Search Results (296)

With the global economic restructuring and the consequent population mobility, urban shrinkage has become a common phenomenon. The Qinling–Daba Mountains, a zone with a key ecological function in China, have long experienced population decline and functional degradation. Clarifying the dynamics and influencing factors of urban shrinkage plays a vital role in supporting the sustainable development of the region. This study, using permanent resident population growth rates and nighttime light data, classified cities in the region into four spatial patterns: expansion–growth, intensive growth, expansion–shrinkage, and intensive shrinkage. It further examined the spatial characteristics of shrinkage across four periods (2005–2010, 2010–2015, 2015–2020, and 2020–2022). A Geographically and Temporally Weighted Regression (GTWR) model was applied to examine core influencing factors and their spatiotemporal heterogeneity. The results indicated the following: (1) The dominant pattern of urban shrinkage in the Qinling–Daba Mountains shifted from expansion–growth to expansion–shrinkage, highlighting the paradox of population decline alongside continued spatial expansion. (2) Three critical indicators significantly influenced urban shrinkage: the number of students enrolled in general secondary schools (X₅), the per capita disposable income of urban residents (X₇), and the number of commercial and residential service facilities (X₁₂), with their effects exhibiting significant spatiotemporal heterogeneity. Temporally, X₁₂ was the most influential factor in 2005 and 2010, while in 2015, 2020, and 2022, X₅ and X₇ became the dominant factors. Spatially, X₇ significantly affected both eastern and western areas; X₅’s influence was most pronounced in the west; and X₁₂ had the greatest impact in the east. This study explored the patterns and underlying drivers of urban shrinkage in underdeveloped areas, aiming to inform sustainable development practices in regions facing comparable challenges. Full article

Karst groundwater systems are critical to water supply and ecological sustainability in northern China, yet their heterogeneity poses challenges for flow characterization. The Yuquan Mountain (YM) Spring, historically a major karst spring in western Beijing, has experienced persistent drying, raising concerns about its recharge and flow mechanisms. This study integrates published isotope data, spatial distributions of Na⁺ and Cl⁻ as hydrochemical tracers, groundwater age estimates, and geophysical survey results to assess the recharge sources and flow pathways within the YM Spring catchment area. The analysis identifies two major recharge zones: the Tanzhesi area, primarily recharged by direct infiltration of precipitation through exposed carbonate rocks, and the Junzhuang area, which receives mixed recharge from rainfall and Yongding River seepage. Three potential flow pathways are proposed, including shallow flow along faults and strata, and a deeper, speculative route through the Jiulongshan-Xiangyu syncline. The synthesis of multiple lines of evidence leads to a refined conceptual model that illustrates how geological structures govern recharge, flow, and discharge processes in this karst system. These findings not only enhance the understanding of subsurface hydrodynamics in complex geological settings but also provide a scientific basis for future spring restoration planning and groundwater management strategies in the regions. Full article

The Kekeya oilfield is located at the southwestern edge of the Tarim Basin, in the southern margin of the Yecheng depression, at the western end of the second structural belt of the northern foothills of the Kunlun Mountains. It is one of the important oil and gas fields in western China, with significant oil and gas resource potential in the X4-X5 section of the Xihepu Formation. This study focuses on the edge of the alluvial fan depositional system, employing various techniques, including core data and well logging data, to precisely characterize the sand body architecture and comprehensively analyze the reservoir architecture in the study area. First, the regional geological background of the area is analyzed, clarifying the sedimentary environment and evolutionary process of the Xihepu Formation. Based on the sedimentary environment and microfacies classification, the sedimentary features of the region are revealed. On this basis, using reservoir architecture element analysis, the interfaces of the reservoir architecture are finely subdivided. The spatial distribution characteristics of the planar architecture are discussed, and the spatial distribution and internal architecture of individual sand body units are analyzed. The study focuses on the spatial combination of microfacies units along the profile and their internal distribution patterns. Additionally, a quantitative analysis of the sizes of various types of sand bodies is conducted, constructing the sedimentary model for the region and revealing the control mechanisms of different sedimentary architectures on reservoir properties and oil and gas accumulation patterns. This study pioneers a quantitative model for alluvial fan fringe in gentle-slope basins, featuring the following: (1) lobe width-thickness ratios (avg. 128), (2) four base-level-sensitive boundary markers, and (3) a retrogradational stacking mechanism. The findings directly inform reservoir development in analogous arid-climate systems. This research not only provides a scientific basis for the exploration and development of the Kekeya oilfield but also serves as an important reference for reservoir architecture studies in similar geological contexts. Full article

