Search Results (8,003)

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

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

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

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

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

Macrozoobenthic communities function as important bioindicators of natural and anthropogenic pressures in transitional ecosystems and contribute to ecosystem processes. Transitional systems, such as lagoons, estuaries and coastal ponds, exhibit strong physico-chemical variability, often intensified by anthropogenic pressures and climate change. Changes in macrozoobenthic communities across five Veneto Po Delta lagoons were assessed through long-term monitoring (2008–2025) conducted within the Water Framework Directive and additional monitoring activities. The macrozoobenthic communities were analysed to assess temporal variability and inter-lagoon differences in the Po Delta system; ecological indices were generally stable, but organism density showed significant interannual fluctuations, with marked declines in 2008, 2009, 2024, and 2025. Univariate and multivariate analyses identified phases of community restructuring driven by temporal shifts in species composition and relative abundance. These patterns may reflect the interacting effects of multiple stressors, including long-term anthropogenic pressures and the recent expansion of the invasive blue crab Callinectes sapidus, although causality was not assessed. Increases in water temperature and suspended solids were observed across all lagoons, potentially affecting benthic communities. Overall, this study provides an assessment of macrozoobenthic variability and a preliminary analysis of the factors that may have influenced it, highlighting patterns that warrant further investigations to elucidate the underlying mechanisms. Full article

Runoff on the Chinese Loess Plateau has declined substantially over recent decades, but the relative roles of climate change and non-climatic disturbance remain debated. Here, we provide a robust regional attribution of runoff reduction across 14 major catchments during 1961–2009 by integrating seven Budyko-based climate elasticity methods with long-term hydro-meteorological analysis and change-point detection. Across the region, runoff and runoff coefficients decreased markedly, while evapotranspiration and leaf area index increased, indicating a widespread reduction in catchment water yield. Runoff showed consistently greater sensitivity to precipitation than to potential evapotranspiration, highlighting precipitation as the primary climatic control on runoff variability. However, the Budyko-based climatic component explained only part of the observed runoff decline, and the residual component not explained by annual precipitation and potential evapotranspiration was large in many catchments, with estimated contributions generally exceeding 50% and reaching more than 80% in several basins. Independent evidence, including vegetation greening, the expansion of ecological engineering measures, and increasing anthropogenic water demand, suggests that this residual was at least partly associated with human disturbance, although other non-Budyko climatic and hydrological processes may also contribute. These results indicate that annual precipitation and potential evapotranspiration alone cannot explain runoff decline across much of the Loess Plateau and underscore the need to jointly consider climatic forcing, land surface alteration, and direct human water use in regional water management. Full article

Due to climate change and its increased variability, as well as the extreme weather events, flooding is becoming a major natural threat causing profound economic, social, and ecological impact. This paper systematically reviews 89 peer-reviewed articles published between 2010 and 2024 on flood risk assessment approaches, including geospatial techniques and methods for flooding, using the PRISMA framework and the ScienceDirect and Web of Science databases. GIS and remote sensing are the most popular tools for flood hazard mapping, and hydrodynamic models such as HEC-RAS and MIKE FLOOD dominate flood simulation. Machine learning algorithms, multi-criteria decision analysis (MCDA), and climate scenario analysis have also emerged as increasingly prominent methodological contributions to flood risk frameworks. This review makes a novel contribution by providing the first systematic synthesis of geospatial flood risk assessment methods, explicitly quantifying both the urban–rural research imbalance and the degree of hazard, vulnerability, and exposure integration across the literature. Specifically, only 13 (2.7%) of all eligible articles addressed rural flooding, despite the profound socioeconomic impacts that disproportionately affect these communities, and only 16% of included studies integrated any combination of hazard, vulnerability, and exposure components within current assessment approaches. This review highlights the importance of interdisciplinary collaboration and sensitivity to rural contexts in cultivating resilience and fostering equitable flood risk management. Full article

The Souss-Massa basin, one of Morocco’s major agricultural regions, is increasingly affected by water scarcity and climatic stress. However, the long-term interactions between hydro-climatic change and farmers’ cropping system adjustments remain insufficiently documented. This study analyzes hydro-climatic trends and agricultural transformations over the period 1995–2021. The methodology combines statistical trend analysis of meteorological data (Mann–Kendall test and Sen’s slope estimator), diachronic land use/land cover mapping using Google Earth Engine, Crop Water Stress Index (CWSI) assessment, and groundwater piezometric analysis. Results reveal declining and highly variable precipitation, together with a significant warming trend reaching +0.116 °C/year. In parallel, cultivated cereal areas (rainfed and irrigated) declined, while irrigated forage crops expanded, particularly Berseem/Maize. Despite increasing aridity, CWSI results indicate maintained crop vigor in irrigated areas, suggesting growing dependence on groundwater extraction. These findings highlight an ongoing agricultural transition that increases pressure on already vulnerable water resources and underscores the need for integrated climate adaptation and groundwater management strategies in the basin. Full article

