Search Results (13,455)

Snow cover plays an important role in ecological stability and seasonal water regulation in the western Greater Khingan Mountains of Inner Mongolia, a cold-region transitional zone where climate warming may intensify environmental vulnerability and sustainability challenges. Using long-term remote sensing, meteorological, and topographic datasets, this study examined the spatiotemporal changes in snow cover and assessed the relative influences of climatic and geographic factors. The results showed pronounced spatial heterogeneity, with greater snow depth and longer snow cover duration occurring in the northeastern, high-altitude, gentle-slope, and north-facing areas. Snow depth showed a slight but marginally significant declining trend during 1982–2024 at a rate of 0.026 cm a⁻¹, while snow cover days decreased by 0.39 d a⁻¹ during 1982–2020. Snow cover onset exhibited a slight but significant delay, whereas snowmelt timing showed strong interannual variability. Compared with precipitation, temperature showed stronger and more persistent associations with snow cover variations, and climatic factors explained a larger proportion of snow-depth variability than geographic factors. Overall, the results suggest that regional warming has played a leading role in recent snow cover decline. These findings improve understanding of climate-sensitive snow dynamics and provide useful evidence for ecological conservation, seasonal water-resource adaptation, and sustainable regional management in cold-region landscapes of northern China. Full article

Crop yield responses to reduced solar radiation are central to the design of agrivoltaic systems, yet crop-specific patterns and critical shading thresholds remain insufficiently characterized across diverse environments. This study evaluates yield responses across a global dataset of 546 observations from 66 studies, including agrivoltaic, shading, and agroforestry systems. Relative yield was analyzed in relation to reduction in solar radiation (RSR), crop type, and environmental variables using exploratory analysis, multiple linear regression, and tree-based ensemble models. Crop responses varied systematically across crop types. Fruits, berries, and fruity vegetables maintained or increased yield under lower shading levels, while forages, leafy vegetables, cereals, and tubers showed gradual declines, and maize and grain legumes exhibited the strongest sensitivity. Across models, yield responses were non-linear, with relatively stable yields at lower shading levels followed by accelerated declines beyond approximately 50–60% RSR. Climatic conditions further influenced these patterns, with crops in higher-radiation and warmer environments maintaining yields more effectively under partial shade. These findings demonstrate that crop yield responses depend on crop type, shading intensity, and environmental context, providing an agronomic basis for crop selection and agrivoltaic system design. Full article

Soybean is one of the most important crops worldwide, but its productivity is frequently challenged by abiotic stresses such as drought and heat, which impair physiological and metabolic processes. Biostimulants have emerged as sustainable tools to improve plant performance under adverse conditions. This study evaluated the effects of foliar application of a new biostimulant, “SB”, on soybean photosynthetic efficiency, antioxidant metabolism, biometric traits, and grain yield. SB was applied at different doses (0.5, 1.0, 1.5, and 2.0 L ha⁻¹) at the V₄ and R₁ growth stages during two seasons (2023/2024 and 2024/2025). Foliar SB application enhanced soybean leaf chlorophyll levels, RuBisCO activity, and gas exchange parameters, resulting in higher photosynthetic rates, carboxylation efficiency, and water use efficiency. In addition, foliar SB application reduced hydrogen peroxide and malondialdehyde accumulation, indicating lower oxidative damage and improved redox balance. These physiological and metabolic improvements contributed to greater root development and plant height and significant increases in yield components. Grain yield was consistently improved by all SB application rates, but the 1.5 L ha⁻¹ dose produced the most stable and positive effects across both seasons, with an average increase of more than 500 kg ha⁻¹ compared to the control. Overall, foliar SB application proved to be an efficient and promising management strategy to enhance soybean resilience and productivity under variable climatic conditions. Full article