Investigating land cover patterns, changes in carbon stocks, and forecasting future conditions are essential for formulating regional sustainable development strategies and enhancing ecological and environmental quality. This study centers on Guiyang, a mountainous urban area in southwestern China, to analyze the dynamic changes in land cover and their effects on carbon stocks from 2000 to 2035. A carbon stocks assessment framework was developed using a cellular automaton-based artificial neural network model (CA-ANN), the InVEST model, and the geographical detector model to predict future land cover changes and identify the primary drivers of variations in carbon stocks. The results indicate that (1) from 2000 to 2020, impervious surfaces expanded significantly, increasing by 199.73 km². Compared to 2020, impervious surfaces are projected to increase by 1.06 km², 13.54 km², and 34.97 km² in 2025, 2030, and 2035, respectively, leading to further reductions in grassland and forest areas. (2) Over time, carbon stocks in Guiyang exhibited a general decreasing trend; spatially, carbon stocks were higher in the western and northern regions and lower in the central and southern regions. (3) The level of greenness, measured by the normalized vegetation index (NDVI), significantly influenced the spatial variation of carbon stocks in Guiyang. Changes in carbon stocks resulted from the combined effects of multiple factors, with the annual average temperature and NDVI being the most influential. These findings provide a scientific basis for advancing low-carbon development and constructing an ecological civilization in Guiyang. Full article

Rockfalls, among the most common natural disasters, pose risks such as traffic congestion, casualties, and substantial property damage. Guizhou Province, with China’s fourth-longest highway network, features mountainous terrain prone to frequent rockfall incidents annually. Consequently, assessing highway rockfall risks in Guizhou Province is crucial for safeguarding the lives and travel of residents. This study evaluates highway rockfall risk through three key components: susceptibility, hazard, and vulnerability. Susceptibility was assessed using information content and logistic regression methods, considering factors such as elevation, slope, normalized difference vegetation index (NDVI), aspect, distance from fault, relief amplitude, lithology, and rock weathering index (RWI). Hazard assessment utilized a fuzzy analytic hierarchy process (AHP), focusing on average annual rainfall and daily maximum rainfall. Socioeconomic factors, including GDP, population density, and land use type, were incorporated to gauge vulnerability. Integration of these assessments via a risk matrix yielded comprehensive highway rockfall risk profiles. Results indicate a predominantly high risk across Guizhou Province, with high-risk zones covering 41.19% of the area. Spatially, the western regions exhibit higher risk levels compared to eastern areas. Notably, the Bijie region features over 70% of its highway mileage categorized as high risk or above. Logistic regression identified distance from fault lines as the most negatively correlated factor affecting highway rockfall susceptibility, whereas elevation gradient demonstrated a minimal influence. This research provides valuable insights for decision-makers in formulating highway rockfall prevention and control strategies. Full article

