Search Results (3,631)

The goal of this work was to determine the effect of soil freeze–thaw processes on the formation of water-stable aggregates (WSA) and plant available water (PAW) in soils of different textures, depending on the intensity of tillage: conventional tillage (CT), reduced tillage (RT) and no-tillage (NT). The WSA value (0.4%) and PAW mean (5.5%) in sandy loam were higher than in loam. The average content of WSA and PAW tended to decrease in the following order: air-dry soil > soil with water content at field capacity > soil near full saturation. These results indicate that WSA in soils that are close to full saturation upon freezing will be less stable after thawing and will decrease the PAW. The content of WSA in NT was 9.4% higher than in RT and 14% higher than in CT. The content of PAW in NT was 5.6% higher than in CT and 13.6% higher than in RT. The effects of various physical and chemical properties on PAW as a function of water level during the freeze–thaw process indicate that WSA content acted as a direct factor for PAW. In a temperate climate zone under dry meteorological conditions, NT would have a promising future for soil stability by maintaining higher WSA and PAW. Full article

Urban heat stress is a growing challenge in hot semi-arid cities, where neighbourhood urban design influences microclimate and outdoor comfort. This study evaluates the effect of neighbourhood spatial layout in Erbil city, using ENVI-met simulations. Five neighbourhoods with varying layouts were modelled under standardized conditions, including uniform building height, surface characteristics, and meteorological forcing. Hourly outputs of air temperature, relative humidity, wind speed, surface temperature, mean radiant temperature, universal thermal climate index, and sky view factor were analyzed after excluding the spin-up period. Results indicate that, while all neighbourhoods exhibited similar diurnal timing of thermal extremes, a key distinctive finding is the identification of a neighbourhood that behaves differently across seasons. The Pavilion neighbourhood remained cooler during summer conditions, while maintaining warmer thermal conditions during winter. This dual seasonal behaviour contrasts with the other neighbourhoods, which generally exhibit a trade-off between reduced summer heat stress and winter cooling. The Pavilion neighbourhood is distinguished by the presence of integrated water lagoons, suggesting that the blue infrastructure, in combination with spatial openness and greenery, can moderate thermal extremes. Overall, the study highlights the importance of neighbourhood-scale spatial design in mitigating urban heat and provides evidence to support the development of sustainable neighbourhoods. Full article

The rice leaf roller (RLR), Cnaphalocrocis medinalis (Guenée), is a major migratory pest that threatens rice production across East Asia. Effective management of migratory pests relies fundamentally on accurately identifying their source areas, population dynamics, and key environmental drivers. Western Hunan is a critical rice-growing region characterized by unique topography and varied climates, making it a principal pathway for RLR migration. Based on 14-year (2011–2024) monitoring datasets, we identified substantial interannual variability in July RLR abundance in Western Hunan, when the population typically peaks, highlighting the episodic and unstable nature of regional infestations. Back-trajectory simulations reveal that heavy occurrence years of RLR feature clear northward migration pathways from the Indo-China Peninsula and South China to Western Hunan in July, supported by strong southerly winds along the route. Multiple linear regression analysis further shows that spring warmth initially facilitates high population accumulation in source regions, and the synergistic effect of source-region precipitation deficits and abundant local rainfall triggers large-scale immigration into Western Hunan. These meteorological factors collectively account for up to 66% of the interannual variability in RLR population fluctuations, confirming that climatic conditions largely determine outbreak severity. This provides a robust quantitative framework for regional early-warning systems and sustainable pest management in migratory corridors. Full article

