In response to the issue that natural disaster knowledge graphs for single disaster types had a narrow coverage of information, making it difficult to extract knowledge from massive and complex information, this study proposed a method for building a natural disaster knowledge graph for the Yellow River Basin oriented toward loss assessment. The natural disaster knowledge graph consisted of a data resources layer, a knowledge extraction layer (including a schema layer and a data layer), and an application service layer. The schema layer was built by using a top-down approach, centered on ontological models of natural disaster events, fundamental geographic information, and disaster loss assessment. Multi-source heterogeneous data were collected, including natural disaster event data, basic geographic information data, and disaster loss data, and the data layer was built by using a bottom-up approach, enabling knowledge extraction, knowledge fusion, and knowledge storage from these diverse data sources. The application situations show that the knowledge graph supports efficient spatial-temporal relationship queries and rapid identification of regional concurrent disasters. The analysis of 160 major disaster events in the Yellow River Basin from 1981 to 2018 reveals an increasing frequency of disasters over time, with floods being the most common disaster type. Additionally, 37 instances of concurrent disasters are identified. This map breaks through the limitations of the traditional single disaster knowledge system and improves the efficiency of disaster loss assessment.

Based on the random forest model and utilizing the sediment and water process data as well as climatic measurement data from four typical hydrological stations in the Zuli River Basin from 2001 to 2020, this study conducted the simulation and attribution analysis of the dynamic characteristics of sediment and water processes at the basin scale. The results show that the random forest model has good applicability for sediment and water simulation in the Zuli River Basin, with high simulation accuracy and a Nash-Sutcliffe Efficiency (NSE) greater than 0.5 for the vast majority of stations, but the simulation of data peaks is relatively poor. During the period from 2001 to 2020, the climate in the Zuli River Basin generally exhibited a trend towards warming and increased humidity, with an overall increasing trend in runoff and a decreasing trend in annual sediment transport. From 2010 to 2020, the changes in runoff in the Zuli River Basin are primarily influenced by climate change, with a contribution rate of 197.59%; whereas the changes in sediment are mainly reduced due to human activities, with a contribution rate of 162.46%.

China is a country with severe earthquake disasters, with a wide range of affected areas, high frequency of occurrence, and high intensity of earthquake activity. Roads, as the "lifeline", play an important role in the transportation of materials and personnel. After an earthquake disaster occurs, quickly and accurately obtaining the location of road damage is of great significance for timely dredging of lifelines and carrying out post disaster rescue. In response to the issues of strong shadow interference, high fault missed detection rate, and severe fragmentation in remote sensing identification of road damage after earthquakes, this paper proposed a road damage layer extraction framework that integrated prior knowledge of OpenStreetMap (OSM). The effectiveness of the method was verified by using the 2023 Jishishan M6.2 earthquake as a typical case. By building a four layer technical system of "vector constraint-image segmentation-topology repair-damage detection", rapid localization of road damage in complex terrain areas had been achieved, providing assistance in improving rescue speed and reducing personnel and property losses.

In order to provide references for landslide hazard prevention and research on the sensitivity of landslide-causing factors, the Longyangxia-Jishixia section of the upper Yellow River basin was selected as the study area, and 16 factors such as elevation, slope, terrain roughness and lithology were taken as typical landslide hazard factors. The collinearity test was carried out by Spearman correlation coefficient method to select landslide hazard factors with strong correlation. GIS was used to reclassify landslide disaster-causing factors and analyze their weights with geographic detectors. The geographic detection model results were coupled with random forest model to obtain landslide prediction probabilities under different causative factors. ROC curve was used to verify the accuracy of prediction results. The results indicate that a）the explanatory power of the interaction between causative factors is greater than that of individual factors, with the synergistic effect of elevation and other topographic parameters being particularly significant. b）The importance of drainage density, topographic roughness, and profile curvature is nearly zero, suggesting that these features may not have a direct or significant correlation with landslide occurrence. c）There are notable differences in the contribution of causative factors to the prediction results, with the elevation-slope combination being the core driving unit for landslide development in the study area. d）The AUC value ofrandom forest model has achieved 0.93, indicating strong classification performance.

Understanding the trend of sediment load variation and future projections in the Ningxia-Inner Mongolia reach of the Yellow River is crucial for its management. This paper analyzed and summarized the characteristics of sediment load changes in the main stream and tributaries of this reach, and identified the causes for the sediment reduction. The characteristics of the sediment load variation in the Ningxia-Inner Mongolia reach (1960-2020) included: a decrease in sediment inflow, with a more pronounced reduction in the mainstem; altered sediment contribution ratio between mainstem and tributaries, showing increased proportional input from tributaries; distinct temporal inflection points in sediment changes for both mainstem and tributaries, with recent reduction phases being largely synchronous and; recent sediment load variations approximately follow a lognormal distribution. The main driving factors influencing these changes of sediment inflow included the detention of the main stream reservoir, water and soil conservation measures in the tributaries, and the variation of rainfall. Considering long-term rainfall stability with cyclical fluctuations and diminishing reservoir sediment retention capacity over time, this study based its analysis on actual sediment yield conditions during 2000-2020, by evaluating reduced sediment interception capacity and newly implemented conservation measures, it is projected that the annual sediment load at Xiaheyan Station on the mainstem, and annual sediment input from interval tributaries will both increase moderately compared to recent levels.