Forest biomass is closely related to carbon sequestration capacity and can reflect the level of forest management. This study utilizes four machine learning algorithms, namely Multivariate Stepwise Regression (MSR), K-Nearest Neighbors (k-NN), Artificial Neural Network (ANN), and Random Forest (RF), to estimate forest aboveground biomass (AGB) in Chenzhou City, Hunan Province, China. In addition, a canopy height model, constructed from a digital surface model (DSM) derived from Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) and an ICESat-2-corrected SRTM DEM, is incorporated to quantify its impact on the accuracy of AGB estimation. The results indicate the following: (1) The incorporation of multi-source remote sensing data significantly improves the accuracy of AGB estimation, among which the RF model performs the best (R² = 0.69, RMSE = 24.26 t·ha⁻¹) compared with the single-source model. (2) The canopy height model (CHM) obtained from InSAR-LiDAR effectively alleviates the signal saturation effect of optical and SAR data in high-biomass areas (>200 t·ha⁻¹). When FCH is added to the RF model combined with multi-source remote sensing data, the R² of the AGB estimation model is improved to 0.74. (3) In 2018, AGB in Chenzhou City shows clear spatial heterogeneity, with a mean of 51.87 t·ha⁻¹. Biomass increases from the western hilly part (32.15–68.43 t·ha⁻¹) to the eastern mountainous area (89.72–256.41 t·ha⁻¹), peaking in Dongjiang Lake National Forest Park (256.41 t·ha⁻¹). This study proposes a comprehensive feature integration framework that combines red-edge spectral indices for capturing vegetation physiological status, SAR-derived texture metrics for assessing canopy structural heterogeneity, and canopy height metrics to characterize forest three-dimensional structure. This integrated approach enables the robust and accurate monitoring of carbon storage in subtropical forests. Full article

Snow cover, as a critical component of the cryosphere, serves as a vital water resource for arid regions in Northwest China. The Qilian Mountains (QLM), situated on the northeastern margin of the Tibetan Plateau, function as an important ecological barrier and water conservation area in western China. This study presents the first high-resolution historical snow cover product developed specifically for the QLM, utilizing a multi-level snow classification algorithm tailored to the complex topography of the region. By employing Landsat satellite data from 1986–2024, we constructed a comprehensive 39-year snow cover dataset at a resolution of 30 m. A dual adaptive cloud masking strategy and spatial interpolation techniques were employed to effectively address cloud contamination and data gaps prevalent in mountainous regions. The spatiotemporal characteristics and driving mechanisms of snow cover changes in the QLM were systematically analyzed using Sen–Theil trend analysis and Mann–Kendall tests. The results reveal the following: (1) The mean annual snow cover extent in the QLM was 15.73% during 1986–2024, exhibiting a slight declining trend (−0.046% yr⁻¹), though statistically insignificant (p = 0.215); (2) The snowline showed significant upward migration, with mean elevation and minimum elevation rising at rates of 3.98 m yr⁻¹ and 2.81 m yr⁻¹, respectively; (3) Elevation-dependent variations were observed, with significant snow cover decline in high-altitude (>5000 m) and low-altitude (2000–3500 m) regions, while mid-altitude areas remained relatively stable; (4) Comparison with MODIS data demonstrated good correlation (r = 0.828) but revealed systematic differences (RMSE = 12.88%), with MODIS showing underestimation in mountainous environments (Bias: −8.06%). This study elucidates the complex response mechanisms of the QLM snow system under global warming, providing scientific evidence for regional water resource management and climate change adaptation strategies. Full article

Jilin Province is an important ecological security barrier and major grain-producing region in northeast China, playing a crucial role in ensuring ecological security and promoting regional sustainable development. This study examines ecological resilience from three dimensions: resistance, adaptability, and resilience. Based on multi-source data from 2000 to 2020, an ecological resilience indicator system was constructed. Spatial autocorrelation and OPGD models were employed to analyze temporal and spatial evolution and the driving mechanisms. The results indicate that ER exhibits an overall spatial pattern of “high in the east, low in the west, and under pressure in the central region.” The eastern mountainous areas demonstrate high and stable resilience, while the central plains and western ecologically fragile regions exhibit weaker resilience. In terms of resistance, the eastern mountainous regions are primarily forested, with high and sustained ESV, while the western sandy edge regions primarily have low ESV, making ecosystems susceptible to disturbance. In terms of adaptability, the large-scale farmland landscapes in the central regions exhibit strong disturbance resistance, while water resource adaptability in the western ecologically fragile regions has locally improved. However, adaptability in the eastern mountainous regions is relatively low due to development impacts. In terms of resilience, the eastern core regions possess stable recovery capabilities, while the central and western regions generally exhibit lower resistance with fluctuating changes. Between 2000 and 2020, the ecological resilience Moran’s I index slightly decreased from 0.558 to 0.554, with the spatial aggregation pattern remaining largely stable. Among the driving factors, DEM remains the most stable. The influence of NDVI has weakened, while temperature (TEM) and NPP-VIIRS have become more significant. Overall, factor interactions have grown stronger, as reflected by the q-value rising from 0.507 to 0.5605. This study provides theoretical support and decision-making references for enhancing regional ecological resilience, optimizing ecological spatial layout, and promoting sustainable ecosystem management. Full article

