. To address this issue, this study develops an r - adaptive approach based on a solid - fluid coupling finite element method (FEM) platform to mitigate mesh distortion problems. This technique optimizes mesh nodes without altering the overall topology, thereby maintaining mesh integrity. However, enhancing mesh accuracy comes at the expense of increased computational costs. To alleviate high computational expenses, this study couples two types of adaptive time stepping techniques, based on mixed error and displacement history curvature, with mesh adaptation. Through verification on two typical dynamic liquefaction models of saturated soil on embankment and subway station, this technique effectively balances the relationship between computational accuracy and costs.

Impacts of water regulation through a reservoir on drought dynamics and propagation in the Pilica River watershed

Study regionThe Pilica River watershed, located in central Poland, is the largest left tributary of the Vistula River basin. Study focusThe study aims to assess the impact of human activities on drought dynamics by considering the specific role of the Sulejów Reservoir. Identifying factors contributing to drought development is essential for decision-makers to implement appropriate management measures. The temporal overlap method is used to distinguish between climate and human-driven droughts. The changes in drought propagation are assessed using the time shift method at various accumulation time scales. To assess the spatial distribution of drought, we applied the Soil and Water Assessment Tool (SWAT) model to simulate soil moisture and streamflow response at a spatial scale. New hydrological insights for the regionThe findings highlight that about 75% of meteorological droughts lead to soil moisture droughts, and about 70% result in hydrological droughts. The results show the contribution of human activities to soil moisture drought (∼25%) and hydrological drought (∼10%), indicating that not all droughts are driven by meteorological factors alone. The key finding suggests a shorter and longer time scale of around 3 and 12 months for soil moisture drought and hydrological drought propagation analysis, respectively. Moreover, the decrease in the duration and severity of drought during 2005–2019 compared to 1997–2004 suggests that the magnitude of the reservoir impact depends on its purpose and operational strategies.

HYSTERESIS OF IMPLICIT EQUATIONS IN HYPOPLASTICITY FOR SOIL MATERIALS WITH GRANULAR HARDNESS DEGRADATION

AbstractWe study a hypoplastic model for soil and granular materials stemming from geomechanical engineering which further incorporates effects of degradation of the granular hardness, therefore allowing for the description of environmental weathering. The governing system is described by a nonlinear system of transcendental-differential equations for stress and strain rate, which is investigated with respect to its long-time dynamic. Under deviatoric stress control, two different solutions of the underlying, implicit differential equations are constructed analytically. The spherical components of stress and strain rate converge asymptotically to an attractor and lead to the sparsification of material states. Whereas under cyclic loading-unloading carried out in a numerical simulation, finite ratcheting of the deviatoric strain rate is observed in the form of a square spiral.

Insights to the Water Balance of a Boreal Watershed Using a SWAT Model.

Abstract The hydrological characteristics of a watershed play a crucial role in shaping ecosystems within the Boreal zone and have a significant impact on regional environments. Knowing these characteristics, such as the distinctive topography, vegetation, soil composition, and climatic conditions in the Canadian Boreal zone, is essential for implementing sustainable water management. This study focuses on assessing the hydrological dynamics of the Upper Humber River Watershed (UHRW) in western Newfoundland, Canada, using the Soil and Water Assessment Tool (SWAT) model. The UHR watershed includes sub-basins and hydrological response units (HRUs), with diverse land uses, soil types, and slope characteristics. Key parameters influencing streamflow simulation were identified through sensitivity analysis, including the runoff curve number, the effective hydraulic conductivity, the temperature lapse rate, the soil evaporation compensation factor, and the available water capacity of the soil layer. The SWAT model, using data from the Reidville hydrometric station, shows favorable performance metrics, with R2 values of 0.79 and 0.83 during the calibration and evaluation periods, respectively. The model effectively captures seasonal and monthly flow patterns, displaying right-skewed distributions and seasonal variations. The analyzed hydrological parameters, such as precipitation, evaporation, transpiration, surface runoff, and groundwater flow, reveal their significant contributions to the water balance. The flow duration curve analysis indicates the model's capability to estimate peak and low flows, with slight under-prediction during the recession phase. Seasonal analysis further supports the model's performance, with positive Nash-Sutcliffe Efficiency (NSE) values ranging from 0.65 to 0.91. The study concludes that the SWAT model is suitable for simulating the hydrological processes in the studied watershed providing valuable insights for sustainable water resource management and decision-making in the UHRW. The results can be useful for other Boreal zone watersheds.

