Search Results (833)

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

Global water scarcity constitutes a critical sustainability challenge, particularly in agricultural and rural contexts exposed to climate variability. Beyond technical and infrastructural solutions, collective and community-based water management practices have gained increasing relevance as sustainable alternatives grounded in local and ancestral knowledge. This study presents a systematic qualitative review of collective practices for alternative water management implemented worldwide between 2018 and 2023, following the PRISMA methodology, and based on a screening of the Scopus database, 31 peer-reviewed studies were selected and analysed through thematic synthesis. The systematic review identified five interconnected dimensions: (1) water management and governance, (2) conservation and storage, (3) hydrological restoration, (4) efficient water use, and (5) recognition of local knowledge. The results show that collective water management practices contribute to water security, ecological resilience, and adaptive capacity in rural territories, particularly when aligned with local socio-environmental conditions. The study highlights the importance of integrating scientific and community-based knowledge to advance context-specific and sustainable water management strategies, contributing to ongoing debates on sustainability, rural development, and adaptive water governance. Full article

The evolution of groundwater in the Puhe River Basin is closely related to the ecological security of the Beijing–Tianjin–Hebei water source conservation zone. Based on 122 groundwater samples, this study systematically investigated the hydrochemical characteristics, evolution mechanisms, and water quality of shallow groundwater using mathematical statistics, Piper diagrams, ionic ratio analysis, and a variable fuzzy pattern recognition model. The results showed that shallow groundwater in the middle and upper reaches is generally weakly alkaline, fresh to hard water, with HCO₃–Ca and HCO₃·SO₄–Ca as the dominant hydrochemical facies. Groundwater hydrochemistry is primarily controlled by rock weathering, and the dissolution of silicate and carbonate rocks is the main source of major ions. Calcite and dolomite are in dynamic equilibrium between dissolution and precipitation, whereas gypsum and halite remain undersaturated. Overall, groundwater quality is generally good; however, anthropogenic activities in cultivated and construction lands have altered local hydrochemical composition and caused water quality deterioration in some areas. These findings improved the understanding of groundwater hydrochemical evolution in typical small watersheds of the northern Hebei hilly region and provided a scientific basis for the sustainable management and protection of groundwater resources in the Beijing–Tianjin–Hebei water source conservation area. Full article

The Dongting Lake Basin is a critical ecological zone in the middle reaches of the Yangtze River, playing a pivotal role in safeguarding regional ecological security and supporting socio-economic development. To investigate the spatiotemporal patterns and underlying drivers of water quality in Dongting Lake, this study developed a Spectral-Attention CNN (SA-CNN) inversion model integrated with the Efficient Channel Attention (ECA) mechanism, utilizing multi-source remote sensing data and convolutional neural networks. Results indicate that the proposed SA-CNN model significantly outperforms traditional machine learning approaches in predicting key water quality parameters, including total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH₃–N), and turbidity. Notably, the model achieved its highest predictive accuracy for TP, with an $R^2$ value of 0.94. By incorporating spectral weight prior knowledge, the model was successfully transferred and trained on Landsat imagery. The validated model was subsequently applied to reconstruct and analyze the spatiotemporal trends from 2015 to 2025, revealing that water quality in Dongting Lake exhibits a fluctuating decline during winter months, while summer periods show an increasing trend in turbidity and TP concentrations. Further analysis suggests that water quality parameters are positively correlated with temperature and negatively correlated with precipitation, with anthropogenic activities also exerting a considerable influence. Full article

