Search Results (4,534)

Long-term monitoring of tree crown condition is essential for assessing forest resilience under increasing climatic variability. This study presents a comprehensive evaluation of oak (Quercus spp.) defoliation trends in Poland from 2015 to 2024, based on national forest health monitoring data. Mean defoliation remained relatively stable until 2018, followed by a significant increase in 2019 (+5.1 percentage points; p < 0.001), coinciding with a major drought event across Central Europe. In subsequent years, defoliation gradually decreased and stabilised, indicating partial canopy recovery. Segmented regression and spline models revealed a consistent breakpoint in 2019 across all age classes, with the most severe crown damage recorded in stands older than 100 years. Younger stands showed lower defoliation levels and higher regenerative capacity. A nonlinear relationship between defoliation and growing-season precipitation was also identified, showing that when rainfall fell below 40 mm, canopy loss exceeded 30%. The results confirm that oak defoliation reflects both short-term climatic stress and long-term structural changes. Integrating monitoring data with climatic analyses and statistical modelling improves the detection of stress-related drivers and the assessment of recovery processes. The combined use of these approaches supports adaptive forest management strategies, including the promotion of mixed-species and multi-aged stands, improvement of soil nutrient conditions, and targeted monitoring of drought-sensitive age classes, thereby enhancing the resilience of oak ecosystems to climate change. Full article

Forest productivity is a critical indicator of forest ecosystem vitality and carbon budget status. Understanding its historical trends and driving mechanisms is essential for assessing forest responses to climate change. Currently, widely used methods for productivity reconstruction, including forest inventories, eddy covariance observations, and remote sensing models, have temporal limitations and cannot adequately meet the demands of long-term ecological research. Tree-ring data, with their advantages of annual resolution and extended time series, have become an important tool for reconstructing historical forest productivity. Research has demonstrated that tree-ring width, stable isotopes, wood density, and anatomical properties are closely related to forest productivity. Mechanistic studies indicate that the climate–canopy–stem coupling relationship exhibits three key nonlinear characteristics: the bidirectional threshold effect of precipitation, the inverted U-shaped temperature response, and the carbon allocation “legacy effect”. Correlation analyses show that the optimal response period between tree rings and productivity is concentrated primarily in the growing season or summer, reflecting the critical regulatory role of temperature and moisture on tree growth. Based on this understanding, existing research has focused predominantly on mid- to high-latitude temperate forests in the Northern Hemisphere that are sensitive to climate, with tree-ring chronologies from arid regions showing stronger correlations with forest productivity. Given current progress and existing limitations, future research should address the impact of stand dynamics on reconstruction accuracy, strengthen linkages between vegetation indices and tree-ring data, integrate belowground productivity, and deepen understanding of the physiological mechanisms underlying forest productivity. Full article

Many small-size ore deposits occur in the Lower Paleozoic strata along the ENE-trending imbricate thrust fault in NW Tarim Basin. Based on field investigations and petrographic examinations, sulfur-related deposits mainly occur within the paleo-karst cavities and are composed of elemental sulfur and anhydrite. Elemental sulfur is extensively present, whereas anhydrite is limited to the Topulang area. The over-dispersed δ³⁴S values (−25.2 to +7.4‰ VCDT) suggest that elemental sulfur and anhydrite typically originate from a multi-phase process involving bacterial sulfate reduction (BSR) superimposed stepwise sulfur disproportionation. The source of sulfate most likely derived from the subsurface Cambrian evaporites. The lower δ¹³C (−6.43 to −3.10‰ VPDB) and δ¹⁸O values (−13.49 to −10.30‰ VPDB) and the higher ⁸⁷Sr/⁸⁶Sr ratios (>0.7105) further suggest that the calcite cements precipitated from near surface aquifer with significant meteoric water influx and were associated with southeastward propagation since the Cenozoic in response to the remote effects of the India–Eurasia collision. This regional tectonic uplift and meteoric water influx created favorable anoxic environments (“sulfur springs”) for subsequent BSR and sulfur disproportionation along the Kepingtage overthrust fault front, resulting in the mineralization of sulfur-bearing species. This study provides a useful example for understanding the repeated processes of BSR and sulfur disproportionation for deep-buried evaporites associated with tectonic-driven mineralization within the Tarim Basin and elsewhere. Full article