In order to effectively support scientific decision-making for ecological protection in the Yellow River Basin, with digitization, networking and intelligence as the main lines, and based on the characteristics of the ecological environment in the basin, a digital twin platform for ecological protection in the Yellow River Basin was constructed. A model framework including information infrastructure, data base, model and technology simulation, business applications was designed. By classifying and compiling monitoring data on water resources, water ecology and water environment, it established a comprehensive data system for the entire basin. Integrated data collection and monitoring, big data integration and analysis, cloud computing and edge computing integration, digital twin modeling and simulation and other technologies were used to realize the data aggregation and presentation of ecological environment factors such as ecological environment status, natural reserve distribution, ecological flow guarantee and so on. Through platform ecological data visualization, ecological simulation and ecological perspective application practice, the water function zoning and statistical situation of the Yellow River Basin, the evolution of key protected animals and plants in the estuarine delta, the evolution of land use types and the inundation range of the Jingtai Stone Forest caused by the Heishanxia Water Control Project were demonstrated, enhancing the multi-source data aggregation governance and scientific decision-making for ecological protection in the Yellow River Basin.

In order to scientifically evaluate the spatial-temporal characteristics of water-saving levels, identify key influencing factors, reveal the spatial equilibrium patterns in the Yellow River water-receiving area of Henan Province, provide a scientific basis for optimizing water resources allocation and formulate differentiated water-saving policies, a water-saving evaluation index system was built, encompassing five dimensions of comprehensive, agricultural, industrial, domestic, and ecological with 12 quantitative indicators. The TOPSIS method was employed to dynamically assess the water-saving levels of 14 prefecture-level cities in the Yellow River water-receiving areas of Henan Province from 2014 to 2023. Additionally, based on the DEMATEL method and spatial equilibrium analysis, the key influencing factors and their regional differentiation characteristics were systematically analyzed. The results indicate that a) from 2014 to 2023, the average water-saving level in the Yellow River water-receiving areas of Henan Province is classified as Level IV on a 5-level evaluation scale, indicating a relatively low level, with significant spatial heterogeneity in water-saving levels among prefecture-level cities. b) Using the DEMATEL method, five main influencing factors are identified and ranked by their degree of impact: the proportion of planned water users (C2) > water consumption per 10 000 yuan of GDP (C1) > effective utilization coefficient of farmland irrigation water (C5) > water consumption per 10 000 yuan of industrial added value (C7) > proportion of saved water (C3). c) The spatial equilibrium of water-saving in the study area generally remains relatively stable but exhibits a slight declining trend over time, reflecting potential risks of regional imbalance in water-saving development. Therefore, it is essential to strengthen water-saving policy guidance and optimize water resources allocation to ensure sustainable development.

While the cascade reservoir group is exerting comprehensive benefits, it has significantly altered the carbon and nitrogen cycle paths of rivers, forming a “source-sink duality” of greenhouse gases (GHGs). This paper systematically reviewed the progress and challenges of research on the GHGs source-sink effect of gradient reservoirs in sediment-laden rivers. In terms of monitoring technology, existing technologies such as flux chambers and eddy covariance complement each other, thereby enhancing GHGs flux observation capabilities, furthermore, acoustic surveys and sediment coring techniques have optimized the assessment of carbon burial. However, the precision of multi-source data monitoring and data fusion continues to constrain the accurate evaluation of source-sink effects. Concerning the spatial-temporal distribution patterns of these sources and sinks, GHGs fluxes exhibit distinct longitudinal gradients along the cascade reservoirs and vertical stratification within the water column; specifically, the drawdown zone emerges as a significant hotspot for enhanced emissions due to frequent wet-dry alternation; the cascade reservoirs trigger the accumulation of GHGs by extending hydraulic retention times, altering dissolved oxygen states, and transforming organic matter composition. Regarding the underlying mechanisms influencing these GHGs dynamics, sediment plays a pivotal role: density currents transport and deposit external organic carbon, serving as a crucial substrate, while sediment resuspension disturbances critically affect redox microenvironments at the sediment-water interface. Simultaneously, hydrodynamic conditions directly govern CO2 diffusion efficiency across the air-water interface, influence CH4 bubble transport pathways and dissolution within the water column, and regulate N2O production dynamics via impacts on nitrification and denitrification processes. Notably, in highly sediment-laden rivers like the Yellow River, suspended sediments uniquely promote the proliferation of methanogens directly within the water column, fostering a distinct emission pattern characterized by methanogenesis occurring in the water itself, rather than solely in the sediments. For optimization and regulation, GHGs models have evolved from empirical statistics to mechanism-machine learning fusion, but the existing multi-objective optimization models still lack quantification of water-sediment-GHGs coupling mechanisms. In view of the above issues, it is urgent to build a tracking observation system of GHGs source-sink effects in the middle reaches of the Yellow River in the group of terraced reservoirs, the spatial-temporal variability of GHGs sources and sinks and their key influencing factors, elucidate the biogeochemical process of water-sediment-GHGs interactions, and optimize the water-sediment regulation model of the cascade reservoirs.

