Search Results (2,564)

Carbon (C) sequestration and nitrogen (N) and phosphorus (P) accumulation in urban forest green spaces are significant for global climate regulation and alleviating nutrient pollution. However, the effects of management and conservation practices across different urban forest vegetation types on soil C, N, and P contents and stoichiometric ratios remain largely unexplored. We selected forest soils from Guangzhou, a major Metropolis in China, as our study area. Soil samples were collected from two urban secondary forests that naturally regenerated after disturbance (108 samples) and six urban forest parks primarily composed of artificially planted woody plant communities (72 samples). We employed mixed linear models and variance partitioning to analyze and compare soil C, N, and P contents and their stoichiometry and its main driving factors beneath suburban forests and urban park vegetation. These results exhibited that soil pH and bulk density in urban parks were higher than those in suburban forests, whereas soil water content, maximum storage capacity, and capillary porosity were higher in urban forests than in urban parks. Soil C, N, and P contents and their stoichiometry (except for N:P ratio) were significantly higher in suburban forests than in urban parks. Multiple analyzes showed that soil pH had the most pronounced negative influence on soil C, N, C:N, C:P, and N:P, but the strongest positive influence on soil P in urban parks. Soil water content had the strongest positive effect on soil C, N, P, C:N, and C:P, while soil N:P was primarily influenced by the positive effect of soil non-capillary porosity in suburban forests. Overall, our study emphasizes that suburban forests outperform urban parks in terms of carbon and nutrient accumulation, and urban green space management should focus particularly on the impact of soil pH and moisture content on soil C, N, and P contents and their stoichiometry. Full article

As the world’s largest loess deposit region, the Loess Plateau’s vegetation dynamics are crucial for its regional water–heat balance and ecosystem functioning. Leaf Area Index (LAI) serves as a key indicator bridging canopy architecture and plant physiological activities. Existing studies have made significant advancements in simulating LAI, yet accurate LAI simulation remains challenging. To address this challenge and gain deeper insights into the environmental controls of LAI, this study aims to accurately simulate LAI in the Loess Plateau using deep learning models and to elucidate the spatiotemporal influence of soil moisture and temperature on LAI dynamics. For this purpose, we used three deep learning models, namely Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Interpretable Multivariable (IMV)-LSTM, to simulate LAI in the Loess Plateau, only using soil moisture and temperature as inputs. Results indicated that our approach outperformed traditional models and effectively captured LAI variations across different vegetation types. The attention analysis revealed that soil moisture mainly influenced LAI in the arid northwest and temperature was the predominant effect in the humid southeast. Seasonally, soil moisture was crucial in spring and summer, notably in grasslands and croplands, whereas temperature dominated in autumn and winter. Notably, forests had the longest temperature-sensitive periods. As LAI increased, soil moisture became more influential, and at peak LAI, both factors exerted varying controls on different vegetation types. These findings demonstrated the strength of deep learning for simulating vegetation–climate interactions and provided insights into hydrothermal regulation mechanisms in semiarid regions. Full article

Earthquake-triggered landslides pose significant hazards to lives and infrastructure. While existing seismic landslide models primarily focus on seismic and terrain variables, they often overlook the dynamic nature of hydrologic conditions, such as seasonal soil moisture variability. This study addresses this gap by incorporating satellite-based soil moisture data from NASA’s Soil Moisture Active Passive (SMAP) mission into the assessment of seismic landslide occurrence. Using landslide inventories from five major earthquakes (Nepal 2015, New Zealand 2016, Papua New Guinea 2018, Indonesia 2018, and Haiti 2021), a balanced global dataset of landslide and non-landslide cases was compiled. Exploratory analysis revealed a strong association between elevated pre-event soil moisture and increased landslide occurrence, supporting its relevance in seismic slope failure. Moreover, a Random Forest model was trained and tested on the dataset and demonstrated excellent predictive performance. To assess the generalizability of the model, a leave-one-earthquake-out cross-validation approach was also implemented, in which the model trained on four events was tested on the fifth. This approach outperformed comparable models that did not consider soil moisture, such as the United States Geological Survey (USGS) seismic landslide model, confirming the added value of satellite-based soil moisture data in improving seismic landslide susceptibility assessments. Full article

