Search Results (24,263)

The Dual-frequency Precipitation Radar (DPR) aboard the Global Precipitation Measurement (GPM) mission provides valuable three-dimensional precipitation structure data on a global scale and has been widely used in hydrometeorological research. However, due to its spatial resolution limitations and inherent algorithmic assumptions, the accuracy of GPM precipitation estimates can exhibit systematic biases, especially under complex terrain conditions or in the presence of variable precipitation structures, such as light stratiform rain or intense convective storms. In this study, we evaluated the near-surface precipitation rate estimates from the GPM-DPR Level 2A product using over 1440 min of disdrometer observations collected across China from 2021 to 2023. Based on three years of stable stratiform precipitation data from the Jinghe station, we developed a least squares linear correction model for radar reflectivity. Independent validation using national disdrometer data from 2023 demonstrated that the corrected reflectivity significantly improved rainfall estimates under light precipitation conditions, although improvements were limited for convective events or in complex terrain. To further enhance retrieval accuracy, we introduced a regionally adaptive R–Z relationship scheme stratified by precipitation type and terrain category. Applying these localized relationships to the corrected reflectivity yielded more consistent rainfall estimates across diverse conditions, highlighting the importance of incorporating regional microphysical characteristics into satellite retrieval algorithms. The results indicate that the accuracy of GPM precipitation retrievals is more significantly influenced by precipitation type than by terrain complexity. Under stratiform precipitation conditions, the GPM-estimated precipitation data demonstrate the highest reliability. The correction framework proposed in this study is grounded on ground-based observations and integrates regional precipitation types with terrain characteristics. It effectively enhances the applicability of GPM-DPR products across diverse environmental conditions in China and offers a methodological reference for correcting satellite precipitation biases in other regions. Full article

Monitoring water quality parameters in coastal and estuarine environments is critical for assessing their ecological status and addressing environmental challenges. However, traditional in situ sampling programs are often constrained by limited spatial and temporal coverage, making it difficult to capture the complex variability in these dynamic systems. This study introduces a novel upscaling framework that leverages limited in situ measurements and airborne hyperspectral data to generate multiple conditional realizations of water quality parameter fields. These pseudo-measurements are statistically consistent with the original data and are used to calibrate inversion algorithms that relate satellite-derived reflectance data to water quality parameters. The approach was applied to Kaštela Bay, a semi-enclosed coastal area in the eastern Adriatic Sea, to map seasonal variations in water quality parameters such as Chlorophyll-a. The upscaling framework captured spatial patterns that were absent in sparse in situ observations and enabled regional mapping using Sentinel-2A satellite data at the appropriate spatial scale. By generating realistic pseudo-measurements, the method improved the stability and performance of satellite-based retrieval algorithms, particularly in periods of high productivity. Overall, this methodology addresses data scarcity challenges in coastal water monitoring and its application could benefit the implementation of European water quality directives through enhanced regional-scale mapping capabilities. Full article

The precipitation of minerals, in particular carbonates, is a widespread phenomenon in all ecosystems, where it assumes a high relevance both from a geological and biogeochemical standpoint. Most carbonate rocks are of biological origin and made in an aquatic environment. In particular, bioprecipitation of carbonates is believed to have started in the Mesoproterozoic Era, thanks to a process often driven by photosynthetic microorganisms. Nevertheless, an important contribution to carbonate precipitation is also due to the metabolic activity of heterotrophic bacteria, which is not restricted to specific taxonomic groups or to specific environments, making this process a ubiquitous phenomenon. In this framework, the relationship between carbonatogenic microorganisms and other living organisms assumes a particular interest. This study aims to isolate and identify the culturable heterotrophic bacterial component associated with the coenosarc of Corallium rubrum in order to evaluate the occurrence of strains able to precipitate carbonates. In particular, the study was focused on the identification and characterisation of bacterial strains isolated from a coral coenosarc showing a high carbonatogenic capacity under laboratory conditions. Samples of C. rubrum were taken in the coastal waters of three Italian regions. The concentration of the aerobic heterotrophic microflora colonising C. rubrum coenosarc samples spanned from 3 to 6∙10⁶ CFU/cm². This variation in microbial populations colonising the C. rubrum coenosarc, spanning over 6 orders of magnitude, is not mirrored by a corresponding variability in the colony morphotypes recorded, with the mean being 5.1 (±2.1 sd). Among these bacteria, the carbonatogenic predominant species was Staphylococcus equorum (93% of the isolates), whereas Staphylococcus xylosus and Shewanella sp. accounted only for 3% of isolates each. All these strains showed a remarkable capacity of precipitating calcium carbonate, in the form of calcite crystals organised radially as well crystalised spherulites (S. equorum) or coalescing spherulites (Shewanella sp.). S. xylosus only produced amorphous precipitates of calcium carbonate. All bacterial strains identified were positive both for the production of urease and carbon anhydrase in vitro at 30 °C. It seems that they potentially possess the major biochemical abilities conducive to Ca²⁺ precipitation, as they showed in vitro. In addition, all our carbonatogenic isolates were able to hydrolyse the phytic acid calcium salt and then were potentially able to induce precipitation of calcium phosphates also through such a mechanism. Full article

