Search Results (1,216)

Under intensifying climate change and anthropogenic pressures, extreme low-flow events increasingly jeopardize water security in the Yellow River water supply region. This study develops the Inter-basin Multi-source Water Joint Allocation and Interconnected Routes Regulation System (IMWA-IRRS) to optimize spatiotemporal allocation of multi-source water and simulate topological relationships in complex water networks. The model integrates system dynamics simulation with multi-objective optimization, validated through multi-criteria calibration using three performance indicators: correlation coefficient (R), Nash-Sutcliffe Efficiency (E_ns), and percent bias (PBIAS). Application results demonstrated exceptional predictive performance in the study area: Monthly runoff simulations at four hydrological stations yielded R > 0.98 and E_ns > 0.98 between simulated and observed data during both calibration and validation periods, with |PBIAS| < 10%; human-impacted runoff simulations at four hydrological stations achieved R > 0.8 between simulated and observed values, accompanied by PBIAS within ±10%; sectoral water consumption across the Yellow River Basin exhibited PBIAS < 5%, while source-specific water supply simulations maintained PBIAS generally within 10%. Comparative analysis revealed the IMWA-IRRS model achieves simulation performance comparable to the WEAP model for natural runoff, human-impacted runoff, water consumption, and water supply dynamics in the Yellow River Basin. The 2035 water allocation scheme for Yellow River water supply region projects total water supply of 59.691 billion m³ with an unmet water demand of 3.462 billion m³ under 75% low-flow conditions and 58.746 billion m³ with 4.407 billion m³ unmet demand under 95% low-flow conditions. Limited coverage of the South-to-North Water Diversion Project’s Middle and Eastern Routes constrains water supply security, necessitating future expansion of their service areas to leverage inter-route complementarity while implementing demand-side management strategies. Collectively, the IMWA-IRRS model provides a robust decision-support tool for refined water resources management in complex inter-basin diversion systems. Full article

Soil erosion undermines the sustainable development of land—a vital resource for human survival. Research into the spatiotemporal dynamics of soil erosion is therefore crucial for formulating effective soil and water conservation strategies and advancing ecological protection efforts. In the domain of soil erosion research, the Universal Soil Loss Equation and Revised Universal Soil Loss Equation (USLE/RUSLE) model represent the dominant approach for quantifying soil erosion volumes. While this methodology yields reliable outcomes, it fails to incorporate an assessment of the relative significance of the factors embedded within the model. This study selected the Henan section of the Yellow River Basin as the research area, using monthly remote sensing data from 2010 to 2025 as the main data source. Taking into account factors such as rainfall, slope, elevation, vegetation coverage, and hydrological conservation measures, the RUSLE model was used to calculate and combine Geographic Information System (GIS) geographic detectors for quantitative analysis of soil erosion factors. The results showed the following: (1) The average soil erosion modulus in the study area from 2010 to 2025 was mainly micro and mild erosion. (2) Soil erosion exhibits a certain periodicity, with a year of significant soil erosion occurring every 3–4 years. The overall trend of soil erosion is a decrease. (3) Geographic detector analysis shows that slope has the greatest impact on soil erosion, with larger slopes leading to more severe soil erosion. The influence of each factor ranges from large to small as slope > water conservation measures > rainfall > vegetation coverage > elevation. (4) The interaction between factors can enhance the influence on soil erosion, and the interaction between vegetation cover factors and other factors significantly increases the influence; after interacting with various factors, the slope factor will significantly increase the influence of soil erosion. The research results can provide technical support and decision-making basis for ecological protection in the Yellow River Basin, such as through soil and water conservation, returning farmland to forests, and slope greening; The dominant factors and obvious interaction factors in the research area can provide a scientific basis for subsequent scholars to optimize the parameters of regional models. Full article