Renewable energy deployment has accelerated globally in recent years, with renewables accounting for 29% of global electricity generation by 2024. In this context, Ecuador has significantly expanded its renewable capacity, relying predominantly on hydropower, which represented 70% of total electricity generation in 2024. Installed capacity increased from 1707 MW in 2000 to 5371 MW in 2024. This study addresses a research gap by integrating climate scenario analysis with long-term energy system modeling, evaluating the viability of Ecuador’s hydropower sector under four Representative Concentration Pathway scenarios through 2060 using the TIMES platform. The results project reductions in hydropower generation of 22%, 19%, and 15% under RCP 8.5, RCP 6.0, and RCP 4.5, respectively, with a modest increase of 1.4% under RCP 2.6, driven by changes in water availability. Overall, an average decline of approximately 14% is projected by 2060. These findings indicate that reductions in hydropower generation may compromise system reliability in hydro-dependent systems such as Ecuador. While the quantified impacts are specific to the national context, the relationship between climate variability, capacity factors, and electricity generation provides insights relevant for other regions with similar hydropower dependence. The study highlights the need to integrate climate projections into future energy planning. Full article

The Antarctic stratospheric ozone plays a crucial role in the polar climate system and is strongly influenced by energetic particle precipitation. Among these processes, medium-energy electron (MEE) precipitation enhances the production of odd nitrogen (NOx) in the polar mesosphere and stratosphere, thereby driving ozone depletion through catalytic reactions. However, quantifying its atmospheric impact remains challenging, largely because the spatial and temporal variability of MEE is poorly constrained, and most current global chemistry–climate models lack a realistic MEE forcing. This study employs the Whole Atmosphere Community Climate Model coupled with Sodankylä Ion Chemistry (WACCM–SIC) to investigate the influence of MEE precipitation during 2013–2014, when moderate geomagnetic storms were more frequent in the winter of 2013. A control simulation (Case1) and two sensitivity experiments (Case 2 and Case 3) were conducted to isolate MEE-driven effects. Model-simulated NO_x (NO + NO₂) and ozone concentrations agree well with satellite observations, indicating that WACCM–SIC captures the key photochemical and dynamical processes. The results further suggest that the direct impact of MEE precipitation on the middle and lower atmosphere during winter is relatively weak. Nevertheless, MEE-generated NO_x can be efficiently transported downward within the polar vortex, reaching altitudes below 15 km. In these regions, MEE-related NO_x enhancement can reach up to 5%, with values during the winter of 2013 approximately twice those in 2014. Sensitivity experiments further reveal that enhanced NO_x leads to pronounced ozone depletion in the lower stratosphere, with ozone losses reaching up to 25%. A clear negative relationship between NO_x and ozone is therefore evident, highlighting the importance of accurately representing MEE precipitation in chemistry–climate models. Full article

Cities are artificial ecosystems that suffer most from environmental issues and climate change. Urban Heat Island (UHI) effects represent an increasing challenge, especially for compact Mediterranean cities characterized by high population density and extensive impervious surfaces. This study assessed localized microclimatic conditions within the small Maltese coastal town of Isla through a 15-day summer field monitoring campaign. Air temperature, relative humidity, and wind speed were measured across urban locations characterized by different levels of vegetation coverage and thermal vulnerability. The analysis combined descriptive statistics, Mann–Whitney U testing, and Multiple Linear Regression (MLR) models. In addition, site-specific Nature-based Solutions (NbS) scenarios were proposed as context-sensitive strategies to support urban heat mitigation and climate resilience. The results highlighted distinct microclimatic responses between the sites investigated. In particular, the MLR analysis suggested that non-vegetated areas were more sensitive to short-term atmospheric variability associated with wind speed and relative humidity fluctuations. These findings suggest that urban vegetation may contribute not only to localized cooling, but also to increased microclimatic stability within compact Mediterranean urban environments. Full article

Cropping systems and agronomic practices play a critical role in regulating soil organic matter dynamics and carbon dioxide (CO₂) emissions, which are key components of the global carbon cycle and climate change mitigation. However, the combined effects of tillage practices and seasonal climatic variability on CO₂ fluxes in chernozem soils (chernozems, WRB classification; highly fertile, humus-rich soils typical of steppe regions) of Northern Kazakhstan remain insufficiently understood. The aim of this study was to quantify soil CO₂ emissions under conventional tillage, no-till, and bare fallow systems during spring wheat cultivation on ordinary chernozems. Field experiments were conducted between 2023 and 2025 in the Kostanay Region (Kazakhstan). Soil CO₂ fluxes were measured using a chamber-based method, while soil temperature, moisture, and microbial community structure were monitored simultaneously. The results revealed pronounced seasonal and interannual variability in CO₂ emissions, ranging from 2 to 27 g CO₂·m⁻²·day⁻¹. Conventional tillage resulted in higher peak emissions due to increased soil aeration and accelerated organic matter mineralization, whereas no-till systems exhibited a more stable seasonal pattern and lower temperature sensitivity of soil respiration (Q₁₀ = 2.40 for no-till and 3.25 for conventional tillage). The application of machine learning techniques (Random Forest) significantly improved the prediction accuracy of CO₂ fluxes (R² = 0.67; RMSE = 3.37 g CO₂·m⁻²·day⁻¹) compared to linear models. These findings provide a scientific basis for the development of climate-smart agricultural practices aimed at improving carbon management in semi-arid steppe agroecosystems. Full article