The Kherlen River Basin is a typical basin in the eastern Mongolian Plateau and is dominated by grassland. This study estimated the grassland yield in the Kherlen River Basin using the Carnegie–Ames–Stanford approach (CASA) model, combined with Theil–Sen median trend analysis and the Geodetector, to explore its spatiotemporal changes and driving factors. This integrated framework links temporal trend detection with spatial interaction analysis to better reveal ecological responses to climatic and anthropogenic influences. The results showed the following: (1) The root mean square error (RMSE) between the estimated grassland yield and the laboratory measurements was 37.88 g/m², with an estimation accuracy (EA) of 73.52%. (2) From 2000 to 2024, the grassland yield increased significantly at a rate of 1.98 g/(m²·a) (p < 0.05), with the fastest growth in the middle reaches. (3) Spatially, 79.78% of the basin exhibited significant increases, mainly in the central and western regions. The proportion of significant increase was highest in the upper reaches (40.36%), followed by the middle (32.89%) and lower reaches (6.53%). (4) Due to limited temporal resolution of socioeconomic data, the driving factor analysis covered the period 2000–2020, during which the overall grassland yield was primarily influenced by the interaction between precipitation and elevation (q = 0.6371). Specifically, the upper, middle, and lower reaches were mainly influenced by the interactions between temperature and precipitation (q = 0.6772), precipitation and elevation (q = 0.6377), and temperature and elevation (q = 0.4255), respectively. The study indicates that grassland yield in the Kherlen River Basin exhibited an overall increasing trend during 2000–2024, with climatic factors (precipitation and temperature) and the geographic factor (elevation) identified as the dominant drivers. The influence of human activities was not significant, although this result may be affected by uncertainties associated with data resolution limitations. Future work should incorporate higher-resolution remote sensing and socioeconomic datasets to better assess the impacts of human activities. Full article

Rare-earth elements (REEs) are integral in a wide range of advanced technologies. Increasing demand for REEs, geopolitical tensions that threaten supply chains, and environmental strain due to extraction operations necessitate the development of new separation and purification methods. Novel selective adsorbents offer a promising alternative to traditional precipitation and solvent extraction due to high selectivity, surface area, and reusability. This research provides insight into the loading and stripping behavior of the 2-D Metal–Organic Framework (MOF) ‘NCU-1’ over multiple pH conditions and time domains in chloride media. NCU-1 structures were synthesized using standard methods, then evaluated for kinetics and equilibria via batch testing. Pseudo-first-order kinetics was used to model the adsorption behavior of all REEs tested. The kinetic trends between elements support a mechanism in which sorption affinity and rate correlate with REE ionic radius and surface interaction strength. The preliminary evaluation presented here suggests that such units are highly useful for both solution purification and the separation of light rare-earth (LREE) from heavy rare-earth elements (HREE). Full article

