Search Results (2,752)

Clarifying the relationship between landscape patterns and runoff coefficient, along with identifying key influencing pathways, is crucial for formulating sustainable water resource management strategies. Since the launch of the Grain-for-Green (GfG) project in 1999, the landscape pattern of the Loess Plateau has been profoundly reshaped, altering regional rainfall-runoff processes. Assessment across 27 catchments selected in the central Loess Plateau demonstrated forest and grassland areas expanded by 738.8 km² and 480.4 km², respectively, paralleled by a 20.1% enhancement in vegetation coverage. Correspondingly, surface runoff decreased by 28.1–90.6% in the 2000s and 12.8–95.5% in the 2010s compared to the 1960s, with a similar decline in runoff coefficient. This study further developed a novel landscape unit mapping method, integrating vegetation coverage, land use, slope, and soil type to compute landscape metrics. Partial least squares regression (PLSR) and piecewise structural equation modeling (piecewiseSEM) were constructed to systematically analyze the linkage between landscape patterns and surface runoff. The constructed landscape metrics explained 64.6% of the variance in the runoff coefficient, with perimeter area fractal dimension (PAFRAC), mean perimeter-area ratio (PARA_MN), and aggregation index (AI) exerting significant influence. The findings provide a scientific basis for water resource management in regions with similar environmental characteristics. Full article

High-spatiotemporal-resolution land surface temperature (LST) data are crucial for analyzing surface energy balance, modeling temperature-related processes, and monitoring thermal environments. However, despite advancements in multi-source fusion and reconstruction techniques, high-frequency LST data remain susceptible to anomalies such as abrupt changes and outliers due to retrieval uncertainties and varying observation conditions. Conventional statistical outlier detection methods risk misidentifying physically plausible rapid weather changes as data errors, introducing systematic biases. To address this, we propose a two-stage anomaly detection framework that follows a “temporal physical pre-screening first, spatial statistical verification later” logic. First, a piecewise empirical model, based on typical diurnal LST variation characteristics, is constructed to identify points violating physical patterns. Subsequently, a spatial consistency test using median absolute deviation (MAD) is introduced to distinguish real weather-driven fluctuations from genuine data anomalies from a spatial synergy perspective. This sequential design effectively reduces the risk of mis-correcting physically reasonable temperature variations. Validated using hourly seamless LST data (2016–2021) and ground observations in the Heihe River Basin, our method outperformed Seasonal-Trend decomposition using Loess (STL), double standardization methods, and robust Holt–Winters. For over 87% of the detected anomalies, the proposed method demonstrated positive improvement rates in RMSE, MAE, R, and R². The overall average improvement rates reached 23.61%, 18.79%, 16.46%, and 61.33%, respectively, indicating robust performance. The results underscore that explicitly incorporating physical constraints enhances the reliability and interpretability of quality control for high-temporal-resolution remote sensing LST data. Full article

Three-dimensional spatial effects in deep excavations critically govern the mechanical response of retaining structures and adjacent soils, yet their quantitative characterization remains a challenge. This study systematically investigates the spatial behavior of row-pile-supported foundation pits through an integrated approach combining model tests, theoretical analysis, and numerical simulations. A novel formulation for the spatial effect influence coefficient K is derived from limit equilibrium principles and subsequently validated via ABAQUS-based finite element simulations. Model test results reveal pronounced spatial heterogeneity in earth pressure and bending moment distributions along the pit perimeter: lateral earth pressure at corner regions exceeds that at mid-side locations at equivalent depths, whereas bending moments in mid-side piles are substantially larger than those at corners. Displacement field measurements further demonstrate that corner zones, constrained bidirectionally, undergo minimal deformation, while maximum displacement occurs at the midpoints of the long sides. These observations collectively confirm the existence of a marked corner effect and a subdued side-midpoint effect under three-dimensional confinement. Complementary numerical analyses indicate that the coefficient K decreases monotonically with increasing half-angle corners and distance from the corner, thereby quantitatively capturing the decay of spatial constraint intensity. Together, these findings establish a theoretical framework for assessing excavation-induced spatial effects and provide actionable guidance for the rational design of deep foundation pit support systems. Full article