Rural tourism plays a crucial role in promoting industrial revitalization in mountainous regions. Drawing inspiration from the site selection mechanisms of nature reserves, this study constructs a gap analysis framework tailored to rural tourism destinations, aiming to provide technical support for their spatial layout and systematic planning. By integrating a potential evaluation system based on tourism resources, market demand, and synergistic factors, the study identifies rural tourism priority zones and proposes a development typology and spatial optimization strategy across five provinces in Southwest China. The findings reveal: (1) First- and second-priority zones are primarily located in the core and periphery of provincial capitals and prefecture-level cities, while third-priority zones are concentrated in resource-rich areas of Yunnan and Guizhou and market-oriented areas of Sichuan, Chongqing, and Guangxi. (2) The Chengdu Plain emerges as the core region for rural tourism development, with hotspots clustered around Chengdu, northern and western Guizhou, central Chongqing, eastern Guangxi, and northwestern Yunnan, whereas cold spots are mainly situated in the western Sichuan Plateau and the Leshan–Liangshan–Zhaotong–Panzhihua–Chuxiong–Pu’er belt. (3) The alignment between tourism resources and rural tourism destinations is highest in Yunnan and Guizhou, while Chongqing exhibits the strongest match between destinations and tourism market potential and synergistic development conditions. Overall, 79.35% of rural tourism destinations in the region are situated within identified priority zones, with Chongqing, Guizhou, and Sichuan exhibiting the highest proportions. Based on the spatial mismatch between potential and existing destinations, the study delineates four development types—maintenance and enhancement, supplementation and upgrading, expansion, and reserve development—and offers regionally tailored planning recommendations. The proposed framework provides a replicable approach for spatial planning of rural tourism destinations in complex mountainous settings. Full article

Geothermal systems play a crucial role in understanding Earth’s heat dynamics. The Yunkai Uplift in southern China exemplifies a geothermally rich region characterized by ancient lithologies and high heat flow. This study investigates the geochemical characteristics of geothermal waters in the Yunkai Uplift. Both geothermal and non-thermal water samples were collected along the Xinyi–Lianjiang (XL) Fault Zone and the Cenxi–Luchuan (CL) Fault Zone flanking the core of the Yunkai Mountains. Analytical techniques were applied to examine major ions, trace elements, and dissolved CO₂ and H₂, as well as isotopic characteristics of O, H, Sr, C, and He in water samples, allowing for an investigation of geothermal reservoir temperatures, circulation depths, and mixing processes. The findings indicate that most geothermal waters are influenced by water–rock interactions primarily dominated by granites. The region’s diverse lithologies, change from ancient Caledonian granites and medium–high-grade metamorphic rocks in the central hinterland (XL Fault Zone) to low-grade metamorphic rocks and sedimentary rocks in the western margin (CL Fault Zone). The chemical compositions of geothermal waters are influenced through mixing contacts between diverse rocks of varying ages, leading to distinct geochemical characteristics. Notably, δ¹³C_CO2 values reveal that while some samples exhibit significant contributions from metamorphic CO₂ sources, others are characterized by organic CO₂ origins. Regional heat flow results from the upwelling of mantle magma, supplemented by radioactive heat generated from crustal granites. Isotopic evidence from δ²H and δ¹⁸O indicates that the geothermal waters originate from atmospheric sources, recharged by precipitation in the northern Yunkai Mountains. After infiltrating to specific depths, meteoric waters are heated to temperatures ranging from about 76.4 °C to 178.5 °C before ascending through the XL and CL Fault Zones under buoyancy forces. During their upward migration, geothermal waters undergo significant mixing with cold groundwater (54–92%) in shallow strata. As part of the western boundary of the Yunkai Uplift, the CL Fault Zone may extend deeper into the crust or even interact with the upper mantle but exhibits weaker hydrothermal activities than the XL Fault Zone. The XL Fault Zone, however, is enriched with highly heat-generating granites, is subjected more to both the thermal and mechanical influences of upwelling mantle magma, resulting in a higher heat flow and tension effect, and is more conducive to the formation of geothermal waters. Our findings underscore the role of geotectonic processes, lithological variation, and fault zone activity in shaping the genesis and evolution of geothermal waters in the Yunkai Uplift. Full article