Leaf Area Index (LAI) is a key biophysical parameter for characterizing terrestrial vegetation dynamics and land surface processes. Time-series MODIS LAI products are widely used in ecological and land-related research, but cloud contamination and sensor noise lead to widespread spatio-temporal gaps, limiting their ability to support long-term, consistent vegetation monitoring over large areas. To address this issue, this study proposes a novel self-supervised LAI reconstruction framework (SSLAI) for generating gap-free and ecologically consistent LAI datasets across China. The framework integrates cross-modal environmental fusion, multi-scale spatio-temporal modeling, and adaptive phenological constraints to ensure the reconstructed LAI aligns with realistic vegetation growth rhythms. SSLAI outperforms seven traditional and state-of-the-art deep learning methods, maintaining a root mean square error (RMSE) below 0.20 even with 16 missing time windows. Field validation confirms its high accuracy, with a coefficient of determination (R²) of 0.885 and an RMSE of 0.477. Furthermore, SSLAI’s response to meteorological changes aligns with ecological principles, demonstrating favorable physical interpretability and ecological rationality. The reconstructed LAI exhibits superior spatial completeness and temporal consistency compared with MODIS, VIIRS, and GLASS products, and performs robustly under variable climatic conditions. This study provides an effective self-supervised solution for MODIS LAI gap-filling over large regions, and the generated high-quality LAI dataset can serve as a reliable data foundation for vegetation dynamics monitoring, land surface modeling, and global change research. Full article

Thermal modernization of existing buildings is an important part of sustainability-oriented retrofit practice because it can reduce operational energy demand, but its environmental effect depends partly on the insulation material selected and on the thermal assumptions used in design. This study examines how the use of declared thermal conductivity (λ_decl) and design conductivity (λ_design) affects the required insulation thickness and the A1–A3 global warming potential (GWP) of alternative insulation materials for an attic ceiling separating heated space from an unheated ventilated attic in a multi-family building. This study supports product-stage sustainability assessment; it does not constitute a comparison of the full life cycle climate effect of the selected material groups. The thickness needed to achieve U_target = 0.15 W/(m²·K) was determined for scenarios based on λ_decl, temperature-corrected λ_design, and a moisture sensitivity analysis for cellulose. Environmental assessment was based on European EN 15804+A2-compliant EPDs, with separate reporting of GWP_fossil and GWP_biogenic. In the analyzed case, differences between material groups were driven mainly by EPD data, whereas conversion from declared to design thermal properties had a smaller, but not negligible, effect. This effect became more important for moisture-sensitive materials. The results show that sustainability-oriented environmental comparisons based only on declared thermal conductivity may be misleading when functionally equivalent solutions are compared. In the analyzed case, the transition from λ_decl to temperature-corrected λ_design resulted in only a small change in the required insulation thickness and the corresponding GWP result. At the same time, the scenario-based sensitivity analysis for cellulose insulation and the variability of data reported in the EPDs indicate that moisture-related assumptions and the selection of input data may be of greater importance. The results show that, when interpreting the environmental performance of insulation solutions in sustainable retrofit design, consistency should be maintained between the adopted functional unit and the method used to define the thermal properties of the material after installation in the building envelope. Full article

The global invasion of the shrub L. camara poses a significant threat to ecosystems. Understanding the roles of human activity and climate in driving its spread is crucial for management. This study aimed to quantify its global invasion dynamics, identify key drivers, and predict future distribution shifts. We constructed a high-precision ensemble species distribution model by integrating historical global occurrence records, multi-source environmental variables (climate and human activity indices), and future climate scenarios (SSP1-2.6 and SSP5-8.5). The global invasion showed a clear four-stage acceleration pattern (1900–1960, 1961–1980, 1981–2000, and 2001–2025). Variable importance and response curve analysis revealed a two-phase “dispersal–colonization” mechanism: human activities (e.g., gross domestic product) acted as a “dispersal amplifier,” while a climatic factor (isothermality) served as a critical “colonization filter.” Under two future climate scenarios assuming unchanged human activity patterns, the potential suitable habitat of L. camara exhibits structural changes while maintaining stable total area. The highly suitable areas continue to shrink, with nearly half the area lost by the end of the century under the high-emission SSP5-8.5 pathway, while low-suitability zones expand significantly—yet the overall suitable habitat remains stable. Under SSP1-2.6, structural changes in suitable habitats occur more gradually. The study clarifies the distinct roles of human activity and climate in the invasion process, providing a scientific basis for differentiated global risk management strategies targeting dispersal pathways and colonization thresholds. Full article