Search Results (225)

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

Lakes account for approximately 87% of the Earth’s surface water resources and serve as sensitive indicators of climate and environmental change. Understanding how lake areas respond to climate change across different elevation gradients is crucial for guiding sustainable water resource management in Xinjiang. We utilized Landsat series remote sensing imagery (1990–2023) on the Google Earth Engine (GEE) platform to extract the temporal dynamics of natural lakes larger than 10 km² in Xinjiang, China (excluding reservoirs). We analyzed the relationships between lake area dynamics, climatic factors, and human activities to assess the sensitivity of lakes at different altitudinal zones to environmental change. The results showed that (1) the total area of Xinjiang lakes increased by 1188.36 km² over the past 34 years, with an average annual area of 5998.54 km²; (2) plain lakes experienced fluctuations, reaching their maximum in 2000 and their minimum in 2015, alpine lakes peaked in 2016, and plateau lakes continued to expand, with the maximum recorded in 2020 and the minimum in 1995; and (3) human activities such as urban and agricultural water use were the primary causes of shrinking plain lakes, while an increased PET accelerates evaporation, alpine lakes were influenced by both climate variability and human disturbance, and plateau lakes were highly sensitive to climate change, with rising temperatures increasing snowmelt and glacial runoff into lakes, which were the main drivers of their expansion. These findings highlight the importance of incorporating elevation-specific lake responses into climate adaptation strategies and sustainable water management policies in arid regions. Full article

Multispectral technology and deep learning are widely used in field crop yield prediction. Existing studies mainly focus on large-scale estimation in plain regions, while integrated applications for small-scale plateau plots are rarely reported. To solve this problem, this study proposes a WT-CNN-BiLSTM hybrid model that integrates UAV-borne multispectral imagery and deep learning for rice yield prediction in small-scale greenhouses on the Yunnan Plateau. Initially, a rice dataset covering five drip irrigation levels was constructed, including vegetation index images of rice throughout its entire growth cycle and yield data from 500 sub-plots. After data augmentation (image rotation, flipping, and yield augmentation with Gaussian noise), the dataset was expanded to 2000 sub-plots. Then, with CNN-LSTM as the baseline, four vegetation indices (NDVI, NDRE, OSAVI, and RECI) were compared, and RECI-Yield was determined as the optimal input dataset. Finally, the convolutional layers in the first residual block of ResNet50 were replaced with WTConv to enhance multi-frequency feature extraction; the extracted features were then input into BiLSTM to capture the long-term growth trends of rice, resulting in the development of the WT-CNN-BiLSTM model. Experimental results showed that in small-scale greenhouses on the Yunnan Plateau, the model achieved the best prediction performance under the 50% drip irrigation level (R² = 0.91). Moreover, the prediction performance based on the merged dataset of all irrigation levels was even better (RMSE = 9.68 g, MAPE = 11.41%, R² = 0.92), which was significantly superior to comparative models such as CNN-LSTM, CNN-BiLSTM, and CNN-GRU, as well as the prediction results under single irrigation levels. Cross-validation based on the RECI-Yield-VT dataset (RMSE = 8.07 g, MAPE = 9.22%, R² = 0.94) further confirmed its generalization ability, enabling its effective application to rice yield prediction in small-scale greenhouse scenarios on the Yunnan Plateau. Full article

This study presents a water vapor gradient (WVG) retrieval method based on Global Navigation Satellite System (GNSS) tropospheric parameter estimation. A case study examined the method’s applicability to the extreme rainstorm event in North China in July 2023. Precipitable water vapor (PWV) and WVG data from 332 GNSS sites in this area were retrieved. Radar and precipitation data were combined to perform a spatiotemporal comparison study. The results show that GNSS PWV and WVG of this weather process were highly consistent with radar reflectivity and precipitation. When a high PWV (>60 mm) was accompanied by WVG convergence, radar reflectivity was significantly strong and precipitation occurred at the leading edge of large gradients and the convergence region. Based on the edge of big WVGs, observed by multiple GNSS stations, the location and movement of rainfall could be identified. In case of large amounts of PWV accompanied by plummeting WVG (down to 0.1–0.4 mm/km), high or persistent precipitation occurs. During the event, compared to the northern plateau, the plain region demonstrated higher PWV, lesser WVG variation, and more intense precipitation, likely caused by the topographic dynamic effect. GNSS PWV and WVG can be key indicators for short-range weather forecasting of extreme rainstorm events. Full article