The Beijiang River Basin is an important ecological security protection area and water source supply area in Guangdong Province. This study assesses the spatiotemporal distribution characteristics of watershed water quality based on on-site monitoring data and multivariate statistical analysis. The results indicate that PO₄³⁻P concentrations peak during the flood season, whereas pH, NO₃⁻-N, and total nitrogen (TN) reach their highest levels during the autumn normal-flow period. Spatially, water quality follows a gradient of upstream > downstream > midstream, with the midstream region identified as the primary zone of water quality degradation. Future non-point source (NPS) pollution characteristics in the Beijiang River Basin are influenced by land use/cover change (LUCC) and climate change, showing significant variation across Shared Socioeconomic Pathway (SSP) scenarios. Under SSP126, precipitation increases at the slowest rate, with a peak annual value of 1599.77 mm during 2031–2040 and an average basin temperature of 19.61 °C. In contrast, SSP245 exhibits a marked increase in precipitation, reaching 1802.92 mm by 2061–2070. Under SSP585, annual precipitation rises to 2200.04 mm, with temperatures approximately 0.5 °C higher than those under SSP126. Simulations based on the improved ESP-PLUS model indicate that, under the natural development scenario (NDS), expansion of construction land increases urban runoff pollution by 32.97%. Under the economic development scenario (EDS), 1023 km² of ecological land is lost, significantly weakening pollution interception capacity, while construction land increases by 26.01%. In contrast, the coordinated development scenario (CDS) reduces ecological land loss by more than 60% compared to EDS through balanced development and conservation, thereby maintaining the basin’s pollutant purification function. Overall, future nitrogen and phosphorus loads in the watershed are projected to first decrease and then increase. Accordingly, differentiated management strategies are recommended, emphasizing the coordinated development of economic growth and ecological protection, and providing a scientific basis for controlling NPS pollution under changing climatic conditions. Full article

Urban water-ecological governance in the Yellow River Basin is shifting from a single administratively dominated model toward a polycentric collaborative system. However, ambiguous responsibilities and overlapping tasks among governments, enterprises, and society often lead to governance conflicts, reduced coordination efficiency, and growing risks to regional ecological security. To address this challenge, this study develops a multi-actor governance analysis framework integrating deep learning, knowledge graphs, and complex network optimization. Stakeholder demands are extracted from multi-source data using a BERT-BiLSTM-CRF model, including policy documents, enterprise reports, and public discourse, and are then organized into a knowledge graph for water-ecological governance. A Relational Graph Attention Network (R-GAT) is subsequently used to transform the knowledge graph into a signed weighted network, enabling the measurement of conflict intensity and the identification of key conflict nodes across governance scenarios. Based on multi-objective optimization, a Pareto frontier is constructed to balance conflict tension, fairness, and governance efficiency, from which a compromise solution for responsibility weighting is identified. An empirical case study of a typical city in the Yellow River Basin shows that the proposed framework can identify core conflict nodes and provide quantitative support for conflict mitigation and coordination adjustment. The findings offer a quantitative reference for institutional innovation and evidence-based decision-making in urban water-ecological governance. Full article

The Yangtze River Economic Belt supports over 400 million people and contributes nearly half of China’s GDP, yet decades of industrialization, urbanization, and agricultural intensification have resulted in severe contamination and pressing environmental challenges. This systematic review synthesizes three decades of peer-reviewed and governmental data to examine the spatiotemporal distribution, sources, and ecological and human health risks of major pollutants, including heavy metals, microplastics, persistent organic pollutants, and excess nutrients. While point-source emission of heavy metals such as cadmium, lead, and mercury have decreased by 35–42% since 2013 following policy interventions like the 10-Point Water Plan and the Yangtze River Protection Law, legacy contaminants in sediments and diffuse agricultural inputs continue to pose significant risks. Cadmium levels in rice still exceed food safety standards, arsenic in groundwater surpasses health guidelines, and microplastic flux into the East China Sea has reached 8.3 × 10¹² particles per year. Nutrient surpluses also drive extensive algal blooms, causing substantial economic losses. This review evaluates remediation strategies such as dredging, phytoremediation, wetland restoration, and AI-enhanced monitoring, which show removal efficiencies of 60–90% at reduced costs. However, critical gaps remain in understanding chronic mixture toxicity, the long-term fate of emerging contaminants, and pollutant–climate interactions. We propose an integrated basin-wide roadmap combining zero-liquid-discharge mandates, green infrastructure, and adaptive, performance-based governance to secure the Yangtze’s ecological and economic sustainability. This framework offers a transferable model for large-scale watershed management worldwide. Full article

