Search Results (4,312)

Dongting Lake is one of the most important floodplain lakes in China and plays a critical role in regional flood regulation, ecological conservation, and water-resource management. Understanding long-term variations in lake water storage is therefore essential for evaluating hydrological responses to climate change and human activities. In this study, a hydrodynamic-simulation-based storage estimation framework was developed to quantify wet-season water storage variations in Dongting Lake during 1990–2022. The proposed estimation equation showed strong agreement with hydrodynamic-simulation-derived water storage, with a coefficient of determination (R²) of 0.972 and a root mean square error (RMSE) of 0.86 billion m³. Results indicate that both annual peak water storage and mean wet-season water storage exhibited significant declining trends over the study period. Mann–Kendall analysis revealed an evident decrease in peak storage after 2008 and a major hydrological transition around 2003, corresponding to the initial operation of the Three Gorges Dam (TGD). Comparative analysis showed that the average annual peak storage decreased from 17.27 billion m³ during 1990–2002 to 14.41 billion m³ during 2003–2022, while mean wet-season storage decreased from 8.16 billion m³ to 7.23 billion m³. Reduced inflow from both the Yangtze River and the Four Rivers was identified as the dominant factor controlling the decline in water storage. Although lakebed erosion increased the potential storage volume associated with bathymetric evolution by approximately 0.95 billion m³, its contribution was substantially smaller than the reduction in inflow runoff. Although long-term storage decreased, Dongting Lake retained substantial flood-regulation capacity, with only limited changes observed in flood-retention performance during major flood events. The findings contribute to a better understanding of long-term hydrological changes in Dongting Lake and provide useful information for flood control and water-resource management in the middle Yangtze River basin. Full article

Global Navigation Satellite Systems (GNSSs) are playing an increasingly important role in monitoring climate change, providing precise and continuous data on processes occurring in the atmosphere, hydrosphere, cryosphere, biosphere, and lithosphere. Initially, GNSSs were used primarily for navigation and geodetic purposes, but the development of satellite signal-processing methods has significantly expanded their applications. This paper presents an overview of climate research with particular emphasis on GNSS-RO, PPP, CORS, GNSS-R, and GNSS-IR techniques. The paper discusses the possibilities for monitoring atmospheric water vapor content, sea-level changes, snow cover, glaciers, soil moisture, vegetation status, and crustal deformation associated with redistribution of the Earth’s mass induced by climate change. The analysis indicates that GNSS observations are currently an important data source for climate and environmental research, as well as in weather forecasting systems, environmental monitoring, and geodynamic analyses. Integration of GNSS data with other remote sensing techniques supports a more comprehensive assessment of changes occurring in the Earth’s climate system as well as supporting the development of methods for adaptation to ongoing climate change. Full article

Early-age cracking severely limits concrete durability in hot, arid environments due to rapid plastic and drying shrinkage. This study evaluates the cracking sensitivity of cement-based materials incorporating four local supplementary cementitious materials (SCMs): limestone filler from Djebel Béchar, natural pozzolan, silica fume, and gypsum under simulated arid conditions (55 °C, 12% relative humidity, 10 km/h wind). Using a custom climatic chamber, prismatic cement-grout specimens with internal restraints were tested. SCMs were evaluated at substitution rates of 2% to 8%. Limestone was further tested at higher rates (up to 40%) and in binary combinations. Findings were validated using micro-concrete with limestone substitutions (0–35%) combined with 4% natural pozzolan. Cracking sensitivity was assessed using maximum crack width and a cracking index, along with setting times and mechanical strengths. Results indicate that limestone filler demonstrated the most favourable performance. A 4% limestone substitution yielded a single crack with a maximum width of 0.1 mm, while an 8% substitution resulted in five cracks of about 0.2 mm. The optimal cracking index was achieved at a 35% limestone substitution rate, which also successfully extended initial and final setting times. While binary SCM combinations significantly reduced cracking compared to the unsubstituted reference, they did not outperform the optimal 35% single limestone substitution. Furthermore, the 28-day compressive and flexural tensile strengths of the micro-concrete were effectively maintained at up to 35% limestone combined with 4% pozzolan. Overall, these preliminary findings demonstrate that crushed limestone fines from Djebel Béchar are highly promising as partial cement replacements to improve concrete durability in arid climates. Further durability assessments and statistical validation are recommended to confirm these benefits for practical field applications. Full article