Soil respiration (Rt), consisting of heterotrophic (Rh) and autotrophic respiration (Ra), plays a vital role in terrestrial carbon cycling and is sensitive to soil temperature and moisture. In dryland agriculture, plastic film mulching (PM) is widely used to regulate soil hydrothermal conditions, but its effects on Rt components and their temperature sensitivity (Q₁₀) across regions remain unclear. A two-year field study was conducted at two rain-fed maize sites: Anding (warmer, semi-arid) and Yuzhong (colder, drier). PM significantly increased Rt, Rh, and Ra, especially Ra, due to enhanced root biomass and improved microclimate. Yield increased by 33.6–165%. Peak respiration occurred earlier in Anding, aligned with maize growth and soil temperature. PM reduced Q₁₀ of Rt and Ra in Anding, but only Ra in Yuzhong. Rh Q₁₀ remained stable, indicating microbial respiration was less sensitive to temperature changes. Structural equation modeling revealed that Rt and Ra were mainly driven by soil temperature and root biomass, while Rh was more influenced by microbial biomass carbon (MBC) and dissolved organic carbon (DOC). Despite increased CO₂ emissions, PM improved carbon emission efficiency (CEE), particularly in Yuzhong (+67%). The application of PM is recommended to enhance yield while optimizing carbon efficiency in dryland farming systems. Full article

This study evaluates machine learning-based methods for retrieving thin Arctic sea ice thickness (SIT) from L-band radiometry, using data from the European Space Agency’s (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite. In addition to the operational ESA product, three alternative approaches are assessed: a Random Forest (RF) algorithm, a Convolutional Neural Network (CNN) that incorporates spatial coherence, and a Long Short-Term Memory (LSTM) neural network designed to capture temporal coherence. Validation against in situ data from the Beaufort Gyre Exploration Project (BGEP) moorings and the ESA SMOSice campaign demonstrates that the RF algorithm achieves robust performance comparable to the ESA product, despite its simplicity and lack of explicit spatial or temporal modeling. The CNN exhibits a tendency to overestimate SIT and shows higher dispersion, suggesting limited added value when spatial coherence is already present in the input data. The LSTM approach does not improve retrieval accuracy, likely due to the mismatch between satellite resolution and the temporal variability of sea ice conditions. These results highlight the importance of L-band sea ice emission modeling over increasing algorithm complexity and suggest that simpler, adaptable methods such as RF offer a promising foundation for future SIT retrieval efforts. The findings are relevant for refining current methods used with SMOS and for developing upcoming satellite missions, such as ESA’s Copernicus Imaging Microwave Radiometer (CIMR). Full article

Evapotranspiration (ET) crucially regulates water storage dynamics and is an essential component of the terrestrial water cycle. Understanding ET dynamics is fundamental for sustainable water resource management, particularly in regions facing increasing drought risks under climate change. In regions like southwestern China, where extreme drought events are prevalent due to complex terrain and climate warming, ET becomes a key factor in understanding water availability and drought dynamics. Using the SWAT model, this study investigates ET dynamics and influencing factors in the Jizi Basin, Yunnan Province, a small basin with over 71% forest coverage. The model calibration and validation results demonstrated a high degree of consistency with observed discharge data and ERA5, confirming its reliability. The results show that the annual average ET in the Jizi Basin is 573.96 mm, with significant seasonal variations. ET in summer typically ranges from 70 to 100 mm/month, while in winter, it drops to around 20 mm/month. Spring ET exhibits the highest variability, coinciding with the occurrence of extreme hydrological events such as droughts. The monthly anomalies of ET effectively reproduce the spring and early summer 2019 drought event. Notably, ET variation exhibits significant uncertainty under scenarios of +1 °C temperature and −20% precipitation. Furthermore, although land use changes had relatively small effects on overall ET, they played crucial roles in promoting groundwater recharge through enhanced percolation, especially forest cover. The study highlights that, in addition to climate and land use, soil moisture and groundwater conditions are vital in modulating ET and drought occurrence. The findings offer insights into the hydrological processes of small forested basins in southwestern China and provide important support for sustainable water resource management and effective climate adaptation strategies, particularly in the context of increasing drought vulnerability. Full article

As an industrial waste, slag powder can be processed and incorporated into cement-based materials as an additive, significantly improving the engineering properties of cement–soil. The strength of slag–cement-stabilized soil is subject to nonlinear interactions among multiple factors, including cement content, slag powder dosage, curing age, and moisture content, forming a complex influence mechanism. To achieve accurate strength prediction and mix proportion optimization for slag–cement-stabilized soil, this study prepared cement-stabilized soil specimens with different slag powder contents using typical sandy soil and clay from the Nantong region, and obtained sample data through unconfined compressive strength tests. A Back Propagation (BP) neural network prediction model was also established. Addressing the limitations of traditional BP neural networks in prediction accuracy caused by random initial weight thresholds and susceptibility to local optima, the sparrow search algorithm (SSA) was introduced to optimize initial network parameters, constructing an SSA-BP model that effectively enhances convergence speed and generalization capability. Research results demonstrated that the SSA-BP model reduced prediction error by 53.4% compared with the traditional BP model, showing superior prediction accuracy and effective characterization of multifactor nonlinear relationships. This study provides theoretical support and an efficient prediction tool for industrial waste recycling and environmentally friendly solidified soil engineering design. Full article