Cloud-aerosol interactions represent a critical uncertainty in climate systems. Using 2006–2021 CALIPSO products, we investigated upper tropospheric clouds and aerosol layers across four Russian regions: Western Plains, West Siberian Plain, Central Siberian Plateau, and Eastern Mountains. Top Cloud Optical Depth (TCOD), Top Depolarization Ratio of clouds (TDRc), and Layer Level (LLc) exhibit pronounced seasonal and diurnal variations, peaking during summer and nighttime when convection intensifies. Upper aerosol layers show low Total Aerosol Optical Depth (TAOD) and Color Ratio (CRa), often displaying multi-layered structures influenced by spring–summer dust transport and biomass burning. We constructed a correlation matrix of 49 parameter pairs (7 cloud × 7 aerosol parameters), revealing moderate positive correlations between cloud and aerosol layer heights under coexistence conditions. TDRc showed weak linear but strong nonlinear relationships with aerosol parameters, indicating complex coupling mechanisms beyond simple linear models. Nighttime observations demonstrated superior signal-to-noise ratios and correlation coefficients compared to daytime measurements. These findings enhance understanding of cloud-aerosol coupling at middle-high latitudes, providing parameterization constraints for improving global climate model representations of these processes. Full article

This study proposes a novel vehicle speed prediction model for plateau transportation—CLS-DW Stacking (Constrained Least Squares Dynamic Weighting Model Stacking)—which holds significant implications for the sustainable development of transportation systems in high-altitude regions. Research on sharp-curved roads on mountainous plateaus remains scarce. Compared with plain areas, data acquisition in such regions is constrained by government confidentiality policies, while complex environmental and topographical conditions lead to substantial variations in road alignment and elevation. To address these challenges, this study presents a sustainable data acquisition and construction method: unmanned aerial vehicle (UAV) video data are processed through road image segmentation, trajectory tracking, and three-dimensional modeling to generate multi-source heterogeneous datasets for both single-curve and continuous-curve scenarios. Building upon these datasets, the proposed framework integrates constrained least squares with multiple deep learning methods to achieve accurate traffic flow prediction. Bi-LSTM (Bidirectional Long Short-Term Memory), Informer, and GRU (Gated Recurrent Unit) are employed as base learners, and the loss function is redefined with non-negativity and normalization constraints on the weights. This ensures optimal weight coefficients for each base learner, with the final prediction obtained via weighted summation. The experimental results show that, compared with single deep learning models such as Informer, the proposed model reduces the mean squared error (MSE) by 1.9% on the single curve dataset and by 7.7% on the continuous curve dataset. Furthermore, by combining vehicle speed predictions across different altitude gradients with decision tree-based interpretable analysis, this research provides scientific support for developing altitude-specific and precision-oriented speed limit policies. The outcomes contribute to accident risk reduction, traffic congestion mitigation, and carbon emission reduction, thereby improving road resource utilization efficiency. This work not only fills the research gap in traffic prediction for sharp-curved plateau roads but also supports the construction of green transportation systems and the broader objectives of sustainable development in high-altitude regions. Full article

The Qinghai-Tibet Plateau, known as the “third pole” of the Earth with an average elevation of approximately 4500 m, offers a unique natural laboratory for probing the dynamic behavior of the global electric circuit. In this study, we conduct a comprehensive analysis of near-surface vertical atmospheric electric field (AEF) measurements collected at the Gar Station (80.1° E, 32.5° N; 4259 m a.s.l.) on the western Tibetan Plateau, spanning the period from November 2021 to December 2024. Fair-weather conditions are imposed. The annual mean AEF at Gar is ∼0.331 kV/m, significantly higher than values observed at lowland and plain sites, indicating a pronounced enhancement in atmospheric electricity associated with high-altitude conditions. Moreover, the AEF exhibits marked seasonal variability, peaking in December (∼0.411–0.559 kV/m) and valleying around July–August (∼0.150–0.242 kV/m), yielding an overall amplitude of approximately 0.3 kV/m. We speculate that this seasonal pattern is primarily driven by variations in aerosol concentration. During winter, increased aerosol loading from residential heating and vehicle emissions due to incomplete combustion reduces atmospheric conductivity by depleting free ions and decreasing ion mobility, thereby enhancing the near-surface AEF. In contrast, lower aerosol concentrations in summer lead to weaker AEF. This seasonal decline in aerosol levels is likely facilitated by stronger winds and more frequent rainfall in summer, which enhance aerosol dispersion and wet scavenging, whereas weaker winds and limited precipitation in winter favor near-surface aerosol accumulation. On diurnal timescales, the Gar AEF curve deviates significantly from the classical Carnegie curve, showing a distinct double-peak and double-trough structure, with maxima at ∼03:00 and 14:00 UT and minima near 00:00 and 10:00 UT. This deviation may partly reflect local influences related to sunrise and sunset. This study presents the longest ground-based AEF observations over the Qinghai–Tibet Plateau, providing a unique reference for future studies on altitude-dependent AEF variations and their coupling with space weather and climate processes. Full article