The resilience of electrical grid infrastructures is increasingly challenged by high penetration of renewables, climate-induced stress events, and complex interdependencies between assets and control systems. This paper proposes a structured Resilience Assessment Cycle (RAC) and operationalizes it through a novel LLM-orchestrated Agentic Resilience Assessment System (A-RAS) for quantitative assessment of resilience to extreme weather events. RAC defines a structured assessment process linking disturbance characterization, operational-state evaluation, resilience quantification, and interpretation of the resulting system response. A-RAS implements this process through coordinated numerical engines and agentic components. First, an anomaly detection engine applies a residual-based Exponentially Weighted Moving Average scheme (OpS-EWMA) to identify incipient operational shifts in heterogeneous equipment from SCADA time series. Second, a labeling and diagnostic engine employs a retrieval-augmented RAG-LLM pipeline to generate structured diagnostic explanations and, in a subsequent step, assign operational state labels using a dual-scoring mechanism that combines two independent “votes”: a quantitative score derived from data-driven anomaly severity and a qualitative score derived from LLM-based semantic assessment. Third, a resilience assessment agent integrates (i) a module that detect extreme weather event windows and (ii) a module that computes a dual-output resilience vector—a service-performance deficit and a residual health-state deficit—derived from the temporal evolution of system performance and asset condition over the defined assessment horizon. Finally, a core orchestration agent manages data flow and task delegation, enabling automated, end-to-end resilience assessment. In contrast to existing approaches that represent equipment condition as a binary—functional or failed—the proposed methodology explicitly integrates the heterogeneity and temporal evolution of operating states. By accounting for intermediate health conditions, it addresses a key limitation of prevailing metrics: their limited ability to explain observed system behavior during stress events. The feasibility and practical value of the approach are demonstrated on a real operational wind power plant, showing that resilience trajectories can be traced to residual health deficits and the contributing equipment. As an initial implementation validated on a single site and hazard class, RAC and A-RAS provide a structured and extensible foundation intended to be generalized across additional assets, hazards, and operational contexts in future work. Full article

In recent decades, there has been a marked increase in the frequency of natural disasters on a global scale. This increase is particularly notable in the context of climatological disasters, such as wildfires, which have become increasingly prevalent and intense in past years. It is evident that as the planet experiences the repercussions of climate change, the severity of these fires will intensify. The present study focuses on the analysis of wildfires that have occurred in Spain in recent years, both in terms of their number and the area affected, using data collected from the national and international databases. Since the beginning of this century, there has been an increasing trend in the number of large wildfires (>500 ha) in this country. In contrast, there has been a decline in the overall number of wildfires. However, when analyzing a more extended period, spanning from 1970 onward, these trends become less discernible. The study also analyzes the differences between some of these databases and notes that, despite the fact that certain databases offer exhaustive documentation of burned areas, others exhibit specific limitations due to a variety of factors. These limitations may include the nature of the recorded data, the resolution of wildfire detection or wildfire perimeter identification detection systems, or the recent initiation of data collection for such events. The development of strategies based on historical data and predictive models is necessary for anticipating future scenarios and mitigating the impacts of wildfires. Full article

Research increasingly examines initial access to higher education (HE) in sub-Saharan Africa, yet less is known about the persistence of enrolled students in fragile contexts where costs are high and outcomes are uncertain. This study provides the first large-scale quantitative analysis of perceived HE worth in Guinea-Bissau, a resource-scarce Lusophone state. Drawing on Human Capital Investment (HCI), Behavioural Economics (BE), and Afrocentric perspectives on resilience and hope, we analysed survey data from 2255 students across six HE institutions (HEIs). A binary indicator of whether HE was worth the cost was used to assess ongoing valuation, and the data were analysed using sequential block-entry logistic regression after multiple imputation. Overall, 30.1% of students did not affirm that HE was worth the cost, indicating that enrolment does not guarantee sustained valuation. While cost-benefit reasoning remains important, perceptions were strongly shaped by campus climate and forward-looking expectations. Perceived advantages of HE, even without completion, outweighed the value of credentials alone, suggesting a pragmatic evaluation. We suggest anticipatory resilience as a lens for understanding students’ sustained valuations of HE amid uncertainty, grounded in anticipated benefits, institutional experience, and relational resources. Policy should prioritise campus climate, reduce financial strain, and reinforce the intrinsic value of HE. Full article

