Search Results (219)

The Yellow River Basin is an important ecological security barrier and agricultural production area in China. Using panel data for nine sprovincial-level regions from 2010 to 2022, this study constructs a multidimensional development index of rural industrial integration and examines its association with regional environmental pollution. Regional pollution pressure is measured from total wastewater discharge, sulfur dioxide emissions, and general industrial solid-waste generation; the measure therefore captures broad regional pollution linked to agricultural and related industrial chains rather than agricultural non-point-source pollution alone. Two-way fixed-effects estimates show that higher integration scores are significantly associated with lower pollution levels. This association is statistically evident in the upper reaches, whereas the middle- and lower-reach estimates are not statistically significant and are interpreted as exploratory because each subsample contains only two provinces. Exploratory channel regressions suggest that agricultural technological progress, rural labor mobility, and agricultural industrial scale may help explain the observed association, but the regressions do not establish causal mediation. The findings indicate potential synergies between rural industrial integration and environmental governance, while also requiring caution regarding causal interpretation, composite-index boundaries, and small-sample regional comparisons. Full article

The Upper Yellow River (UYR) basin is the predominant runoff-yielding area of the entire watershed. Accurately simulating annual runoff is crucial for water resources management. While the Budyko framework effectively captures long-term hydro-thermal equilibrium, it struggles to represent nonlinear dynamics, flow channel routing, and spatial interconnections. Here, we propose a hybrid physics- and data-driven approach by coupling the Budyko framework (Fu’s equation) with an eXtreme Gradient Boosting (XGBoost) model, integrating upstream channel routing and antecedent storage-lag features using long-term hydrologic observations for nonlinear runoff simulation and driver attribution. To resolve the feature multicollinearity on machine learning attributions, input variables were consolidated into three groups: precipitation driven, evaporation limitation, and flow storage lag. The results demonstrate that the Budyko–XGBoost coupled model enhances annual runoff prediction accuracy compared to the standalone Fu equation and pure XGBoost, raising the coefficient of determination (R²) to 0.63–0.86 (mean R² = 0.75) and capturing both nonlinear dynamics and turning points, alongside reductions of 6.2% in the mean RMSE (18.77 mm) and 11.0% in the MAE (13.66 mm) compared to the pure XGBoost model (mean R² = 0.70, RMSE = 20.01 mm, and MAE = 15.35 mm). Group-level SHAP attributions reveal that flow storage-lag drivers (R_lag and $R_{\mathit{lag}}^{\prime }$) exert a primary control on runoff evolution across all the stations. Spatially, secondary drivers exhibit heterogeneity: in relatively humid, energy-limited regions (Maqu), high precipitation promotes positive runoff deviations, whereas in arid/semi-arid, water-limited reaches (e.g., Guide, Xunhua, Xiaochuan, and Lanzhou stations), high precipitation is absorbed by severe soil moisture deficits and reservoir interception, exerting a negative effect on runoff deviation. Full article

The water–energy–carbon (WEC) nexus is central to sustainability, yet the role of water quality as a potential moderator within this nexus remains empirically underexplored. Using a fixed-effects panel regression model applied to provincial-level data from the Yellow River Basin over the period of 2007–2022, this study provides systematic evidence that water quality significantly moderates the WEC nexus. The empirical findings indicate that (1) water quality negatively affects the WEC nexus significantly; (2) the moderating effect presents spatial heterogeneity, showing greater carbon reduction potential in middle and lower reaches; and (3) the marginal impact of water cycle energy consumption on carbon emissions declines monotonically with improvements in water quality, with a sample-specific estimated critical point identified at a compliance rate of 88.159%—below which the marginal effect remains positive and above which it turns negative. (4) The moderating effect exhibits pronounced spatial heterogeneity, being significantly positive in the middle and lower reaches but insignificant in the upper reaches, and is significantly strengthened after the implementation of China’s strictest water resource management policy in 2013. These results suggest that water quality serves as a strategic lever for synergizing WEC governance. The findings offer theoretical and practical insights for adjusting the tightly coupled WEC nexus in the Yellow River Basin, with implications for region-specific and threshold-oriented policy design. Full article

