Search Results (829)

Drought is one of the main climate-induced risks threatening agricultural sustainability in semi-arid regions. Northern Kazakhstan, a key grain-producing region in Central Asia, exhibits increasing vulnerability to droughts due to climatic variability and reliance on rainfed agriculture. This study evaluates the informativeness of drought indices based on the response of agricultural vegetation to dry conditions using remote sensing-based vegetation indices across Northern Kazakhstan from 1990 to 2024. Ground-based meteorological indices—the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), the Hydrothermal Coefficient (HTC), and the Modified China-Z Index (MCZI)—and vegetation indices—the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI), and the Vegetation Health Index (VHI)—were analyzed using data from 11 representative meteorological stations. For the first time in Kazakhstan, the MCZI was calculated, demonstrating high sensitivity to local climate variability and strong agreement with the VHI. The SPI, MCZI, and HTC showed strong seasonal correlations with vegetation indices, whereas the SPEI had a weak correlation, limiting its applicability. The highest correlations (r ≥ 0.82) between meteorological and vegetation indices were recorded in summer, while spring and autumn were influenced by phenological and temperature factors. Persistent drying trends in the southern and southwestern areas contrasted with moderate wetting in the north. The combined use of the SPI, MCZI, HTC, and VHI proved effective for monitoring droughts. The results provide a reproducible foundation for local drought assessment and early warning systems, supporting climate-resilient agricultural planning and sustainable land and water resource management. The results also offer actionable insights to enhance adaptation strategies and support long-term agricultural and environmental sustainability in Central Asia and similar continental agroecosystems. Full article

To improve the physical consistency and interpretability of traditional drought indices, this study proposes a drought assessment model that couples physically based constraints with data-driven approaches, leading to the development of a Multivariate Drought Index (MDI). The model employs convolutional neural networks to achieve physically consistent downscaling, thereby obtaining a high-resolution Normalized Difference Water Index (NDWI), Temperature Vegetation Dryness Index (TVDI), Vegetation Condition Index (VCI), and Temperature Condition Index (TCI). Objective weights are determined using the Criteria Importance Through Intercriteria Correlation method, while random forest and Shapley Additive Explanations are integrated for nonlinear interpretation and physics-guided calibration, forming an ensemble framework that incorporates multi-source and multi-scale factors. Validation with multi-source data from 2000 to 2024 in the major maize-growing areas of Heilongjiang Province demonstrates that MDI outperforms single indices and the Vegetation Health Index (VHI), achieving a correlation coefficient (r = 0.87), coefficient of determination (R² = 0.87), RMSE (0.08), and classification accuracy (87.4%). During representative drought events, MDI identifies signals 16–20 days earlier than the Standardized Precipitation Evapotranspiration Index (SPEI) and the Soil Moisture Condition Index (SMCI), and effectively captures localized drought patches at a 250 m scale. Feature importance analysis indicates that the NDWI and TVDI are consistently identified as dominant factors across all three methods, aligning physically interpretable analysis with statistical contribution. Long-term risk zoning reveals that the central–western region of the study area constitutes a high-risk zone, accounting for 42.6% of the total. This study overcomes the limitations of single indices by integrating physical consistency with the advantages of data-driven methods, achieving refined spatiotemporal characterization and enhanced overall performance, while also demonstrating potential for application across different crops and regions. Full article

Coastal ecosystems are crucial for territorial development but they face increasing pressure from population growth and climate change. These factors threaten ecosystems, communities, and tourism infrastructure. It is essential to assess vulnerability to achieve adaptation and indices are widely used for this purpose due to their simplicity. However, inconsistencies persist in definitions, methodologies, dimensions, and variable selection. This systematic review of 43 second-generation studies analyzes the evolution of conceptual approaches, identifies the most common indicators, and examines index methodologies. The results reveal that, although the IPCC has updated its definition of vulnerability, many publications still use previous conceptual frameworks. While temperature is relevant to tourism, most studies focus on increasing sea level and its effects. In some cases, social and economic dimensions are treated jointly whereas in other studies they are considered separately. Variable selection remains case-specific and a robust, standardized framework is still lacking, especially for social aspects. Despite the undoubted importance of tourism, specific research on this sector is scarce. This review underscores the need for standardized indices tailored to coastal tourism management under climate change. Future research directions are also proposed. Full article