Conventional forest inventories provide standardized but temporally discrete DBH observations, whereas some research and management applications require continuous observations of diameter dynamics between remeasurement campaigns. This study designed and implemented a Smart Tree Monitoring Network based on Internet of Things (IoT) and cloud storage technologies as a complementary intensive-monitoring approach for selected forest plots. The system enables automatic, continuous, networked observation of standing-tree diameter growth and consists of Tree Sensor Nodes (TSNs), Stand Gateways (SGs), and a cloud management platform. The independently designed tree diameter growth monitoring instrument senses micro-variations in DBH and conducts scheduled data acquisition. Low-power wireless transmission from TSNs to gateways is achieved through LoRa/LoRaWAN, while stand gateways aggregate multi-node data and environmental parameters and upload them to the cloud platform via a 4G network. Field deployment involved 426 devices in 10 sample plots with different terrain and climatic conditions in Hubei Province. The results showed that (1) with a 3.6 V, 19,000 mAh lithium battery and a 5 min sampling interval, daily power consumption was 5.37 mAh, corresponding to a theoretical battery-life estimate of 9.69 years under the tested duty-cycle assumptions; (2) at initial deployment, device-measured DBH showed strong agreement with manual measurements, with R² = 0.9996, RMSE = 0.215 cm, MAE = 0.170 cm, and Bias = −0.089 cm, while subgroup analyses indicated larger underestimation for large-diameter trees; and (3) monthly mean RSSI and SNR remained above the adopted reference thresholds throughout 2025, while rainfall and temperature were associated with limited variation in signal quality. These results support the technical feasibility of the system for high-frequency DBH monitoring in selected plots, while long-term measurement drift, end-to-end data completeness, battery life under field aging, and physical durability require further validation. Full article

Peri-urban forest ecosystems play a crucial role in sustaining biodiversity, regulating climate, and providing essential ecosystem services; however, they are increasingly threatened by rapid urban expansion. Despite advances in remote sensing-based forest monitoring, long-term reconstruction of continuous forest canopy cover dynamics remains challenging in highly fragmented peri-urban landscapes. This study developed a machine-learning workflow to reconstruct forest canopy cover dynamics within the “Suelo de Conservación” of Mexico City between 1994 and 2024 using Landsat imagery and forest canopy cover information derived from the Hansen Global Forest Change dataset. A balanced training dataset comprising 5000 samples distributed across five forest canopy cover classes was used to compare four regression algorithms (Multiple Linear Regression, Random Forest, Gradient Boosting, and Multilayer Perceptron) under five-fold spatial cross-validation. Model performance was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), bias, Pearson’s correlation coefficient (r), coefficient of determination (R²), and Lin’s Concordance Correlation Coefficient (CCC). The best-performing model was applied to generate forest canopy cover maps for 1994, 2003, 2014, and 2024, and forest-cover change was quantified using propagated uncertainty and threshold sensitivity analysis. The reconstructed forest canopy cover maps revealed an initial decline between 1994 and 2003, followed by partial recovery during 2003–2014 and relatively stable forest canopy cover conditions through 2024. Independent comparison with the National Forest and Soils Inventory (INFyS) and Global Forest Watch forest canopy cover products indicated moderate agreement in the spatial distribution of canopy cover while highlighting uncertainties associated with differences in reference datasets and acquisition periods. The proposed workflow provides a transparent and reproducible framework for long-term forest canopy cover reconstruction using freely available satellite imagery and supports forest monitoring and conservation planning in peri-urban landscapes. Full article