Road drainage infrastructure in ENSO-vulnerable Andean regions faces compounding threats from climatic variability, geometric inadequacy, and systemic maintenance neglect. This study presents a STEM-integrated Project-Based Learning (PjBL) diagnostic framework applied to 42 road segments along corridors connecting Loja, Ecuador, selected through a purposive-stratified spatial-coverage protocol. Using ArcGIS Survey123, standardised field data were collected on structure presence, geometry, failure modes, and condition across four structure types: crown gutters, road gutters, hydraulic chutes, and culverts. The Composite Drainage Deficiency Index (DDI, 0–100) was derived from five equally weighted binary indicators and validated through Monte Carlo Dirichlet weight-perturbation analysis and jackknife leave-one-out resampling, confirming rank-order invariance to admissible alternative weightings. The results reveal severe systemic deficiencies, including crown gutters absent at 88.1% (95% CI: 75.0–94.8) and road gutters at 81.0% (95% CI: 66.7–90.0) of sites. Every segment exhibited at least one drainage failure (100%; 95% CI: 91.6–100). The DDI identified 73.8% of segments in the High or Critical band (DDI ≥ 60; mean = 60.2 ± 20.4). Hierarchical clustering isolated one geometric outlier whose exclusion altered the aggregate metrics by <1.2%. These findings establish a georeferenced baseline for maintenance prioritisation and validate the methodological reproducibility of academically integrated field protocols for infrastructure diagnostics. Full article

The eastern coast of China, characterized by a pronounced climatic gradient and diverse ecosystems, is an ideal region for exploring the spatiotemporal dynamics of carbon fluxes and their drivers. Based on observations from eight flux tower sites, together with meteorological, remote sensing, and ecohydrological variables from 2001 to 2022, this study developed Back Propagation (BP), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) models to estimate regional gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem productivity (NEP). Among them, RF performed best, achieving validation R² values of 0.92, 0.84, and 0.83 for GPP, ER, and NEP, respectively, and was therefore selected for regional upscaling. The regional mean GPP, ER, and NEP were 1578.38, 1286.05, and 334.56 g C m⁻² yr⁻¹, respectively, indicating that the region functioned as a net carbon sink during the study period. GPP, ER, and NEP exhibited a clear spatial gradient, with higher values in the south and lower values in the north. Total regional NEP increased from 344.12 Tg C in 2001 to 517.73 Tg C in 2022, reflecting a continuous strengthening of terrestrial carbon sink strength. Forests contributed most to the regional carbon sink, while the ecosystem-level NEP contribution of croplands increased over time; by contrast, the total carbon sink of wetlands declined because of area loss. These results suggest that ecological restoration, vegetation greening, and land cover optimization jointly enhanced the carbon sink along the eastern coast of China. These findings have important implications for ecological management and green low-carbon development along the eastern coast of China. Full article

In recent decades, intensified temperature variability has increased the likelihood of abrupt transitions between anomalously cold and warm conditions, exerting substantial ecological and societal impacts. This study identifies rapid temperature flip events (RTFEs), including cold-to-warm transition events (C2Ws) and warm-to-cold transition events (W2Cs), across China using the CN05.1 gridded daily mean temperature data for 1961–2022, and further reveals their regional heterogeneity and long-term changes. Eastern China represents a hotspot of RTFEs, exhibiting higher frequencies and stronger intensities compared with western China. RTFEs are most frequent in spring, followed by summer. Over the period 1961–2022, both C2W and W2C became more frequent and more intense, with W2C showing a larger increase in frequency of 0.54 events century⁻¹ and a larger increase in intensity of 0.29 s.d. century⁻¹, compared with increases of 0.01 events century⁻¹ and 0.11 s.d. century⁻¹, respectively, for C2W. In addition, significant decadal changes in both types of events were observed across large areas of China during the 1990s–2000s and 2010s, following a high–low–high pattern. Analysis across the seven natural sub-regions reveals distinct high-hazard areas where RTFE hotspots coincide with increasing frequency and intensity: the eastern monsoonal regions of China for W2Cs and Inner Mongolia for both event types. These findings contribute to addressing climate change and mitigating the risk of RTFEs. Full article