In arid regions with limited water resources, improving cropland water-use efficiency (WUEc) is crucial for maintaining crop production. This study aims to investigate how changes in meteorological and vegetation factors affect WUEc in drylands and to identify its primary drivers, which are essential for understanding how cropland ecosystems respond to complex environmental changes. Using remote sensing data, we analyzed the spatiotemporal patterns of WUEc in Xinjiang from 2002 to 2022 by applying STL decomposition, Sen’s slope combined with the Mann–Kendall test, and an XGBoost–SHAP model, quantifying its key controlling factors. The results indicate that from 2002 to 2022, WUEc in Xinjiang showed an overall declining trend. Prior to 2007, WUEc increased at 0.05 gC·m⁻¹·m⁻²·a⁻¹, after which it fluctuated downward at −0.01 gC·m⁻¹·m⁻²·a⁻¹. Intra-annual peaks consistently occurred in May and during September–October. Spatially, WUEc exhibited significant heterogeneity, increasing from south to north, with 53.26% of the region showing declines. Temperature (T) and leaf area index (LAI) emerged as the primary meteorological and vegetation drivers, respectively, influencing WUEc change in 45.7% and 17.6% of the area. Both variables were negatively correlated with WUEc, with negative correlations covering 60% of the region for T and 83% for LAI. These findings provide scientific guidance for optimizing crop structure and water-resource management strategies in arid regions. Full article

Planting patterns and irrigation amounts are key factors affecting maize yield. This study adopted a two-factor experimental design, with planting pattern as the main plot and irrigation amount as the subplot, to investigate the effects of irrigation levels under different planting patterns (including uniform row spacing and alternating wide-narrow row spacing) on spring maize yield and water use efficiency in Xinjiang. Through this approach, the study examined the mechanisms by which planting pattern and irrigation amount influence maize growth, yield formation, and water use efficiency. Experiments conducted at the Agricultural Science Research Institute of the Ninth Division of Xinjiang Production and Construction Corps demonstrated that alternating wide-narrow row spacing combined with moderate irrigation (5400 m³/hm²) significantly optimized maize root distribution, improved water use efficiency, and increased leaf area index and net photosynthetic rate, thereby promoting dry matter accumulation and yield enhancement. In contrast, uniform row spacing under high irrigation levels increased yield but resulted in lower water use efficiency. The study also found that alternating wide-narrow row spacing enhanced maize nutrient absorption from the soil, particularly phosphorus utilization efficiency, by improving canopy structure and root expansion. This pattern exhibited comprehensive advantages in resource utilization, providing a theoretical basis and technical pathway for achieving water-saving and high-yield maize production in arid regions, which holds significant importance for promoting sustainable agricultural development. Full article