To investigate the influence of complex terrain on precipitation characteristics in the Jinsha River Basin (JRB), this study analyzes the spatiotemporal distribution of precipitation amount, frequency, and intensity under different topographic factors in the middle and lower reaches of the JRB (MLJRB), based on hourly precipitation observations from 1745 ground stations deployed by the China Meteorological Administration. The results indicate the following: (1) Precipitation amount increases gradually from low altitudes, peaks at sub-high altitudes, and then decreases. The highest precipitation frequency occurs at high altitudes, while the greatest precipitation intensity is observed at mid altitudes. (2) Spatially, a high-precipitation center with high frequency and intensity is formed in the lower reaches of the JRB, whereas the northern part of the study area exhibits a low center for both frequency and intensity. (3) Pronounced diurnal and monthly variations are observed at all altitudes. Precipitation amount and intensity peak during nighttime hours. On a monthly scale, both precipitation amount and intensity increase from May to July or August and then decrease, while the trend for precipitation frequency is not entirely consistent. (4) Precipitation amount shows little change with increasing slope gradient. Precipitation frequency increases with slope gradient, whereas precipitation intensity exhibits a clear decreasing trend. Eastern slopes receive higher precipitation amount and frequency compared to other aspects, followed by southern slopes, with western slopes receiving the lowest; however, differences in precipitation intensity among different slope aspects are minimal. In conclusion, the MLJRB exhibits strong spatiotemporal variability, distinct vertical differentiation, and pronounced periodic variation in precipitation. Precipitation frequency and intensity in this region are also associated with micro-topography. Full article

Airborne microplastics (MPs) are increasingly recognized as a pervasive pollutant with potential implications for environmental and human health. Despite growing concern, the influence of seasonal dynamics and environmental conditions on MP distribution remains poorly understood. This study investigates the temporal variability and environmental drivers of MPs across outdoor settings, highlighting how factors such as temperature, wind speeds, and precipitation modulate their behaviors. Using a combination of shielded gravitational deposition sampling (Sigma-2) and bulk deposition sampling over four seasons, coupled with μ-FTIR single particle analysis, we quantified MP abundance, size distribution, morphology, and polymer composition across contrasting environments. Deposition fluxes differed between samplers, with bulk samplers yielding 131–1589 MP/m²/d and Sigma-2 samplers yielding 4208–39,126 MP/m²/d. Multivariate analyses indicate that temperature was significantly correlated with MP loading in the Sigma-2 sampler, whereas precipitation effects were not detectable within the temporal resolution of our dataset. Polymer profiles differed between samplers, with Sigma-2 samples enriched in polyamide (PA) and resin-type particles, and bulk samples containing higher proportions of rubber and acrylate. Spherical and irregular particles were the predominant morphologies across both samplers. Together, these findings provide new insights into the environmental controls governing airborne MP deposition and underscore the need for long-term, meteorology-integrated, and methodologically standardized monitoring strategies to improve exposure assessment and inform mitigation efforts. Full article

Net surface shortwave radiation (NSSR) is a key parameter in the Earth’s energy cycle, greatly affecting global water and heat balance. Currently, a comprehensive comparative analysis regarding the accuracy of different models remains severely lacking, and there is also a notable deficiency in the systematic exploration of seasonal radiative drivers. Therefore, we developed a machine learning-based seasonal NSSR estimation model. By integrating in-situ observational data with multi-source remote sensing datasets, we achieved precise quantification of radiative fluxes. This proposed model framework employed three cutting-edge algorithms, namely Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), to capture the non-linear interactions among radiative drivers across the four seasons. Through mechanistic sensitivity analysis, we quantified the impacts of key variables on NSSR prediction. The results unequivocally demonstrated that the RF algorithm demonstrated the best performance. Its seasonal R² were 0.95 (spring), 0.89 (summer), 0.95 (autumn), and 0.96 (winter). The Solar Zenith Angle (SZA) dominated in spring and winter; its absence reduced R² by 0.23 and raised RMSE by 20.66–26.42 W/m². Meteorological factors mattered most in summer; excluding them cut R² by 0.17 and hiked RMSE by 23.82 W/m². This study provides actionable insights for terrestrial radiation budget research. Full article