As a fundamental metric for assessing carbon sequestration, Net Primary Productivity (NPP) and the mechanisms driving its spatiotemporal dynamics constitute a critical research domain within global change science. This research centered on the Huang–Huai–Hai Plain (HHHP), combining 2001–2023 MODIS-NPP data with natural (landform, temperature, precipitation, soil) and socio-economic (population density, GDP density, land use) drivers. Trend analysis, coefficient of variation, and Hurst index were applied to clarify the spatiotemporal evolution of NPP and its future trends, while geographic detectors and structural equation models were used to quantify the contribution of drivers. Key findings: (1) Across the HHHP, the multi-year average NPP ranged between 30.05 and 1019.76 gC·m⁻²·a⁻¹, with higher values found in Shandong and Henan provinces, and lower values concentrated in the northwestern dam-top plateau and central plain regions; 44.11% of the entire region showed a statistically highly significant increasing trend. (2) The overall fluctuation of NPP was low-amplitude, with a stable center of gravity and the standard deviation ellipse retaining a southwest-to-northeast direction. (3) Future changes in NPP exhibited persistence and anti-persistence, with 44.98% of the region being confronted with vegetation degradation risk. (4) NPP variations originated from the synergistic impacts of multiple elements: among individual elements, precipitation, soil type, and elevation had the highest explanatory capacity, while synergistic interactions between two elements notably enhanced the explanatory capacity. (5) Climate variation exerted the strongest influence on NPP (direct coefficient of 0.743), followed by the basic natural environment (0.734), whereas human-related activities had the weakest direct impact (−0.098). This research offers scientific backing for regional carbon sink evaluation, ecological security early warning, and sustainable development policies. Full article

This study focuses on Greece, providing a comprehensive climatological analysis of drought conditions from 1960 to 2022. The Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) were employed on a 1-month timescale to assess meteorological drying conditions over the study period. The Drought Occurrence Probability (DOP), Total Drought Duration (TDD) and drought severity were analyzed spatially, while temporal trends were examined using rolling time windows and the Mann–Kendall test. The findings reveal regional differences in drought characteristics and indicate more intense drought conditions under the SPEI compared to the SPI, underscoring the increasing role of temperature in drought intensification. Full article

Alpine wetlands, key carbon sinks and biodiversity hubs, remain understudied, especially under climate change pressures. Hence, the present study was conducted to assess the variability in soil enzyme activity (SEA) and the carbon management index (CMI) and to utilize principal component analysis (PCA) to explore the variation and correlation between SEA and CMI as influenced by altitudinal gradients in alpine wetlands. This information is essential for exploring the impacts of soil degradation and guiding restoration efforts. The study was designed in blocks (catchments) with six altitudinal variations (from 2500 to 3155 m a.s.l), equivalent to alpine wetlands from three catchments (Senqunyane, Khubelu and Sani) as follows: Khorong and Tenesolo in Senqunyane; Khamoqana and Khalong-la-Lichelete in Sani; and Lets’eng-la-Likhama and Koting-Sa-ha Ramosetsana in Khubelu. The soil samples were collected in February 2025 (autumn season, i.e., wet season) at depths of 0–15 and 15–30 cm and analyzed for bulk density, texture, pH, electrical conductivity (EC), soil organic carbon (SOC), SEA, and carbon pools, and the CMI was computed following standard procedures. The results demonstrated that the soil was loam to sandy loam and was slightly acidic and non-saline in nature in the 0–15 cm layer across the wetlands. The significant decreases in SEA were 45.33%, 32.20% and 15.11% (p < 0.05) for dehydrogenase, fluorescein di-acetate and β-Galactosidase activities, respectively, in KSHM compared with those in Khorong (lower elevated site). The passive carbon pool (C_PSV) was dominant over the active carbon pool (C_ACT) and contributed 76–79% of the SOC to the total organic carbon, with a higher C_PSV (79%) observed at KSHM. The CMI was also greater (91.05 and 75.88) under KSHM at the 0–15 cm and 15–30 cm soil depths, respectively, than in all the other alpine wetlands, suggesting better carbon management at higher altitudinal gradients and less enzymatic activity. These trends shape climate change outcomes by affecting soil carbon storage, with high-altitude regions serving as significant, though relatively less active, carbon reservoirs. The PCA-Biplot graph revealed a negative correlation between the CMI and SEA, and these variables drove more variation across sites, highlighting a complex interaction influenced by higher altitude with its multiple ecological drivers, such as temperature variation, nutrient dynamics, and shifts in microbial communities. Further studies on metagenomics in alpine soils are needed to uncover altitude-driven microbial adaptations and their role in carbon dynamics. Full article