Interactions of Brominated Flame Retardants with Membrane Models of Dehalogenating Bacteria: Langmuir Monolayer and Grazing Incidence X-ray Diffraction Studies.

Brominated flame retardants (BFRs) are small organic molecules containing several bromine substituents added to plastics to limit their flammability. BFRs can constitute up to 30% of the weight of some plastics, which is why they are produced in large quantities. Along with plastic waste and microplastic particles, BFRs end up in the soil and can easily leach causing contamination. As polyhalogenated molecules, multiple BFRs were classified as persistent organic pollutants (POPs), meaning that their biodegradation in the soils is especially challenging. However, some anaerobic bacteria as Dehaloccocoides can dehalogenate BFRs, which is important in the bioremediation of contaminated soils. BFRs are hydrophobic, can accumulate in plasma membranes, and disturb their function. On the other hand, limited membrane accumulation is necessary for BFR dehalogenation. To study the BFR-membrane interaction, we created membrane models of soil dehalogenating bacteria and tested their interactions with seven legacy and novel BFRs most common in soils. Phospholipid Langmuir monolayers with appropriate composition were used as membrane models. These membranes were doped in the selected BFRs, and the incorporation of BFR molecules into the phospholipid matrix and also the effects of BFR presence on membrane physical properties and morphology were studied. It turned out that the seven BFRs differed significantly in their membrane affinity. For some, the incorporation was very limited, and others incorporated effectively and could affect membrane properties, while one of the tested molecules induced the formation of bilayer domains in the membranes. Thus, Langmuir monolayers can be effectively used for pretesting BFR membrane activity.

Streamflow Variation under Climate Conditions Based on a Soil and Water Assessment Tool Model: A Case Study of the Bailong River Basin

We coupled the global climate models (GCMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and Future Land Use Simulation model (FLUS) to evaluate land use change in the Bailong River Basin (BRB) under three shared socioeconomic pathway and representative concentration pathway scenarios (SSP1–2.6, SSP2–4.5, SSP5–8.5). Additionally, we used calibrated soil and water assessment tools (SWATs) to evaluate the streamflow in the BRB from 2008 to 2100 under the combined influence of climate and land use changes. The results indicate that (1) under the SSP126-EP scenario, forests have been well preserved, and there has been an increase in the combined area of forests and water bodies. The SSP245-ND scenario has a similar reduction pattern in agricultural land as SSP126-EP, with relatively good grassland preservation and a moderate expansion rate in built-up land. In contrast, the SSP585-EG scenario features a rapid expansion of built-up land, converting a significant amount of farmland and grassland into built-up land. (2) From 2021 to 2100, the annual average flow increases under all three scenarios, and the streamflow change is most significant under SSP5–8.5. (3) Compared to the baseline period, the monthly runoff increases, with the most significant increase occurring during the summer months (June to August). This study offers a thorough assessment of potential future changes in streamflow. Its findings are expected to be applied in the future to improve the management of water resources at a local level.

Predicting Ili River streamflow change and identifying the major drivers with a novel hybrid model

Study regionIli River, the main river in the Lake Balkhash basin. Study focusThis study introduces a novel hybrid model by coupling the Soil and Water Assessment Tool (SWAT) hydrological model with machine learning algorithms. It aims to simulate and forecast the streamflow of the Ili River, clearly delineating the roles of meteorological factors and anthropogenic factors in the process of streamflow change. New hydrological insights for the regionDuring the period 1960–2020, the contribution of climate change to streamflow variation was 104.59%-113.07%, the contribution of land use ranged from −10.75% to −4.59%, and the impact of reservoir construction was −2.27%. The predictive outcomes of the hybrid model indicate that, under the SSP2–4.5 and SSP5–8.5 scenarios, the streamflow of the Ili River is projected to increase by 12.8% and 14.3% respectively in the future period (2021–2100), in comparison to the historical period (1960–2020). The warming and humidification from 2021 to 2100 will lead to changes in the streamflow components of the Ili River, with a decrease in the proportion of groundwater flow and an increase in the proportion of surface flow. The results of this study provide a reference for the rational utilization and scientific management of water resources in the Ili River Basin.