Against the backdrop of global warming and the “warming and moistening” trend in northwestern China, arid inland river basins are highly sensitive to climate change, with their vegetation dynamics strongly controlled by upstream snowmelt water supply. The Keriya River Basin, situated on the northern slope of the Kunlun Mountains and the southern edge of the Taklamakan Desert, exhibits pronounced vertical zonation in vegetation cover and relies heavily on upstream snowmelt water supply for its water resources. To date, there has been a lack of systematic research into the spatiotemporal evolution patterns of long-term NDVI time series in this basin, its multiscale climate responses, and, in particular, future vegetation projections based on CMIP6 multi-scenario analyses and machine learning methods. To address this, this study utilised MODIS NDVI remote sensing data, historical data from the CMIP6 BCC-CSM2-MR model, and monthly temperature, precipitation, and snow cover data for three SSP scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) and systematically analysed the spatiotemporal differentiation characteristics of NDVI in the Keriya River Basin and its multiscale coupling relationships with climatic factors. A multi-model selection and forecasting framework was developed, integrating feature engineering with the XGBoost machine learning algorithm. The study innovatively introduced a physically constrained scenario scaling factor based on historical correlations and future climate mean values, thereby addressing the bias where machine learning models’ predicted NDVI means converged across different SSP scenarios. This enabled the monthly estimation of NDVI under various emission pathways from 2015 to 2100. The results indicate: (1) During the historical period (2001–2024), the basin’s annual average NDVI showed an overall slight increase; the annual pattern was unimodal, peaking in July and reaching its trough in January–February; NDVI was highest in summer and lowest in winter. (2) NDVI initially increases and then decreases with altitude; the highest NDVI values are observed in the 3000–4000 m altitude band; in the mid-altitude band, NDVI rose significantly after 2010 and peaked in 2017; the low-altitude band exhibits the greatest interannual stability. (3) During the historical period, both temperature and precipitation in the catchment exhibited high levels of fluctuation, with annual mean temperatures ranging from 1.90 to 3.92 °C and annual precipitation ranging from 434.5 to 621.0 mm. NDVI showed a strong positive correlation with temperature (R = 0.86), a relatively strong negative correlation with snow cover (R = −0.71), and virtually no correlation with precipitation, indicating that upstream snowmelt is heat-driven and water-dependent. (4) Under the future SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, temperature increases are projected to be 0.83 °C, 2.68 °C, and 5.35 °C, respectively, whilst snow cover is projected to decrease by 2.0%, 14.3%, and 34.0%, respectively; The multi-year mean NDVI values predicted using the XGBoost model (validation R² = 0.9097) are 0.0726, 0.0683, and 0.0690, respectively, all characterised by strong seasonal fluctuations. Given that these future projections are based on a single CMIP6 model and a statistical forecasting framework, they are subject to a degree of uncertainty; however, the low-emission scenario (SSP1-2.6) still indicates a trend that is relatively more conducive to maintaining vegetation stability in this region and may provide preliminary scientific guidance for water resource management along the southern margin of the Tarim Basin. Full article

Sustainable agriculture in Southeast Asia requires balancing economic viability with climate mitigation. This study investigated whether integrated intercropping can mitigate early-stage economic “dead zones” in perennial crops while simultaneously enhancing long-term carbon sequestration. A 25-year bio-economic simulation in Thailand compared three scenarios: oil palm monoculture (OPM), cassava monoculture (CM), and cassava-oil palm intercropping (COPI) during the initial 5-year establishment phase. Secondary national data were analyzed to quantify crop yields, carbon footprint, carbon sequestration, and inclusive income (incorporating crop revenues, self-employment wages, and potential carbon credits). Results demonstrate that COPI and OPM achieve identical accumulated carbon sequestration (−389.50 tCO₂eq/ha with the same number of palm plants, with carbon footprints of 65.07 and 63.57 tCO₂eq/ha, respectively (CM: 30.78 tCO₂eq/ha)). Economically, COPI successfully bridges early-stage cash deficits, achieving the highest cumulative inclusive income (2.95 × 10⁶ THB/ha) and commercial profit (2.93 ×10⁶ THB/ha), outperforming OPM (2.78 × 10⁶ THB/ha and 2.77 × 10⁶ THB/ha) and CM (2.91 × 10⁶ THB/ha for both). Although OPM yields a slightly higher net cash flow (0.77 × 10⁶ THB/ha vs. COPI’s 0.74 × 10⁶ THB/ha), COPI optimizes smallholder livelihood stability via retained household self-employment wages. Overall, integrated intercropping improves smallholder climate resilience and financial stability, providing a scalable model for sustainable tropical agriculture. Full article

