Search Results (354)

The mechanical properties of moraine soil in cold regions are significantly influenced by freeze–thaw cycles (FTCs). However, current understanding of the quantitative characteristics of its shear behavior under the coupled effect of FTCs and confining pressure is still insufficient. To address this, a series of triaxial unconsolidated-undrained shear tests were conducted on saturated moraine soil, with different numbers of FTCs (N = 0, 1, 4, 8, 10, 12, 15, 20) and various confining pressures (σ₃ = 100, 200, 300, 400 kPa). The experimental results reveal that: (1) With an increase in the number of FTCs, the stress–strain curves gradually change from strain-softening to strain-hardening types. Correspondingly, the pore water pressure development shifts gradually from a peak-decay pattern to a growth-stabilization pattern. The peak pore water pressure rises linearly with increasing confining pressure, whereas it decays linearly with an increasing number of FTCs. (2) Both the secant modulus E₅₀ and the shear strength increase with higher confining pressure and decrease with more FTCs. Confining pressure exerts a significant inhibitory and compensatory effect on freeze–thaw-induced damage, markedly reducing the deterioration rate under high confining pressure. (3) Quantitative prediction models for E₅₀ and q_max were established, effectively capturing the coupled effect of confining pressure and FTCs. It can be inferred that confining pressure mitigates structural damage by compressing frost-induced cracks and enhancing interparticle contacts, while FTCs exacerbate the degradation of soil mechanical properties because of ice crystal expansion or contraction and weakening of cementation. This study quantifies the coupled effect of confining pressure and FTCs, and the proposed prediction model provides a useful reference or preliminary estimation for relevant geotechnical engineering designs. Full article

Effective flood planning requires spatially complete hazard information, yet regulatory flood products can contain unresolved hazard classifications and provide limited uncertainty information. In addition, existing models for flood mapping often rely on random sampling that neglects spatial dependence among neighboring areas, struggle to model zero-inflated, bounded area shares (i.e., shares with many zero values and a bounded 0–1 range), and offer limited distribution-level explainability. To address these limitations, this study developed a calibrated hurdle conditional diffusion framework to estimate the Federal Emergency Management Agency (FEMA) Special Flood Hazard Area (SFHA) share and the probability that coverage exceeds 10% for Louisiana census block groups. Within this framework, a hurdle component separates zero from positive coverage, a conditional diffusion model estimates bounded positive-share distributions, spatial blocking evaluates geographic transfer, and probabilistic calibration supports exceedance probabilities and prediction intervals. Terrain, land-cover, wetland, hydrographic, climate, and soil predictors were evaluated across 3776 block groups with resolved FEMA information. The conditional regression component achieved a mean absolute error of 0.172 for the primary continuous SFHA-share prediction and strong parish-level agreement (Pearson r = 0.834). For the secondary screening task of identifying census block groups with ≥10% SFHA share, the calibrated hurdle diffusion model yielded an area under the receiver operating characteristic curve of 0.852. Statewide predictions were generated for all 4294 census block groups, including 518 unresolved units with a mean predicted SFHA share of 33.3%. Distributional Reliability Explanation Attribution (DREA) identified flooded soils, elevation, topographic wetness, wetlands, and water proximity as reliable predictors of distributional displacement, uncertainty, exceedance probability, and probabilistic skill. Overall, the framework supports leakage-safe, uncertainty-aware screening while complementing authoritative FEMA flood maps. Full article

Frost heave in seasonally frozen loess regions is fundamentally governed by pore water migration towards the freezing front driven by temperature gradients, forming ice lenses that damage engineered infrastructure. Because both freezing intensity and moisture availability evolve with climate, frost heave susceptibility is itself dynamic, yet existing assessments remain static and ignore future climate trajectories. This paper presents a physics-constrained machine learning framework that couples soil freezing characteristic curve (SFCC) prior knowledge with multi-source open data and CMIP6 climate projections to achieve dynamic frost heave susceptibility mapping for the Loess Plateau. Monotonicity constraints derived from the coupled phase-transition and cryosuction mechanisms described by the SFCC and the segregation potential theory are enforced during gradient-boosted tree training, ensuring that predictions respect the established relationships among freezing intensity, fine-grained content and ice segregation potential. An ordinal decomposition strategy is adopted to guarantee that the monotonicity constraint on each binary sub-model translates into monotonicity of the predicted ordinal susceptibility level. The best performer, physics-constrained XGBoost, reaches an overall accuracy of 88.7% and an AUC of 0.942 on a four-class susceptibility scheme. Independent validation against 156 field records and Sentinel-1 InSAR observations confirms that the model captures genuine frost heave patterns. Under SSP5-8.5, the area classified as high or very-high susceptibility contracts by approximately 38% by the 2080s owing to warming, while under SSP1-2.6 the reduction is only 12%, and transitional zones of moderate risk expand in both scenarios. These findings provide a temporally explicit and physically grounded basis for climate-adaptive infrastructure planning in cold loess regions. Full article