Against the backdrop of China’s advancing strategy for ecological protection and high-quality development in the Yellow River Basin, improving urban land green-use efficiency is critical to reconciling ecological conservation, resource efficiency, and coordinated regional development. Using data for 64 cities across seven urban agglomerations, this study applies a Super-SBM model to estimate urban land green-use efficiency from 2012 to 2024 and combines Dagum Gini decomposition, kernel density estimation, and conventional and spatial Markov chains to examine its spatial disparities and dynamic evolution. Four principal findings emerge. First, efficiency increased overall but exhibited a clear spatial gradient, with higher levels in the upper and middle reaches and lower levels downstream. Second, overall disparities initially widened, subsequently narrowed, and rebounded slightly toward the end of the study period. Between-agglomeration disparities were the largest component on average and during most of the study period; however, their contribution declined, and transvariation density became the largest component in 2023–2024, indicating greater overlap among the efficiency distributions of urban agglomerations. Third, both within- and between-agglomeration disparities exhibited marked heterogeneity, with distinct trajectories across and within urban agglomerations. Fourth, the conventional Markov-chain analysis revealed strong state persistence, a pronounced tendency for high-efficiency states to persist, and transitions occurring predominantly between adjacent classes. The spatial Markov results further showed that local transition probabilities varied across neighborhood efficiency conditions. Overall, efficiency disparities did not exhibit sustained unidirectional convergence; instead, they were characterized by phased adjustment, increasing cross-agglomeration overlap, and neighborhood-conditioned state transitions. By establishing a sequential framework of “efficiency measurement–disparity decomposition–distributional evolution–state transition,” this study provides empirical evidence for understanding the spatial disparities and dynamic evolution of urban land green-use efficiency in the Yellow River Basin. Full article

Achieving synergistic governance between carbon emissions and ecosystem service value (ESV) in the Yellow River Basin (YRB) remains a significant challenge. Existing studies often rely on a single administrative scale and rarely extend spatial clustering results toward governance-oriented zoning frameworks that support differentiated management. To bridge these gaps, we developed a multi-scale remote sensing framework by integrating the China Land Cover Dataset (CLCD), NPP/VIIRS nighttime light (NTL) imagery, and energy consumption statistics to examine carbon emissions and ESV across the YRB at both county and grid scales. We estimated land-use carbon emissions by integrating emission coefficients with NTL modeling, while ESV was quantified via the equivalent factor method. By coupling dynamic carbon–ESV trends with their spatial association patterns (derived from bivariate LISA), we established a dual-dimensional Trend–Pattern framework for synergistic governance. Our findings reveal a pronounced asymmetry in regional development: while carbon emissions surged from 235 to 1033 million tons, ESV grew by a marginal 0.13% annually. Although carbon emissions and ESV are significantly negatively correlated (p < 0.001), this relationship diverges across scales—weakening at the county level but intensifying at the grid level. Notably, grid-scale analysis identified a contraction of over 56% in “Low carbon–High ESV” synergistic clusters in the upper reaches. This critical signal of declining carbon–ecological synergy was completely obscured by the averaging effect inherent in county-level assessments. Capitalizing on these scale-dependent insights, we delineated seven distinct governance zones, including Trend Control and Ecological Conservation Zones, thereby transitioning from mere spatial description to decision support for zoning-based governance. This study highlights the potential limitations of relying solely on single-tier administrative evaluations and offers a robust, multi-scale decision-support tool for carbon–ecological governance in the YRB. Full article

