Search Results (265)

Under the background of global climate warming, glaciers in the Three-River Headwaters Region, as a crucial component of the “Asian Water Tower,” exert profound influences on regional water resource security and ecological stability through their mass balance variations. Due to the scarcity of in situ observations caused by the harsh high-altitude environment, long-term monitoring based on remote sensing techniques is urgently required. In this study, the geodetic method was employed, using the SRTM-C DEM acquired in 2000 as the reference, and recent glacier surface DEMs were generated from high-resolution ZiYuan-3 tri-stereo imagery obtained during 2024–2025. Through refined DEM co-registration, differencing, and systematic error corrections, the glacier mass balance in the Three-River Headwaters Region from 2000 to 2025 was systematically estimated. The results indicate that the glaciers in the study area exhibited an overall negative mass balance during the study period, with significant spatial heterogeneity. Among the sub-regions, the Lancang River source region experienced the most pronounced mass loss (−0.70 ± 0.07 m w.e. yr⁻¹), whereas the Yellow River source region showed the lowest mass loss (−0.37 ± 0.09 m w.e. yr⁻¹). Compared with earlier studies, glacier mass loss has accelerated in recent years and is closely associated with regional climatic characteristics. This study provides a scientific basis for understanding glacier changes and their hydrological and ecological impacts in the Three-River Headwaters Region. Full article

Accurate identification of acid-producing layers is key to controlling acid mine drainage (AMD) in abandoned coal mines. This study collected 337 core samples from 34 boreholes in the Modigou mining area, Shanxi, China, and established a combined static–mineralogical–kinetic approach to evaluate the acid-generating and neutralization potentials of sulfur-bearing rocks. Three-stage net acid generation (NAG) tests identified the pyrite-bearing layer of the Benxi Formation and the No. 10 coal seam of the Taiyuan Formation as the main acid producers, with NAG values of 360.41 and 97.87 kg H₂SO₄/t, respectively, while the Taiyuan limestone showed a high neutralization capacity (ANC = 490 kg H₂SO₄/t). NAG pH was strongly negatively correlated with sulfur content (Pearson r = −0.75, p < 0.01). Sulfide oxidation acid production showed staged attenuation, with average decreases of 64.81% and 47.65% in the second and third stages. Humidity cell experiments demonstrated continuous acid production over 63 days under dry–wet cycles, with increased acid generation rates at higher flow velocities (Darcy flux: 3.54 × 10⁻³ cm/s for accelerated vs. 8.84 × 10⁻⁴ cm/s for standard conditions). Multi-dimensional flow-through simulations confirmed the AMD formation mechanism of “acid supply, buffer, and fracture conduction”. The identified acid-producing layers matched well with field discharge points. This multi-method coupling system provides a theoretical basis for source control of AMD in abandoned high-sulfur coal mines in the Yellow River Basin. This study did not account for microbial catalysis, which is a key limitation of the static chemical oxidation method used. Full article

Pisha sandstone (PS) is a weakly cemented soft rock widely distributed in the middle reaches of the Yellow River, China. PS disintegrates rapidly upon contact with water and has poor erosion resistance, making it a major source of coarse sediment in the Yellow River. However, PS is rich in aluminosilicate minerals and clay fractions, offering great potential as a sustainable precursor for geopolymer cementitious materials and as an amendment for degraded soils. The sustainable resource utilization of PS provides a new pathway for coordinated ecological and economic development in the PS areas. This paper first reviews the mineralogical and chemical characteristics of PS, clarifying that low diagenetic degree and high montmorillonite content cause poor erosion resistance, and that compound erosion from freeze–thaw, water, wind, and gravity erosion creates a superimposed amplification effect, which is the primary driver of severe soil erosion. Subsequently, three major control measures for soil erosion in the PS areas are summarized, namely biological measures using sea-buckthorn (Hippophae rhamnoides), chemical solidification, and microbially induced calcium carbonate precipitation (MICP), with analyses of their mechanisms, efficiency, and limitations. Furthermore, the research progress on the sustainable resource utilization of PS in the preparation of geopolymer cementitious materials and the amelioration of degraded soils is elaborated. Finally, future research directions are discussed to support the control of soil erosion and the green, sustainable resource utilization of PS. Full article