When red-bed mudstone is directly used as fill material for building foundations, road subgrades, and similar applications, it is prone to seepage-induced deformation and instability. Amending it with montmorillonite bentonite can effectively regulate its permeability characteristics. Meanwhile, rapid and accurate prediction of the permeability coefficient is crucial for building foundations and road subgrade seepage analysis and stability assessment. This study investigated red-bed mudstone fill material modified with montmorillonite bentonite at different blending ratios. Soil–water characteristic curve tests, saturated/unsaturated permeability tests, and nuclear magnetic resonance (NMR) tests were conducted on the specimens to examine the pore evolution patterns and permeability characteristics of the modified red-bed mudstone, and a predictive model for coefficients was proposed. The results indicate that the incorporation of montmorillonite-based bentonite markedly affects the permeability properties of modified red-bed mudstone fillers. The NMR T₂ spectrum exhibits a bimodal distribution; the incorporation of bentonite and the saturation process result in a marked reduction in large pores and an increase in microporosity. The predictive model achieves higher accuracy when the montmorillonite bentonite content is high. Sensitivity analysis revealed that the maximum pore radius has a far greater influence on permeability than the pore fractal dimension and tortuosity. The research findings provide experimental evidence and theoretical models for the rapid estimation of permeability and seepage stability analysis of modified red-bed mudstone fill materials. Full article

Selecting suitable agricultural land is critical for food security and sustainable development, particularly in arid regions facing resource scarcity and environmental degradation. This study assesses agricultural land suitability in Sohag governorate, Egypt, using a hybrid Geographic Information System (GIS), Fuzzy Analytical Hierarchy Process (FAHP) and geostatistical approach. Thirty-four representative soil profiles were morphologically described and analyzed for ten physical (slope, depth, erosion, texture, stoniness, drainage) and chemical (EC, pH, CaCO₃, organic matter) criteria, weighted using both the Analytical Hierarchy Process (AHP) and FAHP. Geostatistical analysis characterized the spatial variability in soil properties across the study area. The two suitability maps were validated using Receiver Operating Characteristic (ROC) curves and Kappa statistics: the FAHP achieved higher prediction accuracy (AUC = 0.83, Kappa = 0.91) than the AHP (AUC = 0.81, Kappa = 0.88). The AHP-based map classified 40% (1154.92 km²) as moderately suitable (S2), 33% (952.81 km²) as marginally suitable (S3), and 27% (779.57 km²) as unsuitable (N); the FAHP-based map classified 42% (1212.67 km²) as S2, 30% (866.19 km²) as S3, and 28% (808.44 km²) as N. The proposed framework may serve as a reference for similar arid environments and support progress toward Sustainable Development Goals 2, 6, 8, 9, 11, 13 and 15, conditional on local soil, water, climate and management conditions. Full article

The surface soil in the Shendong mining area is dominated by aeolian sand and sandy sediment, while precipitation is limited, and evaporation is intense. Under these conditions, shallow reconstructed soil has difficulty retaining plant-available water, which constrains vegetation restoration. This study evaluated how low-range increases in fine-particle clay content affect both water retention and upward water conduction in sandy reconstructed soil. Sandy material from the Shangwan mining area and exogenous river clay were mixed into four treatments, and soil water characteristic curves (SWCCs) were determined by centrifuge over 10–1000 kPa matric suction. The data were fitted with the Van Genuchten model and combined with capillary-rise tests. The results showed that increasing fine-particle content shifted the SWCC upward and raised both saturated and residual volumetric water contents. SN10 reached 17.18% and 5.55% volumetric water content at 10 and 1000 kPa, respectively, and its effective water capacity in the 33–1500 kPa range was 17.9% higher than that of ST. At the same time, fine-particle enrichment in the bottom layer reduced wetting-front rise during capillary testing, indicating a trade-off between water storage and upward replenishment. Within the tested fine-particle range, moderate clay addition improved the hydraulic performance of sandy reconstructed soil, but soil design should balance precipitation retention, infiltration, and capillary supply. Because each treatment and soil-column configuration was represented by only one independently prepared experimental unit, experimental variability and reproducibility could not be evaluated. This study should therefore be regarded as a preliminary and exploratory laboratory assessment conducted under a specific set of material-preparation procedures, specimen geometries, and boundary conditions. The results describe specimen-level hydraulic contrasts rather than reproducible treatment effects and should not be directly generalized to field-scale soil reconstruction. They support a preliminary hypothesis for future replicated testing: fine-particle enrichment may increase water retention while slowing upward capillary replenishment. Full article