This study investigates the potential of residual clays from a traditional ceramic-producing region in southern Portugal as raw materials for red ceramic applications. This work aims to support more sustainable ceramic practices through the local valorization of naturally available, underutilized clay resources. A multidisciplinary approach was employed to characterize clays, integrating mineralogical (XRD), chemical (XRF), granulometric, and thermal analyses (TGA/DTA/TD), as well as technological tests on plasticity, extrusion moisture, shrinkage, and flexural strength. These assessments were designed to capture both the intrinsic properties of the clays and their behavior across key ceramic processing stages, such as shaping, drying, and firing. The results revealed a broad diversity in mineral composition, particularly in the proportions of kaolinite, smectite, and illite, which strongly influenced plasticity, water demand, and thermal stability. Clays with higher fine fractions and smectitic content exhibited excellent plasticity and workability, though with increased sensitivity to drying and firing conditions. Others, with coarser textures and illitic or feldspathic composition, demonstrated improved dimensional stability and lower shrinkage. Thermal analyses confirmed expected dehydroxylation and sintering behavior, with the formation of mullite and spinel-type phases contributing to densification and strength in fired bodies. This study highlights that residual clays from varied geological settings can offer distinct advantages when matched appropriately to ceramic product requirements. Some materials showed strong potential for direct application in structural ceramics, while others may serve as additives or tempering agents in formulations. These findings reinforce the value of integrated characterization for optimizing raw material use and support a more circular, resource-conscious approach to ceramic production. Full article

Eucalyptus has become a major plantation crop in southern China, with a carbon sequestration capacity significantly higher than that of other species. However, its long-term carbon sequestration capacity and regional-scale potential remain highly uncertain due to commonly applied short-rotation management practices. The InTEC (Integrated Terrestrial Ecosystem Carbon) model is a process-based biogeochemical model that simulates carbon dynamics in terrestrial ecosystems by integrating physiological processes, environmental drivers, and management practices. In this study, the InTEC model was enhanced with an optimized eucalyptus module (InTEC_euc) and a data assimilation module (InTEC_DA), and driven by multiple remote sensing products (Net Primary Productivity (NPP) and carbon density) to simulate the carbon budgets of eucalyptus plantations from 2003 to 2023. The results indicated notable improvements in the performance of the InTEC_euc model when driven by different datasets: carbon density simulation showed improvements in R² (0.07–0.56), reductions in MAE (5.99–28.51 Mg C ha⁻¹), reductions in RMSE (8.1–31.85 Mg C ha⁻¹), and improvements in rRMSE (12.37–51.82%), excluding NPP_Lin. The carbon density-driven InTEC_euc model outperformed the NPP-driven model, with improvements in R² (0.28), MAE (−8.15 Mg C ha⁻¹), RMSE (−9.43 Mg C ha⁻¹), and rRMSE (−15.34%). When the InTEC_DA model was employed, R² values for carbon density improved by 0–0.03 (excluding ACD_Yan), with MAE reductions between 0.17 and 7.22 Mg C ha⁻¹, RMSE reductions between 0.33 and 12.94 Mg C ha⁻¹ and rRMSE improvements ranging from 0.51 to 20.22%. The carbon density-driven InTEC_DA model enabled the production of high-resolution and accurate carbon budget estimates for eucalyptus plantations from 2003 to 2023, with average NPP, Net Ecosystem Productivity (NEP), and Net Biome Productivity (NBP) values of 17.80, 10.09, and 9.32 Mg C ha⁻¹ yr⁻¹, respectively, offering scientific insights and technical support for the management of eucalyptus plantations in alignment with carbon neutrality targets. Full article

In the current era, Indian agriculture faces a significant demand for increased food production, which has led to the integration of advanced technologies to enhance efficiency and productivity. Drones have emerged as transformative tools for enhancing precision agriculture, reducing costs, and improving sustainability. This study provides a comprehensive review of drone adoption in Indian agriculture by examining its effects on precision farming, crop monitoring, and pesticide application. This research evaluates technological advancements, regulatory frameworks, infrastructure, farmers’ perceptions, and the financial accessibility of drone technology in the Indian agricultural context. Key findings indicate that, while drone adoption enhances efficiency and sustainability, challenges such as high costs, lack of training, and regulatory barriers hinder widespread implementation. This paper also explores the growing market for agricultural drones in India, highlighting key industry players and projected market growth. Furthermore, it addresses regional differences in adoption rates and emphasizes the increasing social acceptance of drones among Indian farmers. To bridge the gap between potential and practice, the study proposes several policy and institutional recommendations, including government-led financial incentives, training programs, and public–private partnerships to facilitate drone integration. Moreover, this review article also highlights technological advancements, such as AI and IoT, in agriculture. Finally, open issues and future research directions for drones are discussed. Full article