Remote-sensing land surface deformation (LSD) is a powerful and effective approach for investigating regional alpine permafrost variations. However, alpine permafrost is often distributed in areas characterized by earthquakes, and the LSD of alpine permafrost is potentially contaminated or diminished by earthquake-related LSD. Therefore, this study aimed to derive the effective LSD in the alpine permafrost of the Source Area Yellow River (SAYR) by removing LSD originating from the M_w 7.4 Maduo earthquake in 2021-05-22 and analyzing the spatiotemporal variations in LSD during 2017–2024. Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) was used to obtain the initial LSD time series from Sentinel-1 images acquired during 2017–2024. The LSD of the M_w 7.4 Maduo earthquake, its aftershocks and the post-seismic relaxation in SAYR was simulated separately by considering its temporal process and removed from the LSD time series in SAYR. The final LSD was validated against in situ Global Navigation Satellite System (GNSS) measurements, and the spatiotemporal variations in LSD in SAYAR were subsequently analyzed. The study found the following: (1) the removal of the earthquake-related LSD was successful both spatially and temporally and the final LSD has mean absolute error (MAE) of 3.22 mm and root mean squared error (RMSE) of 3.92 mm; (2) during 2017–2024, the vertical LSD in SAYR was mostly −8–8 mm/y; (3) soil moisture determined the spatial distribution of the LSD direction in SAYR as a result of local drainage conditions, air temperature, precipitation and snow melt. This study demonstrated the necessity of removing the earthquake-related LSD when investigating the alpine permafrost LSD in tectonically active areas. The strategy adopted in this study serves as a technical reference for future investigations of this kind. The findings in this study provide insight for a thorough understanding of permafrost evolution on the Tibetan Plateau in the context of climate change. Full article

To address the environmental evolution and management needs of emerging contaminants in the Yellow River Basin (Henan Section), China, nine typical functional cross-sections, covering industrial outfalls, sewage treatment plant (STP) effluents, human activity-dense areas, and baseline tributaries, were selected to systematically investigate the occurrence, potential sources, and multi-dimensional risks of perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) in surface water. The results indicated a 100% detection rate of the target pollutants across all sites, with PFOA (0.45–7.46 ng/L) being the absolute dominant analogue. The spatial distribution exhibited an evident industrial point-source-driven pattern, where the pollution loads at the Jili District industrial outfall (S7) and STP effluent (S5) were significantly higher than those in non-point sources and natural baseline waters. Source apportionment suggested that direct wastewater discharge and secondary release from regional industrial clusters were likely key contributors to PFAS spatial heterogeneity. Multi-dimensional risk assessments revealed that the current ecological risk quotients (RQ < 0.01) for aquatic organisms and the human health risk values (HR < 0.1) via drinking water ingestion for various age groups were well within safe and controllable ranges. However, PFOS contributed significantly more to the ecological risk than PFOA, and children exhibited slightly higher health exposure vulnerability than adults. Although the overall risk is minimal, PFOA concentrations at high-load cross-sections have exceeded the latest stringent maximum contaminant level (4.0 ng/L) mandated by the US EPA in 2024. This study suggests an urgent need to establish a dynamic, life-cycle monitoring network for PFASs in the basin and to prioritize targeted deep-reduction strategies for high-risk industrial point sources. Full article