The net ecosystem CO₂ exchange (NEE) of spruce forest ecosystems is poorly understood by the lack of measurements of CO₂ in the Qilian Mountain of Western China. Thus, we conducted consecutive measurements of CO₂ fluxes using tower-based the eddy covariance method from 2021 to 2022. These results indicated that daily NEE of spruce forest indicated a robust temporal pattern ranging from −28.43 to 29.62 g C m⁻² from 2021 to 2022. Remarkable carbon sink characteristics were presented from late May to late September. Month accumulative NEE fluxes ranged from −336.57 to 142.22 g C m⁻² in two years. Additionally, average carbon sink was 591.51 ± 37.41 g C m⁻² in Qilian Mountain. NEE was negatively driven by vapor pressure deficit (VPD) and average air temperature (p < 0.05), as determined using the structural equation model. However, the direct effect coefficient of precipitation on NEE was weak. VPD was positively driven by air temperature and negatively determined by precipitation. In conclusion, a future warming scenario would significantly decrease the carbon sink of the spruce forest in Qilian Mountain. Full article

The China–Mongolia arid region adjacent to the Altai Mountain (CMA) has a sensitive ecosystem that relies heavily on both terrestrial water (TWS) and groundwater storage (GWS). However, during the 2003–2016 period, the CMA experienced significant glacier retreat, lake shrinkage, and grassland degradation. To illuminate the TWS and GWS dynamics in the CMA and the dominant driving factors, we employed high-resolution (0.1°) GRACE (Gravity Recovery and Climate Experiment) data generated through random forest (RF) combined with residual correction. The downscaled data at a 0.1° resolution illustrate the spatial heterogeneity of TWS and GWS depletion. The highest TWS and GWS decline rates were both on the north slope of the Tianshan River Basin (NTRB) of the Junggar Basin of Northwestern China (JBNWC) (27.96 mm/yr and −32.98 mm/yr, respectively). Human impact played a primary role in TWS decreases in the JBNWC, with a relative contribution rate of 62.22% compared to the climatic contribution (37.78%). A notable shift—from climatic (2002–2010) to anthropogenic factors (2011–2020)—was observed as the primary driver of TWS decline in the Great Lakes Depression region of western Mongolia (GLDWM). To maintain ecological stability and promote sustainable regional development, effective action is urgently required to save essential TWS from further depletion. Full article

The Middle Yangtze River Urban Agglomeration, a critical ecological barrier in China, faces escalating pressures from rapid urbanization and climate change, leading to fragmented landscapes and degraded ecosystem services. To address the synergistic challenges of ecological protection and risk management, this paper takes the urban agglomeration in the middle reaches of the Yangtze River as the study area, and obtains the source patches through morphological spatial pattern analysis. Based on the spatial distribution of risky source areas, ecological blind zones are cut down by optimizing buffer zones and merging fragmented patches. Finally, a composite ecological network is constructed through circuit theory superimposed on the dual network method. The results showed that (1) there are 16 ecological source patches and 16 risk source patches in the study area. Six complementary ecological sources and four new ecological sources were obtained through the blind zone reduction strategy. The percentage of ecological blind zones reduced from 58.4% to 39.5%. (2) The integrated nodes with 11,366 connecting edges were identified. The integrated nodes are distributed around the central Jiuling-Mafushan Mountains, mainly in the western and southern areas of the Dongting Lake Plain. (3) Primary integration nodes are critical for network stability, with a 75% node failure threshold triggering systemic collapse. The proposed strategy of “mountain protection–plain control–railway monitoring” is consistent with China’s territorial and spatial planning. By incorporating the risk network into the conservation framework, this study provides feasible insights for balancing development and sustainability in ecologically fragile areas. Full article