Climate change threatens grain production in East Asia. This study assesses the impacts of climate variables and climate change on rice, wheat, and maize total production using an improved economy–climate model (C-D-C model). The innovation is to model a roughly inverted U-shaped relationship between dry-wet conditions (measured by Standardized Precipitation Evapotranspiration Index, SPEI) and production. Building on this, this study introduces a new metric reflecting extent of future climate change impact, the Impact Ratio of Climate Change (IRCC), to project the impact on production under three climate scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) for 2021–2050. Key findings include: The dry–wet conditions exhibit a significant roughly inverted U-shaped relationship with grain production in some crop areas, with optimal production levels observed near an SPEI of zero. Effective accumulated temperature positively affects wheat production in most regions, while higher effective accumulative temperatures reduce production in warm southern areas. Future climate change in 2021–2050 will likely increase rice production in northern China but decrease it in the south (IRCC > −30%). Overall impacts on wheat will be modestly negative, accounting for about 10% of future total production. Impacts in Japan and Korea will be minimal, with absolute values of IRCC not exceeding 2.5% across all scenarios. Full article

The Athabasca River Basin (ARB) is the location of the Canadian oil sands industry and 70.8% of global estimated bitumen deposits. The Athabasca River is the water source for highly water-intensive bitumen processing. Our objective is to project ARB temperature, precipitation, total runoff, climate moisture index (CMI), and standardized precipitation evapotranspiration index (SPEI) for 2011–2100 using the superior modelling skill of seven regional climate models (RCMs) from Coordinated Regional Climate Downscaling Experiment (CORDEX). These projections show an average 6 °C annual temperature increase for 2071–2100 under RCP 8.5 relative to 1971–2000. Resulting increases in evapotranspiration may be partially offset by an average 0.3 mm/day annual precipitation increase. The projected precipitation increases are in the winter, spring, and autumn, with declines in summer. CORDEX RCMs project a slight increase (0.04 mm/day) in annual averaged runoff, with a shift to an earlier springtime melt pulse. However, these are countered by projected declines in summer and early autumn runoff. There will be significant decreases in annual and summertime CMI and annual SPEI. We conclude that there will be increasingly stressed ARB water availability, particularly in summer, doubtless resulting in repercussions on ARB industrial activities with their extensive water allocations and withdrawals. Full article

With government, industry, and public pressure to decarbonize the building sector through reducing embodied and operational emissions, there have been a wide range of innovative materials used in building envelope retrofits. Although these innovative materials, such as super insulating materials, bio-based insulation, and many others, are assessed on thermal performance and code requirements before use in retrofits, there is no unified standard assessment metric for hygrothermal performance of innovative materials in building envelope retrofits. This paper performs a rapid review of the available literature from January 2013 to March 2025 on hygrothermal performance assessment metrics used in retrofits. Using rapid review methods to search for records in Scopus, Web of Science, and Google Scholar, fifty-nine publications were selected for bibliometric and qualitative analysis. Most selected publications include discussions and analysis of relative humidity in the wall assembly post retrofit, moisture content, and mould index within the envelope. There is a research gap in publications considering hygrothermal damage functions such as freeze–thaw index, relative humidity and temperature (RHT) index, or condensation prediction. There is also a research gap in country and climate studies and analyses of in situ retrofits with innovative materials, and occupant comfort post retrofit. Full article

With the rapid development of edge computing and deep learning, the efficient deployment of deep neural networks (DNNs) on resource-constrained terminal devices faces multiple challenges (background), such as execution delay, high energy consumption, and resource allocation costs. This study proposes an improved Multi-Objective Particle Swarm Optimization (MOPSO) algorithm for PSO. Unlike the conventional PSO, our approach integrates a historical optimal solution detection mechanism and a dynamic temperature regulation strategy to overcome its limitations in this application scenario. First, an end–edge–cloud collaborative computing framework is constructed. Within this framework, a multi-objective optimization model is established, aiming to minimize time delay, energy consumption, and cloud configuration cost. To solve this model, an optimization method is designed that integrates a historical optimal solution detection mechanism and a dynamic temperature regulation strategy into the MOPSO algorithm. Experiments on six types of DNNs, including the Visual Geometry Group (VGG) series, have shown that this algorithm reduces execution time by an average of 58.6%, the average energy consumption by 61.8%, and optimizes cloud configuration costs by 36.1% compared to traditional offloading strategies. Its Global Search Capability Index (GSCI) reaches 92.3%, which is 42.6% higher than the standard PSO algorithm. This method provides an efficient, secure, and stable cooperative computing solution for multi-constraint task unloading in an edge computing environment. Full article