Plastic film mulching (PFM) is widely used in arid, semiarid, and seasonally arid regions, where it plays a key role in regulating agricultural productivity and CO₂ emissions. Our study aims to clarify the effects of PFM on crop yield, CO₂ emissions, and the associated tradeoffs, providing a theoretical basis for the sustainable use of PFM in agriculture. We conducted a meta-analysis to compare differences in crop yield, CO₂ emissions, and yield-scaled CO₂ emissions (YSC) between mulching and no mulching treatments while identifying factors influencing these outcomes. Our findings demonstrated that PFM enhanced crop yields of maize, wheat, and cotton by 33.2% (p < 0.001), 21.8% (p < 0.05), and 26.3% (p < 0.05), respectively. PFM stimulated CO₂ emissions in maize fields by 36.8% (p < 0.001), while decreasing them in wheat and cotton fields by 11.8% (p < 0.05) and 8.1% (p > 0.05), respectively. Consequently, PFM significantly lowered YSC for maize by 39.3% (p < 0.05) and reduced it for cotton by 27.4% (p > 0.05), but led to a 38.3% increase in YSC for wheat (p > 0.05). For maize and cotton, when crop yields exceeded 6 t/ha, the YSC under plastic film mulching was higher than that under non-mulching. In contrast, for wheat, within the conventional yield range (below 10 t/ha), the YSC under plastic film mulching was lower than that under non-mulching. For cotton, the lowest YSC under PFM was achieved under the combined conditions of water inputs > 500 mm, air temperature > 8 °C, soil pH > 8, and N inputs < 200 kg N ha⁻¹. For wheat, the lowest YSC under PFM was obtained under water inputs < 350 mm, air temperature < 8 °C, light-texture soils, and N inputs < 200 kg N ha⁻¹. For maize, the lowest YSC under PFM was achieved under water inputs < 350 mm, air temperature < 8 °C, heavy-texture soils, soil pH < 8, and N inputs < 200 kg N ha⁻¹. These insights offer guidance for the optimal use of PFM to enhance carbon efficiency and crop yield in agricultural systems. Full article

Rainfall-induced shallow loess landslides pose a significant threat to human life and property. Early warning and risk assessment of these landslides are critical prerequisites for engineering control and disaster loss reduction. The Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model (TRIGRS)-Three-dimensional Slope Stability Analysis Tool (Scoops 3D) joint model can overcome the shortcomings of using a single TRIGRS model for hydrological analysis and a single Scoops 3D model for slope stability analysis. Landslide risk assessment based on expected economic loss, on the other hand, can overcome the issue of maintaining the risk level edge and sorting at the same level. In this paper, the TRIGRS model’s head pressures were put into the Scoops 3D model, with the southeast of Fangta, a town in Shaanxi province, China, as the study area. The relationship between the slope gradient and the number of grids in each stable grade was certified. The rainfall thresholds for landslides, based on both rainfall intensity and rainfall duration, were obtained by rerunning the TRIGRS-Scoops 3D joint model. The landslide range and land uses of each dangerous slope were determined by maximum likelihood classification, and then the expected economic loss was calculated. To verify the reliability of the TRIGRS-Scoops 3D joint model, the identified dangerous slopes were compared with the results from landslide susceptibility mapping. The results show that the unstable grids are concentrated within a slope gradient of 30° to 35°, and the landslide early warning levels are divided into Tier 3, Tier 2, and Tier 1 Warnings. The occurrence of shallow loess landslides is affected by both rainfall intensity and rainfall duration, and the combined effect should be considered in early warning. The distribution of both extreme susceptible grids and high susceptible grids across all 23 dangerous slopes demonstrates the reasonableness of the TRIGRS-Scoops 3D joint model. The landslide susceptible probability within some dangerous slopes exhibits spatial variability. The mapping relationship between the slope gradient and loess landslides is extremely complex. This paper can provide a theoretical basis for the early warning and risk management for rainfall-induced shallow loess landslides; the proposed method is also applicable to other regions with similar geological and meteorological conditions. Full article