The Cascade Reservoir System (CRS) in the upper Yellow River delivers integrated benefits (flood control, water supply, hydro-power generation, and ecological regulation), but it also alters the natural runoff regime and exerts non-negligible impacts on the regional eco-environment. However, the long-term trajectory of reservoir-cascade effects on land use/land cover (LULC) in alpine basins has not yet been systematically quantified. Here, we focused on the Hualong-Xunhua reach and delineated two impact domains—the Reservoir Influence Zone (RIZ, enclosed by the first-order mountain ridge lines closest to the river channel representing direct hydrological impacts) and the Local Microclimate Influence Zone (LMIZ, spanning from the first-order ridges to the outer watershed boundary representing indirect climatic impacts)—to investigate the spatiotemporal dynamics of LULC associated with reservoir development. Results show that, from 1985 to 2023 in the CRS area, cropland and shrub-land decreased by 89.56 km² (−16.09%) and 9.41% (−9.41%), respectively, whereas forest and grassland increased by 79.92 km² (+14.36%) and 7.74% (+7.74%). Within the RIZ, cropland declined by 29.49 km² (−20.14%), while water bodies increased markedly by 32.19 km² (+22%); forest cover also expanded by 9.09 km² (+6.21%). In the LMIZ, forest and grassland exhibited pronounced increases of 70.83 km² (+17.27%) and 39.37 km² (+9.60%), respectively. Correlation analysis indicates that GDP and air temperature are strongly and positively correlated with forest, water bodies, and impervious surfaces (Pearson’s r > 0.9), whereas cropland shows significant negative correlations with GDP, forest, and grassland (Pearson’s r < −0.8). Overall, the distinct spatiotemporal contrasts between the RIZ and LMIZ, coupled with the temporal alignment of cropland-to-forest transitions post-2000, suggest that reservoir-cascade construction and the Grain-for-Green Project are associated with these major LULC transitions, serving as contributing factors, while temperature rise and GDP growth represented the background environmental and socioeconomic context. These findings provide data support and a conceptual basis for long-term monitoring and assessment of eco-environmental responses to reservoir cascade development, and offer scientific evidence particularly relevant to reservoir planning and management in high-altitude cold regions. Full article

As a critical alpine transition zone linking the Qinghai–Tibet Plateau and the Loess Plateau, Gannan Prefecture acts as an important water conservation area in the upper Yellow River basin of China. Based on GLDAS-2.1 surface soil moisture (SSM) datasets spanning 2000–2022 and interpretable machine learning tools (SHAP and ALE), this paper analyzes the spatiotemporal evolution, future trend sustainability, and nonlinear statistical associations between environmental predictors and SSM. The main results were as follows: (1) SSM exhibited a significant upward trend with an annual growth rate of 0.18 kg·m⁻²·a⁻¹ (p < 0.001), and an abrupt turning point occurred in 2017. The spatial pattern of high SSM in the southeast and low SSM in the northwest remained relatively stable, with the centroid migration distance being less than 1.81 km; most regions presented statistically significant moistening trends (p < 0.05). (2) Natural environmental predictors jointly carried 95.79% of the total statistical explanatory weight for modeled SSM variability. Precipitation possessed the highest explanatory proportion (37.93%), followed by temperature (27.30%), potential evapotranspiration (ET_p, 12.26%), elevation (10.44%), and fractional vegetation cover (FVC, 7.77%). One-dimensional ALE curves identified sample-limited statistical breakpoints: SSM gradually plateaued when precipitation reached 650–700 mm, while modeled SSM decreased substantially once ET_p exceeded 800 mm·a⁻¹. Two-dimensional ALE further characterized combined statistical correlations among precipitation, temperature, and ET_p. Model outputs also indicated that FVC above 0.45 corresponded to enhanced soil water retention within the observed sample range, which only reflects statistical patterns captured in this dataset rather than universal regulatory standards. This study offers quantitative statistical understanding of SSM variations across alpine transition zones. Full article