Existing research on the landscape patterns of urban parks and green spaces demonstrates a disproportionate focus across tiers within China’s urban hierarchy. Numerous studies have concentrated on economically developed first-tier cities, such as Beijing, Shanghai, and Guangzhou. In contrast, medium-to-large non-first-tier cities, especially provincial capitals and emerging cities within the first- and second tiers, have been relatively understudied, although they have received increasing attention in recent years. This bias extends regionally, with studies predominantly examining cities in the more developed central and eastern regions, while less-developed areas and lower-tier cities receive significantly less attention. This study tracks changes in park quantity, spatial concentration, patch structure and driver associations at three planning-related time points. Shenyang provides a distinct cold-region and old industrial city case, shaped by long winters, industrial renewal and outward urban growth. Furthermore, to inform park and green-space planning in Northeast China’s cold-climate cities, exemplified here by Shenyang, a major metropolis with a monsoon-influenced humid continental climate (Köppen Dwa), long cold winters, and relatively short warm summers, we document a shift in park distribution from the urban core to peripheral areas. Based on park vector layers reconstructed from planning documents, remote sensing interpretation and field verification, this study combined spatial analysis, landscape metric calculation and driver-association modeling. ArcGIS Pro was used to identify changes in distribution centers, directional extension and local clustering; FRAGSTATS 4.2 was used to calculate park landscape metrics; and SIMCA-P 14.1 was used to examine the statistical associations between selected landscape indicators and potential driving variables. The results show that the number and total area of parks in central Shenyang increased substantially from 2000 to 2024. Spatially, park distribution became less concentrated in the traditional inner city, while new clusters gradually appeared in peripheral districts and newly developed urban areas. The old urban core remained important, but its dominance weakened as park provision expanded outward. The landscape metric results further indicate that park expansion was accompanied by more irregular patch forms, stronger fragmentation and declining structural continuity. The driver association analysis suggests that climate conditions, population change, industrial restructuring, real estate investment, road construction and urban greening policies were related to different aspects of park landscape change. These associations should be interpreted as statistical relationships rather than direct causal effects. Overall, this study clarifies the spatial restructuring of park green spaces in a cold-region old industrial city and provides planning evidence for improving park connectivity, coordinating green space expansion with urban construction and supporting sustainable park system development in Northeast China. Full article

Gravity Recovery and Climate Experiment (GRACE) satellites primarily monitor changes in land water storage, including groundwater, soil moisture, lake and river surface water, and canopy and snow water. However, its coarse spatial resolution of 0.25 degrees limits its ability to observe smaller basins. To assess aquifer depletion and evaluate a long-term water resource management framework, GRACE data are crucial. It remains rare for GRACE-focused studies to be conducted in great depth. A comprehensive review of 80 articles published between 2011 and 2025 was conducted using the Scopus and Web of Science databases. These articles focused on downscaling GRACE data using machine learning (ML) methods. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines were used in this review. This study highlights the attributes of ML models, the input variables used, the evaluation metrics, and the output resolution. Based on the analysis of the articles, random forest (RF) methods were used in the majority of the papers. Gradient boosting (GB), artificial neural networks (ANN), support vector machines (SVM), support vector regression (SVR), and long short-term memory (LSTM) were the most widely used ML methods. As input variables, rainfall (Pr), soil moisture (SM), and runoff (Qs) are essential. In 2011, there were very few journal articles; since 2021, the number has increased. The number of published studies from China was the highest (24), followed by the USA (12) and Iran (9). A total of 38 journals published reviewed articles. In terms of articles, Remote Sensing generates 19%, Journal of Hydrology has 10%, and Journal of Hydrology: Regional Studies has 8%. The paper also discusses limitations, challenges, recommendations, and potential future directions for improving the accuracy of the GWS change prediction model. Full article