Given that the Sunkoshi River watershed (located in the southern foot of the Himalayas) is sensitive to climate change and its mountain ecosystem provides important services, we aim to evaluate its spatial and temporal variation patterns of vegetation, represented by the Normalized Difference Vegetation Index (NDVI), during 2000–2021 and identify the dominant driving factors of vegetation change. Based on the NDVI dataset (MOD13A1), we used the simple linear trend model, seasonal and trend decomposition using loess (STL) method, and Mann–Kendall test to investigate the spatiotemporal variation features of NDVI during 2000–2021 on multiple scales (annual, seasonal, monthly). We used the partial correlation coefficient (PCC) to quantify the response of the NDVI to land surface temperature (LST), precipitation, humidity, and soil moisture. The results indicate that the annual NDVI in 52.6% of the study area (with elevation of 1–3 km) increased significantly, while 0.9% of the study area (due to urbanization) degraded significantly during 2000–2021. Daytime LST dominates NDVI changes on spring, summer, and winter scales, while precipitation, soil moisture, and nighttime LST are the primary impact factors on annual NDVI changes. After removing the influence of soil moisture, the contributions of climate factors to NDVI change are enhanced. Precipitation shows a 3-month lag effect and a 5-month cumulative effect on the NDVI; both daytime LST and soil moisture have a 4-month lag effect on the NDVI; and humidity exhibits a 2-month cumulative effect on the NDVI. Overall, the study area turned green during 2000–2021. The dominant driving factors of NDVI change may vary on different time scales. The findings will be beneficial for climate change impact assessment on the regional eco-environment, and for integrated watershed management. Full article

Loess, a typical soil widely distributed in China, exhibits engineering properties that are highly sensitive to environmental changes, leading to increased erosion and the development of surface cracks. This article examines the influence of initial moisture content, dry density, and thickness on crack formation in compacted loess subjected to wet–dry cycles, using both laboratory experiments and numerical simulation analysis. It quantitatively analyzes the process of crack evolution using digital image processing technology. The experimental results indicate that wet–dry cycles can cause cumulative damage to the soil, significantly encouraging the initiation and expansion of secondary cracks. New cracks often branch out and extend along the existing crack network, demonstrating that the initial crack morphology has a controlling effect over the final crack distribution pattern. Numerical simulations based on MultiFracS software further revealed that soil samples with a thickness of 0.5 cm exhibited more pronounced surface cracking characteristics than those with a thickness of 2 cm, with thinner layers of soil tending to form a more complex network of cracks. The simulation results align closely with the indoor test data, confirming the reliability of the established model in predicting fracture dynamics. The study provides theoretical underpinnings and practical guidance for evaluating the stability of engineering slopes and for managing and mitigating fissure hazards in loess. Full article

Traditional stabilization techniques, such as lime and cement, widely used for their effectiveness, albeit with economic and environmental limitations, are leading to the search for sustainable approaches that utilize agricultural and industrial waste, such as rice husk ash, bagasse, and natural fibers. These have been shown to improve key geotechnical properties, even under saturated conditions, significantly. In particular, the combination of rice husk ash and recycled ceramics has shown notable results in Ecuadorian coastal soils. The article emphasizes the importance of selecting techniques that balance effectiveness, cost, and sustainability and identifies existing limitations, such as the lack of long-term data (ten years) and predictive models adapted to the Ecuadorian climate. From a bibliographic perspective, this article analyzes the challenges posed by expansive soils in the western coastal region of Ecuador, whose high plasticity and instability to moisture negatively affect civil works such as roads and buildings. The Ecuadorian clay contained 30% kaolinite and only 1.73% CaO, limiting its chemical reactivity compared to soils such as Saudi Arabia, which contained 34.7% montmorillonite and 9.31% CaO. Natural fibers such as jute, with 85% cellulose, improved the soil’s mechanical strength, increasing the UCS by up to 130%. Rice husk ash (97.69% SiO₂) and sugarcane bagasse improved the CBR by 90%, highlighting their potential as sustainable stabilizers. All of this is contextualized within Ecuador’s geoenvironmental conditions, which are influenced by climatic phenomena such as El Niño and La Niña, as well as global warming. Finally, it is proposed to promote multidisciplinary research that fosters more efficient and environmentally responsible solutions for stabilizing expansive soils. Full article