Agrivoltaic (AV) systems represent a promising strategy for integrating renewable energy production and agricultural activity on the same land unit, while contributing to soil water conservation under increasingly frequent drought conditions. This preliminary, single-site case study investigates the effects of a horizontal biaxial tracking AV system on soil water dynamics in a durum wheat field in the Po Valley (Borgo Virgilio, Mantua, Italy) over a full monitoring period, covering the final crop growth stages and the post-harvest bare soil phase (May–December 2024). Monitoring of soil temperature, volumetric water content (VWC), and soil water potential (SWP) was conducted at four depths (15, 30, 45, and 60 cm) at one representative monitoring station per treatment, comparing soil under AV cover (AVC) and in unshaded conditions (UC), located 10 m apart. Paired VWC and SWP measurements were used to derive site-specific soil water characteristic curves (SWCCs) and to calibrate the agro-hydrological model CRITERIA-1D, which was used to estimate available water (AW) in the first 80 cm of depth for both treatments. Measured VWC values were higher in the AVC profile than in the UC profile at all monitored depths throughout the May–September period, with differences persisting, although at lower values through October–December. Estimated AW was consistently higher under AVC than in UC during both the dry and wet periods. Despite higher VWC, the AVC profile showed more negative average SWP values at all depths during summer. This pattern is consistent with the shape of the derived SWCCs and may point to differences in water-retaining capacity between the two profiles, possibly related to structural modifications induced by 13 years of AV system operation. These preliminary findings suggest that AV systems could potentially improve soil water availability in the root zone of rainfed cereal crops and propose the hypothesis that long-term AV cover may act as a driver of changes in soil hydraulic properties, with implications for the sustainability and climate resilience of dryland farming systems. However, given the design of this case study, with only one monitoring point per treatment, the observed differences reflect the specific monitored locations and cannot fully disentangle the AV treatment effect from pre-existing spatial heterogeneity in soil properties. The preliminary results obtained in this study should therefore not be generalised beyond the specific conditions of this case study, and the interpretations proposed here should be treated as unproven hypotheses rather than established conclusions. Further studies with spatial replication and multi-year monitoring are needed to confirm these patterns. Full article

Biochar is a porous, carbon-rich soil amendment that can enhance soil water retention capacity by modifying pore structure and physicochemical properties. Understanding the soil−water characteristic curve (SWCC) of biochar-amended soils is essential for evaluating their hydrological behavior and promoting the application of biochar in engineering practice. Given the demonstrated feasibility and accuracy of machine learning methods for predicting soil parameters, this study employed six machine learning models, namely, decision tree, random forest, XGBoost, LightGBM, CatBoost, and artificial neural network, to predict the SWCC of biochar-amended soils based on a constructed dataset. Feature importance analysis and partial dependence analysis were further conducted to reveal the influence patterns of key variables. The results indicate that all six models exhibit good predictive capability, with gradient boosting models (XGBoost, CatBoost, and LightGBM) performing best. Suction is the dominant factor controlling the volumetric water content variation, while soil particle-size distribution and dry density provide the physical basis for water retention. Biochar content, pyrolysis temperature, and feedstock type further modulate the water retention capacity of amended soils. Overall, the findings demonstrate that machine learning approaches can effectively predict the SWCC of biochar-amended soils and provide insights into the controlling mechanisms of soil water retention. Full article

Landslide susceptibility mapping (LSM) is an essential tool for hazard assessment and land-use planning in landslide-prone areas. This study compares three statistical models—Frequency Ratio (FR), Weight of Evidence (WoE), and Logistic Regression (LR)—with six machine-learning algorithms: Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Artificial Neural Network (ANN), k-Nearest Neighbor (KNN), and Decision Tree (DT), for regional landslide susceptibility assessment in Hiroshima Prefecture, Japan. A balanced dataset comprising 1936 landslide and 1936 non-landslide samples was developed from the 2018 rainfall-induced landslide inventory, utilizing seven conditioning factors: slope angle, profile curvature, aspect, elevation, lithology, soil water index, and 24 h cumulative rainfall. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1-score. Among the statistical models, WoE exhibited the highest performance, while SVM provided the most balanced results among the machine-learning models. Both modeling approaches consistently identified lithology and slope angle as the primary controls on landslide occurrence. Independent validation demonstrated comparable predictive performance for both models; however, spatial validation showed that WoE assigned 96.72% of observed landslides to the High and Very High susceptibility classes, compared to 72.54% for SVM. These findings underscore the importance of integrating conventional classification metrics with spatial validation to enhance the evaluation and interpretation of landslide susceptibility models for regional hazard assessment. Full article