This study develops an integrated framework combining emergy analysis, carbon footprint accounting, and long short-term memory neural network modeling to investigate the effects of nature-based solutions on coastal ecosystem services and blue carbon functions from the perspective of river–coast connectivity. Three transects along a connectivity gradient were established in the Yellow River Delta, a typical large river delta in temperate China, covering riparian zones, estuarine transition areas, intertidal wetlands, and seagrass beds, with multi-source data collected over three consecutive hydrological years. Emergy–carbon coupling analysis based on this case study indicates that the high-connectivity transect shows a higher emergy yield ratio and net carbon sink compared to the low-connectivity transect, with salt marshes being most sensitive to connectivity change. Threshold analysis, specific to this delta, identifies a three-phase response pattern of carbon burial rate with increasing sediment connectivity, and reveals that wave attenuation efficiency declines notably when hydrological connectivity falls below approximately 0.5, although this value may vary across different coastal settings. A higher sea level rise rate raises the critical connectivity level required to maintain carbon sink function. The long short-term memory neural network trained on observational data achieves better prediction accuracy for blue carbon accumulation rates than traditional statistical methods, and SHAP value analysis suggests the possible existence of synergistic effects among connectivity dimensions. Based on these findings, three optimization strategies including tiered restoration, a dynamic pathway, and spatial configuration are proposed as case-specific recommendations for the Yellow River Delta. Framework-based simulations indicate the potential for connectivity-informed strategy adjustments to improve restoration efficiency under local conditions. This study concludes that river–coast connectivity represents an important lever regulating the ecological benefits of nature-based solutions, but emphasizes that all quantitative thresholds and benefit magnitudes reported here are case-specific estimates that require recalibration when applied to other coastal systems. Full article

Peri-urban zones are critical interfaces where urban expansion, agricultural production, and ecological processes overlap. However, ecological-network planning in these areas is often constrained by uncertain boundary definition and insufficient integration between habitat quality and landscape connectivity. Taking Zhengzhou, China, as a case study, this paper proposes a resilience-oriented Ecological Security Membrane planning framework that links peri-urban boundary delineation with the prioritization of ecological sources, corridors, and critical points. A deep neural network was used to distinguish the urban core, urban fringe, and peri-urban zone from multi-source land-use and socioeconomic indicators, achieving an overall classification accuracy of 93.1%. Priority ecological sources were then identified by coupling biodiversity quality, patch morphology, area thresholds, and connectivity contribution, while corridors and critical points were prioritized to support network reinforcement. The results reveal a peri-urban ecological structure characterized by source concentration in the western mountainous and eastern agroforestry areas, insufficient ecological continuity along the Yellow River corridor, and key bottlenecks at transport and urban-expansion interfaces. The proposed framework advances peri-urban ecological planning by translating source–corridor–node analysis into a spatially explicit planning structure. Future research should test the robustness of this framework under multi-year, multi-seasonal, and scenario-based urban-growth conditions. Full article

Accurate, high-resolution irrigation-related spatial information is paramount to diverse applications, including water resources management, food security, and agricultural planning. To address this need, our study leveraged machine learning algorithms and integrated multi-source data to extract and analyze land use types and spatiotemporal dynamics of irrigated farmland across provinces in the lower reaches of the Yellow River Basin over the 2008–2022 period. The results indicate that cultivated land remained dominant and largely stable, although localized losses occurred in peri-urban areas due to urban expansion. Construction land increased significantly, particularly in Shandong where it expanded by more than 15%, while forest and grassland areas grew under national ecological programs. The Random Forest (RF) algorithm achieved robust performance in identifying irrigated farmland, with overall accuracy exceeding 85% and regression with statistical irrigation data yielding R² values above 0.9 over the past 15 years at the city level. Spatiotemporal analysis showed strong variability in Henan, with irrigated area declining by 8–12% during drought years and recovering in wetter years, while Shandong experienced relative stability but a gradual 5% decline since 2015, driven by groundwater depletion and stricter regulation. The findings suggest irrigation expansion has reached near-saturation, given stable cultivated land and continuous improvements in water use efficiency. Future strategies should prioritize water use efficiency, water saving technologies, and equitable allocation to ensure sustainable agricultural development. Full article

