Search Results (9,449)

Urban flooding poses an escalating threat to riverine cities in Southern Asia’s tropical regions, primarily driven by rapid urban expansion. This study develops future projections of Intensity–Duration–Frequency (IDF) curves for major urban centers in Punjab, Pakistan, utilizing downscaled satellite-derived precipitation data. Baseline IDF curves were established using historical rainfall records from multiple meteorological stations. Among eight General Circulation Models (GCMs) assessed, EC-Earth3-Veg-LR demonstrated the highest skill in capturing extreme rainfall patterns relevant to the region. Future precipitation projections from this model were downscaled using the Equidistant Quantile Matching (EQM) technique and applied to generate IDF curves under two CMIP6 scenarios: SSP2-4.5 and SSP5-8.5. The results reveal a substantial increase in extreme rainfall intensities, particularly under the SSP5-8.5 scenario, with projected 100-year return period rainfall intensities rising by approximately 30–55% across key cities. The downscaled projections reveal more pronounced variations than the raw GCM outputs, emphasizing the importance of high-resolution climate data for accurate regional hydrological risk evaluation and effective urban flood resilience planning. Full article

Tectona grandis L.f. (teak) is a timber species of exceptional commercial value, widely cultivated in Ecuador for export to international markets. This study aimed to ecogeographically characterise current production and identify zones with high potential for exploitation, using tools from CAPFITOGEN v3.0 and the MaxEnt maximum entropy algorithm, based on data from 1023 plantations. The territory was classified into 26 ecogeographic categories, of which teak is present in 13. Categories 17, 19, and 21 were predominant, collectively accounting for 88.27% of the analysed plantations. Sixteen relevant variables (comprising four climatic, four edaphic, and eight geophysical factors) served as predictors in MaxEnt, with model validation demonstrating strong accuracy (AUC = 0.924). The most influential factors for teak suitability were precipitation seasonality, altitude, annual precipitation and September wind speed. Areas with elevated and high probabilities for teak exploitation were quantified at 6,737.83 km² and 10,154.70 km², respectively, with Guayas, Los Ríos, and Manabí provinces showing the most favourable conditions. This integrative framework provides an evidence-based basis for land-use planning and resource management, supporting more sustainable and efficient development of Ecuador’s teak forestry sector. Full article

The Dvorak technique has represented a fundamental tool for understanding the power of tropical cyclones based on their shape and geometric evolution. However, it should be noted that the Dvorak technique is purely morphological in nature and was developed for wind, not precipitation. The role of shape methods in precipitation prediction remains uncertain, particularly in the context of modern multi-sensor capabilities. This uncertainty forms the motivation for the present study. In an attempt to enrich Dvorak’s technique, this study proposes a novel hypothesis. This study tests the hypothesis that higher precipitation intensity is associated with more organized cloud-system morphology, as captured by simple geometric descriptors and indicative of dynamically coherent convection. A total of 3419 cloud-system objects (after size filter) were utilized to establish geometric relationships in each of them. For the case study of Hurricane Patricia over the Mexican coast in 2015, 3858 geometric shapes were processed. The cloud-system morphology was derived from geostationary imagery (GOES-13) and collocated with satellite precipitation estimates in order to isolate intense-rainfall objects (>50 mm/h). For each object, simple geometric descriptors were computed, and shape variability was summarised via Principal Component Analysis (PCA). The present study sought to evaluate the associations with rain-rate metrics (mean, mode, maximum) using rank correlations and k-means clustering. Furthermore, sensitivity analyses were conducted on the rain threshold and minimum object size. A Shape Descriptor: ratio between perimeter and diameter was identified as a promising tool to enhance early prediction models of extreme rainfall, contributing to enhanced meteorological risk management. The study indicates that cloud shape can serve as a valuable indicator in the classification and forecasting of intense cloud systems. Full article