It has always been a research topic for some meteorologists to design a new and reasonable calculation scheme of the intensity of the Tibetan Plateau (TP) summer monsoon (TPSM). Existing indices are defined based on dynamic factors. However, the intensity of the TPSM can also be influenced by thermal factors. We therefore propose defining a TPMI in terms of horizontal temperature advection within the main body of the TP. This provides a new index that directly quantifies the extent to which the thermal forcing in the TP region regulates the monsoon system. The new index emphasizes the importance of the atmospheric asymmetry structure in measuring TPSM strength, represents the variability of the TPSM circulation system, effectively reflects the meteorological elements, and accurately represents the climate variation. Tropospheric temperature (TT) and TPSM are linked by the new index. These significant centers of correlation are characterized by alternating positive and negative phases along the Eastern European Plain, across the Turan Plain, and into southwestern and northeastern China. The correlation coefficients are found to be significantly out of phase between high and low altitudes in the vertical direction. This research broadens our minds and helps us to develop a new approach to measuring TPSM strength. It can also predict extreme weather events in advance based on TPMI changes, providing a scientific basis for disaster warnings and the management of agriculture and water resources. Full article

Understanding the dynamics and drivers of groundwater storage (GWS) is crucial for sustainable resource management. Most studies attribute GWS changes to climate change or human activities, often neglecting external hydrological influences. In this study, we categorize the driving factors influencing GWS changes into three groups: climate change, human activity, and regional hydrological pressure. We emphasize that the coupling effects and potential disturbances from adjacent hydrological systems may significantly affect local groundwater evolution. This perspective differs from conventional approaches that focus solely on local factors. This study analyzes the spatiotemporal evolution of GWS in Shanxi Province, located in the eastern Loess Plateau, from 2003 to 2023 using GRACE and GLDAS data. We examine the linkage between GWS in Shanxi and the North China Plain through correlation analysis, Engle–Granger cointegration tests, and Granger causality tests. The results show that GWS in Shanxi showed an average annual reduction of −17.27 ± 1.4 mm/yr, with the most severe depletion occurring in the southeastern region, which is geographically adjacent to the North China Plain. The results of the Engle–Granger cointegration test and Granger causality analysis reveal a bidirectional causal relationship between GWS changes in the two regions, indicating that changes in GWS in either region may have a significant impact on the other. The results of the contribution analysis indicate that the North China Plain’s groundwater decline contributes approximately −53.89% to the reduction of GWS in Shanxi, while human activities and external hydrological influences together explain over 98% of the change. This result suggests that relying solely on climatic and human activity factors to explain groundwater changes may lead to significant biases, as ignoring interregional hydrological linkages can amplify or obscure the attribution of local groundwater variations, resulting in distorted conclusions. These findings highlight the value of remote sensing in capturing regional hydrological interactions and underscore the need to integrate interregional groundwater connectivity into policy design for sustainable groundwater governance. Full article

Finding out the dynamics of soil organic carbon and inorganic carbon is paramount for sustaining terrestrial carbon cycling and climate change mitigation. From the 1980s to 2010s, substantial changes in land use, climate, and agricultural practices have occurred across North China. This study systematically quantified the stratified dynamics of soil carbon stocks (0–100 cm with 20 cm intervals) and their compositional shifts by using the geographically weighted regression kriging model. The model integrated soil sample data from provincial surveys across North China with key environmental covariates (e.g., elevation, precipitation, air temperature, and the vegetation index) to spatially predict and analyze vertical carbon stock changes. The results indicated that soil carbon stocks decreased considerably by 5.86 Gt in the one-meter soil profile from the 1980s to the 2010s. Significant losses in soil inorganic carbon stocks directly contributed to net soil carbon sources. These significant soil inorganic carbon losses of 7.03 Gt, originating primarily from losses of 7.35 Gt in deeper soil layers (20–100 cm), effectively offset increases of 1.17 Gt in soil organic carbon. About two-thirds of regions in North China have been categorized as carbon source regions. These are distributed for the most part in arid and semi-arid areas and the Qinghai–Tibet Plateau. The remaining one-third of regions have been classified as carbon sink regions which are primarily found in the Loess Plateau, the Huang–Huai–Hai Plain, the Middle-lower Yangtze Plain, and the Northeast China Plain. Significant losses in soil inorganic carbon stocks caused by strong carbon sources may undermine global measures aimed at enhancing terrestrial ecosystem carbon sequestration and fixation. Our results highlight the urgent need to account for vulnerable subsurface inorganic carbon pools in regional carbon sequestration strategies and climate models. Full article