There is a lack of systematic research on the comprehensive regulatory effects of urea and organic fertilizer application on soil quality and cotton yield in summer direct-seeded cotton fields in the Yangtze River Basin. Additionally, there is a redundancy of indicators in the cotton field soil quality evaluation system and a lack of reports on constructing a minimum dataset to evaluate the soil quality status of cotton fields. We aim to accurately and efficiently evaluate soil quality in cotton fields and screen nitrogen application measures that synergistically improve soil quality, cotton yield, and nitrogen fertilizer utilization efficiency. Taking the summer live broadcast cotton field in Jiangxi Province as the research object, four treatments, including CK without nitrogen application, CF with conventional nitrogen application, N1 with nitrogen reduction, and N2 with nitrogen reduction and organic fertilizer application, were set up for three consecutive years from 2022 to 2024. A total of 15 physical, chemical, and biological indicators of the 0–20 cm plow layer soil were measured in each treatment. A minimum dataset model was constructed to evaluate and verify the soil quality status of different nitrogen application treatments and to explore the physiological mechanisms of nitrogen application on yield performance and stability from the perspectives of cotton source–sink relationship, nitrogen use efficiency, and soil quality. The minimum dataset for soil quality evaluation in cotton fields consisted of five indicators: soil bulk density, moisture content, total nitrogen, organic carbon, and carbon-to-nitrogen ratio, with a simplification rate of 66.67% for the evaluation indicators. The soil quality index calculated based on the minimum dataset (MDS) was significantly positively correlated with the soil quality index of the total dataset (TDS) (R² = 0.904, p < 0.05). The model validation parameters RMSE was 0.0733, nRMSE was 13.8561%, and the d value was 0.9529, all indicating that the model simulation effect had reached a good level or above. The order of soil quality index based on MDS and TDS for CK, CF, N1, and N2 treatments was CK < N1 < CF < N2. The soil quality index of N2 treatment under MDS significantly increased by 16.70% and 26.16% compared to CF and N1 treatments, respectively. Compared with CF treatment, N2 treatment significantly increased nitrogen fertilizer partial productivity by 27.97%, 31.06%, and 21.77%, respectively, over a three-year period while maintaining the same biomass, yield level, yield stability, and yield sustainability. Meanwhile, N1 treatment had the risk of significantly reducing both boll density and seed cotton yield. Compared with N1 treatment, N2 treatment could significantly increase the biomass of reproductive organs during the flower and boll stage by 23.62~24.75% and the boll opening stage by 12.39~15.44%, respectively, laying a material foundation for the improvement in yield and yield stability. Under CF treatment, the cotton field soil showed a high degree of soil physical property barriers, while the N2 treatment reduced soil barriers in indicators such as bulk density, soil organic carbon content, and soil carbon-to-nitrogen ratio by 0.04, 0.04, 0.08, and 0.02, respectively, compared to CF treatment. In summary, the minimum dataset (MDS) retained only 33.3% of the original indicators while maintaining high accuracy, demonstrating the model’s efficiency. After reducing nitrogen by 20%, applying 10% total nitrogen organic fertilizer could substantially improve cotton biomass, cotton yield performance, yield stability, and nitrogen partial productivity while maintaining soil quality levels. This study also assessed yield stability and sustainability, not just productivity alone. The comprehensive nitrogen fertilizer management (reducing N + organic fertilizer) under the experimental conditions has high practical applicability in the intensive agricultural system in southern China. Full article

To meet the requirements for radar echo acquisition and feature extraction from stratified media and rough-surfaced targets, a vehicle was geometrically modelled in CAD. Monte Carlo techniques were applied to generate the rough interfaces at air–snow and snow–soil boundaries and over the vehicle surface. Soil complex permittivity was characterized with a four-component mixture model, while snow permittivity was described using a mixed-media dielectric model. The composite electromagnetic scattering from a rough-surfaced vehicle on snow-covered soil was then analyzed with the finite-difference time-domain (FDTD) method. Parametric studies examined how incident angle and frequency, vehicle orientation, vehicle surface root mean square (RMS) height, snow liquid water content and depth, and soil moisture influence the composite scattering coefficient. Results indicate that the coefficient oscillates with scattering angle, producing specular reflection lobes; it increases monotonically with larger incident angles, higher frequencies, greater vehicle RMS roughness, and higher snow liquid water content. By contrast, its dependence on snow thickness, vehicle orientation, and soil moisture is complex and shows no clear trend. Full article