Vegetation is a fundamental component of ecosystems and plays a vital role in maintaining ecological processes. It contributes to soil conservation, climate regulation, and landscape quality. Kunming, widely known as the “Spring City,” relies heavily on vegetation to sustain its ecological and social environment. This study employs moderate resolution imaging spectroradiometer (MODIS) and Normalized Difference Vegetation Index (NDVI) data in combination with temperature, precipitation, population, and gross domestic product (GDP) records to analyze the spatiotemporal dynamics and driving factors of NDVI-derived vegetation index in Kunming from 2000 to 2020 using trend and correlation analyses. We derived fractional vegetation coverage (FVC) from MODIS NDVI using the pixel dichotomy model, analyzed its temporal trends with linear regression, and applied pixel-wise Pearson correlation analysis to identify the spatial relationship between FVC and precipitation. The main findings can be summarized as follows: (1) The NDVI-derived vegetation index pattern in Kunming is generally higher in the west than in the east and higher in mountainous areas than in plains and basins. From 2000 to 2020, overall NDVI-derived vegetation index increased, with the mean NDVI rising from 0.48 to 0.545. Notably, the NDVI values in 2010 and 2012 declined sharply, likely due to drought conditions caused by reduced rainfall in the preceding years. (2) During the study period, 26.86% of the area showed moderate (NDVI slope: 0.005–0.016) improvement and 10.35% showed significant (NDVI slope: 0.016–0.063) improvement, while 10.28% exhibited degradation. Spatially, improvements were concentrated in Xundian County, parts of Dongchuan District, northern Luquan County, and northern border areas adjoining Yiliang and Shilin Counties. Areas with clear degradation were primarily located in Kunming’s main urban area and along the corridor from the airport to Songming. (3) Correlation analysis revealed that 53.3% of areas exhibited a positive relationship between temperature and NDVI-derived vegetation index, while 18.6% showed a significant negative correlation, mainly in the lower Pudu River basin, the Fumin–Luquan border, and the basin areas of Songming and Shilin Counties. This negative relationship may be attributed to increased evapotranspiration under higher temperatures, which exacerbates soil moisture loss and imposes drought stress on vegetation, thereby inhibiting plant growth. Similarly, 53% of areas showed a positive correlation between precipitation and FVC, whereas only 8.3% showed a significant negative correlation, underscoring the strong influence of precipitation on vegetation dynamics in Kunming. (4) Over the past two decades, Kunming’s GDP increased tenfold. In comparison with NDVI-derived vegetation index data for the same period, this indicates that areas of higher GDP are often associated with lower NDVI-derived vegetation index. Full article

This article presents a detailed review of methodologies for estimating crop yields in the context of growing global concern for food security and agricultural sustainability, with the main objective of analyzing, synthesizing, and comparing recent studies that apply artificial intelligence to yield prediction, identifying their strengths, limitations, and emerging trends. Approaches that integrate climatic variables, soil conditions, and agricultural management practices are examined. Artificial intelligence techniques, such as machine learning and neural networks, are effective at building robust predictive models. In several reviewed studies, these methods have achieved coefficients of determination (R²) greater than 0.85 and error reductions of 15% to 30% compared to traditional statistical approaches, confirming their high predictive potential. These models consider key elements such as temperature, precipitation, soil fertility, and agronomic decisions related to planting, crop choice, and fertilizer use. The article also discusses the challenges associated with model calibration and selection, given the complexity of agricultural systems and the variability of available data. The review covers studies published between 2016 and 2024, a period in which there has been a notable advance in the application of hybrid and deep learning approaches in the agricultural field. The importance of further research into hybrid approaches that integrate various techniques to improve prediction accuracy is highlighted. Finally, the strategic role of artificial intelligence in agricultural decision-making, in promoting sustainable practices, and in strengthening global food security is underlined. Full article

Urban areas face increasing flood risks due to extreme precipitation and anthropogenic activities, which threaten residents’ livelihoods. However, conventional research often lacks a forward-looking perspective, failing to integrate future flood vulnerability assessments with pre-disaster resource allocation. To address this gap, the combination of spatiotemporal flood vulnerability distributions and a pre-disaster funding allocation model serves to enhance urban flood resilience and recovery capabilities. Using Wenzhou City as a case study, a Hydrodynamic Flood Vulnerability Framework (VHCF) was applied to assess current and future vulnerabilities based on hydrodynamic modeling, which revealed distinct spatial patterns in vulnerability. Specifically, a coupled hydrological–hydrodynamic model and the Patch-generating Land Use Simulation (PLUS) model were integrated to simulate flood dynamics under future land-use scenarios for the years 2020 and 2030. A subsequent funding optimization model, based on the VHCF, was developed to prioritize disaster prevention resources for both current and projected high-risk areas. This approach achieves efficient resource allocation by balancing multidimensional flood vulnerability dynamics. The results indicate that extremely high-risk and high-risk zones are predominantly distributed along river corridors and urban centers. From 2020 to 2030, the areal proportion across all vulnerability levels exhibited an increasing trend. Following funding optimization, the coverage rates for low-risk and extremely low-risk zones reached 88.29% and 87.93% in 2020 and 2030, respectively. This methodology provides a scientific basis for decision-makers to enhance urban flood resilience, facilitate post-disaster recovery, and advance sustainable disaster prevention and mitigation strategies. Full article