Seismic Response Study of S-Wave Incident Stratified Unsaturated Soil Site Under Thermal Effects

ABSTRACT Drawing upon the fundamental principles of mixture continuum and thermodynamics, planar S-wave incident model for layered unsaturated soil is formulated. Employing the Helmholtz vector decomposition principle, the wave field within the layered unsaturated soil is analyzed using the transfer matrix method, accounting for the influence of thermal effects. An analytical solution is obtained for the seismic response in layered unsaturated soil under the action of thermal effect. The results of the numerical analyses show that the thermo-physical parameters such as relative heat of solid, temperature, and thermal expansion coefficient have a notable effect on the seismic response of the site.

Predicting Current and Future Habitat Suitability of an Endemic Species Using Data-Fusion Approach: Responses to Climate Change

Fritillaria imperialis L., an indicator plant species in Iran, is facing threats and its population has declined in recent years. To provide insights into the drivers affecting its loss, this research aims to identify the effects of three groups of factors, including climate, soil, and physiographic variables, on the current and future spatial distributions of F. imperialis. For this purpose, we used five machine learning algorithms as well as an ensemble forecasting of species distribution approach to explain the geographical distributions of the species as a function of these factors. In addition, we used two shared socio-economic pathways scenarios – SSP 1-2.6 and SSP 5-8.5 – to project the future distributions of F. imperialis in 2030, 2050, 2070, and 2090. Based on evaluation indices, area under the ROC curve (AUC) and true skill statistic (TSS), the Random Forest (RF) model generated the strongest prediction of the distribution of F. imperialis (TSS>0.9 and AUC>0.9). No significant difference observed among the three datasets (climate-only variables, climate + physiography variables, and climate + physiography + soil variables) in terms of AUC values. In models using climate + physiography + soil datasets, soil electrical conductivity, clay, and pH emerged as the most important variables affecting the growth and development of F. imperialis while climate factors played a lesser role in its distribution. Future projections revealed different patterns when using the optimistic (SSP 1-2.6) and pessimistic (SSP 5-8.5) socio-economic pathway scenarios and either the climate only or climate + physiography models. The climate + physiography + soil model produced similar prediction patterns for the scenarios. The climate-only models predicted larger areas suitable for crown imperial in the future than did the climate + physiography + soil model. These results emphasize the consideration of factors beyond climate scenarios when modeling biological responses to global warming and regional climate change.

Temperature-Dependent SWCC Model for Unsaturated Soil

Temperature-Dependent SWCC Model for Unsaturated Soil

Inverse modeling of frequency domain-based one-dimensional soil water flow in layered soils

Soils are heterogeneous at multiple scales. Estimates of spatially varying saturated hydraulic conductivity (Ks) of soils dictate the resolution of modeling water flux based on the Richardson-Richards equation (RRE), but direct measurements of Ks everywhere in a field are practically impossible. As a result, this study develops an inverse approach to estimate spatially varying Ks utilizing soil moisture observations driven by periodic weather variability at different frequencies. Using a numerical solution to vertical, one-dimensional RRE based on Gardner-Kozeny (GK) model in a heterogeneous soil with periodic flux, we investigate the spatial cross-correlation between Ks and observations (the amplitude or phase shift) of soil moisture fluctuation at different frequencies without high computational cost in the conventional time-domain model. These correlation patterns vary with the spatial location and frequency, suggesting that soil moisture fluctuations at different frequencies at one monitoring location carry non-redundant information about the heterogeneous distribution of Ks. Guided by cross-correlation maps, several inverse experiments reveal the effectiveness of multi-frequency data of soil moisture fluctuation for mapping the heterogeneous distribution of Ks. Monte Carlo simulations suggest that multi-frequency data can provide non-redundant and useful information about the estimation of heterogeneous Ks. Synthetic data based on a commonly-applied nonlinear van Genuchten-Mualem (VG) model is then tested in our inversion algorithm. The results indicate that the spatial pattern of the inverted GK Ks is very similar to that of the VG Ks, although these two parameters are inherently not identical since they are used in two different models. In addition, the inverted GK Ks can successfully predict the soil moisture fluctuation of frequencies that were not used in the inversion. Despite having no field experiments to validate this model, we believe this is the first attempt to conduct frequency-domain inverse modeling in vadose zone hydrology using soil moisture fluctuation.