Accurate and rapid identification of microalgae in ship ballast water is critical for preventing the spread of invasive aquatic species and ensuring ecological security. However, traditional manual microscopic examination is labor-intensive and limited by challenges such as high intra-class morphological variability, frequent cell aggregation, and inter-class similarity among microalgae. This study proposes YOLO11-DBalgae, a specialized end-to-end object detection framework designed for fine-grained microalgae recognition in complex aquatic environments. Two key architectural innovations are introduced into the YOLO11n baseline—a Detail-enhanced Vanishing-prevention Block (DVB), which processes input features through a VoVGSCSP cross-stage aggregation module followed by parallel Conv and DSConv paths, preserving fine-grained boundary signals of morphologically diverse algal cells during repeated downsampling, and a Bidirectional Feature Pyramid Network (BiFPN), which employs learnable cross-scale weighting to optimize multi-scale feature fusion across the extreme size range of co-occurring microalgal targets. Experimental results demonstrate that YOLO11-DBalgae achieves an mAP@0.5 of 97.3%, representing an improvement of 7.0 percentage points over the baseline YOLO11n model. The model sustains an inference speed of 240 FPS with 2.83 M parameters, maintaining a lightweight and deployment-viable profile. Qualitative analysis via per-class precision–recall curves, detection visualization, and Grad-CAM attention maps confirms the model’s robustness in recovering near-invisible weak-feature targets, minimizing false detections within dense cell clusters, and accurately distinguishing morphologically convergent species. The proposed framework provides a practical and deployable solution for automated microalgae monitoring, offering maritime regulatory bodies an efficient and reliable tool for ballast water management. 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

Under the context of global change, forest cover, as a critical component of terrestrial ecosystems, exerts a profound influence on regional ecological security and sustainable development through its spatiotemporal evolution. Current research on forest cover change primarily focuses on pattern description and single-factor driver analysis, with insufficient in-depth exploration of the interactions among multiple factors and their associated nonlinear mechanisms. To address this gap, this study focuses on the Wumeng Mountain area, a typical ecologically fragile karst region in Southwest China. By comprehensively employing methods such as Theil–Sen Median trend analysis, land use transfer matrix, standard deviation ellipse, and spatial autocorrelation analysis, this study systematically reveals the spatiotemporal evolution characteristics of forest cover from 1985 to 2024. On this basis, an integrated eXtreme Gradient Boosting–SHapley Additive exPlanations (XGBoost-SHAP) model is introduced to construct an indicator system comprising 16 driving variables, including elevation, slope, aspect, temperature, precipitation, soil type, soil pH, soil thickness, soil organic matter, soil moisture content, GDP, population, distance from water, distance from railway, distance from grade highway, and distance from government. This model quantifies the influence intensity of each driving factor on forest change. The main findings are as follows: (1) From 1985 to 2024, the forest cover rate in the Wumeng Mountain area significantly increased from 54.7% to 60.2%, exhibiting a “high-low-high” heterogeneous spatial distribution pattern along the northeast-southwest axis; (2) Forest increase primarily originated from the conversion of cropland and grassland, with contribution rates reaching 93.58% and 5.9%, respectively, indicating an overall trend of “increase in low-value areas and decrease in high-value areas”; (3) Forest cover change is driven by both natural and anthropogenic factors, with dominant driving factors exhibiting phased replacement over time. Overall, this is manifested as long-term stable constraints exerted by natural background factors, alongside strong disturbances from anthropogenic factors such as social-economic, and transportation-related activities. Natural factors remain the primary driving force behind changes in forest cover. The core findings of this study elucidate the complex driving factors of forest change in karst mountainous areas, thereby providing scientific support for the precise management of regional forest resources, the planning of ecological restoration projects, and the implementation of sustainable development strategies. Full article