In the context of global climate change, drought events significantly impact plants, and studying the response of plant sap flow to environmental factors is of great significance for plant protection and sustainable development. This article uses the thermal pulse method to obtain trunk sap flow data, combined with real-time monitoring of environmental factor data, to compare the differences in sap flow characteristics between the subsequently deceased and surviving Zanthoxylum planispinum var. dingtanensis during a natural drought event. Additionally, the xylem vulnerability curve of Z. planispinum was fitted through laboratory measurements to estimate its hydraulic safety margin ($HSM$). The results showed that: (1) Compared with the surviving individuals, the subsequently deceased Z. planispinum exhibited a higher and more fluctuating sap flow rate, maintaining a consistently higher transpiration rate even after the onset of drought. (2) As soil moisture decreased, the sap flow of surviving Z. planispinum was promptly constrained by soil water availability and remained at a low, conservative level. In contrast, the sap flow of the deceased individuals continued to be driven by meteorological factors, failing to downregulate transpiration. (3) The xylem vulnerability curve revealed a $P_{50}$ of −2.2 MPa. During the severe drought, the minimum field branch water potential (${\psi }_{min}$) dropped to −3.0 MPa, resulting in a negative $HSM$ ($HSM$ = −0.8 MPa). This suggests that the xylem tension severely breached the embolism threshold, likely triggering catastrophic hydraulic failure, which emerged as the primary driver of the observed mortality in Z. planispinum. Therefore, for plants with low $HSM$s, when they are subjected to drought stress accompanied by highly fluctuating sap flow—a condition indicating that water supply cannot match the transpirational demand—timely manual intervention (e.g., supplemental irrigation) may be considered to mitigate hydraulic risks and ensure survival. Full article

This study presents the development and evaluation of a hydrogel-based superabsorbent polymer sensor (HSPS) for measuring soil suction and establishing the soil–water characteristic curve (SWCC) of unsaturated soils. Polyacrylamide (PAM) hydrogels were synthesized via UV-induced free radical polymerization using acrylamide with varying crosslinking degrees. The polymers were characterized through FT-IR and TGA analyses, confirming successful synthesis and high thermal stability. Swelling, water retention, and kinetic behavior were systematically investigated. Results indicated that lower crosslinking density significantly enhanced swelling capacity, reaching up to 3000% in distilled water, while saline environments reduced absorption due to ionic screening effects. Swelling kinetics followed anomalous (non-Fickian) diffusion behavior and were well described by the pseudo-second-order Schott model. The synthesized polymers were integrated into a modified high-sensitivity pressure sensor operating on the osmotic principle to measure matric suction. The system was validated using natural soil. Among the tested formulations, the HSPS-3 demonstrated the most reliable suction measurements, reaching values up to approximately 1 MPa without significant temperature sensitivity. The resulting SWCC exhibited bimodal characteristics consistent with the soil’s dual pore structure. The proposed method provides a cost-effective, simple, and efficient alternative for suction measurement, expanding the practical range of SWCC determination in unsaturated soil mechanics. Full article

Vegetation-impervious surface-soil (V-I-S) fractions offer a continuous sub-pixel representation of urban surface heterogeneity. In this study, the influence of urban surface characteristics represented through V-I-S fractions on hydrological processes is analyzed at decadal intervals, i.e., 2000, 2010, 2020, and the projected year 2030 for the Mula–Mutha river catchment, Maharashtra, India. Pune city, as a major urban centre in this region, is experiencing significant changes in land surface characteristics over time, which have direct implications for its hydrology. The analysis uses the Soil and Water Assessment Tool (SWAT) to model these changes and their effects on water resources. Results show that the urban area has increased from 14% (2000) to 25% (2020), with projections indicating a further rise to 34% (2030). Such transitions yielded an increase in surface runoff from 47% (2000) to 53% (2020) and projected to reach 54% (2030). Groundwater recharge has declined from 10% to 6% and is expected to fall to 4% by 2030. Model validation using discharge data at Mirawadi outlet yielded a coefficient of determination of 0.72 using land use/land cover (LULC) data and 0.79 for simulations based on runoff Curve Number (CN) derived from V-I-S fractions, indicating the improved model performance. This study presents a novel framework, which incorporates remote sensing-derived V-I-S fractions to assess the spatiotemporal impact of urban expansion on water balance components. Full article