In evaluating energy consumption in building complexes, the influence of urban microclimate variations—primarily driven by the urban heat island (UHI) effect—is often overlooked, leading to modeling inaccuracies. This study develops a numerical simulation framework integrating Weather Research and Forecasting (WRF) and EnergyPlus to assess the energy consumption of university dormitories while accounting for regional microclimate conditions. This is because university dormitories serve as a key indicator for measuring the type of high-density residential buildings in China. The model incorporates dynamic microclimate variables, including ambient temperature, relative humidity, wind speed, solar radiation, and cloud cover, to simulate dormitory energy consumption profiles. Simulation results are validated against measured data, yielding an annual energy consumption error of −1.03%. Quantitative analysis indicates that ignoring the microclimate effect and directly using data from nearby meteorological stations or TMY data has a limited impact on the annual total energy consumption but has a significant impact on seasonal results. To improve the simulation accuracy of building complexes, more attention should be paid to temperature and relative humidity. Moreover, for areas with low occupant density and a high shape coefficient, energy consumption simulation should also consider the local microclimate factors. Full article

In view of the limitations of existing studies, in which remote sensing extraction of algal blooms is easily affected by cloud interference, and mechanistic models are constrained by excessive parameters and inadequate representation of nonlinear relationships, resulting in limited timeliness and accuracy, this study took Taihu Lake as the study area and constructed a research framework of bloom extraction-scale matching-spatial prediction-scenario response based on Landsat imagery and gridded meteorological data, constructing the relationship between meteorological factors and algal blooms using machine learning methods. First, the Tasseled Cap transformation (TCap) and Floating Algae Index (FAI) were combined to extract the spatial distribution and area of algal blooms, while cloud interference was addressed to improve recognition stability under complex background conditions. Next, the spatial scales of bloom rasters and meteorological factors were unified to build a matched bloom-meteorological dataset. On this basis, a U-Net model driven by multiple meteorological factors was developed to predict remote-sensing-based bloom distribution/extent patterns under three warming scenarios. The results showed that: (1) the combination of TCap and FAI improved the accuracy and efficiency of bloom extraction; FAI was more sensitive but tended to overestimate bloom area, whereas TCap was more stable under cloud interference; (2) the U-Net model achieved an overall accuracy of 95% and a prediction accuracy of 88%; and (3) bloom area increased under all three warming scenarios, and the extent of expansion generally became more pronounced with increasing warming magnitude, although the response was not strictly monotonic across all cases. Based on the seasonal mean bloom-area increase relative to the baseline condition (S0), the warming response was strongest in spring, followed by summer and autumn, and weakest in winter. This study can provide a reference for cyanobacterial bloom early warning and water environment management in Lake Taihu. Full article

This study confirmed that cultivation technologies, cultivar, and meteorological conditions significantly influenced the contents of ascorbic acid, total polyphenols, and phenolic acids in sweet potato roots. Ascorbic acid content ranged from 27.22 to 111.9 mg·100 g⁻¹ DW, with the highest values recorded in the traditional cultivation system (TC), reaching 111.9 mg·100 g⁻¹ DW in ‘Carmen Rubin’ and 111.4 mg·100 g⁻¹ DW in ‘Beauregard’. In contrast, in the ‘Satsumo Imo’ cultivar grown under nonwoven fabric (WC), ascorbic acid content decreased to 49–58% of the values obtained in TC. Genetic factors strongly differentiated the contents of bioactive compounds. The ‘Purple’ cultivar showed the highest contents of total polyphenols (up to 963.5 mg·100 g⁻¹ DW) and phenolic acids (17,067.42 mg·100 g⁻¹ DW), whereas the lowest values were recorded in ‘Satsumo Imo’ (858.82–1225.89 mg·100 g⁻¹ DW). Cultivation under polyethylene film (FC) increased and stabilized phenolic compounds. The ‘Carmen Rubin’ cultivar also exhibited high phenolic acid content (5332.04–5447.60 mg·100 g⁻¹ DW), while ‘Beauregard’ was characterized by high stability of this trait (1535.93–1581.46 mg·100 g⁻¹ DW). From a practical perspective, the results highlight the importance of appropriate cultivar selection and cultivation technology for obtaining raw material with high functional value. These findings may serve as a basis for developing agrotechnical recommendations aimed at producing sweet potatoes with enhanced nutritional and health-promoting qualities under the climatic and soil conditions of Poland. Full article