This study focuses on the assessment of the ecological vulnerability of lands in the western regions of Kazakhstan (WKR) using the MEDALUS (Mediterranean Desertification and Land Use) model in combination with satellite remote sensing data. Particular attention is given to the influence of climatic factors, soil properties, vegetation condition, and anthropogenic pressure. As part of the analysis, key indicators were calculated, including the Soil Quality Index (SQI), Vegetation Quality Index (VQI), Climate Quality Index (CQI), and Management Quality Index (MQI). Based on these parameters, an Environmental Sensitivity Area (ESA) index was developed, allowing the classification of the territory into five vulnerability classes ranging from low to critical sensitivity. The results indicate that 52.7% of the territory of the WKR falls within the high-risk zone for land degradation. The most pronounced changes were observed in the southern oblasts of the region, particularly in Mangystau oblast (MAN), where 98.7% of the land is classified as degraded and 74.3% of the territory falls under the category of extremely high ecological vulnerability. In addition, a steady decline in precipitation levels has been identified, contributing to the intensification of aridization processes across the region. Correlation analysis showed that the strongest relationships with the final ESA index were observed for the Vegetation Quality Index (VQI) and Climate Quality Index (CQI), both with correlation coefficients of r = 0.93 and an average coefficient of determination R² = 0.87. The Soil Quality Index (SQI) also demonstrated a strong correlation (r = 0.86). In contrast, the Management Quality Index (MQI) exhibited a generally weak correlation, except in the MAN oblast, where within the Very Low Quality (VLQ) class areas, it showed a moderate correlation (r = 0.68, p < 0.0001). The results highlight the critical role of natural factors—particularly vegetation condition, climate, and soil quality—in shaping the ecological vulnerability of the region. Findings emphasize the need for a comprehensive, multi-criteria approach in developing strategies for sustainable land management under conditions of ongoing climate change. Full article

This bioinformatic study provides a comprehensive theoretical assessment of oxalate-degrading enzymes in probiotics. Kidney stone disease is a common urological disorder with rising global incidence, largely driven by the precipitation of insoluble calcium oxalate salts. Current treatments—including thiazides, lithotripsy, or ultrasound fragmentation—often show variable outcomes and high recurrence rates. Here, we systematically assessed the oxalate-degrading potential of 38 probiotic species listed in the List of Cultures Available in Food (China National Health Commission) along with selected next-generation probiotics. Using BLASTp homology searches, we identified seven strains carrying both oxalyl-CoA decarboxylase (OXC) and formyl-CoA transferase (FRC) genes, one encoding oxalate decarboxylase (OXDC), and three harboring subunits of oxalate oxidoreductase (OOR). Additionally, seven species from international probiotic lists (EFSA QPS and AEProbio) were analyzed, among which two carry both OXC and FRC genes. We prioritized strains with the coupled OXC-FRC pathway or OOR enzymes, examined catalytic site conservation by multiple sequence alignment, and performed AlphaFold-based structural prediction with Template Modeling (TM)-align scoring. Species with TM-scores >0.8 exhibited highly conserved folds, suggesting functional oxalate degradation capacity. These findings provide theoretical guidance for identifying probiotic candidates with oxalate-degrading activity and establish a framework for developing next-generation functional probiotics to alleviate kidney stone disease. Full article

Global ecosystems have undergone significant degradation and deterioration, making the identification of ecosystem changes essential for promoting sustainable development and enhancing quality of life. Hami City, a representative region characterized by the complex “desert–oasis–mountain” ecosystem in Xinjiang, China, provides a critical context for examining ecosystem changes in extremely arid environments. This study utilizes remote sensing data alongside the Revised Wind Erosion Equation and Revised Universal Soil Loss Equation models to analyze the transformations within the desert–oasis ecosystems of Hami City and their driving forces. The findings reveal that (1) over the past 24 years, there have been substantial alterations in the ecosystem patterns of Hami City, primarily marked by an expansion of cropland and grassland ecosystems and a reduction in desert ecosystems. (2) Between 2000 and 2023, there has been an upward trend in Fractional Vegetation Cover, Net Primary Productivity, and windbreak and sand fixation amount in Hami City, whereas soil retention has shown a declining trend. (3) The overall ecosystem change in Hami City is moderate, encompassing 61.85% of the area, with regions exhibiting positive change comprising 16.79% and those with negative change comprising 21.33%. (4) Temperature, precipitation, and evapotranspiration are the primary drivers of ecosystem change in Hami City. Although the overall changes in ecosystems in Hami City have shown an improving trend, significant spatial heterogeneity still exists. The natural climatic conditions of Hami City constrain the potential for further ecological improvement. This study enhances the understanding of ecosystem change processes in extremely arid regions and demonstrates that strategies for mitigating or adapting to climate change need to be implemented as soon as possible to ensure the sustainable development of ecosystems in arid areas. Full article