Net primary productivity (NPP) is a key indicator of the carbon sequestration capacity of terrestrial ecosystems, and its dynamics are jointly influenced by climate change and human activities. However, quantitatively disentangling their respective contributions and clarifying their non-linear interactions remains challenging. In this study, remote sensing, meteorological, and anthropogenic data were integrated to investigate the spatiotemporal dynamics of vegetation NPP in the Yellow River Basin (YRB) from 2000 to 2024. Six scenarios were constructed to quantify the relative contributions of climate change and human activities. Furthermore, an XGBoost-SHAP framework was employed to elucidate the underlying non-linear driving mechanisms. The results indicate that vegetation NPP exhibited a significant increasing trend over the study period, with a rapid recovery phase after 2012 and a peak in 2024 (351.75 gC·m⁻²·a⁻¹), representing a 71.43% increase compared with the baseline period. Spatially, the upper reaches were primarily climate-driven (58.74%), the middle reaches showed a strong synergistic effect between climate and human factors (97.41%), while the lower reaches were dominated by human activities (73.02%). The XGBoost-SHAP analysis identifies land surface temperature (LST) as the primary moderator of carbon sequestration across river basins (mean SHAP > 12.0). The driving mechanisms exhibit a clear longitudinal shift, transitioning from a heat-dominated regime in the upper reaches to a complex interplay of precipitation and intense urbanization in the middle and lower reaches. These non-linear interactions reveal critical feedback loops between natural hydrological constraints and urban expansion pressures. These findings clarify the drivers of regional carbon sequestration, providing a scientific basis for targeted ecological management and carbon neutrality strategies in the YRB. Full article

Soil heavy metal pollution poses a threat to agricultural sustainability, food safety, and human health. The ecologically fragile Yellow River Basin is a critical hub for agriculture, energy, and mining; however, soil heavy metal studies remain fragmented, and basin-wide syntheses are limited almost entirely to agricultural soils. This study presents a basin-wide analysis of As, Cd, Cr, Cu, Ni, Pb, and Zn in topsoil, based on 2498 sampling locations compiled from 347 publications, using an integrated framework of receptor modeling, multivariate spatial statistics, self-organizing maps, and probabilistic human health and ecological risk assessment. Four pollution sources, namely agricultural–industrial, emissions, mining–smelting, and geogenic/lithogenic, were resolved. Agriculture–industry and emissions posed considerable ecological risks (mean PER = 367.9 and 353.4), with Cd and Pb accounting for 95.7% of the risk. The non-carcinogenic hazard was negligible for adults, but 8.6% of sites exceeded the safe threshold for children, and the carcinogenic risk surpassed 10⁻⁶ for all groups, with 2.6–9.6% of sites exceeding 10⁻⁴. Spatially, the strongest multimetal contamination corridors are the Baiyin–Lanzhou corridor (upper–middle reaches) for Cu-Pb-Zn (mining–smelting) and the Xi’an–Weinan belt (middle reaches) for Cd-Pb (agricultural–industrial and emissions). Multivariate clustering was more extensive (56.1% of sites) than single-metal clustering (13.1–26.2%), confirming coherent source-linked zones. Ecological risks were driven by Cd and Pb, whereas human health risks were driven by As, Cr, and Ni. This divergence and the strong spatial organization of the risk clusters highlight the need for source-specific, spatially targeted mitigation, which requires monitoring across all land use types. The compiled dataset, although extensive, is constrained by heterogeneity in sampling periods and analytical methods and by sparse coverage in some grassland, desert, and plateau regions. Full article

Amid tightening ecological constraints, accelerating urbanization transition, and increasingly complex spatial governance, the coordinated evolution of Urban Green Space (UGS) and New-Type Urbanization (NTU) has become central to green transition and high-quality development in major river basins. Drawing on city-level panel data for the Yangtze River Economic Belt (YREB) and the Yellow River Basin (YRB) from 2006 to 2022, this study integrates a Coupling Coordination Degree (CCD) model, spatial statistical analysis, and interpretable machine learning to investigate UGS-NTU coordination, with emphasis on spatiotemporal evolution, spatial differentiation, and nonlinear contribution pathways. The findings indicate that: (1) UGS and NTU levels rise in both basins, but their spatial trajectories differ substantially. The YREB exhibits river-oriented expansion and gradient diffusion, whereas the YRB features nodal agglomeration and discontinuous expansion. (2) The CCD improves overall in both basins, with downstream areas leading, the middle reaches following, and the upper reaches lagging behind; UGS lag is widespread in the middle and upper reaches. (3) The YRB shows stronger spatial agglomeration, more pronounced regional differentiation, and more persistent low-value clustering, while the YREB is characterized by stable high-value clustering in the Yangtze River Delta. (4) The YREB is mainly associated with green space system optimization, whereas the YRB is more closely associated with improvements in the foundational capacities of NTU. Both associations exhibit clear nonlinear characteristics. This study provides empirical support for differentiated green transition and high-quality development strategies in major river basins. Full article