Analyzing the propagation dynamics from meteorological drought (MD) to hydrological drought (HD) is essential for sustainable water resource management, particularly under climate change. This study analyzed the multidimensional propagation characteristics and their driving factors from MD to HD in the Jialing River Basin from 1993 to 2020. The temporal characteristics of drought propagation were analyzed using monthly and daily drought indices, with a focus on variations in initiation lag times across seasons and drought grades. The attenuation and amplification effects during drought propagation were quantified using event propagation ratios, while examining the differential propagation patterns across different drought grades. Additionally, the Geographical Detector Model was employed to identify the main drivers of spatial heterogeneity in hydrological drought response rates. The main findings are as follows: (1) at the daily scale, the initiation stage had the shortest lag, while peak and termination stages showed longer lags. Seasonal and drought grade variations were observed in the initiation lag, with shorter lags in summer and autumn. (2) Drought propagation from MD to HD resulted in an attenuation of maximum intensity, while duration and severity were amplified. (3) Spatial heterogeneity in HD response rate was mainly influenced by evaporative conditions, vegetation cover, and topography. Full article

Accurate prediction of forest fires is crucial for enhancing regional fire prevention and control. Existing models frequently rely on static factors such as weather and terrain, while insufficiently taking into account the Fuel Moisture Content (FMC), a critical internal factor that directly determines fire behavior. Instead, proxies like the Normalized Difference Vegetation Index (NDVI) are commonly employed, which weakens the physical foundation of predictions. This study assesses the marginal contribution of integrating dynamic FMC into fire prediction models. Concentrating on California, we developed a random-forest-based model that incorporates high-resolution FMC products retrieved by our team, along with meteorological, topographic, vegetation, and anthropogenic data. Through comparative experiments and SHapley Additive exPlanations (SHAP) analysis, we evaluated model improvements and the contribution mechanisms of key drivers. The results indicated that: (1) Incorporating FMC significantly enhanced model performance, with precision and specificity increasing by 3.93% and 3.60%, respectively, and the Area Under the Curve (AUC) showing improvements, suggesting heightened sensitivity in detecting actual fire occurrences. (2) SHAP analysis disclosed nonlinear effects and threshold dynamics: temperature was the dominant positive driver (the fire risk soared above 20 °C); FMC demonstrated a negative correlation with fire risk, with 100% serving as a potential threshold; elevation presented an inverted U-shaped pattern (the peak risk occurred at 1000–1500 m); and population density exhibited a shifting influence from positive to negative. (3) The monthly risk maps for California in 2023 captured the seasonal progression of fire risk and spatial patterns consistent with historical fire points. The fire risk map for 9 September 2020 also demonstrated consistency with the spatial distribution of the actual fire points on that day. This study validates that the integration of dynamic FMC strengthens the mechanistic foundation and early-warning capacity of fire prediction models, providing scientific backing for targeted fire management. Full article

The primary aim of this study is to identify the driving mechanisms behind long-term water-level changes and drought–flood transitions in Dongting Lake. To achieve this, we employed methods including the Improved Standardized Water Level Index (ISWI), Mann–Kendall test, Sen’s slope estimator, and a random forest–SHAP model to analyze hydro-meteorological data from 1992 to 2023. The results demonstrate a significant overall decline and spatial heterogeneity in water levels, alongside a systemic shift in the regional pattern from flood-dominated conditions to frequent droughts with intense drought–flood abrupt alternations. Crucially, during the critical autumn water recession period, runoff anomalies from the Yangtze River’s three outlets emerged as the dominant factor driving water-level changes, far exceeding the influence of local precipitation. Furthermore, a recent downward shift in the water level–discharge relationship indicates that under identical inflow conditions, water levels are now 1.5 to 2.0 m lower than in previous decades. These general findings highlight that critical-period inflow reductions and altered boundary hydrodynamic conditions mutually amplify low-water-level risks, providing a scientific reference for adaptive water resource management in complex river-connected lakes. Full article