Wheat (Triticum aestivum L.) is considered one of the most important cereals globally, contributing significantly to the human population’s caloric and protein requirements. Therefore, ensuring a sufficient yield of wheat for global consumption plays a significant role in maintaining food security in different parts of the world. With increasing demand and dwindling production capacity, due to increasingly uncertain growing conditions, projections indicate that there should be an upswing of 60–70% in wheat productivity by 2050 to fulfill the requirement. However, drought represents the most significant and widespread abiotic limitation to global wheat production, currently resulting in approximately 10% yield losses worldwide. Furthermore, each additional 1 °C increase in temperature is anticipated to decrease staple calorie production by 4.4%. The factors contributing to drought in wheat, as well as its impact on the plant’s biochemical, physiological, and morphological structures, include altered rainfall patterns, elevated atmospheric CO₂ levels, increased temperatures, hot and dry winds, and restricted soil water availability. These factors initiate a series of morphological, physiological, and biochemical disruptions that hinder wheat growth and productivity. Drought impact on wheat starts at biochemical levels through reactive oxygen species (ROS) generation and degradation of chlorophylls, and tolerance to stress is influenced by a polygenic system where numerous genes contribute minor effects and interact significantly with environmental factors transitioning to osmoprotectants. At the physiological level, drought alters the water content in the plant body, leading to reduced net photosynthetic rates, stomatal conductance, transpiration rates, and water utilization efficiency. At the morphological level, drought impacts all kinds of structures such as roots, shoots, leaves and reproductive parts. To counter these effects, wheat develops a set of tolerant mechanisms called drought escape, avoidance and tolerance. An increase in trichomes and leaf waxes, alteration of root–shoot ratios, the staying green phenomenon, production of stress proteins like proline, activity of enzymes including superoxide dismutase (SOD), ascorbate peroxidase, catalase, etc., osmotic adjustment, abscisic acid (ABA) accumulation, expression of dehydration proteins called dehydrin, etc., contribute towards drought tolerance. This comprehensive review investigates the intricate interactions between drought and various wheat genotypes, emphasizing their substantial impacts on plant physiology, biochemistry, growth dynamics, and grain yield. Additionally, this review assesses a variety of genetic and biotechnological strategies aimed at enhancing the resilience of wheat genotypes to drought stress. By integrating recent research findings with practical applications, this review provides a detailed framework for improving the adaptive capacity of wheat plants to withstand the escalating threats of drought stress, thereby supporting sustainable wheat production in a changing climate. Addressing drought stress through genetic and biotechnological management practices is crucial for maintaining wheat productivity. Full article

One adaptive silvicultural strategy to address the increasing frequency and severity of climate extremes is the promotion of genetic material best suited to a changing environment. Using a long-term provenance trial established in 1966 in central Poland, we evaluated how climate humidity at the parental site (provenance origin) influences individual-tree growth–drought resilience trade-offs in Scots pine (Pinus sylvestris L.). Stem discs from 240 trees representing 16 provenances were initially assessed, of which 202 trees were retained following cross-dating and quality control. Drought responses were quantified using complementary growth resilience indices for major drought events identified using the Standardized Precipitation–Evapotranspiration Index. Mixed-effects models followed by Type III Wald chi-square tests and Sidák-adjusted pairwise comparisons revealed significant provenance-level differences in drought responses. Structural equation modeling showed that parental-site climate directly and indirectly affected drought resistance through its influence on relative tree size. Trees from provenances originating in more water-rich environments tended to be smaller but exhibited higher resistance, indicating a potential trade-off between growth and drought tolerance. Relative tree size was also negatively associated with resilience, whereas recovery and relative resilience were not significantly associated with provenance climate or relative tree size. These findings indicate that provenance climate is associated with drought resistance in Scots pine, with tree size emerging as an important mediator of this relationship. Full article