The interaction between salt solutions and expansive soils is critical for engineering in chemically aggressive environments. However, the effect of iodide salts on pore water distribution in expansive soils remains poorly understood. This study investigated the transverse relaxation time (T₂) characteristics of pore water in expansive soils under varying KI concentrations (0–20%), moisture content (8.7–26.0%), and dry density (1.26–1.79 g/cm³) using nuclear magnetic resonance (NMR). All T₂ curves exhibited a single peak. Increasing moisture content from 8.7% to 26.0% resulted in increases of approximately 63% in T₂ at peak and 408–439% in peak area. Increasing KI concentration decreased both T₂ at peak by up to 33.3% and peak area by up to 44.0% within the tested range, attributed to diffuse double-layer compression and signal loss. Increasing moisture content broadened the T₂ distribution and linearly increased T₂ at peak and peak area, indicating water gradually occupied larger pore spaces as moisture content rose. T₂ at peak was independent of dry density, while the peak area showed a linear relationship with dry density, consistent with mass balance. The observed systematic linear relationships among T₂ at peak, peak area, and the three experimental variables suggest that NMR is a promising tool for the quantitative assessment of salt solution effects on pore water in expansive soils. These findings provide a theoretical basis for evaluating salt-affected expansive soils in coastal and arid regions. Full article

Soil hydraulic hysteresis—the divergence between drying and wetting trajectories of the water retention curve—encodes quantitative information on soil structural organization that traditional modelling approaches do not explicitly capture. This study introduces the State Resilience Index (SRI), a dimensionless descriptor defined as the normalized area enclosed between the drying and wetting branches of a hysteresis loop and evaluates its interpretive value across five contrasting datasets from the published literature. The datasets encompass compaction gradients, repeated wetting–drying cycles, depth-resolved soil profiles, and water vapor sorption isotherms from soils differing in clay content and mineralogy. SRI values ranged from 0.023 to 0.333, responding consistently to the dominant structural drivers of hysteresis in each system. High values were associated with heterogeneous, structurally complex pore networks characteristic of productive soils; declining values reflected progressive loss of structural memory under compaction, repeated cycling, or mineralogical rigidity. A provisional interpretive framework is proposed, linking SRI ranges to soil structural quality and agronomic potential. The index is dimensionless, model-free, and applicable across measurement domains and forcing regimes, positioning it as a broadly transferable tool for the comparative assessment of soil hydraulic path asymmetry. Full article

With the occurrence of global climate change and the depletion of agricultural water resources, there is a growing need to develop rapid, non-destructive, and autonomous plant health monitoring systems. As an economically valuable crop, Ocimum basilicum L. (basil) is sensitive to changes in water availability and may exhibit stress-related morphological variations under drought and over-irrigation conditions. However, due to the visual similarity of leaf symptoms under drought stress, waterlogging stress, and optimal irrigation conditions, accurately distinguishing these conditions remains challenging in practical applications. To address this challenge, this paper presents an attention-based dual-branch deep learning framework designed to extract both subtle leaf details and channel-related features from high-resolution plant images. By combining the Convolutional Block Attention Module (CBAM) and Squeeze-and-Excitation (SE) mechanism in a parallel structure, the proposed network improves the analysis of high-resolution images with an input size of 720 × 720 pixels. Under controlled environmental conditions, with ground-truth labels obtained using soil moisture sensor measurements, the proposed model was compared with eight deep learning architectures, including DenseNet121, InceptionV3, and VGG16. The proposed model achieved a hold-out evaluation accuracy of 99.54%, outperforming the second-best model, DenseNet121, which achieved 96.43%. In addition, the proposed model reached a class-specific precision value of 100% for the Drought Stress category and achieved an area under the receiver operating characteristic curve of 1.00 under the controlled experimental setting. Taylor Diagram analysis also indicated that the model closely preserved the variability pattern of the reference data. These results suggest that the proposed application-specific framework may support non-destructive basil water-stress detection under controlled conditions. After further validation with larger datasets, different cultivars, variable environmental conditions, and real-world agricultural scenarios, the proposed approach may contribute to precision irrigation management and sustainable agricultural production. The contribution of this study should be interpreted as an application-specific implementation and evaluation of complementary attention mechanisms for controlled-environment basil water-stress classification, rather than as the introduction of a fundamentally new deep learning methodology. Full article