Numerical Evaluation of Pile Group Behavior Subject to Earthquake Loads

The seismic design of pile foundations mainly relies on analyzing the seismic response of layered, liquefiable locations. Two design scenarios are taken into consideration from the case histories; the first is how pile foundations react to the stresses and lateral displacements brought on by the lateral dispersion of liquefied soil. The second is how to piles reaction to seismic activity that occurs with the development of high pore water pressures. The PLAXIS 3D software is utilized in this research with a non-linear soil constitutive model (hypoplastic model) for both dry and saturated loose sandy soils under the impact of two earthquakes and the motion of different features to give a complete understanding of the dynamic piled foundation response. The findings from this study show that the site profile, pile diameter, pile length, and excitation of ground motion significantly affect the dynamic response of the layered liquefied site. So, in the saturated case, the increase in the piles length to (L/D = 55) in comparison to the original length (L/D = 35) decreased the peak acceleration at the raft foundation by about (24.4 and 41.9) % under the effect of Kobe and Upland earthquake motion, respectively, while in the dry case, the reduction in peak acceleration was about (22.8 and 40.9) % under the effect of Kobe and Upland earthquake motion, respectively.

Climate change in the Biebrza Basin—Projections and ecohydrological implications

Over the last decades observed climate change in Poland had a significant impact on the condition and functioning of the environment. Thus, it is crucial to analyze further future changes to be able to cope with the potential effects of changing climate conditions. In this study, we aimed to assess the impact of projected climate change on meteorological and hydrological conditions in the Biebrza Basin. We analyzed seasonal and annual changes in air temperature, precipitation, streamflow and flood characteristics using the hydrological Soil and Water Assessment Tool (SWAT) model. We examined projected changes for two future time horizons (2024–2050 and 2074–2100) under the Representative Concentration Pathways (RCP) 4.5 and 8.5 using an ensemble of nine EUROCORDEX model scenarios. Climate change projections indicated an increase in precipitation by up to +17 % (+117 mm) and air temperature by up to 3.8 °C by the end of the 21st century. In the analyzed flow gauges a considerable increase in low and mean flows is projected in the future. High flows are projected to slightly decrease for Sztabin, remain at a similar level for Dębowo and slightly increase for Osowiec and Burzyn. Flood area and volume will slightly increase in future horizons. The greatest increase in flood duration (by up to 16 days) is projected for RCP8.5 by the end of the 21st century. It seems, that the expected hydrological conditions, both in the short and long term, will become more stable and improve the conditions for the development of wetlands.

Spatial calibration and uncertainty reduction of the SWAT model using multiple remotely sensed data

Remotely sensed products are often used in watershed modeling as additional constraints to improve model predictions and reduce model uncertainty. Remotely sensed products also enabled the spatial evaluation of model simulations due to their spatial and temporal coverage. However, their usability is not extensively explored in various regions. This study evaluates the effectiveness of incorporating remotely sensed evapotranspiration (RS-ET) and leaf area index (RS-LAI) products to enhance watershed modeling predictions. The objectives include reducing parameter uncertainty at the watershed scale and refining the model's capability to predict the spatial distribution of ET and LAI at sub-watershed scale. Using the Soil and Water Assessment Tool (SWAT) model, a systematic calibration procedure was applied. Initially, solely streamflow data was employed as a constraint, gradually incorporating RS-ET and RS-LAI thereafter. The results showed that while 14 parameter sets exhibit satisfactory performance for streamflow and RS-ET, this number diminishes to six with the inclusion of RS-LAI as an additional constraint. Furthermore, among these six sets, only three effectively captured the spatial patterns of ET and LAI at the sub-watershed level. Our findings showed that leveraging multiple remotely sensed products has the potential to diminish parameter uncertainty and increase the credibility of intra-watershed process simulations. These results contributed to broadening the applicability of remotely sensed products in watershed modeling, enhancing their usefulness in this field.