In Brazil, more than 80% of households rely on electricity for water heating, representing approximately 13% of residential electricity consumption and significantly contributing to peak grid demand. As a prominent alternative for supplying household thermal energy and reducing grid stress, this study experimentally evaluates, under tropical climate conditions, the performance of a modular water-based photovoltaic–thermal (PVT) system and compares it with a conventional photovoltaic (PV) system operating simultaneously under identical environmental conditions. The PVT system, based on commercial PV modules coupled to roll-bond heat exchangers, a storage tank, and a shower outlet, was tested under three hydraulic regimes: natural thermosiphon, closed-loop, and Forced circulation. A dedicated ESP32-based data acquisition system, integrated with a cloud platform, continuously monitors electrical, thermal, and meteorological variables. Results show that PVT modules exhibit a small electrical efficiency reduction due to increased cell temperatures, which is largely compensated by the simultaneous thermal generation, yielding overall efficiency gains of 74.04%, 76.53%, and 7.62% over the reference PV system for Normal, Forced, and Closed circulation, respectively. The comparative analysis identifies Forced-circulation scheduling and the matching between thermal generation and consumption as key factors for performance optimization. The findings provide practical guidelines for deploying PVT systems to replace electric showers in tropical regions, reducing residential electricity consumption and mitigating peak-demand stress on the grid. Full article

Yet-to-develop infrastructures like biorefineries are exposed to many uncertainties compared to established systems such as fossil-based ones. The exposure to fluctuations of biomass supply is a growing concern due to the increasingly magnified consequences of climate change. This paper presents a two-stage stochastic mixed integer linear programming framework to design circular production systems using biomass wastes subjected to yield uncertainty. The modelling framework embeds an expected profit objective function in a spatially explicit, multi-echelon, multi-period, multi-feedstock, and multi-product lignocellulose-based biorefining supply chain network. The modelling framework integrates a risk-constrained formulation based on downside risk to represent decision-makers’ propensity towards risk. A case study based on real data from south-west Hungary is presented. Results show that biobased biorefining systems remain a risky capital-intensive investment, but profitable configurations of the network can be achieved, despite the inclusion of large variabilities in the biomass yields. Although they exhibit expected profits either comparable or slightly lower than risk-neutral configurations, the solutions subjected to risk-based regularisation (risk-constrained), are more stable than their stochastic counterpart. Furthermore, biomass supply chains, that can develop either a centralised or a decentralised configuration, would correspond to different risk profiles. While the localisation of centralised plants generates higher expected profits compared to sparsely distributed facilities, the latter, with a more diffuse presence of plants in the territory, can lead to a more stable system and to a more homogenous integration with local communities. Full article

Long-term balanced mineral fertilization is essential for sustainable sweet cherry production under variable climatic conditions. This seven-year field study (2016–2022) evaluated the effects of NP, NPK, and NPKMg fertilization including the control on six parameters: trunk cross-sectional area (TCSA), fruit yield (FY), crop load (CL), fruit diameter (FD), water-soluble dry matter content (BRIX), and cherry leaf spot incidence (CLS) in two sweet cherry cultivars (‘Vera’ and ‘Carmen’). TCSA increased continuously in both cultivars, while fertilization effects on growth, FY, CL, and FD varied among years and were significantly higher under NPK and NPKMg treatments compared with the control, particularly in specific years. Leaf spot incidence was reduced in the NPKMg treatment in epidemic years, although strong interannual and cultivar-dependent variability was observed, with ‘Carmen’ being more susceptible than ‘Vera’. Correlation and regression analyses revealed significant relationships among key traits, particularly for CL vs. FY, FD vs. CLS, TCSA vs. CLS, and BRIX vs. CL, indicating strong vegetative–generative interactions. Principal component analyses further showed that tree and fruit traits as well as disease incidence were structured along a limited number of integrated multivariate components explaining most of the variance. In conclusion, balanced fertilization improved productivity and partly reduced disease incidence, but treatment effects were strongly influenced by complex multivariate interactions and interannual climatic variability. These findings highlight the importance of integrative analytical approaches to optimize nutrient management under Central European conditions. Full article