Terrestrial water storage includes soil water storage, groundwater storage, surface water storage, snow water equivalent, plant canopy water storage, biological water storage, etc., which can comprehensively reflect the total change in water volume during processes such as precipitation, evapotranspiration, runoff, and human water use in the basin hydrological cycle. The Gravity Recovery and Climate Experiment (GRACE) satellite provides a powerful tool and a new approach for observing changes in terrestrial water storage and groundwater storage. The North China Plain (NCP) is a major agricultural region in the northern arid area of China, and long-term overexploitation of groundwater has led to increasingly prominent ecological vulnerability issues. This study uses GRACE and Global Land Data Assimilation System (GLDAS) hydrological model data to assess the spatiotemporal patterns of groundwater drought in the NCP and its various sub-regions from 2003 to 2022, identify the locations, occurrence probabilities, and confidence intervals of seasonal and trend mutation points, quantify the complex interactive effects of multiple climate factors on groundwater drought, and reveal the propagation time from groundwater drought to agricultural drought. The results show that: (1) from 2003 to 2022, the linear tendency rate of groundwater drought index (GDI) was −0.035 per 10 years, indicating that groundwater drought showed a gradually worsening trend during the study period; (2) on an annual scale, the most severe groundwater drought occurred in 2021 (GDI = −1.59). In that year, the monthly average GDI in the NCP ranged from −0.58 to −2.78, and the groundwater drought was most severe in July (GDI = −2.02); (3) based on partial wavelet coherence, the best univariate, bivariate for groundwater drought were soil moisture (PASC = 19.13%); and (4) in Beijing, Tianjin and Hebei, the propagation time was mainly concentrated in 1–5 months, with average lag times of 2.87, 3.20, and 2.92 months, respectively. This study can not only reduce and mitigate the harm of groundwater drought to agricultural production, social life, and ecosystems by monitoring changes in groundwater storage, but also provide a reference for the quantitative identification of the dominant factors of groundwater drought. Full article

High-mountain regions such as the Ile-Alatau range of the Northern Tien Shan are highly sensitive to climatic fluctuations, where even minor variations in temperature and precipitation significantly influence glacier mass balance and hydrology. Despite this sensitivity, few long-term studies have examined the links between hydro-climatic trends, glacier retreat, and moraine lake development. This study investigates multi-decadal glacier and lake dynamics (1955–2024) in relation to observed climate variability, using an integrated hydro-climatic and remote-sensing approach. Temperature and precipitation records from four high-altitude meteorological stations were assessed using linear regression and the Mann–Kendall test, while glacier and lake extents were derived from aerial photographs and Landsat, Sentinel-2, and PlanetScope imagery across ten river basins. Results show statistically significant warming at all stations, with mean annual temperatures increasing by 0.14–0.28 °C per decade and summer temperatures by 0.15–0.30 °C, while precipitation remained stable or slightly decreased. Glacierized area decreased from approximately 269.6 km² in 1955 to 141.7 km² in 2021, representing a 47.4% reduction (≈−0.72% yr⁻¹) over six decades and underscoring the rapid regional cryospheric response to sustained climatic warming. Simultaneously, moraine-dammed lakes increased by 16–18% between 2017 and 2024. These trends highlight the dominant climatic control on glacier loss and lake evolution, emphasizing growing glacial lake outburst floods (GLOFs) and the need for adaptive water-resource management in Central Asia. Full article