The Source Region of the Yellow River (YRSR) is a key ecological barrier and a major water supply area, where water conservation is highly sensitive to ongoing climate change (CC) and land use/cover change (LUCC). However, the relative roles of CC and LUCC in regulating water conservation remain insufficiently quantified. In this study, we applied the Soil and Water Assessment Tool (SWAT) to simulate the spatiotemporal dynamics of water conservation in the YRSR and to disentangle the respective contributions of CC and LUCC using a fixing–changing approach, in which one driver is fixed and the other is varied across paired scenarios, followed by projections driven by CMIP6 forcing under SSP2–4.5 and SSP5–8.5. Water conservation showed a pronounced southeast–northwest contrast and increased over 2000–2019 (+4.56 mm/year). Attribution analysis revealed that CC dominated changes in water conservation, whereas LUCC exerted a weak net negative influence. Most increasing regions were precipitation-driven, whereas declining regions were concentrated where evapotranspiration and surface runoff increased concurrently. Under SSP2–4.5, water conservation is projected to continue increasing (+1.16 mm/year). In contrast, under SSP5–8.5, water conservation is projected to slightly decline (−0.26 mm/year). These findings highlight the primary role of climate in regulating water conservation in the YRSR and provide scientific support for adaptive watershed management under a changing climate. Full article

The Yellow River Delta, a young alluvial plain in China, is experiencing severe land subsidence that threatens its ecological security and sustainable development. However, the driving mechanisms of this subsidence exhibit strong spatial heterogeneity, which traditional global models fail to capture. This study integrates high-precision subsidence measurements from Sentinel-1A imagery and SBAS-InSAR technology (2017–2023) with multi-source environmental factors (topography, geology, land use, precipitation) to propose a Spatially Adaptive Ensemble Learning Model with feature selection (SA-GSE). The model concatenates predictions from base learners (CatBoost, XGBoost, Random Forest) with spatial features (e.g., distance to salt pans, local topographic variance) to form meta-features, which are then input into a multilayer perceptron meta-learner. Through 5-fold spatial cross-validation, SA-GSE learns spatially dynamic base-model weights, implicitly adapting to regional variations in subsidence drivers. The model achieves an R² of 0.7810 and RMSE of 40.55 mm/yr on the test set, outperforming individual base models and ordinary stacking. Residual spatial autocorrelation is substantially reduced, with SA-GSE yielding the lowest Moran’s I (0.0334, p = 0.206) among all evaluated models, confirming effective capture of spatial heterogeneity. Driving force analysis reveals that distance to salt pans is the most important predictor (permutation importance: 0.4456), underscoring the dominant role of brine extraction-induced aquifer compaction. Lagged precipitation importance (0.3191) exceeds that of current precipitation (0.2453), indicating a recharge lag effect. SHAP interaction analysis uncovers a nonlinear “precipitation decoupling” mechanism in salt pan areas, where high precipitation paradoxically exacerbates subsidence. The resultant map of predicted subsidence rates highlights elevated rate zones in the northern salt pans and along the Guangli River. While the map does not represent a full risk assessment—as it does not include exposure or vulnerability—it provides a spatially explicit estimate of hazard likelihood. This ensemble framework yields novel perspectives on subsidence drivers in heterogeneous regions and can support land subsidence prevention and groundwater management planning. Full article