The role of ponds in pesticide dissipation at the catchment scale: The case of the Save agricultural catchment (Southwestern France)

Pesticides are a major source of pollution for ecosystems. In agricultural catchments, ponds serve as buffer areas for pesticide transfers and biogeochemical hotspots for pesticide dissipation. Some studies have highlighted the specific impact of ponds on the dynamics of pesticides, but knowledge of their cumulative effect at the watershed scale is scarce. Hence, using a modelling approach, we assessed the cumulative role of ponds in pesticide transfer in an agricultural basin (Southwest of France, 1110 km2). The Soil and Water Assessment Tool (SWAT) model was used to model the Save basin, including 197 ponds selected with a Multi-Criteria Decision Aiding Model based on their pesticide interception capacities. The daily discharge, the suspended sediment loads and two herbicide loads (i.e. S-metolachlor and aclonifen) in dissolved and particulate phases were accurately simulated from January 2002 to July 2014 at a daily time step. The presence of ponds resulted in a yearly mean reduction at the watershed outlet of respectively 61 % and 42 % of aclonifen and S-metolachlor fluxes compared to the simulations in the absence of ponds. Sediment-related processes were the most efficient for pesticide dissipation, leading to a mean dissipation efficiency by ponds of 51.0 % for aclonifen and 34.4 % for S-metolachlor. This study provides a first quantification of the cumulative role of ponds in pesticide transfer at the catchment scale in an intensive agricultural catchment. Environmental implicationThe article focuses on two pesticides (aclonifen and S-metolachlor) and their transfer and fate at the catchment scale. The cumulative role of ponds in pesticide dissipation was studied for the first time at this scale. The new methodology introduced by the study improves the modelling of pesticide transfers at the catchment scale. The study of the cumulative role of ponds allowed the assessment of their efficiency in pesticide removal and highlighted the different mitigation pathways. This study is environmentally relevant: it led to better and more precise pesticide modelling at catchment scale and confirmed the role of ponds as potential off-field mitigation strategies.

Soil data augmentation and model construction based on spectral difference and content difference

Soil analysis makes for developing precision agriculture and monitoring land quality, while the models available for spectroscopy-based chemometrics are constrained by limited samples from small areas. The paper proposed sample expansion and model construction based on spectral difference and content difference, realizing data augmentation and deep learning applied to original samples with limited numbers. The spectral subtraction based on maximum or minimum values exploited the maximum or minimum values to acquire the spectral difference and content difference, which provided a new data form for model construction. Keeping enhanced samples whose spectral difference and content difference were all zero was useful for improving model performance. Augmentation of all data or training data based on maximum or minimum values-based spectral subtraction, which sorted the contents and made them the maximum or minimum values in sequence, achieved sample expansion by the spectral difference and content difference. The model utilized the random vector functional link (RVFL) network, extreme learning machine (ELM), and one-dimensional convolutional neural network (1D CNN), which could predict the content of new samples through ensemble averaging when predicting content difference. The experimental result showed the model of the spectral subtraction based on maximum or minimum values had a similar performance to that of the original samples. Augmentation of all data improved model performance by only RVFL and ELM. Augmentation of training data verified 1D CNN was better than RVFL and ELM. The paper implements a new data augmentation method and applies CNN to original samples with inadequate numbers, which lays the foundation for an improved model and applying spectral preprocessing.