Urban–rural integration (URI) represents a pivotal pathway to realizing sustainable development within urban–rural spatial systems. It is of paramount importance in addressing the challenge of reconciling ecological conservation with high-quality development in the Yellow River Basin. Leveraging panel data from 78 cities in the Yellow River Basin spanning the years 2006–2023, this research constructs an evaluation index system that encompasses five dimensions: population, economy, society, ecology, and space. Through the comprehensive application of kernel density estimation, exploratory spatiotemporal data analysis, and panel quantile regression models, a systematic analysis of the spatiotemporal evolution patterns and transition mechanisms of URI is conducted. The results disclose that URI in the Yellow River Basin demonstrates a trend of “overall enhancement with regional disparities”. From 2006 to 2023, the URI of the basin witnessed an average annual growth rate of 2.86%. Spatially, it presented distinct features: high-level agglomeration in the lower reaches, accelerating-growth path dependency accompanied by internal divergence in the middle reaches, and balanced yet low-level development in the upper reaches. The local spatial evolution of URI follows a pattern characterized as “predominant stability and limited transitions”. In detail, high-level regions sustain their advantages, low-level regions encounter obstacles in achieving breakthroughs, and the spillover effects between adjacent regions remain relatively restricted. The driving mechanisms exhibit significant “phase-spatial” dual heterogeneity, with four distinct patterns identified. In light of these findings, policy recommendations are put forward, including the establishment of a multi-scale, coordinated spatial governance system. Full article

Taking the Yellow River Basin (YRB) as an example, this study explores the non-stationary drought evolution features in large river basins under climate change. This study utilized precipitation and multiple climate factor data to establish the non-stationary standardized precipitation index (NSPI) through the GAMLSS model. Combined with the run theory, Copula function and a cascaded RF-LSTM machine learning model, the drought characteristics and retrospective predictive patterns were systematically assessed. The results show that: (1) The Arctic Oscillation, the Pacific Decadal Oscillation, the Southern Oscillation and the North Pacific Index are the primary climate drivers of non-stationary precipitation variation in the YRB, with the former three being selected most frequently and NPI additionally influencing April–June and September, and their effects are both different and lagging. Compared with the traditional SPI, the NSPI assigned higher drought grades and greater severity to typical drought years (e.g., the 1974 event was rated D3 with a severity of 17.935 by NSPI versus D2 with 11.733 by SPI), and thus better captured non-stationary drought evolution. (2) The duration of droughts exhibited a decreasing trend that was not statistically significant (p > 0.05), whereas drought intensity and severity decreased significantly (p < 0.05); the peak severity showed a significant upward trend (p = 0.0078). Spatially, the northwest of the Loess Plateau was a compound core area with high severity, high frequency and long duration of droughts, while the upper reaches were mainly characterized by low severity, short duration and sudden droughts. (3) The drought risk in the YRB shows a higher frequency in the lower reaches and a lower frequency in the upper reaches. The middle and lower reaches were high-risk areas, with shorter AND-type joint exceedance return periods for moderate drought (2.46–5.83 years) and severe drought (3.77–9.15 years). The upper reaches were low-risk areas, with longer return periods reaching up to 5.83 years for moderate drought and 9.15 years for severe drought. The study shows that the NSPI, considering the driving of multiple climate factors, can more effectively identify and assess non-stationary drought risks, providing a scientific basis for drought prevention and control in river basins. Full article