In the era of the digital economy, enhancing the resilience of industrial chains is a core task in building a modern industrial system. This paper views the cotton industrial chain as a system composed of multiple segments and entities, aiming to explore how the digital economy drives the collaborative evolution of the chain’s constituent elements, organizational structure, and overall functions, ultimately enhancing its resilience to respond to shocks and adapt to changes. The study focuses on the cotton industrial chain, systematically analyzing the mechanisms and spatiotemporal characteristics of the digital economy’s impact on its resilience, aiming to provide theoretical support and practical pathways for constructing a secure, efficient, and sustainable cotton industrial chain. Based on panel data from nine provinces in China’s three major cotton-producing regions from 2013 to 2022, the study uses the entropy method to measure the technological innovation vitality and the resilience of the cotton industrial chain, employing a semi-parametric panel model to empirically test the systemic association between them, and utilizing a mediation effect model to identify the roles of market information utilization and the scale of planting in this relationship. The findings indicate the following: (1) The development of the digital economy significantly enhances the resilience of the cotton industrial chain and exhibits an inverted U-shaped nonlinear relationship. (2) The digital economy enhances the overall resilience and synergy of the cotton industrial chain through two key pathways: improving the technological innovation vitality and increasing the level of planting scale. (3) The influence of the digital economy on the resilience of the cotton industrial chain shows geographical heterogeneity, with the order being “Yangtze River Basin cotton areas > Northwest Inland cotton areas > Yellow River Basin cotton areas.” The impact of the digital economy on the resilience of the cotton industrial chain also exhibits temporal heterogeneity, with “2013–2017 > 2018–2022.” From the perspective of system optimization, future efforts should focus on constructing regionally differentiated collaborative mechanisms, improving the integrated platform for market information services, strengthening incentives for large-scale planting policies, enhancing the digital literacy of practitioners, and conducting skills training, in order to strengthen the overall resilience and sustainable evolution of China’s cotton industrial chain. Full article

Canopy ammonia (NH₃) exchange is a major contributor to agricultural NH₃ emissions and is closely linked to nitrogen-use efficiency. Glutamine synthetase (GS) mediates plant NH₃ assimilation, yet the specific roles of different GS isoenzymes in regulating wheat canopy NH₃ exchange remain unclear. This study aimed to clarify the functional differences of wheat TaGS isoenzymes in modulating canopy–atmosphere NH₃ exchange dynamics using two wheat cultivars (Yumai 49-198 and Xinong 509) under two nitrogen application levels (120 and 225 kg N ha⁻¹). Field experiments combined with FTIR-based NH₃ flux measurement, biochemical assays, and molecular analyses were conducted at anthesis and 16, 24, and 30 days after anthesis (DAA). Results showed that the leaf NH₃ compensation point, determined by apoplastic NH₄⁺ concentration, is a key factor influencing canopy NH₃ exchange. Leaf NH₃ sources exhibited distinct temporal specificity: photorespiration and nitrate reduction dominated at anthesis to 16 DAA, whereas nitrogenous compound degradation prevailed at 24–30 DAA. This temporal partitioning was highly coordinated with TaGS isoenzyme expression: TaGS2 was highest in early grain filling, potentially supporting assimilate NH₃ from photorespiration/nitrate reduction, while TaGS1;1 expression increased progressively, aligning with the scavenging of NH₃ from organic nitrogen degradation. These coordinated patterns suggest that the TaGS isoenzymes play differentiated roles in influencing wheat canopy NH₃ exchange. This study thus provides correlative insights that point to potential molecular targets for breeding nitrogen-efficient wheat cultivars and mitigating agricultural NH₃ emissions sustainably. Full article

Current agricultural irrigation management practices are often extensive, and traditional soil moisture content (SMC) monitoring methods are inefficient, so there is a pressing need for innovative approaches in precision irrigation. This study proposes a Multi-Feature Fusion Network (MFF-Net) for SMC inversion. The model uses a designed Channel-Changeable Residual Block (ResBlockCC) to construct a multi-branch feature extraction and fusion architecture. Integrating the Channel Squeeze and Spatial Excitation (sSE) attention module with U-Net-like skip connections, MFF-Net inverts root-zone SMC from summer maize leaf images. Field experiments were conducted in Zhengzhou, Henan Province, China, from 2024 to 2025, under three irrigation treatments: 60–70% θ_fc, 70–90% θ_fc, and 60–90% θ_fc (θ_fc denotes field capacity). This study shows that (1) MFF-Net achieved its smallest inversion error under the 60–70% θ_fc treatment, suggesting the inversion was most effective when SMC variation was small and relatively low; (2) MFF-Net demonstrated superior performance to several benchmark models, achieving an R² of 0.84; and (3) the ablation study confirmed that each feature branch and the sSE attention module contributed positively to model performance. MFF-Net thus offers a technological reference for real-time precision irrigation and shows promise for field SMC inversion in summer maize. Full article