Electricity demand in the United States is steadily increasing due to rapid technological growth, especially the expansion of AI data centers and electric vehicles, which are becoming major power consumers. At the same time, rising renewable energy integration, changing weather patterns, and the deployment of battery energy storage systems are increasing variability and complexity in grid operations. These evolving conditions require advanced forecasting methods to ensure reliability and efficiency, as traditional statistical and machine learning models struggle with nonlinear and temporal dependencies. To address these challenges, this study proposes a hybrid deep learning framework that combines convolutional neural networks, long short-term memory, and bidirectional LSTM models to forecast electricity generation across both conventional and renewable energy sources. The framework incorporates seasonal-trend decomposition using loess to extract trend, seasonal, and residual components, enhancing the learning of multi-scale temporal patterns. A key contribution of this work is the development of a unified, source-specific forecasting system in which each energy source is assigned its best-performing hybrid architecture. The proposed framework achieves superior accuracy, with the CNN-Bi-LSTM model yielding the best total power results (MAPE 2.60%, RMSE 13,745 MWh, MAE 9542 MWh), while Bi-LSTM models excel for wind, biomass, geothermal, and nuclear. This enables scalable, high-precision national-level forecasting. Full article

With the widespread adoption of electric vehicles, predicting user charging consumption can enhance the operational efficiency of charging infrastructure. However, differences in user charging habits result in charging station operators obtaining data that is non-continuous and event-driven, lacking internal battery state information. This makes traditional methods difficult to apply directly. This paper explores how to accurately predict user charging consumption based on non-continuous observation data from charging stations. To this end, we propose a three-stage solution: (1) Design a method for segmenting the temporal sequence of users’ internal charging behavior based on statistical significance testing, enabling unsupervised recognition of homogeneous sequences of user behavior patterns; (2) establish a continuous-time reconstruction mechanism based on a physics-inspired power decay model to convert discrete homogenous sequences into equidistant daily sequences of charging consumption; (3) utilize seasonal and trend decomposition using Loess (STL) time-series decomposition to extract the component from the reconstructed sequence and input it as a feature into the Long Short-Term Memory (LSTM) prediction model. Through experimental validation using real charging data, the proposed method significantly enhances prediction performance, providing an effective solution for forecasting user charging consumption in actual charging stations. Full article

To address the engineering problems of high cement content, high brittleness, and weak frost resistance of cement-improved loess in the seasonal frozen soil area of Northwest China, F1 ion curing agent (F1) and cement composite improved loess (FCIL) were used in this paper. Through unconfined compressive (UC) strength tests, consolidated undrained (CU) triaxial shear tests, and microscopic pore characteristics analysis, the mechanical properties, freeze–thaw cycle deterioration law, and microscopic pore structure of FCIL were studied. The effects of cement content (C_c), F1 dosage (C_F), number of freeze–thaw cycles (N_F-T), and confining pressure (σ₃) on the strength, deformation behavior, and pore characteristics of FCIL were analyzed. The synergistic improvement mechanism of FCIL, as well as the freeze–thaw damage mechanism, was elucidated. The results show that C_c is the primary factor controlling the strength of improved loess. The incorporation of F1 can further increase UCS and markedly enhance the failure strain (ε_f), thereby achieving simultaneous improvements in strength and ductility. An appropriate mix proportion was identified as C_F = 0.2 L/m³ and C_c = 6%. After 7 d curing, FCIL exhibited a UCS of 1.35 MPa, a cohesion (c) of 205 kPa, an internal friction angle (φ) of 36.2°, and ε_f 1.8 times that of loess improved with C_c = 6% cement alone. CU triaxial shear tests indicate that, under all tested conditions, the stress–strain responses of FCIL exhibit σ3-sensitive strain-softening behavior. As C_c and σ₃ increase, triaxial peak strength (q_max) and secant modulus (E₅₀) increase significantly. Compared with natural loess (NL), FCIL shows a markedly lower porosity (n), a substantial increase in the proportion of micropores, and reductions in medium and small pores. After multiple freeze–thaw cycles, the evolution of the pore structure is effectively restrained. This indicates that the combined use of F1 and cement promotes the formation of a dense layered stacking structure, significantly improves the microscopic pore-size distribution, and enhances the mechanical performance of loess under freeze–thaw environments. Full article