This study analyses the dynamics of snow cover in the Neelum Watershed of Pakistan and the expected changes in temperature and precipitation. Google Earth Engine was used to analyze the variability of winter snow cover with the help of MODIS 8-day data from 2000 to 2020. Two model combinations totaling five CMIP6 General Circulation Models were used to interpret future climate projections based on three Shared Socioeconomic Pathways (SSP2-4.5, SSP3-7.0, and SSP5-8.5) for 2021–2050. The modified Mann–Kendall test was used to identify trends, and the Theil–Sen estimator was used to analyze the impact. The results demonstrate that the extent of snow-covered area increased significantly between 2000 and 2020, and approximately 6448.83 km² (approximately 87% of the watershed) was covered by snow in winter. All SSP scenarios indicated positive trends in winter precipitation with average rates of 1.87, 0.44, and 0.80 mm/yr under SSP2-4.5, SSP3-7.0, and SSP5-8.5. In all the scenarios, the minimum temperature (0.0405 °C yr⁻¹) and maximum temperature (0.0305 °C yr⁻¹) are consistently growing, as per temperature predictions. These projected changes indicate the danger of more frequent extreme weather events that will put a strain on the region’s ecosystems, agriculture, and hydropower operations. The findings offer the necessary information to inform strategies regarding climate adaptation and mitigation in the Neelum River basin. Full article

Inner Mongolia is an important energy producer and the sixth-largest grain-supplying region in China. To address crucial water security challenges, the spatiotemporal variations in terrestrial water storage (TWS) and groundwater storage (GWS) in semiarid Inner Mongolia from April 2002 to January 2025 were evaluated on the basis of the synergistic use of multisource data, including satellite gravimetry, hydrological models, and meteorological data. There was a loss of TWS in Inner Mongolia (−1.69 ± 0.17 mm/year), which was caused mainly by the depletion of groundwater (−4.90 ± 0.12 mm/year), and it offset a slight increase in surface water (+3.21 ± 0.19 mm/year). Marked declines were clustered mainly in the central/southern regions (e.g., Ordos: GWS of −10.20 ± 0.19 mm/year), whereas the northeastern region (e.g., Hulun Buir) experienced an increase (+5.09 mm/year), which was related to abundant rainfall. Notably, the declining trend of GWS across all of Inner Mongolia before 2022 (−5.49 ± 0.17 mm/year) achieved an unprecedented reversal after 2022 (+17.80 ± 0.21 mm/year), indicating the significant influence of policy interventions and precipitation changes. In the central/eastern agro-pastoral zones, water loss was driven mainly by human-related activities such as coal mining and farming; in contrast, aridity in the west was worsened by climate variability. Therefore, it is crucial to formulate urgent water redistribution strategies, promote efficient irrigation methods, and improve monitoring systems for the purpose of protecting energy and food security and strengthening ecological adaptability in the context of climate change. Full article