Against the backdrop of global climate change and the “carbon neutrality” target, the ecological quality improvement of the Loess Plateau—a key region for ecological restoration in China—and its impact on vegetation carbon sources hold significant importance for regional carbon balance and ecological security. Based on MODIS and meteorological reanalysis data from 2002 to 2024, this study constructed the Remote Sensing Ecological Index (RSEI). Combined with a carbon source/sink model, it systematically assessed the spatiotemporal coupling evolution characteristics of ecological environment quality and vegetation carbon storage capacity in the Loess Plateau, and explored the synergistic driving mechanisms of major hydrothermal and surface factors. The results indicate the following: (1) From 2002 to 2024, the ecological environment of the Loess Plateau improved significantly, with the RSEI rising from moderate to good. This improvement was accompanied by a marked decrease in surface dryness, an increase in surface wetness, and notable growth in vegetation cover, revealing a positive coupling relationship characterized by “reduced surface dryness—increased surface wetness—enhanced vegetation restoration.” (2) Regional vegetation carbon storage capacity strengthened markedly. Gross Primary Productivity (GPP), Net Primary Productivity (NPP), and Net Ecosystem Productivity (NEP) all showed significant increasing trends, and the proportion of area classified as carbon sink increased substantially. (3) Spatially, carbon sink distribution exhibited a pattern of “higher in the southeast, lower in the northwest.” Sub-regions A and D were identified as core areas with higher ecological quality and carbon sink capacity, whereas sub-regions B and C were more ecologically fragile and served as primary carbon source areas. (4) The implementation of soil and water conservation measures on the Loess Plateau has effectively enhanced regional carbon storage capacity. Vegetation restoration, improved water conditions, and reduced surface dryness have jointly driven the transition of the Loess Plateau ecosystem from a “vulnerable type” to a “recovering type”, while ecological restoration projects have played a certain role in enhancing the carbon sink. This study provides a theoretical basis and scientific–technological support for ecological protection and high-quality development in the Yellow River Basin. Full article

Air–sea CO₂ flux (FCO₂) in the estuary–coastal continuum plays a vital role in global carbon sequestration; however, the mechanisms governing FCO₂ spatial heterogeneity during early spring remain poorly understood, particularly the roles of distinct dissolved inorganic carbon (DIC) sources. In March 2025, we investigated the FCO₂ spatial variability and DIC sources across the Yangtze River estuary and adjacent coastal areas using DIC concentration, pH, and δ¹³C_DIC analyses. The study area was a net CO₂ source (7.3 ± 8.7 mmol m⁻² d⁻¹), with the intensity declining progressively from the inner estuary to offshore areas. Physical mixing of three principal water masses established the following pattern: high-pCO₂ Changjiang Diluted Water and Yellow Sea Coastal Current drove CO₂ outgassing, while low-pCO₂ East China Sea Shelf Water weakened it. Quantitative apportionment revealed atmospheric CO₂ invasion as the dominant DIC source, followed by carbonate dissolution and organic matter degradation, with the latter declining from the inner estuary to offshore areas. The spatial variation in DIC source contributions further confirms that, superimposed on the physical mixing, biogeochemical processes—particularly biological activity—modulated reginal source intensities. This early-spring case captures a critical transitional window and highlights the necessity of integrating multi-factor regulation with DIC source partitioning to resolve carbon dynamics in the estuarine–coastal continuum. Full article

The Yellow River Basin contains abundant coal resources; however, its ecological environment is inherently fragile, and vegetation degradation has been further intensified by extensive mining activities. Accurate classification of individual tree species in mining-affected areas is therefore essential for assessing ecological conditions and establishing a scientific foundation for targeted restoration and sustainable management. To address this need, an evaluated machine learning framework was developed and evaluated for individual tree species classification in a coal mining area of the Yellow River Basin using integrated unmanned aerial vehicle (UAV) data. A comprehensive feature set was constructed by extracting 278 attributes per tree. These attributes included 224 spectral bands and 29 hyperspectral indices derived from hyperspectral imagery, 24 textural metrics obtained from RGB orthophotos, and one canopy height feature generated from a LiDAR-derived model. Based on ground-truth data from 1095 individual trees, seven machine learning algorithms were trained and systematically compared: Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT), Gradient Boosting (GB), Logistic Regression (LR), and XGBoost. Statistical significance testing using 5 × 5 repeated cross-validation, together with the Friedman test and post hoc Nemenyi test, and additional model stability analysis consistently identified XGBoost as the optimal classifier. On an independent test set, XGBoost achieved high accuracy (Overall Accuracy = 0.897, Kappa = 0.811) with an efficient training time of 2.36 s. Further analysis demonstrated the critical and complementary roles of hyperspectral and structural features in species discrimination. The optimized model was subsequently applied to generate a detailed wall-to-wall tree species map across the entire mining area. Overall, this study presents a statistically informed comparison of classifiers for multi-source feature-based species discrimination and delivers an evaluated and practical pipeline for effective vegetation monitoring. The proposed framework provides a scientific tool for assessing and managing ecological recovery in complex mining environments, particularly within ecologically sensitive regions such as the Yellow River Basin. Full article