Regional drought monitoring based on the Temperature Vegetation Drought Index (TVDI) holds significant potential in efforts to ensure food safety. However, its empirical determination of dry and wet edges introduces subjectivity and uncertainty, limiting its accuracy and applicability. An improved TVDI (iTVDI) was developed by optimizing boundary parameters using reinforcement learning, based on maximizing the correlation between the TVDI and the ERA5-Land soil moisture dataset. The findings are as follows: (1) The enclosed area and the absolute value of dry edge slope of iTVDI was 34.83–39.97% and 0.79–33.75% larger than TVDI, indicating that the iTVDI can be used to achieve better representation of drought conditions. (2) The iTVDI showed stronger correlations with ERA5 soil moisture (r: −0.416 to −0.174), with average |r| values 17.25% higher than TVDI; its correlations with Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), and the Vegetation Condition Index (VCI) were also 12.69–75.43% higher. (3) From 2005 to 2024, the spring drought in the Huaihe Basin intensified, with the annual iTVDI increasing by 0.008–0.011, primarily driven by rising temperature, potential evapotranspiration, and vapor pressure deficit. Overall, the iTVDI is proved to be more accurate and reliable for monitoring drought dynamics and driving factors. Full article

Temperature and precipitation are the primary factors restricting litter decomposition in desert ecosystems. The desert ecosystems in Central Asia are ecologically fragile regions, and the climate shows a trend of “warm and wet” due to the regional climate change. However, the influencing mechanisms of warming and winter snow changes on litter decomposition are still poorly understood in desert ecosystems. Furthermore, the litter decomposition rate cannot be directly compared due to the large variations in litter quality across different ecosystems. Here, we simulated warming and altered winter snow changes in the field, continuously monitored litter decomposition rates of standard litter bags (i.e., red tea and green tea) and a dominant plant species (i.e., Erodium oxyrrhynchum) during a snow-cover and non-snow-cover period over five months. We found that warming and increased snow cover increased the litter decomposition rate of red tea, green tea, and Erodium oxyrhinchum, and had significant synergistic effects on litter decomposition. The effects of warming and winter snow changes on litter decomposition were more pronounced in April, when the hydrothermal conditions were the best. The decomposition rates of all three litter types belowground were higher than those on the soil surface, highlighting the important roles of soil microbes in accelerating litter decomposition. Furthermore, we found that warming and winter snow changes altered litter decomposition by influencing soil enzyme activities related to soil carbon cycling during the snow-cover period, while influencing soil enzyme activities related to soil phosphorus cycling during the non-snow-cover period. And, notably, decreased snow cover promoted soil enzyme activities during the snow-cover period. More interestingly, our results indicated that the decomposition rate (k) was the lowest, but the stability factor (S) was the highest in the Gurbantünggüt Desert based on the cross-ecosystem comparison using the “Tea Bag Index” method. Overall, our results highlighted the critical roles of warming and winter snow changes on litter decomposition. In future research, the consideration of relationships between litter decomposition and soil carbon sequestration will advance our understanding of soil carbon cycling under climate change in desert ecosystems. Full article

In this research, thermoplastic waste (polyethylene and propylene) from waste electrical and electronic equipment (WEEE) was used to manufacture polymer composite materials that included talc, fly ash, and elastomers, with tailored electromagnetic interference shielding properties, for the potential use for electric car components. A distribution of inorganic components within the polymer structures without particle clustering were observed, illustrating an effective melt compounding process. The gradual replacement of talc with fly ash lowered both the fluidity index and the softening temperature values. The increase in fly ash content resulted in higher values of both permittivity and dielectric loss factor. The novelty was related to a significant increase in both dielectric characteristics at increased quantities of fly ash at higher temperatures, an aspect more relevant at higher frequencies where they approached a steady value. The permittivity values surpassed five, and the dielectric loss factor values exceeded 0.04, fulfilling the requirements for their application in electrical equipment. The recipes containing 10% fly ash may guarantee an electromagnetic shielding effectiveness of at least 99% within the frequency domain of 0.1–4 GHz. Composites with greater amounts of fly ash can conduct heat more efficiently, leading to improved diffusivity and thermal conductivity values, with significant thermal conductivity values surpassing 0.2 W/(m*K). Finally, it was concluded that the composites with 10% talc, 10% fly ash, and elastomer using recycled high-density polyethylene might be the best choice for electric vehicle parts, in line with all required standards for these uses. Full article