The pepper weevil Anthonomus eugenii is a devastating pest native to Central America that can cause severe damage to over 35 pepper varieties. Global trade in peppers has significantly increased the risk of its spread and expansion. Moreover, future climate change may add more uncertainty to its distribution, resulting in considerable ecological and economic damage globally. Therefore, we employed an ensemble model combining Random Forests and CLIMEX to predict the potential global distribution of A. eugenii in historical and future climate scenarios. The results indicated that the maximum temperature of the warmest month is an important variable affecting global A. eugenii distribution. Under the historical climate scenario, the potential global distribution of A. eugenii is concentrated in the Midwestern and Southern United States, Central America, the La Plata Plain, parts of the Brazilian Plateau, the Mediterranean and Black Sea coasts, sub-Saharan Africa, Northern and Southern China, Southern India, Indochina Peninsula, and coastal area in Eastern Australia. Under future climate scenarios, suitable areas in the Northern Hemisphere, including North America, Europe, and China, are projected to expand toward higher latitudes. In China, the number of highly suitable areas is expected to increase significantly, mainly in the south and north. Contrastingly, suitable areas in Central America, northern South America, the Brazilian Plateau, India, and the Indochina Peninsula will become less suitable. The total land area suitable for A. eugenii under historical and future low- and high-emission climate scenarios accounted for 73.12, 66.82, and 75.97% of the global land area (except for Antarctica), respectively. The high-suitability areas identified by both models decreased by 19.05 and 35.02% under low- and high-emission scenarios, respectively. Building on these findings, we inferred the future expansion trends of A. eugenii globally. Furthermore, we provide early warning of A. eugenii invasion and a scientific basis for its spread and outbreak, facilitating the development of effective quarantine and control measures. Full article

Corydalis yanhusuo W. T. Wang, commonly known as Yanhusuo, is an important and rare medicinal plant resource in China. Its habitat integrity is facing severe challenges due to climate change and human activities. Establishing an integrative quality zoning system for this species is of significant practical importance for resource conservation and adaptive management. This study integrates multiple data sources, including 121 valid distribution points, 37 environmental factors, future climate scenarios (SSP126 and SSP585 pathways for the 2050s and 2090s), and measured content of tetrahydropalmatine (THP) from 22 sampling sites. A predictive framework for habitat suitability and spatial distribution of effective components was constructed using a multi-model coupling approach (MaxEnt, ArcGIS spatial analysis, and co-kriging method). The results indicate that the MaxEnt model exhibits high prediction accuracy (AUC > 0.9), with the dominant environmental factors being the precipitation of the wettest quarter (404.8~654.5 mm) and the annual average temperature (11.8~17.4 °C). Under current climatic conditions, areas of high suitability are concentrated in parts of Central and Eastern China, including the Sichuan Basin, the middle–lower Yangtze plains, and coastal areas of Shandong and Liaoning. In future climate scenarios, the center of suitable areas is predicted to shift northwestward. The content of THP is significantly correlated with the mean diurnal temperature range, temperature seasonality, and the mean temperature of the wettest quarter (p < 0.01). A comprehensive assessment identifies the Yangtze River Delta region, Central China, and parts of the Loess Plateau as the optimal integrative quality zones. This research provides a scientific basis and decision-making support for the sustainable utilization of C. yanhusuo and other rare medicinal plants in China. Full article