Hippophae rhamnoides L. is an ecologically and medicinally significant species widely distributed across Eurasia, the suitable habitat of H. rhamnoides subsp. sinensis (is hereinafter referred to as sinensis) is concentrated in Northwest and Southwest China (approximately 34–40° N, 100–115° E). H. rhamnoides subsp. yunnanensis (hereinafter referred to as yunnanensis) is mainly distributed in the Hengduan Mountains and surrounding areas (approximately 25–30° N, 98–103° E). H. rhamnoides subsp. mongolica (hereinafter referred to as mongolica) is native to Central Asia to Siberia and is mainly distributed in Northern Xinjiang and Western Inner Mongolia in China (approximately 40–50° N, 100–120° E). H. rhamnoides subsp. turkestanica (hereinafter referred to as turkestanica) is mainly distributed in Western Xinjiang (approximately 40–45° N, 70–85° E). Climate change poses a considerable challenge, affecting its distribution and leading to shifts in its habitat ranges. This study applies the MaxEnt model to assess climate-driven distribution patterns of Hippophae species in China, and predicts current and future suitable habitats under climate change scenarios. This study employs the MaxEnt model and ArcGIS to simulate the potential distribution of four subspecies of H. rhamnoides during the current period and future projections under scenarios SSP1–2.6 and SSP5–8.5. The analysis reveals that the distributions of sinensis, mongolica, yunnanensis, and turkestanica are influenced primarily by climate variables such as temperature and precipitation, while yunnanensis is predominantly restricted by altitude. Future projections indicate that under the extreme climate of SSP5–8.5, centroid migration will be disrupted; specifically, sinensis is expected to migrate northeast or oscillate, mongolica will expand southwest but be limited by desert steppe zones, and turkestanica may face risks associated with groundwater depletion. This study advocates for integrating climate, ecological, and genetic data into conservation planning, with an emphasis on groundwater restoration and exploring genetic resources for stress resilience. The insights offered here contribute significantly to understanding climate adaptation mechanisms in arid and mountainous ecosystems and guide biodiversity conservation efforts. Full article

With the intensification of global climate change and human activities, coniferous species as the main components of natural forests in the Altay Mountains are facing the challenges of aging and regeneration. This study systematically analyzed structural heterogeneity and regeneration of three coniferous stand groups, Larix sibirica Ledeb. stand group, Abies sibirica Ledeb.-Picea obovata Ledeb.-Larix sibirica mixed stand group, and Picea obovata stand group, respectively, across western, central, and eastern forest areas of the Altay Mountains in Northwest China based on field surveys in 2023. Methodologically, we integrated Kruskal–Wallis/Dunn’s post hoc tests, nonlinear power-law modeling (diameter at breast height (DBH)–age relationships, validated via R², root mean square error (RMSE), and F-tests), static life tables (age class mortality and survival curves), and dynamic indices. Key findings revealed structural divergence: the L. sibirica stand group exhibited dominance of large-diameter trees (>30 cm DBH) with sparse seedlings/saplings and limited regeneration; the mixed stand group was dominated by small DBH individuals (<10 cm), showing young age structures and vigorous regeneration; while the P. obovata stand group displayed uniform DBH/height distributions and slow regeneration capacity. Radial growth rates differed significantly—highest in the mixed stand group (average of 0.315 cm/a), intermediate in the P. obovata stand group (0.216 cm/a), and lowest in the L. sibirica stand group (0.180 cm/a). Age–density trends varied among stand groups: unimodal in the L. sibirica and P. obovata stand groups while declining in the mixed stand group. All stand groups followed a Deevey-II survival curve (constant mortality across ages). The mixed stand group showed the highest growth potential but maximum disturbance risk, the L. sibirica stand group exhibited complex variation with lowest risk probability, while the P. obovata stand group had weaker adaptive capacity. These results underscore the need for differentiated management: promoting L. sibirica regeneration via gap-based interventions, enhancing disturbance resistance in the mixed stand group through structural diversification, and prioritizing P. obovata conservation to maintain ecosystem stability. This multi-method framework bridges stand-scale heterogeneity with demographic mechanisms, offering actionable insights for climate-resilient forestry. Full article