Particle Swarm Optimization for Inverse Modeling of Soils in Urban Green Stormwater Infrastructure Sites

Particle Swarm Optimization for Inverse Modeling of Soils in Urban Green Stormwater Infrastructure Sites

Development of transparent soil grouting test system and its application in grouted gravel pile

To visualize the grouting process of grouted gravel piles, a transparent soil model test system was developed. This system allows for the study of soil movement around the piles during grouting reinforcement and enables load-bearing capacity testing after grout solidification into piles. The experimental system comprises a grouting system, a vertical load-bearing capacity testing system, a continuous physical field measurement system, and a model box. The grouting system facilitates sequential processes: pre-consolidation, constant-pressure grouting, and vertical loading. In transparent clay with a pre-consolidation pressure of 60 kPa, constant-pressure grouting and vertical load-bearing tests for grouted gravel piles were conducted. The results captured the disturbance deformation of the soil around the pile during both grouting and vertical loading. The study indicates that disturbance deformation of the soil surrounding the pile increases with prolonged grouting time. Soil beneath the pile end predominantly undergoes vertical compression, while soil at the pile sides experiences horizontal compression. The mid-section of the soil adjacent to the pile displays oblique upward bulging and vertical deformation. Under constant-pressure grouting, the filling rate of gravel voids in the pile hole by epoxy resin chemical grout with strong time-dependent viscosity shows a decreasing trend. During vertical loading, the displacement of the soil around the pile is significantly greater than that at the pile end. Consequently, this test system facilitates the visualization and simulation of grouting and vertical load-bearing tests for grouted gravel piles.

Study of the dynamic interactions between helix-stiffened cement mixing piles and soft soil under oblique incidence of SV waves in arbitrary space

Helix stiffened cement mixing (HSCM) piles are extensively utilized in soft soil sites due to their excellent seismic and pullout resistance. Initially, the equivalent nodal force formulas for each face under the oblique incidence of SV waves in any spatial direction are derived based on the viscous-spring artificial boundary. The method's applicability in studying pile–soil interactions is verified based on existing pile–soil tests. Subsequently, the HSCM pile–soil model is constructed using finite element software through a Python program. Various analyses of accelerations, relative displacements, soil reactions, and pile–soil dynamic p-y curves are conducted using two different directions of incidence angles (horizontal and vertical) and various types of seismic waves. Subsequently, the backbone line of the dynamic p-y curve is plotted, revealing the pile–soil interaction mechanism under spatial oblique incidence. Finally, Matlock's formula is modified to ensure its applicability to pile–soil interactions under spatially oblique incidence. The results demonstrate that loading the Kobe wave causes the peak acceleration in the x direction, the peak pile–soil relative displacement, and the peak pile-side soil reaction force to increase by 22.64 %, −18.90 % and 18.42 %, respectively, compared to those of the El Centro wave.

Meta-analysis reveals that the effects of precipitation change on soil and litter fauna in forests depend on body size.

Anthropogenic climate change is altering precipitation regimes at a global scale. While precipitation changes have been linked to changes in the abundance and diversity of soil and litter invertebrate fauna in forests, general trends have remained elusive due to mixed results from primary studies. We used a meta-analysis based on 430 comparisons from 38 primary studies to address associated knowledge gaps, (i) quantifying impacts of precipitation change on forest soil and litter fauna abundance and diversity, (ii) exploring reasons for variation in impacts and (iii) examining biases affecting the realism and accuracy of experimental studies. Precipitation reductions led to a decrease of 39% in soil and litter fauna abundance, with a 35% increase in abundance under precipitation increases, while diversity impacts were smaller. A statistical model containing an interaction between body size and the magnitude of precipitation change showed that mesofauna (e.g. mites, collembola) responded most to changes in precipitation. Changes in taxonomic richness were related solely to the magnitude of precipitation change. Our results suggest that body size is related to the ability of a taxon to survive under drought conditions, or to benefit from high precipitation. We also found that most experiments manipulated precipitation in a way that aligns better with predicted extreme climatic events than with predicted average annual changes in precipitation and that the experimental plots used in experiments were likely too small to accurately capture changes for mobile taxa. The relationship between body size and response to precipitation found here has far-reaching implications for our ability to predict future responses of soil biodiversity to climate change and will help to produce more realistic mechanistic soil models which aim to simulate the responses of soils to global change.