Accurate solar radiation estimation is critical for optimizing solar energy applications. This study divided 819 meteorological stations in China into six solar radiation zones using k-means, hierarchical, and bisecting k-means clustering based on daily relative sunshine duration. Correlation analysis and feature importance evaluation were conducted to quantify the contributions of key meteorological variables. A comparison of models considering regional heterogeneity was performed. Six sunshine-based empirical models, three machine learning models (Random Forest, Support Vector Machine, and Extreme Gradient Boosting), and two deep learning models (Long Short-Term Memory and Gated Recurrent Unit) were systematically evaluated across 98 stations with observed solar radiation data. Model performance was assessed using the coefficient of determination (R²), mean absolute error (MAE), root mean square error (RMSE), and normalized RMSE (NRMSE). Results showed that k-means clustering outperformed the other two methods and was adopted for final zoning. The correlation analysis identified sunshine duration (S), extraterrestrial radiation (R_a), temperature difference (ΔT), and maximum temperature (T_max) as the dominant influencing factors, with clear regional heterogeneity. The deep learning models, particularly LSTM (R² = 0.939, RMSE = 1.702 MJ/m/²/d¹, MAE = 1.319 MJ/m/²/d¹, NRMSE = 0.046), achieved the highest accuracy, followed by GRU, XGB, SVM, and RF. Among the empirical models, Model 5 performed best in Zones 1, 3, 4, and 5, while Model 6 was optimal in Zones 2 and 6. The key novelty of the study is an integrated zoning–prediction framework for regional solar radiation estimation, combining clustering validation, correlation analysis, empirical model calibration, and deep learning benchmarking, with enhanced physical interpretability and prediction accuracy. Full article

The rapid expansion of renewable energy has raised the demand for accurate, long-term wind power forecasting. However, wind power series are strongly affected by meteorological factors and exhibit pronounced volatility, making long-term prediction challenging. To model these characteristics more comprehensively, we propose STFNet, a dual-branch neural architecture that integrates time-domain and frequency-domain modeling. STFNet contains two key modules: (1) an MLFE module, which explicitly captures lag effects and non-stationary transitions through parallel multi-scale convolutions and a difference-convolution branch and further enhances multivariate dependency learning via cross-variable interaction modeling, and (2) an FGFE module, which applies DCT to capture long-cycle trends and uses a learnable low-pass filter for noise suppression. Experiments on two real-world wind farm datasets (LY and HG) show that STFNet consistently outperforms strong baselines, achieving average MSE reductions of 15.9–26.6% while maintaining a high computational efficiency. Ablation studies further confirm the effectiveness of each module, indicating the strong practical potential of STFNet for wind farm operation and management. Full article

Against the background of a warming and humidifying climate on the Qinghai–Tibet Plateau, increasing attention has been paid to the sustainability of water resources and ecosystems in the Qinghai Lake Basin. Investigating the characteristics of precipitation stable isotopes and moisture sources provides critical insights into the driving mechanisms of the regional hydrological cycle. In this study, precipitation samples collected at the Qinghai Lake Wetland Ecosystem National Observation and Research Station from June 2023 to October 2024 were analyzed for hydrogen (δ²H) and oxygen (δ¹⁸O) stable isotopes. The temporal variations of δ²H, δ¹⁸O, and deuterium excess (d-excess) were characterized, and their relationships with air temperature and precipitation amount were examined. In addition, a backward trajectory model was employed to identify the moisture sources of precipitation during the observation period. The results indicate that: (1) precipitation stable isotopes and d-excess exhibit pronounced seasonal variability, characterized by enrichment in summer and depletion in spring and autumn; (2) the Local Meteoric Water Line (LMWL) for the basin is defined as δ²H = 8.15δ¹⁸O + 38.71 (R² = 0.93), with both slope and intercept exceeding those of the Global Meteoric Water Line (GMWL); (3) precipitation isotopes show a discernible temperature effect but are jointly controlled by multiple moisture sources and meteorological factors; and (4) backward trajectory analysis combined with d-excess values reveals that precipitation moisture is primarily derived from westerly transport, while locally recycled moisture and continental air masses also exert significant influences. Overall, these findings reveal the multi-source driving mechanisms of the regional hydrological cycle and provide critical scientific support for understanding hydrological processes in alpine inland basins and their responses to future climate change, thereby contributing to the sustainable management of regional water resources. Full article