Soil organic carbon (SOC) and total nitrogen (TN) are key indicators of soil fertility; however, the dynamics of carbon (C) and nitrogen (N) fractions during winter wheat growth under different tillage systems remain poorly understood. This study examined the effects of three tillage practices: no tillage (NT), subsoiling tillage (SS), and deep tillage (DT) on four soil organic carbon fractions (SOC, soil organic carbon; EOC, easily oxidized organic carbon; DOC, dissolved organic carbon; POC, particulate organic carbon) and four nitrogen fractions (TN, total nitrogen; NO₃⁻-N, nitrate nitrogen; NH₄⁺-N, ammonium nitrogen; DON, dissolved organic nitrogen) across five winter wheat growth stages (sowing, overwintering, jointing, filling and harvest) in the 0–50 cm soil profile. The results showed that SOC, its labile fractions, and TN all decreased with increasing soil depth, with tillage effects mainly confined to the 0–20 cm layer. SS achieved the highest SOC and TN contents in the topsoil, while NT and SS significantly enhanced the surface enrichment of C and N. In contrast, DT promoted more uniform nutrient distribution into the 30–50 cm subsoil. DON continuously accumulated throughout the growing season with faster accumulation rates under SS and NT; DOC peaked at the jointing stage, while EOC and NH₄⁺-N followed a consistent “decline–recovery–decline” seasonal pattern. SS yielded the highest total SOC stock (166.20 t ha⁻¹) in the 0–50 cm profile, particularly in the 0–30 cm layer. Correlation analysis showed that the coupling relationships among C and N indicators varied with soil depth, with the strongest positive correlation between SOC and EOC in the topsoil. Both SS and DT maintained higher soil water content (SWC) than NT in the 20–50 cm layers throughout the experimental period. In conclusion, SS emerges as the optimal balanced tillage strategy for dryland wheat fields on the Loess Plateau, simultaneously improving topsoil fertility, water retention, and C sequestration; meanwhile, DT is more effective for enhancing subsoil water and nutrient conditions. These findings provide a scientific basis for targeted tillage management to sustain soil fertility and productivity in rainfed dryland farming systems. Full article

Land use change profoundly impacts the trade-offs and synergies among ecosystem services in ecologically fragile watersheds. Optimizing land use patterns based on ecological function zoning is an important approach to coordinate multiple ecosystem services and promote sustainable watershed management. This study focuses on the Wuding River Basin within the Chinese Loess Plateau, using Self-Organizing Map, multi-objective genetic algorithms, and the Future Land-Use Simulation model to explore land use optimization schemes. The results show that the windbreak and sand fixation service in the Wuding River Basin presents a spatial pattern of higher values in the northwest and lower values in the southeast, while the other six services exhibit a pattern of higher values in the east and lower values in the west. Based on the ecosystem service cluster characteristics, the basin can be divided into soil and water conservation zones, habitat conservation zones, and ecologically fragile zones. The trade-offs and synergies between ecosystem services within different zones differ significantly, with the trade-off between food supply, soil conservation, and habitat quality being particularly prominent. After optimization, the food supply and soil conservation in the soil and water conservation zones increased by an average of 0.63 × 10⁴ t and 1.94 × 10⁵ t, respectively. The food supply in the habitat conservation zones increased by 0.11 × 10⁴ t, while habitat quality remained stable. In the ecologically fragile area, water production and carbon sequestration services increased by an average of 0.26 × 10⁴ t and 0.58 × 10⁵ t, respectively. During the optimization process, the reasonable allocation of grassland and unused land played a key role in balancing service conflicts. This study provides a scientific basis for coordinating trade-offs in watershed ecosystem services and achieving land use optimization management through the framework of service clusters, functional zones, and multi-objective optimization. Full article