Electromagnetic heating (EMH) using microwaves has emerged as a promising enhanced oil recovery (EOR) technique, particularly for heavy crude oils where conventional thermal methods encounter technical and environmental challenges. However, its large-scale implementation remains limited due to incomplete understanding of its energy transfer mechanisms. This study proposes an experimental–numerical approach integrating magnetic graphene oxide nanoparticles (Fe₃O₄@GO) with microwave heating to enhance energy absorption near the wellbore. The nanomaterial was synthesized via a modified Hummer’s method followed by in situ magnetite precipitation and studied through multiple material characterization techniques showing uniform 80 nm particles with superparamagnetic behavior—ideal for EMH applications. Nine experiments were conducted on sand–heavy-oil–water systems with nanoparticle concentrations up to 500 ppm using a laboratory microwave heating prototype. A simulation model was then developed in CMG-STARS for history matching to estimate energy absorption as a function of saturation and nanoparticle concentration. Experiments reached temperatures up to 240 °C, with 653 MJ of effective heat transferred to the target zone over 55 h, as estimated from the input heat required in the simulator for history matching. The results confirm that magnetic graphene oxide nanoparticles enhance thermal efficiency and heat distribution in microwave-assisted EOR. Full article

Forests play a critical role in regulating hydrological processes and reducing soil erosion and sediment load. However, climate change has increased the frequency and severity of wildfires, which can significantly impact these ecosystem services. A historical megafire burned in January of 2017 in Central Chile, affecting the Purapel in Sauzal experimental watershed (an area dominated by Pinus radiata plantations), providing a unique opportunity to study post-fire sediment load dynamics. We hypothesized that sediment load would significantly increase following the wildfire, especially in areas with exotic commercial plantations. To test this, we analyzed daily sediment load and streamflow data collected the Purapel River during the 1991–2018 period, as well as other variables. Descriptive statistics and a sediment rating curve model were used to assess temporal variations in sediment load. Contrary to expectations, results showed no significant increase in sediment concentration following the devastating 2017 wildfire event. In fact, the Mann–Kendall test revealed a significant decreasing trend in winter sediment production over the study period. These findings may be explained by a reduction in precipitation during the mega-drought of the 2010s and, importantly, a rapid and dense post-fire pine seedling regeneration. This study highlights the complex interactions between climate, vegetation, and geomorphic processes, as well as the need for further research on post-fire sediment dynamics in Mediterranean plantation forests. Full article

This study utilizes data from the eighth and ninth Chinese National Forest Inventories of Qinghai Province to establish a climate-sensitive transfer matrix growth model for natural forests in Qinghai Province. The model considers tree species diversity (S_d), size diversity (D_c), mean annual temperature (MAT), and mean annual precipitation (MAP) and their impacts on tree growth, mortality, and recruitment. Additionally, the forest stand growth and development were predicted under different climate scenarios (RCP2.6, RCP4.5, RCP8.5) for the next 50 years. The results show that the number of Qinghai spruce (Picea crassifolia Kom.) and White birch (Betula platyphylla Sukaczev) trees per hectare gradually decreases, but the stock volume continues to increase. The number of trees per hectare remains relatively stable (from 2235 to 855), with stock volume increasing annually for the first 30 years of the simulation and then stabilizing (from 76.96 to 798.02). Other tree species groups exhibit a continuous annual increase. Comparing the changes in stock volume and tree numbers under three different climate scenarios, there was no significant difference, and the overall trend remained similar. The finding fills a gap in the research on climate-sensitive transfer matrix growth models for natural forests in Qinghai Province. Compared to single-tree and whole-stand models, this model can predict forest stand growth more quickly and effectively, providing a reliable reference for future forest management. It helps formulate policies to address climate change and promote the sustainable development of forest health. This achievement will contribute to a better understanding of future forest stand growth trends, offering valuable insights for sustainable forest management. Full article

This paper investigates the collection of aerosol particles (APs), ranging from 0.002 μm to 0.2 μm in diameter, by solid hydrometeors such as cloud ice, snow, and graupel. It specifically examines electrostatic scavenging (ESS) of APs and compares it with our previously studied scavenging by cloud droplets and raindrops. ESS by solid hydrometeors is contrasted with other scavenging mechanisms. The original two-moment aerosol scheme, which includes prognostic equations for the number and mass of APs within the numerical model, is employed in this work. It is concluded that ice crystals are most effective at electrostatic scavenging of APs compared to other solid hydrometeors. The reduction in the total mass of APs in the air caused by ESS from liquid hydrometeors exceeded six times the reduction caused by ESS from cloud ice after one hour of integration. ESS by solid hydrometeors increases the relative aerosol precipitation mass (RAPM) by less than 0.1%, whereas ESS by liquid hydrometeors raises RAPM by over 24%. Full article