This study examines long-term trends in maximum (Tmax) and minimum (Tmin) near-surface air temperatures and precipitation across South America, focusing on Brazilian biomes and national capitals, using ERA5 reanalysis data for 1979–2024. To isolate the underlying climate signal, seasonal cycles were removed using Seasonal-Trend decomposition based on Loess (STL), which effectively separates short-term variability from long-term trends. Temperature trends were quantified using ordinary least squares (OLS) regression, allowing consistent estimation of linear changes over time, while precipitation trends were assessed using the non-parametric Mann–Kendall test combined with Theil–Sen slope estimation, a robust approach that minimizes the influence of outliers and serial correlation in hydroclimatic data. Results indicate widespread but spatially heterogeneous warming, with Tmax increasing faster than Tmin, consistent with reduced cloudiness and evaporative cooling. A meridional precipitation dipole is evident, with drying across the Cerrado, Pantanal, Caatinga, and Pampa, contrasted by rainfall increases in northern South America linked to ITCZ shifts. The Pantanal emerges as the most vulnerable biome, showing strong warming (+0.51 °C decade⁻¹) and the steepest rainfall decline (−10.45 mm decade⁻¹). Satellite-based fire detections (2013–2024) reveal rising wildfire activity in the Amazon, Pantanal, and Cerrado, aligning with the “hotter and drier” climate regime. In the capitals, persistent Tmax increases suggest enhanced urban heat island effects, with implications for public health and energy demand. Although ERA5 provides coherent spatial coverage, regional biases and sparse in situ observations introduce uncertainties, particularly in the Amazon and Andes, these do not alter the principal finding that the magnitude and persistence of the 1979–2024 warming lie well above the range of interdecadal variability typically associated with the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO). This provides strong evidence that the recent warming is not cyclical but reflects the externally forced secular warming signal. These findings underscore growing fire risk, ecosystem stress, and urban vulnerability, highlighting the urgency of targeted adaptation and resilience strategies under accelerating climate change. Full article

The Dongting Lake Basin (DLB), a region of key importance in the national project of the Yangtze River Protection and Economic Belt Construction, experienced dramatic land use changes caused by anthropogenic disturbances and climate change. Understanding vegetation dynamics is crucial for improving ecosystem structure and function and environmental sustainability. Here, a long-term (2000–2020) Normalized Difference Vegetation Index (NDVI) dataset, integrated with multiple statistical methods, was applied to investigate the spatiotemporal characteristics of vegetation coverage in the DLB. The Geodetector model and partial correlation analysis were then applied to determine the main factors affecting spatial and temporal vegetation coverage change. The results showed the following: (1) The DLB showed an overall increasing NDVI at a rate of 0.37% per year from 2000 to 2020; NDVI dynamics shifted in 2010, changing from a slow to a significant increase. The seasonal average NDVI increased differently among the four seasons, in the following descending order: winter (0.56%) > spring (0.22%) > summer (0.17%) > autumn (0.05%). (2) The area with an upward NDVI trend was primarily distributed in the forest zones in the eastern and western parts, accounting for 87.55% of the total area, whereas the area with a decreasing trend was mainly clustered in the northern plains of the DLB, accounting for 6.27% of the total area. (3) The annual variation rate of the NDVI during 2010–2020 was faster than that from 2000 to 2010; the gains and losses of the transmission area were varied among different vegetation levels. (4) The DEM and slope comprised a stronger influence on the NDVI spatial variation, while the annual average temperature was the controlling climate factor, with a q-value of 26.09%. The interaction of each independent factor showed a strengthening effect for explaining the spatial variability of the NDVI. (5) Climatic factors exerted a positive correlation with the NDVI, and the temperature had a stronger influence on vegetation coverage change than that of precipitation. These results can guide the development of ecosystem models to enhance their predictive accuracy, which can provide a scientific basis for the sustainable management of vegetation resources. Full article