The Liujiaxia–Heishanxia reach is critical for water and sediment regulation in the upper Yellow River, where changes in runoff–sediment relationships greatly affect downstream channel stability and flood safety. Climate change and intensive human activities have substantially altered local hydrological regimes in recent decades. Using long-term hydrological records from five stations during 1956–2020, this study applied the Mann–Kendall test, moving t-test, wavelet analysis and XGBoost algorithms to analyze the trends, abrupt changes and periodic features of runoff and sediment load, and quantify the contributions of natural and human drivers. The results show that both runoff and sediment load decreased significantly, with a sharper decline in sediment load. Major abrupt changes occurred in 1969, 1986, 1996 and 2008, and both variables presented a dominant 40-year interdecadal cycle. Human-induced landscape changes became the leading factor driving hydrological variations after 1996. Our findings suggest that future watershed management should combine landscape optimization and climate adaptation to maintain stable runoff-sediment conditions. This work provides scientific references for water resource management and the construction of the Heishanxia Water Conservancy Project. Full article

Polycyclic aromatic hydrocarbons (PAHs) in farmland soils pose potential ecological and human health risks, yet their contamination characteristics and source-related risks in farmland soils across different river basins in China remain insufficiently understood. This present study analyzed 84 farmland soil samples from northeast (primarily the middle and lower reaches of the Songhua River and Liao River basin), central (primarily the middle reaches of the Yellow River basin and Dongting Lake system), northwest (primarily the middle and upper reaches of the Yellow River and Yarlung Zangbo River basin), and southern (primarily the upper reaches of the Pearl River and Yangtze River basin) China in order to assess the contamination characteristics, sources, ecological risks, and human health risks associated with 16 US EPA priority PAHs in the samples. The findings suggest that the 16 aggregate PAHs’ concentrations in Chinese farmland soils varied from 63.9 to 9637.7 μg/kg, with an average of 1919.3 μg/kg. A gradual decline was observed from north to south, with dibenz[a,h]anthracene (DahA) accounting for the highest proportion at 14.3%. Correlation analysis, principal component analysis, and positive matrix factorization jointly indicated that fossil fuel combustion, high-temperature combustion, and traffic-related emissions were the main PAH inputs to farmland soils. The results of the ecological risk assessment indicated that the northeastern region exhibited the highest PAH ecological risk, with 41.2% of sample plots demonstrating severe PAH contamination. Conversely, the southern region exhibited the lowest PAH ecological risk, with 73.9% of the sample plots demonstrating no ecological risk. The human health risk assessment found that non-carcinogenic risks for both children and adults were within safe limits, while carcinogenic risks for both groups were relatively high. DahA was identified as the primary carcinogen, accounting for 45.9% and 70.3% of the total carcinogenic risk for children and adults, respectively. Oral ingestion was the primary route of exposure. This study provides an integrated basin-scale assessment of PAH contamination and source-related risks in Chinese farmland soils, supporting targeted management of PAH inputs in agricultural environments. Full article

Rhinogobio nasutus, an endangered fish species endemic to the upper and middle reaches of the Yellow River in China, lacks essential genomic information on gonadal development, hindering research into its reproductive biology. To address this, mRNA-seq and miRNA-seq datasets derived from adult testis (n = 3) and ovary (n = 3) were integrated to characterize sex-biased expression profiles and potential regulatory mechanisms. A total of 34,813 genes and 68,623 transcripts were detected, and 16,105 differentially expressed genes (DEGs) were identified between testis and ovary, including 9365 testis-biased and 6740 ovary-biased genes. Small-RNA profiling identified 51 differentially expressed miRNAs (DEMs: 31 testis-biased; 20 ovary-biased). The sex-biased mRNA profiles highlighted conserved candidate genes associated with germ-cell maintenance, somatic regulation, ovarian differentiation, and oocyte maturation, including vasa, piwi, dmrt1, amh, cyp19a1a, zar1, zar1l, and rbpms2. Integrated miRNA–mRNA analysis further predicted potential interactions involving key sex-related genes, suggesting that DEMs may contribute to post-transcriptional regulation during gonadal differentiation. Functional enrichment (GO and KEGG analyses) highlighted pathways associated with gonadal differentiation, germline maintenance, and signal transduction pathways. qRT-PCR validation of nine mRNAs and nine miRNAs showed expression patterns consistent with the sequencing results. Collectively, these results provide an integrated mRNA and miRNA resource for R. nasutus gonads and identify candidate genes and miRNAs for future studies on sex-biased gonadal development, reproductive regulation, and artificial propagation. Full article