Floating offshore wind turbines (FOWTs) exhibit complex motions under marine environmental loads and frequently undergo coupled oscillations across multiple degrees of freedom (DOFs). Accurate short-term motion prediction of these responses is crucial for operational safety and maintenance. To overcome the limitations of traditional “black-box” models under complex aero-hydrodynamic loads, this study proposes STL–TimesNet–NLinear, a novel physics-informed deep learning framework. The framework utilizes STL decomposition to explicitly decouple motion signals: NLinear captures non-stationary low-frequency slow drifts, while TimesNet extracts multi-periodic wave-frequency responses. The model was evaluated across different platform typologies—a 5 MW semi-submersible and a larger-scale 15 MW Spar-type platform—under various typical operational and extreme environmental conditions. Model performance was evaluated using comparative and ablation experiments. At a prediction-ahead time (PAT) of 5 s, the proposed model achieves coefficients of determination (R²) exceeding 0.95. Even at longer PATs, the R² remains above 0.90, consistently outperforming multiple benchmark models. Compared to traditional recurrent neural networks (e.g., LSTM), it decreases the Mean Absolute Error (MAE) for pitch motion under extreme sea states by 54.7% and increases the R² to 0.9573. Furthermore, the inference latency is only 2.4 ms per step. These findings confirm that the proposed STL–TimesNet–NLinear model provides fast and accurate solutions for the short-term motion response prediction of FOWTs, demonstrating valuable potential applications for enhancing the safety planning of offshore wind turbine operation and maintenance. Full article

In the fragile Loess Plateau ecosystem, straw return is a key measure to improve its low soil organic matter. However, the short-term carbon retention efficacy of straw return, which depends on the initial balance between carbon mineralization and sequestration, remains unclear across different soil textures. This study investigated the short-term impacts of straw return on organic carbon fractions in three soils with varying textures via laboratory incubation. Results showed that while straw return universally increased active organic carbon pools, its accumulation in the mineral-associated organic carbon (MAOC) pool was texture-dependent. Straw incorporation, especially maize straw, effectively promoted MAOC formation in clayey soils (Phaeozems and Anthrosols) with large specific surface areas. Conversely, in Arenosols, carbon was retained in active pools, limiting long-term retention potential. The mechanism involves a combined regulation by soil physicochemical properties, where clay content and specific surface area are fundamental physical drivers for MAOC accumulation, synergistically influenced by chemical factors like pH and electrical conductivity through processes such as cation bridging. These findings provide critical scientific evidence for developing texture-specific straw return management strategies for the Loess Plateau. Full article

Soil nitrogen availability is a major constraint to crop productivity in rainfed arid and semi-arid regions. The influence of straw strip mulching on nitrogen availability and transformation across soil layers remains unclear. This study investigates the effect of straw strip mulching on soil nitrogen dynamics and crop-specific variation in wheat- and potato-cultivated soils under rainfed semi-arid conditions. This study consisted of five mulching treatments, including without mulching (Tck), black plastic film mulching (Tp), straw strip mulching (Tss), plant strip without mulch (Tps), and composite strip of straw strip mulching and plant strip without mulch (Tcs) applied in wheat and potato cultivation during 2019 and 2020, and soil nitrogen fractions were determined across different soil depths. Tss mulching showed the highest increase in urease activity (48%), nitrite reductase activity (48%), microbial biomass nitrogen (52%), NH₄ (11%), acid-hydrolyzed total nitrogen (10%), acid-soluble NH₄ (6%), acid-hydrolyzed amino sugar (16%) and acid-hydrolyzable unknown nitrogen (59%) relative to Tck without mulching. While total nitrogen (11%) and acid-hydrolyzed amino acid (9%) were highest in the Tps treatment compared to Tck treatment, the mulching treatment had no significant effect on soil organic nitrogen-derived functional traits. Across all treatments, the 0–20 cm soil layer consistently showed the highest concentrations of observed soil traits, which declined with increasing soil depth. Furthermore, potato-cultivated soils showed consistently higher concentrations of these traits than wheat-cultivated soils, and the concentrations of these traits in 2020 exceeded those observed in 2019. This study highlights that maize straw mulching in strips significantly promotes soil organic nitrogen fractions, particularly in the upper soil layers, and promotes higher nitrogen availability in potato than in wheat-cultivated soils, and is recommended as an effective soil management practice to improve soil nitrogen availability in rainfed semi-arid Loess Plateau conditions. Full article