Earth’s early history provides the only natural record for evaluating how planetary evolution can generate environments capable of initiating life. Here we review early Earth evolution through the lens of urability: the time-dependent capacity of planetary environments to support prebiotic chemistry progressing toward compartmentalized, self-propagating, information-bearing systems. We argue that urability is not a single globally habitable state, but a transient overlap among several coupled dimensions: liquid water availability, permissive temperature, ocean pH and salinity, access to bioessential elements, atmospheric shielding and volatile retention, and exposed or shallow environments that enable concentration, mineral catalysis, and wet–dry cycling. During the Hadean–early Archean transition, these dimensions were shaped by magma-ocean degassing, late accretion history, atmospheric compositional evolution from CO₂-rich to more N₂-dominated states, ferruginous ocean chemistry, tectonic recycling, continental growth, and intermittent land emergence. These processes created tradeoffs: high pCO₂ may have enhanced abiotic nitrogen fixation but imposed hot and acidic conditions, whereas CO₂ drawdown improved climate and ocean pH while weakening some fixed-nitrogen sources; ferruginous chemistry could locally enhance phosphate availability while also promoting nutrient scavenging; and tectonic recycling could stabilize the carbon cycle while generating chemically diverse but spatially intermittent land environments. We therefore frame life’s origin as a planetary timing problem, in which prebiotic opportunities opened and closed as multiple environmental constraints came into and out of overlap. This perspective motivates coupled models that resolve when and where water, temperature, pH, nutrients, energy, atmospheric photochemistry, and exposed land surfaces jointly produced urable environments on Earth and other rocky planets. Full article

In 2014, the Meteorological Service of Catalonia established systematic daily observations of anthropogenic clouds, defined in 2017 by08 the WMO as homogenitus. This initiative aimed to quantify the human contribution to cloud cover through direct observation. Since 2015, a comprehensive database of daily observations has been maintained at La Selva del Camp observatory (Spain). Using this 11-year dataset, two distinct analyses were performed. First, a statistical analysis focused on both high homogenitus clouds (contrails) from aircraft and low homogenitus clouds from a nearby chemical complex. To uncover the underlying periodicities of these phenomena, a Fast Fourier Transform (FFT) was applied to the monthly time series, revealing a dominant 12-month seasonal cycle alongside a significant long-term increasing trend. This analysis quantified average cloud cover and identified seasonal and annual trends spanning daily to interannual scales. Furthermore, the environmental impact of these persistent tracks was evaluated through a radiative forcing assessment, weighing the cooling albedo effect against longwave terrestrial radiation trapping. Second, the study examines ‘peak events’ where contrail coverage exceeded 2 and 3 oktas. Ultimately, this work connects multi-year visual observations with mathematical frequency spectra and energy budget assessments, providing a robust framework to evaluate the local climatic and radiative footprint of persistent anthropogenic cloudiness. Full article

Extensive ecosystem degradation along the coastal areas of Ghana highlights the need for targeted landscape restoration. This study identified areas with high restoration potential by evaluating environmental, climatic, and anthropogenic determinants of mangrove distribution. We tested multiple habitat suitability models, including Random Forest (RF), Generalized Additive Model (GAM), Generalized Linear Model (GLM), Maximum Entropy (MaxEnt), and an ensemble approach. Mangrove occurrence records were compiled from field observations, drone-derived data, and publicly available biodiversity and mapped datasets. The ensemble GAM-RF model emerged as the most robust model, with the highest predictive performance. Hydrological and topographic factors were the strongest predictors of mangrove habitat suitability, particularly proximity to rivers and the coastline, low-elevation terrain (<5 m asl), and temperature-related bioclimatic variables, while precipitation metrics and anthropogenic proxies played secondary roles. Using the ensemble approach, the estimated potentially suitable habitat ranged from 417 to 1313 km², indicating that substantial areas remain available for mangrove restoration. These findings align with ongoing national and international restoration initiatives, including the coastal restoration plan, and have important implications for coastal protection, carbon sequestration, fisheries habitat, and local livelihoods. Our findings demonstrate that multi-model habitat suitability analyses can guide spatially targeted, and evidence-based restoration planning. Full article