Existing baseflow estimation methods—such as exponential recession models, linear reservoir approaches, and digital filtering techniques—seldom account for anthropogenic disturbances or evapotranspiration-induced streamflow alterations. To address this limitation, a physically based baseflow equation that explicitly integrates human water withdrawals and evapotranspiration losses has been introduced. The governing equation was reformulated from a nonlinear storage–discharge relationship and validated against multi-decadal streamflow records in the Middle Yellow River Basin (MYRB). Results demonstrate that the proposed model accurately reproduces observed recession behavior across diverse sub-basins (NSE ≥ 0.94; RMSE ≤ 152 m³ s⁻¹). By providing reliable baseflow estimates, the equation enables quantitative assessment of eco-hydrological benefits and informs cost-effective water-resource investments. Furthermore, long-term baseflow simulations driven by climate projections offer a scientific basis for evaluating climate-change impacts on regional water security. Full article

Against the backdrop of accelerating urbanisation in China, the urban-rural divide continues to widen, while cave dwellings along the Yellow River have been largely abandoned, facing the challenge of cultural erosion. This study breaks from conventional conservation approaches by empirically exploring the viability of living heritage in promoting sustainable rural revitalisation and integrated urban-rural development. Employing participatory action research, it engaged multiple stakeholders—including villagers, returning migrants, and urban designers—across 60 villages in the middle reaches of the Yellow River. This collaboration catalysed a “collective-centred” adaptive reuse model, generating multifaceted solutions. The case of Fangshan County’s transformation into a cultural ecosystem demonstrates how this model simultaneously fosters endogenous social cohesion, attracts tourism resources and investment, while disseminating traditional culture. Quantitative analysis using the Yao Dong Living Heritage Sensitivity Index (Y-LHSI) and Living Heritage Transmission Index (Y-LHI) indicates that the efficacy of collective action is a decisive factor, revealing an inverted U-shaped relationship between economic development and cultural preservation. The findings further propose that living heritage regeneration should be reconceptualised from a purely technical restoration task into a viable social design pathway fostering mutually beneficial urban-rural symbiosis. It presents a replicable “Yao Dong Solution” integrating cultural sustainability, community resilience, and inclusive economic development, offering insights for achieving sustainable development goals in similar contexts across China and globally. Full article

Growing agricultural water demand, driven by climate change and land-use intensification, is accelerating global water scarcity and threatening food and environmental security. This study quantifies spatiotemporal changes in crop water requirements (CWR) and irrigation water requirement (IWR) from 1980 to 2017 for wheat, maize, and soybean. A corrected FAO crop coefficient method was used to estimate global CWR, while the logarithmic mean Divisia index (LMDI) was applied to decompose its drivers into climate and crop area changes. IWR was calculated to evaluate the increasing water stress in four representative river basins: the Haihe (HRB), Yellow (YRB), Mississippi (MRB), and Ganges (GRB) river basins. Multiple linear regression models were used to identify dominant drivers of water stress. Results show that from 1980 to 2017, CWR increased significantly for maize (+210 × 10⁸ m³) and soybean (+523 × 10⁸ m³) primarily due to crop area expansion, while wheat CWR declined (−109 × 10⁸ m³). Area growth contributed over +850 × 10⁸ m³ to global CWR increases. At the basin scale, IWR rose notably in HRB, YRB, and GRB, but declined in MRB. Regression analysis confirms that crop area change was the dominant driver of variations in IWR, particularly for soybean in HRB and maize in YRB, while precipitation exerted strong negative effects in some regions. This study provides a scalable framework for diagnosing agricultural water stress and its key drivers, supporting climate adaptation and irrigation planning under global change. Full article