Dust and sand storms occurring in northern China are strongly controlled by near-surface aerodynamics, yet the spatial heterogeneity of these processes remains poorly understood. We obtained field measurements of the wind above gobis, sandy surfaces, and dry lakebeds in the Hexi Corridor Desert and Heihe River Basin, and sandy surfaces in northern China. First, the slope of wind profile (a₁) reveals distinct drag reversal with increasing wind speed: under low winds, a₁ increases from sandy to dry lakebed to gobi surfaces, whereas under high winds, actively saltating sandy surfaces exhibit the highest a₁, surpassing gobi and dry lakebed. Second, the dynamic feedback between sediment transport and aerodynamics is clear: at below-threshold winds, friction velocity ($u_{*}$) and aerodynamic roughness length (z₀) are lowest for sand; however, as wind speed increases to initiate significant saltation, the sandy surface develops the highest $u_{*}$ and z₀, highlighting the dominant role of grain-borne roughness. Third, the focal height (z_f) shows regional disparity, varying by up to two orders of magnitude for both sandy and gobi surfaces, with a strong correlation to local gravel coverage. This work provides spatially explicit parameterizations of surface type, offering a physical basis for modeling dust emission and transport in northern China and similar arid regions globally. Such parameterizations are essential for developing reliable early warning systems and evidence-based land management strategies. These advances contribute directly to ecosystem sustainability and community resilience in vulnerable arid and semi-arid regions under climate change. Full article

The Loess Plateau has experienced persistent vegetation greening over the past two decades, yet this recovery has occurred under a concurrent intensification of atmospheric evaporative demand and drying. This raises a key land–atmosphere question: which hydroclimatic processes most strongly constrain greening, and where do vegetation responses shift across environmental regimes? To address this issue, we integrated spatiotemporal trend analysis, Geographical Convergent Cross Mapping (GCCM)-based directional attribution, and an interpretable machine-learning framework combining Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) to diagnose the dominant controls and threshold-like response patterns of vegetation activity. Using 1 km kernel Normalized Difference Vegetation Index (kNDVI) and eight hydroclimatic variables during 2000–2019, we found that regionally averaged kNDVI increased from 0.099 in 2000 to 0.164 in 2019, with a significant trend of 0.003 year⁻¹, and greening trends covered 65.503% of the Loess Plateau. Over the same period, Vapor Pressure Deficit (VPD) increased from 0.142 to 0.275 kPa (+0.133 kPa), indicating that vegetation recovery did not occur under a more humid atmospheric background. GCCM results consistently showed stronger directional influence from hydroclimatic drivers to kNDVI than the reverse, with evaporation and thermal conditions, especially Tmin, emerging as the dominant constraints, followed by Tmax, VPD, and wind speed, whereas precipitation showed comparatively weaker recoverable influence. The tuned XGBoost model achieved strong out-of-sample performance (R² = 0.9611, RMSE = 0.0188, MAE = 0.0131), and SHAP revealed clear nonlinear thresholds: evaporation and Tmin shifted into persistently positive contribution regimes beyond 302 mm and −17.6 °C, respectively; Tmax became predominantly inhibitory beyond −1.9 °C, and Palmer Drought Severity Index (PDSI) exhibited a multi-stage non-monotonic transition around −0.7. These results provide a coherent evidence chain linking directional influence, relative contribution, and threshold boundaries, offering quantitative support for identifying climate-sensitive zones and restoration risk regimes under continued warming and rising atmospheric dryness. Full article