Rapid urbanization in the Yellow River Basin intensifies the conflict between urban expansion and the thermal environment, threatening ecological security and sustainable development. Utilizing multi-source data (2000–2023) including nighttime light (NTL) and land surface temperature (LST), this study applies spatial analysis and Geographically Weighted Regression (GWR) to explore the spatial associations between urban development and LST and its drivers across core cities. The results indicate significant spatiotemporal differentiation: mid-downstream cities exhibited contiguous urban expansion, whereas upstream growth remained constrained by local topography, with heat islands consistently concentrating in built-up areas. The warming rate decreased gradually from downstream (0.29–0.40 °C/year) to upstream (0.20–0.30 °C/year). The LST-NTL correlation strengthened notably in mid-downstream regions but remained moderate upstream. GWR analysis revealed that urban development intensity, represented by NTL, is the primary driver of LST increase downstream, while natural factors predominantly mitigate warming upstream. This long-term, multi-city comparison provides a scientific basis for precise urban heat island management and sustainable planning in the basin. Full article

Crop planting structure adjustments in irrigated agricultural regions alter irrigation and drainage regimes, with potential consequences for regional surface water dynamics. However, the nature and scale dependence of these linkages remain insufficiently understood. This study investigates the spatiotemporal dynamics of crop planting structure and surface water bodies in the Ningxia Plain from 2004 to 2023, and systematically quantifies their scale-dependent coupling mechanisms. Annual crop maps were generated using a Random Forest classifier (Sentinel-2, 2019–2023) and a Transformer-based model applied to multi-source satellite imagery (2004–2018). Surface water bodies were derived from long-term remote sensing datasets covering the full study period. Results show that the agricultural system underwent a pronounced transition toward maize dominance. Maize area expanded by 50.8%, whereas wheat and rice declined by 74.3% and 44.6%, respectively. Crop diversity also decreased, with the Shannon Diversity Index declining from 1.41 to 1.06 in 2023, indicating progressive system simplification. Meanwhile, surface water bodies exhibited a sustained downward trend, decreasing at an average rate of −5.32 km² per year after 2013 and reaching a minimum in 2022. The Yellow River water surface area also contracted by 14.41% (p = 0.001), indicating a basin-scale reduction in surface water extent. Lake classification results reveal strong scale-dependent hydrological responses. Small lakes (≤18 ha), accounting for 73.2% of lake numbers, are primarily controlled by local irrigation–drainage processes. Medium lakes (18–80 ha) are influenced by both anthropogenic regulation and natural variability. Large lakes (>80 ha), although representing only 4.9% of lake numbers but 62.9% of total water area, are mainly sustained by climatic variability and ecological water supplementation. Principal component analysis explains 84.44% of total variance, highlighting agricultural structural change and irrigation–drainage dynamics as key system drivers. Correlation analysis further reveals strong climate sensitivity of large lakes and the Yellow River (ρ = 0.50, p = 0.031), while small lakes are predominantly influenced by agricultural drainage processes. Overall, crop planting structure affects regional water dynamics through scale-dependent processes, with maize expansion altering irrigation and diversion patterns and local irrigation–drainage processes controlling small water bodies. Full article