Climate change is increasingly recognized as a critical factor of food contamination risks, particularly through its influence on pesticide behavior and usage. Rising temperatures, altered precipitation patterns, and the proliferation of crop pests are leading to intensified and extended pesticide application across agricultural systems. These shifts increase the likelihood of elevated pesticide residues in food and water and affect their environmental persistence, mobility, and accumulation within the food chain. At the same time, current regulatory frameworks and risk assessment models often fail to account for the synergistic effects of chronic low-dose exposure to multiple residues under climate-stressed conditions. This review provides a multidisciplinary overview of how climate change intensifies the pesticide residue burden in food, emphasizing emerging toxicological concerns and identifying critical gaps in current mitigation strategies. In particular, it examines sustainable adsorbent technologies, primarily carbon-based materials derived from agro-industrial waste, which offer promising potential for removing pesticide residues from water and food matrices, aligning with a circular economy approach. Beyond their technical performance, the real question is whether such materials and the thinking behind them can be meaningfully integrated into next-generation food safety systems that are capable of responding to a rapidly changing world. Full article

Dynamical downscaling is an essential approach for bridging the gap between coarse-resolution global climate models and regional details required for climate impact assessment and sustainable development planning. Thailand, a climate-sensitive country in Southeast Asia, requires robust climate information to support its adaptation and resilience strategies. This study evaluated the Weather Research and Forecasting (WRF) model in dynamically downscaling selected Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations over Thailand during the baseline period of 1976–2005. A two-way nested WRF configuration was employed, with domains covering Southeast Asia (36 km) and Thailand (12 km) in the model. Model outputs were compared with gridded observations from the Climatic Research Unit (CRU TS), and spatial variations were analyzed across six administrative regions in Thailand. The WRF successfully reproduces broad climatological patterns, including the precipitation contrast between mountainous and lowland areas and the north–south gradient of temperature. Seasonal cycles of rainfall and temperature are generally well represented, although systematic biases remain, specifically the overestimation of orographic rainfall and a cold bias in high-elevation regions. The 12 km WRF simulations demonstrated improved special and temporal agreement with the CRU TS dataset, showing a national-scale wet bias (MBE = +17.14 mm/month), especially during the summer monsoon (+65.22 mm/month). Temperature simulations exhibited seasonal derivations, with a warm bias in the pre-monsoon season and a cold bias during the cool season, resulting in annual cold biases in both maximum (−1.25 C) and minimum (−0.80 C) temperatures. Despite systematic biases, WRF-CMIP5 downscaled framework provides enhanced regional climate information and valuable insights to support national-to-local climate change adaptation, resilience planning, and sustainable development strategies in Thailand and the broader Southeast Asian region. Full article

Cities use 311 platforms for residents to report flooding, offering insight into flood-prone areas. The combined role of environmental and socioeconomic factors shaping these reports remains unexplored. This study analyzes five years of 311 flood reports in Norfolk, VA, using a logistic regression model to identify salient predictors and assess their influence on flood reporting. The model includes environmental variables (precipitation, tide level, and topographic wetness index) and socioeconomic indicators (race, income, and education). The model performed well with an area under the receiver operator characteristic (ROC) curve (AUC) of 0.8. Permutation-based feature importance revealed precipitation as the most important predictor (AUC contribution: 0.27), followed by the percentage of Black residents (0.02); tide only contributed ~0.01. The influence of the percentage of Hispanics was also ~0.01. Increases in the percentage of Black residents were associated with increased reporting, while the converse was true for a higher percentage of Hispanic residents. Higher reporting in Norfolk from locations with more Black residents is distinct from findings in other cities, suggesting Norfolk may have more effective communication with these residents about 311 reporting. However, lower reporting in locations with more Hispanic residents suggests Norfolk could improve outreach to non-native speakers, for example, by adding Spanish language options to their 311 platform. Full article