The aim of this study was to conduct a comparative analysis of the effects of native quinoa grain with a high saponin content and quinoa grain subjected to preliminary saponin removal with low saponin content on growth, meat quality, biochemical blood composition, and the expression of genes related to muscle growth, gut health, and nutrient transport in broiler chickens. The control group of chickens received a standard diet. The SAP group feed contained quinoa grain without saponin removal (saponin level—5.20%) at 3% of the “Starter” feed mass and 5% of the “Grower” and “Finisher” feeds, maintaining the same nutritional values as the control group. The SAP-FREE group feed contained quinoa grain that was pre-treated to remove saponins by washing with water for 60 min at a temperature of 50 °C (saponin level—0.24%) in the same amount as the SAP group. The research results indicated certain advantages of unprocessed quinoa grain in relation to saponin content. Specifically, in the SAP group, the broiler performance index was at the same level as the control, while the SAP-FREE group had a high mortality rate (10%), resulting in a performance index that was 23.82 units lower than the control. The use of quinoa grain with high saponin content promoted better development of thigh muscles by 9.6% compared to the control (p = 0.008) and increased yields of wing, neck, and back muscles by 2.9 abs.% (p = 0.007) compared to the use of purified quinoa grain. The fat yield decreased by 1.7 abs.% (p = 0.015) with saponin-free quinoa compared to the control and by 2% (p = 0.008) compared to the high saponin group, making this feeding system viable for producing dietary meat. Upon stopping the feeding of purified quinoa, chickens showed a 34.0% increase in AST activity (p = 0.019) and a 15.7% increase in creatinine levels (p = 0.008), likely indicating intensified protein metabolism upon cessation of the inhibiting factor of purified quinoa. Molecular genetic studies revealed a 1.6-fold increase in IGF1 gene expression (p = 0.014) in breast muscle and a 69.12-fold increase (p = 0.010) in AvBD9 in the cecum due to high-saponin quinoa grain, while purified quinoa increased GHR gene expression by 3.29 times (p = 0.039) in breast muscle and decreased IRF7 activity to 2^−ΔΔCT = 0.54 (p = 0.017). The expression of transporter protein genes decreased to low or undetectable levels, indicating the presence of anti-nutritional factors and the need for further research on feeding quinoa with the addition of proteases. Thus, high-saponin quinoa grain, unlike purified quinoa, positively influences gut health and bird survival, maintaining performance levels similar to the control, suggesting the feasibility of using unprocessed quinoa in poultry nutrition, thus avoiding additional costs in feed preparation. Full article

This study investigated how different extraction parts of raw materials and different flocculation methods affect the extraction yield, structure, and properties of sodium alginate. The aim was to improve the quality of sodium alginate and provide theoretical guidance for upstream enterprises. In this study, Lessonia nigrescens (LN) was used as a raw material. The alkali treatment conditions were optimized. The optimal extraction conditions were determined to be a 2% sodium carbonate concentration, a duration of 4 h, a material-to-liquid ratio of 1:40, and a temperature of 60 °C, achieving an extraction yield of 43.03%. LN was categorized into blades, stipes, holdfasts, and whole seaweed for comparative analysis, and sodium alginate was flocculated using the acid, calcium, and ethanol methods. Structural and physicochemical analyses showed that the mannuronic acid/guluronic acid (M/G) ratios of the twelve sodium alginate samples ranged from 5.73 to 8.76. The LN part had a greater influence on the M/G ratio than the flocculation method. The relative molecular weight (2343–3074 kDa) and viscosity (170–331 mPa·s) exhibited consistent trends. For the same part, the effect of the flocculation method on the molecular weight followed the order ethanol > acid > calcium. The physicochemical properties of the extracted sodium alginate met the requirements specified in the physicochemical index standard GB 1886.243-2016 of China. Full article