Rainfall, as the main driving force of natural disasters such as floods and droughts, has strong non-linear and abrupt characteristics, which makes it difficult to predict. As extreme weather events occur frequently in the Yellow River Basin, it is especially critical to reveal the physical mechanism of rainfall in the basin and integrate monthly scale meteorological data to achieve monthly rainfall prediction. In this paper, we propose a rainfall prediction model coupled with a physical mechanism and a temporal convolutional network (TCN) to achieve the prediction of monthly rainfall in the basin, aiming to reveal the physical mechanism between rainfall factors in the basin based on the transfer entropy and the multidimensional Copula function and based on the physical mechanism which is embedded into the TCN to construct a dual-driven prediction model with both physical knowledge and data, while the SHAP is used to analyze the interpretability of the prediction model. The results are as follows: (1) Temperature, relative humidity, and evaporation are key characteristic factors driving rainfall. (2) The physical mechanism features between temperature, relative humidity, and evaporation can be described by the three-dimensional Gumbel–Hougaard Copula function, with a more concentrated data distribution of their joint distribution probability. (3) The PHY-TCN model can accurately fit the extremes of the rainfall series, improving the model accuracy in the training set by 3.82%, 1.39%, and 9.82% compared to TCN, CNN, and LSTM, respectively, and in the test set by 6.04%, 2.55%, and 8.91%, respectively. (4) Embedding physical mechanisms enhances the contribution of individual feature variables in the PHY-TCN model and increases the persuasiveness of the model. This study provides a new research framework for rainfall prediction in the YRB and analyzes the physical relationship between the input data and output results of the deep learning model. It has important practical significance and strategic value for guiding the optimal scheduling of water resources, improving the risk management level of the basin, and promoting the ecological protection and high-quality development of the YRB. Full article

Understanding the structure of zooplankton communities in water contaminated with per- and polyfluoroalkyl substances (PFAS) is essential to the conservation of aquatic biodiversity. This study focused on the Weihe River and systematically characterized the PFAS pollution. By employing environmental DNA metabarcoding, multivariate statistics, and Partial Least Squares Path Modeling (PLS-PM), we systematically analyzed the associations between PFAS and zooplankton within the context of water parameters. The results showed that short-chain PFAS were the dominant PFAS compounds in the Weihe River (accounting for 70.89% of ΣPFAS), and that both PFAS and the zooplankton community exhibited similar spatial patterns. PLS-PM identified a key pathway: water chemistry promoted PFAS accumulation, which in turn exerted taxon-specific effects. Short-chain PFAS were primarily associated with Cercozoa, and path analysis indicated negative relationships, whereas long-chain PFAS were correlated with Ciliophora and Rotifera. Specific taxon within Ciliophora showed potential as bioindicators. Additionally, higher community relative abundance was associated with reduced diversity loss under anthropogenic stress, indicating a potential buffering response. Overall, short-chain PFAS, in combination with water parameters, were associated with higher ecological risk to zooplankton communities. This study highlights the importance of including indirect pathways and taxon-specific responses into risk assessments of emerging contaminants. Full article

Despite its rich cultural heritage, the Yellow River Basin (YRB) faces challenges of ecological fragility and unbalanced development that constrain residents’ welfare improvement. Cultural–tourism integration (CTI)—aimed at creating employment, optimizing industrial structure, and improving public services—is increasingly promoted as a pathway to enhance people’s livelihood and well-being (PLW). Grounded in industrial integration theory and welfare economics, this study examined the impact effects, transmission mechanisms, and spatial spillovers of CTI on PLW. Panel data from 75 prefecture-level cities in the YRB, spanning 2011 to 2023, were utilized, and multi-dimensional indices were constructed for both CTI and PLW. Impact effects, mediating mechanisms, and spatial spillovers were examined through kernel density estimation, a dynamic system generalized-method-of-moments (SYS-GMM) model, mediation analysis, and a spatial Durbin model (SDM). The results showed that CTI and PLW both improved over time and displayed a spatial pattern of “midstream and downstream leading, upstream lagging”. CTI significantly promoted PLW, after controlling for dynamics and endogeneity (SYS-GMM coefficient = 0.130, p < 0.01). Industrial structure upgrading acted as a positive mediator, whereas digital infrastructure exhibited a short-term suppressing (negative mediating) effect, implying a phased mismatch between CTI investment priorities and digital input. Spatial estimates further indicated that CTI generated positive spillovers, improving PLW in neighboring cities, in addition to local gains. These findings suggest that basin-wide coordination and better alignment between CTI projects and digital infrastructure are essential for inclusive and sustainable well-being improvements, supporting regional progress toward the Sustainable Development Goals. Full article