The livestock sector underpins food security, employment, and rural livelihoods across the Southern African Development Community (SADC), contributing up to 50% of agricultural GDP and supporting more than 60% of rural households. Yet climate change poses escalating threats through heat stress, declining pasture productivity, water scarcity, and vector-borne diseases that compromise productivity and economic resilience. This review identifies and locates effective climate change mitigation strategies along the livestock value chain, spanning production, processing, transport, and consumption, to promote sustainable, low-emission, and inclusive growth in the SADC region. A broad review of 46 peer-reviewed and institutional sources (2000–2024) was undertaken, focusing on livestock-related mitigation within SADC and comparable agro-ecological systems. Strategies were thematically categorized by value-chain stage and assessed for their emission-reduction and livelihood-enhancement potential. Local strategies include genetic improvement for low-methane and heat-tolerant breeds, adaptive rangeland and feed management, renewable-energy adoption in processing, climate-resilient transport infrastructure, and consumer awareness of low-emission products. Evidence suggests potential GHG-emission reductions of 18–30%, coupled with productivity gains and improved smallholder incomes. Coordinated implementation through the SADC Regional Agricultural Investment Plan (2021–2030) and national policies can transform the livestock sector into a climate-resilient driver of inclusive growth. Further research should quantify the socioeconomic feasibility and scaling potential of these strategies across production systems. Successful integration of climate change mitigation imperatives must be tailored to local biophysical conditions (e.g., rainfall, soil type) and socioeconomic contexts (e.g., market access, cultural practices). Full article

Responding to rapid urbanization, this study examines the trade-offs and synergies of ecosystem services (ESs) at the county scale in the Zhengzhou metropolitan area and constructs an ecological security pattern. Using the InVEST model, we quantified carbon storage (CS), soil conservation (SC), habitat quality (HQ), water yield (WY), and food production (FP). We then analyzed their trade-offs and synergies using the geographically weighted regression model, identified driving factors with an optimal parameter-based geographical detector model, detected ecosystem service bundles via a Self-organizing map model, and constructed an ecological security pattern based on circuit theory. The results showed that: (1) From 2003 to 2023, ES spatial distribution remained stable overall, with weak trade-offs and synergies. Locally, WY and HQ declined, while SC and FP increased. (2) Slope and DEM enhanced SC, whereas urban expansion consistently weakened CS, HQ, and FP. Moreover, slope played an increasingly prominent role in regulating WY. (3) Key synergistic bundles with stable spatiotemporal distribution were identified as ecological sources, leading to the construction of ecological security pattern characterized by “four districts, one corridor, and one belt.” This provides a framework for integrating ecological space protection and restoration into urban development. Full article

Small-scale inland fisheries in the Amazon are critical for food security, yet their sustainability is increasingly threatened by overexploitation and environmental degradation. In data-limited contexts such as the northern Ecuadorian Amazon, the absence of continuous monitoring constrains the development of adaptive management strategies. This study develops an integrated socio-ecological baseline to support the establishment of fisheries agreements in five Indigenous communities of the Napo and Aguarico rivers. Through a participatory monitoring approach, we generated reproductive parameters (gonadosomatic index, fecundity, size at first maturity), population structure metrics, and length–weight relationships for key subsistence species across three hydrological phases. Reproductive investment exhibited marked seasonality, with peak gonadosomatic indices during rising waters in most species, identifying a critical period for protection. Life-history strategies ranged from high-fecundity periodic strategists to low-fecundity equilibrium species, implying differentiated vulnerability to harvesting. Community perceptions prioritized large migratory catfish and floodplain habitats, aligning with biological indicators of vulnerability. High performance in technical training demonstrated the feasibility of long-term local monitoring systems. By linking biological indicators with local ecological knowledge, this study proposes a pathway from baseline assessment to adaptive co-management. The framework presented here provides a transferable model for strengthening sustainability, governance, and food security in tropical small-scale fisheries facing persistent data limitations. Full article