Dental implant components are typically fabricated using subtractive manufacturing, often involving metal materials that can be costly, inefficient, and time-consuming. This study explores the use of additive manufacturing (AM) with zirconia for dental implant overdenture bars, focusing on mechanical performance, stress distribution, and fit. Solid and lattice-structured bars were designed in Fusion 360 and produced using LithaCon 210 3Y-TZP zirconia (Lithoz GmbH, Vienna, Austria) on a CeraFab 8500 printer. Post-processing included cleaning, debinding, and sintering. A 3D-printed denture was also fabricated to evaluate fit. Thermography and optical imaging were used to assess adaptation. Custom fixtures were developed for flexural testing, and fracture loads were recorded to calculate stress distribution using finite element analysis (ANSYS R2025). The FEA model assumed isotropic, homogeneous, linear-elastic material behavior. Bars were torqued to 15 Ncm on implant analogs. The average fracture loads were 1.2240 kN (solid, n = 12) and 1.1132 kN (lattice, n = 5), with corresponding stress values of 147 MPa and 143 MPa, respectively. No statistically significant difference was observed (p = 0.578; α = 0.05). The fracture occurred near high-stress regions at fixture support points. All bars demonstrated a clinically acceptable fit on the model; however, further validation and clinical evaluation are still needed. Additively manufactured zirconia bars, including lattice structures, show promise as alternatives to conventional superstructures, potentially offering reduced material use and faster production without compromising mechanical performance. Full article

In arid regions, water scarcity and soil potassium destruction are major constraints on the sustainable development of the jujube industry. In this regard, the use of crop models can compensate for time-consuming and costly field trials to screen for better irrigation regimes, but their predictive accuracy is often compromised by parameter uncertainty. To address this issue, we utilized data from a three-year (2022–2024) field trial (with irrigation at 50%, 75%, and 100% of evapotranspiration and potassium applications of 120, 180, and 240 kg/ha) to simulate the growth process of jujube trees in arid regions using the WOFOST model. In this study, parameter sensitivity analyses were conducted to determine that photosynthetic capacity maximization (A_max), the potassium nutrition index (K_status), the water stress factor (SWF), the water–potassium photosynthetic coefficient of synergy (α), and potassium partitioning weight coefficients (β_i) were the important parameters affecting the simulated growth process of the crop. Path analysis using segmented structural equations also showed that water stress factor (SWF) and potassium nutrition index (K_status) indirectly controlled yield by significantly affecting photosynthesis (path coefficients: 0.72 and 0.75, respectively). The ability of the crop model to simulate the growth process and yield of jujube trees was improved by the introduction of water and potassium parameters (R² = 0.94–0.96, NRMSE = 4.1–12.2%). The subsequent multi-objective optimization of yield and crop water productivity of dates under different combinations of water and potassium treatments under a bi-objective optimization model based on the NSGA-II algorithm showed that the optimal strategy was irrigation at 80% ET_c combined with 300 kg/ha of potassium application. This management model ensures yield and maximizes crop water use efficiency (CWP), thus providing a scientific and efficient irrigation and fertilization regime for jujube trees in arid zones. Full article