The Inner Mongolia Plateau (IMP), situated in the arid and semi-arid ecological transition zone of northern China, is particularly vulnerable to both climate change and human activities. Understanding the spatiotemporal vegetation dynamics and their driving forces is essential for regional ecological management. This study employs Sen’s slope estimation, BFAST analysis, residual trend method and Geodetector to analyze the spatial patterns of Normalized Difference Vegetation Index (NDVI) variability and distinguish between climatic and anthropogenic influences. Key findings include the following: (1) From 1982 to 2022, vegetation cover across the IMP exhibited a significant greening trend. Zonal analysis showed that this spatial heterogeneity was strongly regulated by regional hydrothermal conditions, with varied responses across land cover types and pronounced recovery observed in high-altitude areas. (2) In the western arid regions, vegetation trends were unstable, often marked by interruptions and reversals, contrasting with the sustained greening observed in the eastern zones. (3) Vegetation growth was primarily temperature-driven in the eastern forested areas, precipitation-driven in the central grasslands, and severely limited in the western deserts due to warming-induced drought. (4) Human activities exerted dual effects: significant positive residual trends were observed in the Hetao Plain and southern Horqin Sandy Land, while widespread negative residuals emerged across the southern deserts and central grasslands. (5) Vegetation change was driven by climate and human factors, with recovery mainly due to climate improvement and degradation linked to their combined impact. These findings highlight the interactive mechanisms of climate change and human disturbance in regulating terrestrial vegetation dynamics, offering insights for sustainable development and ecosystem education in climate-sensitive systems. Full article

Xylosalsola chiwensis (Popov) Akhani & Roalson is listed in the Red Data Book of Kazakhstan as a rare species with a limited distribution, occurring in small populations in Kazakhstan, Uzbekistan, and Turkmenistan. The aim of this study is to deepen the understanding of the ecological conditions of its habitats, the floristic composition of its associated plant communities, the species’ morphological and anatomical characteristics, and its molecular phylogeny, as well as to identify the main threats to its survival. The ecological conditions of the X. chiwensis habitats include coastal sandy plains and the slopes of chinks and denudation plains with gray–brown desert soils and bozyngens on the Mangyshlak Peninsula and the Ustyurt Plateau at altitudes ranging from −3 to 270 m above sea level. The species is capable of surviving in arid conditions (less than 100 mm of annual precipitation) and under extreme temperatures (air temperatures exceeding 45 °C and soil surface temperatures above 65 °C). In X. chiwensis communities, we recorded 53 species of vascular plants. Anthropogenic factors associated with livestock grazing, industrial disturbances, and off-road vehicle traffic along an unregulated network of dirt roads have been identified as contributing to population decline and the potential extinction of the species under conditions of unsustainable land use. The morphometric traits of X. chiwensis could be used for taxonomic analysis and for identifying diagnostic morphological characteristics to distinguish between species of Xylosalsola. The most taxonomically valuable characteristics include the fruit diameter (with wings) and the cone-shaped structure length, as they differ consistently between species and exhibit relatively low variability. Anatomical adaptations to arid conditions were observed, including a well-developed hypodermis, which is indicative of a water-conserving strategy. The moderate photosynthetic activity, reflected by a thinner palisade mesophyll layer, may be associated with reduced photosynthetic intensity, which is compensated for through structural mechanisms for water conservation. The flow cytometry analysis revealed a genome size of 2.483 ± 0.191 pg (2n/4x = 18), and the phylogenetic analysis confirmed the placement of X. chiwensis within the tribe Salsoleae of the subfamily Salsoloideae, supporting its taxonomic distinctness. To support the conservation of this rare species, measures are proposed to expand the area of the Ustyurt Nature Reserve through the establishment of cluster sites. Full article

The spatiotemporal changes and driving mechanisms of rain-on-snow (ROS) events and their intensities are crucial for responding to disasters triggered by such events. However, there is currently a lack of detailed assessment of the seasonal variations and driving mechanisms of ROS events and their intensities in China. Therefore, this study utilized daily meteorological data and daily snow depth data from 513 stations in China during 1978–2020 to investigate spatiotemporal variations of ROS events and their intensities. Also, based on the detrend and partial correlation analysis model, the driving factors of ROS events and their intensity were explored. The results showed that ROS events primarily occurred in northern Xinjiang, the Qinghai–Tibet Plateau, Northeast China, and central and eastern China. ROS events frequently occurred in the middle and lower Yangtze River Plain in winter but were easily overlooked. The number and intensity of ROS events increased significantly (p < 0.05) in the Changbai Mountains in spring and the Altay Mountains and the southeast part of the Qinghai–Tibet Plateau in winter, leading to heightened ROS flood risks. However, the number and intensity of ROS events decreased significantly (p < 0.05) in the middle and lower Yangtze River Plain in winter. The driving mechanisms of the changes for ROS events and their intensities were different. Changes in the number of ROS events and their intensities in snow-rich regions were driven by rainfall days and quantity of rainfall, respectively. In regions with more rainfall, these changes were driven by snow cover days and snow water equivalent, respectively. Air temperature had no direct impact on ROS events and their intensities. These findings provide reliable evidence for responding to disasters and changes triggered by ROS events. Full article