Marine organisms that episodically aggregate near coastal nuclear power plant water intakes pose a substantial risk to cooling water security. Predicting the spatial distribution of such high-risk species remains challenging because their occurrence is shaped not only by environmental conditions but also by complex trophic interactions. In this study, we model the habitat distribution of three high-risk nektonic species, Dotted gizzard shad (Konosirus punctatus), Japanese swimming crab (Charybdis japonica) and squid (Loligo sp.), in the cooling water intake area of a coastal nuclear power plant in eastern Liaodong Bay using generalized linear models (GLMs) and joint species distribution models (JSDMs). Based on summer surveys conducted in 2024–2025, we explicitly incorporated trophic linkages among target species, their prey, and predators within JSDMs. Model performance was evaluated using cross-validation based on AUC, RMSE, and coefficient of determination (R²). Our results indicate that water depth was the dominant environmental driver for all three species, while chlorophyll-a concentration and distance to the intake exerted species-specific effects. By incorporating interspecific trophic associations and environmental responses, JSDMs showed consistently improved predictive performance relative to GLMs, with approximately 1.5-fold higher R² values and 10–30% lower RMSE, while offering enhanced ecological interpretability. The models revealed strong positive associations between target species and both lower-trophic prey and higher-trophic predators, suggesting that top–down and bottom–up processes jointly regulate aggregation dynamics. This study demonstrates that integrating trophic interactions into species distribution modeling substantially improves predictions of high-risk marine species near coastal infrastructure and provides an ecological basis for proactive management of cooling water intake systems. Full article

The Linpan in western Sichuan is a composite rural landscape of “household-water-forest-field” on the Chengdu Plain. Under the interference of human activities, problems such as landscape fragmentation and ecological function degradation have become increasingly serious, threatening regional ecological security. The specific components involved in the “study on ecological risk sequence” include landscape disturbance degree, landscape vulnerability degree, landscape connectivity, and human activity intensity. Given the lack of long-term ecological risk research on the Linpan landscape in Chongzhou City to support conservation decisions, this study takes it as the object. Based on five phases of land use data from 2003 to 2023, a landscape ecological risk assessment model was constructed. This model is a deterministic and nonlinear comprehensive evaluation model. The determinism is reflected in the fact that, based on specific influencing factors, a unique and definite result can be obtained through a fixed indicator system and calculation method. The nonlinearity is reflected in the fact that the comprehensive risk index does not involve a simple linear superposition of the various factors; instead, the evaluation result is obtained by integrating the factors through nonlinear approaches such as weighted coupling. Using ArcGIS and spatial analysis methods, based on a temporal resolution of 5 years and a spatial resolution of 30 m, the spatiotemporal evolution characteristics were revealed. The results show that: (1) From 2003 to 2023, the Linpan landscape pattern in Chongzhou City underwent significant evolution, characterized by “reduction in agricultural land, expansion of construction land, and slight recovery of ecological land”. Landscape fragmentation intensified, connectivity decreased, but overall aggregation remained stable. (2) The evolution of the landscape pattern drove the ecological risk to show a stable pattern of “low in the northwest and high in the southeast”. The global Moran’s I value decreased from 0.887 to 0.832, indicating that risk aggregation intensified in the early period and was alleviated in the later period. (3) Landscape disturbance degree is the key factor dominating the change in the comprehensive ecological risk index. Compared with similar studies, this research shares the commonality of urbanization-driven fragmentation exacerbation risk, but also exhibits the uniqueness of Linpan structural resilience and conservation policies promoting a reduction in high-risk areas. This study can provide a scientific basis for Linpan protection, land use optimization, and ecological security pattern construction in Chongzhou City. Full article