Soil salinization in arid regions has drawn considerable attention due to its constraints on agricultural productivity and ecological security. Climate and soil texture, as key drivers at the macroscale, still lack systematic quantitative assessments regarding their mechanisms in shaping the long-term dynamics of salinity, and comparative evaluations of their relative contributions remain insufficient. Therefore, there is an urgent need to explore the spatiotemporal variations in soil salinization in arid regions and its responses to climate and soil texture. This study was based on salinity sampling sites collected in southern Xinjiang in 2023. A Random Forest (RF)-based inversion model was constructed using spectral indices derived from Landsat-9 and Sentinel-2 as environmental predictors. The predictive performance of models using all variables was compared with those using RF-based feature selection. The optimal model was then applied to retrieve soil salinity concentrations for 2008, 2013, 2018, and 2023 at four equidistant time points, enabling the spatiotemporal evolution of soil salinization across the study area to be assessed. Finally, a Boosted Regression Tree (BRT) model was employed to quantify the driving contributions of climate and soil texture. Results showed that the feature-selected Landsat-9 model performed best, with an R² of 0.747, significantly outperforming the Sentinel-2 model. The mean soil salinity concentration declined rapidly from 2008 to 2013, followed by a relatively slower but sustained decrease thereafter. The proportion of non-salinized land increased from 3.08% to 30.81%. The Sen’s slope−Mann−Kendall test indicated that 78.6% of salinity levels exhibited a significant downward trend, while 18.8% showed a slight increase. The relative contribution analysis indicated that climatic factors consistently exerted a stronger influence on the evolution of soil salinization than soil texture. Specifically, the contribution of climatic variables increased from 65.2% in 2008 to 66.8% in 2023, whereas that of soil texture decreased slightly from 34.8% to 33.2%. Among the climatic variables, the effect of potential evapotranspiration gradually weakened, while the impacts of temperature and precipitation continued to intensify. In contrast, soil texture variables played a comparatively minor yet stable role throughout the study period. These findings provide an effective framework for long-term monitoring of soil salinization and offer critical insights for adaptive management in arid regions under climate change. Full article

Japanese swords have a history of more than one thousand years and are recognized as metallic art objects. The sword-making process is not clearly understood, especially for old swords made before about 1600 A.D. Knowledge of structural information such as crystallite sizes and anisotropy is important to understand the sword characteristics and the sword-making process. Bragg-edge transmission imaging is a useful noninvasive method that can extract this structural information continuously over a wide area of the sword. Neutron CT is powerful enough to detect quenched areas, voids, and precipitates. Using both methods, we measured more than 10 swords and obtained information on the two-dimensional crystallite size distribution, anisotropy parameter, lattice plane spacing, and quenched regions. Comparison of the results indicated the following features: the crystallite size distributions showed two patterns: an almost uniform distribution of small-sized crystallites, and mixed distributions of large- and small-sized crystallites. The patterns were observed in different eras and places. The preferred orientation showed different patterns, and strain areas due to quenching were observed in many swords. The quenched area showed a trend that the quenching was weaker for old swords than newer ones. CT images showed the boundaries of the quenched regions and a void in the layered structure for one sword, for which a layered structure was confirmed. Full article

China’s Yellow River basin encounters widespread risks of reduced runoff and intensified hydrological drought. This study focuses on the middle and upper reaches of the Dahei River, the Yellow River’s primary tributary. In this region, the Soil & Water Assessment Tool (SWAT) hydrological model was employed to simulate hydrological processes, identify runoff changes and hydrological drought characteristics, and conduct attribution analysis from 1983 to 2022, as well as to project trends over the next 40 years. The results indicate that total runoff during the impact period (1999–2022) decreased by 55.26% compared to the baseline period (1983–1998). Climate change accounted for a contribution rate of 38.6% to this decline, while human activities accounted for 61.4%. Additionally, climate primarily altered surface runoff (SURQ) and lateral groundwater flow (LATQ) through precipitation changes, while land use had a predominant influence on total runoff volume by modifying SURQ. Both factors exhibited relatively minor effects on LATQ. Moreover, human activities contribute to hydrological drought at a rate of 36.11% to 94.25%. Drought probability is significantly influenced by climate through precipitation and temperature changes, while land use primarily mitigates hydrological drought by impacting the three runoff components. It is predicted that over the next 40 years, total runoff will decrease by 2.08% to 60.16%, along with hydrological droughts that are more frequent, longer in average duration, and more intense; however, the Maximum Drought Duration is anticipated to shorten. In the east and northeast, hydrological drought presents a trend of intensification, with central and western regions exhibiting weaker or declining changes. Full article