Intensive irrigation in arid and semi-arid regions can cause significant environmental issues, including salinity, waterlogging, and water quality deterioration. Watershed modeling helps us understand essential water balance components in these areas. This study implemented a modified SWAT (Soil and Water Assessment Tool) model tailored to capture irrigation practices within a 15,900 km² area of the Arkansas River Basin from 1990 to 2014. The model analyzed key water balance elements: surface runoff, evapotranspiration, soil moisture, lateral flow, and groundwater return flow, distinguishing between wet and dry years. Over 90% of precipitation is consumed by evapotranspiration. The average watershed water yield comprises 19% surface runoff, 39% groundwater return flow, and 42% lateral flow. Various irrigation scenarios were simulated, revealing that transitioning from flood to sprinkler irrigation reduced surface runoff by over 90% without affecting crop water availability in the intensively irrigated region of the watershed. Canal sealing scenarios showed substantial groundwater return flow reductions: approximately 15% with 20% sealing and around 57% with 80% sealing. Scenario-based analyses like these provide valuable insights for optimizing water resource management in intensively irrigated watersheds. Full article

Straw mulching is an important strategy for regulating soil moisture, nutrient availability, and thermal conditions in agricultural systems. However, the mechanisms by which the mulching period, thickness, and planting density interact to influence yield formation in wheat–soybean rotation systems remain insufficiently understood. In this study, we systematically examined the combined effects of straw mulching at the seedling and jointing stages of winter wheat, as well as varying mulching thicknesses and soybean planting densities, on soil properties and crop yields through field experiments. The experimental design included straw mulching treatments during the seedling stage (T₁) and the jointing stage (T₂) of winter wheat, with soybean planting densities classified as low (D₁, 1.8 × 10⁵ plants·ha⁻¹) and high (D₂, 3.6 × 10⁵ plants·ha⁻¹). Mulching thicknesses were set at low (S₁, 2830.19 kg·ha⁻¹), medium (S₂, 8490.57 kg·ha⁻¹), and high (S₃, 14,150.95 kg·ha⁻¹), in addition to a no-mulch control (CK) for each treatment. The results demonstrated that (1) straw mulching significantly increased soil water content in the order S₃ > S₂ > S₁ > CK and exerted a temperature-buffering effect. This resulted in increases in soil organic carbon, available phosphorus, and available potassium by 1.88−71.95%, 1.36−165.8%, and 1.92−36.34%, respectively, while decreasing available nitrogen content by 1.42−17.98%. (2) The T₁ treatments increased wheat yields by 1.22% compared to the control, while the T₂ treatments resulted in a 23.83% yield increase. Soybean yields increased by 23.99% under D₁ and by 36.22% under D₂ treatments. (3) Structural equation modeling indicated that straw mulching influenced yields by modifying interactions among soil organic carbon, available nitrogen, available phosphorus, available potassium, bulk density, soil temperature, and soil water content. Wheat yields were primarily regulated by the synergistic effects of soil temperature, water content, and available potassium, whereas soybean yields were determined by the dynamic balance between organic carbon and available potassium. This study provides empirical evidence to inform the optimization of straw return practices in wheat–soybean rotation systems. Full article

Lateritic soils in tropical regions feature cohesive textures and high specific resistance, driving up energy demands for tillage and harvesting machinery. However, current equipment designs lack discrete element models that account for soil moisture variations, and the microscopic effects of water content on lateritic soil deformation remain poorly understood. This study aims to calibrate and validate discrete element method (DEM) models of lateritic soil at varying moisture contents of 20.51%, 22.39%, 24.53%, 26.28%, and 28.04% by integrating the Hertz–Mindlin contact mechanics with bonding and JKR cohesion models. Key parameters in the simulations were calibrated through systematic experimentation. Using Plackett–Burman design, critical factors significantly affecting axial compressive force—including surface energy, normal bond stiffness, and tangential bond stiffness—were identified. Subsequently, Box–Behnken response surface methodology was employed to optimize these parameters by minimizing deviations between simulated and experimental maximum axial compressive forces under each moisture condition. The calibrated models demonstrated high fidelity, with average relative errors of 4.53%, 3.36%, 3.05%, 3.32%, and 7.60% for uniaxial compression simulations across the five moisture levels. Stress–strain analysis under axial loading revealed that at a given surface displacement, both fracture dimensions and stress transfer rates decreased progressively with increasing moisture content. These findings elucidate the moisture-dependent micromechanical behavior of lateritic soil and provide critical data support for DEM-based design optimization of soil-engaging agricultural implements in tropical environments. Full article