Heavy rainfall events, characterized by extreme downpours that exceed 100 mm per day, pose an intensifying hazard to the densely settled valleys of the western Himalaya; however, their coupling with expanding urban land cover remains under-quantified. This study mapped the spatiotemporal exposure of built-up areas to heavy-day rainfall (HDR) across Jammu, Kashmir, and Ladakh and the adjoining areas by integrating daily Climate Hazards Group InfraRed Precipitation with Stations product (CHIRPS) precipitation (0.05°) with Global Human Settlement Layer (GHSL) built-up fractions within the Google Earth Engine (GEE). Given the limited sub-hourly observations, a daily threshold of ≥100 mm was adopted as a proxy for HDR, with sensitivity evaluated at alternative thresholds. The results showed that HDR is strongly clustered along the Kashmir Valley and the Pir Panjal flank, as demonstrated by the mean annual count of threshold-exceeding pixels increasing from 12 yr⁻¹ (2000–2010) to 18 yr⁻¹ (2011–2020), with two pixel-scale hotspots recurring southwest of Srinagar and near Baramulla regions. The cumulative high-intensity areas covered 31,555.26 km², whereas 37,897.04 km² of adjacent terrain registered no HDR events. Within this hazard belt, the exposed built-up area increased from 45 km² in 2000 to 72 km² in 2020, totaling 828 km². The years with the most expansive rainfall footprints, 344 km² (2010), 520 km² (2012), and 650 km² (2014), coincided with heavy Western Disturbances (WDs) and locally vigorous convection, producing the largest exposure increments. We also performed a forecast using a univariate long short-term memory (LSTM), outperforming Autoregressive Integrated Moving Average (ARIMA) and linear baselines on a 2017–2020 holdout (Root Mean Square Error, RMSE 0.82 km²; measure of errors, MAE 0.65 km²; R² 0.89), projecting the annual built-up area intersecting HDR to increase from ~320 km² (2021) to ~420 km² (2030); 95% prediction intervals widened from ±6 to ±11 km² and remained above the historical median (~70 km²). In the absence of a long-term increase in total annual precipitation, the projected rise most likely reflects continued urban encroachment into recurrent high-intensity zones. The resulting spatial masks and exposure trajectories provide operational evidence to guide zoning, drainage design, and early warning protocols in the region. Full article

The rainy season characteristics are directly modulated by atmospheric circulation and moisture transport dynamics. Focusing on the characteristics of the rainy season onset date (RSOD), this study aims to advance the understanding and prediction of climate change impacts on agricultural production and disaster mitigation strategies. Based on rainfall data from 66 meteorological stations in northeast China (NEC) from 1961 to 2020, this study determined the patterns of the RSOD in the region and established its mechanistic linkages with atmospheric circulation and water vapor transport mechanisms. This study identifies a climatic regime shift around 2000, with the RSOD transitioning from low to high interannual variability in NEC. Further analysis reveals a strong correlation between the RSOD and atmospheric circulation characteristics: cyclonic vorticity amplifies before the RSOD and dissipates afterward. Innovatively, this study reveals a significant transition in the water vapor transport paths during the early rainy season in NEC around 2000, shifting from eastern Mongolia–Sea of Japan to the northwestern Pacific region. Moreover, the advance or delay of the RSOD directly influences the water vapor transport intensity—an early (delayed) RSOD is associated with enhanced (weakened) water vapor transport. These findings provide a new perspective for predicting the RSOD in the context of climate change while providing critical theoretical underpinnings for optimizing agricultural strategies and enhancing disaster prevention protocols. Full article

Precipitable water vapor (PWV) is a critical component of the Earth’s atmosphere, playing a pivotal role in weather systems, climate dynamics, and hydrological cycles. Accurate estimation of PWV is essential for numerical weather prediction, climate modeling, and atmospheric correction in remote sensing. Ground-based observation stations can only provide PWV measurements at discrete points, whereas spaceborne infrared remote sensing enables spatially continuous coverage, but its retrieval algorithm is restricted to clear-sky conditions. This study proposes an innovative approach that uses ensemble learning models to integrate infrared and microwave satellite data and other geographic features to achieve all-weather PWV retrieval. The proposed product shows strong consistency with IGRA radiosonde data, with correlation coefficients (R) of 0.96 for the ascending orbit and 0.95 for the descending orbit, and corresponding RMSE values of 5.65 and 5.68, respectively. Spatiotemporal analysis revealed that the retrieved PWV product exhibits a clear latitudinal gradient and seasonal variability, consistent with physical expectations. Unlike MODIS PWV products, which suffer from cloud-induced data gaps, the proposed method provides seamless spatial coverage, particularly in regions with frequent cloud cover, such as southern China. Temporal consistency was further validated across four east Asian climate zones, with correlation coefficients exceeding 0.88 and low error metrics. This algorithm establishes a novel all-weather approach for atmospheric water vapor retrieval that does not rely on ground-based PWV measurements for model training, thereby offering a new solution for estimating water vapor in regions lacking ground observation stations. Full article