Agricultural activities are major contributors to global greenhouse gas (GHG) emissions, with methane (CH₄) and nitrous oxide (N₂O) emissions accounting for 40% and 60% of total agricultural emissions, respectively. Therefore, developing effective emission reduction pathways in agriculture is crucial for achieving carbon budget balance. This article synthesizes the impact of farmland management practices on GHG emissions, evaluates prevalent accounting methods and their applicable scenarios, and proposes mitigation strategies based on systematic analysis. The present review (2000–2025) indicates that fertilizer management dominates research focus (accounting for over 50%), followed by water management (approximately 18%) and tillage practices (approximately 14%). Critically, the effects of these practices extend beyond GHG emissions, necessitating concurrent consideration of crop yields, soil health, and ecosystem resilience. Therefore, it is necessary to conduct joint research by integrating multiple approaches such as water-saving irrigation, conservation tillage and intercropping of leguminous crops, so as to enhance productivity and soil quality while reducing emissions. The GHG accounting framework and three primary accounting methods (In situ measurement, Satellite remote sensing, and Model simulation) each exhibit distinct advantages and limitations, requiring scenario-specific selection. Further refinement of these methodologies is imperative to optimize agricultural practices and achieve meaningful GHG reductions. Full article

Under the combined influences of climate change and anthropogenic activities, the variability of basin streamflow has intensified, posing substantial challenges for accurate prediction. Although Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models characterize volatility in time series, many previous studies have neglected changes in series structure, leading to inaccurate identification of the form of volatility. Building on tests for structural breaks (SBs) in time series, this study first removes the series mean using an Autoregressive Integrated Moving Average (ARIMA) model and then incorporates Markov-switching (MS) to develop a multi-state MS-GARCH model. An asymmetric MS-GARCH (MS-gjrGARCH) variant is also incorporated to describe the volatility of streamflow series with SBs. Daily streamflow data from five hydrological stations in the middle reaches of the Yellow River are used to compare the predictive performance of SB-ARIMA-MS-GARCH, SB-ARIMA-MS-gjrGARCH, ARIMA-GARCH, and ARIMA-gjrGARCH models. The results show that daily streamflow exhibits SBs, with the number and timing of breakpoints varying among stations. Standard GARCH and gjrGARCH models have limited ability to capture runoff volatility clustering, whereas MS-GARCH and MS-gjrGARCH effectively characterize volatility features within individual states. The multi-state switching structure substantially improves daily streamflow prediction accuracy compared with single-state volatility models, increasing R² by approximately 5.8% and NSE by approximately 36.3%.The proposed modeling framework offers a robust new tool for streamflow prediction in such changing environments, providing more reliable evidence for water resource management and flood risk mitigation in the Yellow River basin. Full article

The goal of this scientific study is to clarify whether and how digital–intelligent integration contributes to urban carbon efficiency and to identify the conditions under which this contribution becomes nonlinear and policy-relevant. Focusing on 39 prefecture-level cities in the middle reaches of the Yellow River Basin during 2011–2022, we adopt an integrated measurement–modelling approach that combines efficiency evaluation, machine-learning interpretation, and dynamic–spatial validation. Specifically, we construct two super-efficiency DEA indicators: an undesirable-output SBM incorporating CO₂ emissions and a conventional super-efficiency CCR index. We then estimate nonlinear city-level relationships using XGBoost and interpret the marginal effects with SHAP, while panel vector autoregression (PVAR) and spatial diagnostics are employed to validate the dynamic responses and spatial dependence. The results show that digital–intelligent integration is positively associated with both carbon-related and conventional efficiency, but its marginal contribution is strongly conditioned by human capital, urbanisation, and environmental regulation, exhibiting threshold-type behaviour and diminishing returns at higher digitalisation levels. Green efficiency reacts more strongly to short-run shocks, whereas conventional efficiency follows a steadier improvement trajectory. Heterogeneity across urban agglomerations and evidence of spatial clustering further suggest that uniform policy packages are unlikely to perform well. These findings highlight the importance of sequencing and policy complementarity: investments in digital infrastructure should be coordinated with institutional and structural measures such as green finance, environmental standards, and industrial upgrading and place-based pilots can help scale effective digital applications toward China’s dual-carbon objectives. The proposed framework is transferable to other regions where the digital–climate nexus is central to smart and sustainable urban development. Full article