The sustainable utilization of saline water resources represents an effective strategy for alleviating water scarcity in arid regions. However, the mechanisms by which prolonged saline water irrigation influences soil salinization and dryland crop growth are not yet fully understood. This study examined the effects of six irrigation water salinity levels (CK: 0.87 g·L⁻¹, S1: 2 g·L⁻¹, S2: 4 g·L⁻¹, S3: 6 g·L⁻¹, S4: 8 g·L⁻¹, S5: 10 g·L⁻¹) on soil salinization dynamics and jujube growth during a three-year field experiment (2020–2022). The results showed that soil salinity within the 0–1 m profile significantly increased with rising irrigation water salinity and prolonged irrigation duration, with the 0–0.4 m layer accounting for 50.27–74.95% of the total salt accumulation. A distinct unimodal salt distribution was observed in the 0.3–0.6 m soil zone, with the salinity peak shifting downward from 0.4 to 0.5 m over time. Meanwhile, soil pH and sodium adsorption ratio (SAR) increased steadily over the study period. The dominant hydrochemical type shifted from SO₄²⁻-Ca²⁺·Mg²⁺ to Cl⁻-Na⁺·Mg²⁺. Crop performance exhibited a nonlinear response to irrigation salinity levels. Low salinity (2 g·L⁻¹) significantly enhanced plant height, stem diameter, leaf area index (LAI), vitamin C content, and yield, with improvements of up to 12.11%, 3.96%, 16.67%, 16.24%, and 16.52% in the early years. However, prolonged exposure to saline irrigation led to significant declines in both plant growth and water productivity (WP) by 2022. Under high-salinity conditions (S5), yield decreased by 16.75%, while WP declined by more than 30%. To comprehensively evaluate the trade-off between economic effects and soil environment, the entropy weight TOPSIS method was employed to identify S1 as the optimal irrigation treatment for the 2020–2021 period and control (CK) as the optimal treatment for 2022. Through fitting analysis, the optimal irrigation water salinity levels over 3 years were determined to be 2.75 g·L⁻¹, 2.49 g·L⁻¹, and 0.87 g·L⁻¹, respectively. These findings suggest that short-term irrigation of jujube trees with saline water at concentrations ≤ 3 g·L⁻¹ is agronomically feasible. Full article

Heat stress is a critical constraint to poultry production in tropical regions, where the temperature–humidity index (THI) frequently exceeds thermoneutral thresholds. Despite growing interest in climate-resilient livestock, limited research has explored the genetic sensitivity of local chicken breeds to increasing THI levels. This study aimed to evaluate the genetic effects of increasing THI on growth performance traits in two tropical chicken breeds. The data included body weight (BW), average daily gain (ADG), and absolute growth rate (AGR) from 4,745 black-boned and 3,001 Thai native synthetic chickens across five generations. Growth data were collected from hatching to 12 weeks of age, whereas temperature and humidity were continuously recorded to calculate daily THI values. A reaction norm model was used to estimate genetic parameters and rate of decline of BW, ADG, and AGR traits under varying THI thresholds (THI70 to THI80). Results indicated that the onset of heat stress occurred at THI72 for black-boned chickens and at THI76 for Thai native synthetic chickens. Heritability estimates for BW, ADG, and AGR decreased as the THI increased in both chicken breeds. However, the Thai native synthetic chickens consistently exhibited higher genetic potential across all THI levels (average heritability: BW = 0.28, ADG = 0.25, AGR = 0.36) compared to the black-boned chickens (average heritability: BW = 0.21, ADG = 0.15, AGR = 0.23). Under mild heat stress (THI72), black-boned chickens showed sharp declines in all traits (average reduction in BW = −10.9 g, ADG = −0.87 g/day, AGR = −3.20 g/week), whereas Thai native synthetic chickens maintained stable performance. At THI76, both breeds experienced significant reductions, particularly in males. Estimated breeding values (EBVs) for AGR decreased linearly with THI, though Thai native synthetic chickens showed greater individual variability, with some birds maintaining stable or positive EBVs up to THI80—suggesting the presence of heat-resilient genotypes. In conclusion, Thai native synthetic chickens demonstrated superior thermotolerance and genetic robustness under increasing THI conditions. The identification of breed-specific THI thresholds and resilient individuals provides novel insights for climate-smart poultry breeding. These findings offer valuable tools for genetic selection, environmental management, and long-term adaptation strategies in response to global climate change. Full article