In order to empower the promotion of the new stage of high-quality development of water conservancy, and continue to promote the construction of the digital twin Yellow River, we constructed the digital twin-based Jiaozuo Qinhe “Four Predictions” integration platform, applied it to the Qinhe River Basin in Jiaozuo section, and explored the key technologies and application scenarios for the construction of digital twins of the Qinhe River in Jiaozuo section. The key technologies included the construction of hydraulic professional models and high-fidelity digital twin simulation engines, which utilized a high-performance aggregate hydrological model, a high-efficiency flood evolution model, and a visual disaster assessment model for flood forecasting, preview simulation and disaster assessment, and built an integrated data base for “air, space and ground” through multi-dimensional and multi-temporal and spatial scales. In addition, it formed an integrated platform for the “Four Predictions” of the Jiaozuo Qinhe River, which included dynamic monitoring, forecasting, early warning, rehearsal and preplanning, and described the platform’s infrastructure, functions and application achievements. After the platform is put into application, it will realize the dynamic interaction, real-time integration and simulation of the flood forecast and dispatching results of the Qinhe River in the digital map, can more accurately assess the flood situation, achieve timely early warning and rapid response, gain the greatest initiative for flood disaster prevention, effectively enhance the flood monitoring and early warning capabilities and scientific decision-making capabilities of the Jiaozuo Qinhe River Basin, and at the same time provide construction ideas and case references for the construction of the “Four Predictions” platform in other small and medium-sized river basins.

It is a common method to extract water areas in SAR remote sensing images based on the threshold segmentation method, which has the advantages of clear physical meaning and low algorithm complexity. The determination of threshold is the key of this type of method, and the existing manual and automatic threshold determination methods suffer from poor timeliness and weak adaptability to water distribution. This article firstly analyzed the reasons for the fluctuation of the optimal segmentation threshold when extracting water bodies based on SAR remote sensing images from the perspective of microwave scattering characteristics of water bodies, mainly including imaging conditions, polarization methods, water surface roughness, and other factors; Secondly, on this basis, a regression model between SAR imaging conditions and water backscattering coefficient was built , and an adaptive threshold segmentation method was proposed for water extraction based on this model. Finally, experiments were conducted by using actual high-resolution SAR remote sensing image data. The test results show that the method has strong adaptability to changes in imaging conditions and other factors, with an extraction accuracy of 94.8%. The extraction process can achieve full process automation, which can effectively improve the accuracy and timeliness of remote sensing flood monitoring.

With the advancement of water-saving technologies, China's industrial water use efficiency has been continuously improved, and the total industrial water use has shown a downward trend. The LMDI model was applied to analyze the driving factors of changes in industrial water use, and the rebound effect was used to quantitatively analyze the actual offset degree of the effectiveness of technological water-saving measures. An empirical analysis of the long-series panel data of Henan Province and its 18 prefecture-level cities from 2014 to 2022 shows that the improvement of industrial water use efficiency has effectively reduced the industrial water use, with the role of this factor being significantly enhanced. Meanwhile, economic scale and industrial structure have also contributed to a significant reduction in industrial water use, though there are large differences in their driving directions across different years and prefecture-level cities. On the whole, industrial water use in Henan Province is in a state of partial rebound effect. Driven by technological progress, the rebound effect value has decreased year by year, and the decoupling phenomenon between industrial water use and industrial added value has become obvious since 2019. Additionally, industrial water use in all 18 provincial-level cities also experience partial rebound, but there are significant regional differences (the rebound effect is relatively low in Xuchang, Pingdingshan and Kaifeng, while it is higher in Luohe, Anyang and Jiyuan). In order to alleviate the pressure of water resources shortage, reduce water environmental pollution and improve the level of industrial water conservation, controlling the rebound effect of industrial water use is essential. In order to achieve the high-quality development of Henan’s industrial economy, suggestions are put forward, including continuing to promote the improvement of industrial water use efficiency, coordinately advancing the formulation of industrial policies and water-saving policies, and building a regionally differentiated collaborative governance system.

In order to investigate the characteristics of microbial community and influencing factors in aquatic bodies during the rainy season in coastal cities, based on Illumina MiSeq sequencing, this paper studied the distribution characteristics of microbial community structure and its correlation with environmental factors in Xixiang River, Shenzhen, and analyzed the effects of environmental factors on microbial community structure. The results show that the nitrogen and phosphorus pollution of Xixiang River is serious, and the water quality of Xixiang River is poor Class V with the risk of eutrophication. There is no significant difference in microbial diversity among sampling sites. At the phylum level, Proteobacteria, Bacteroidetes and Actinobacteria are the dominant species, and Proteobacteria is the first dominant species with a relative abundance of 62.25%-77.28%. At the level of genera, the dominant genera at all sampling points are not completely consistent.  NH+4-N, TDS, NO-3-N, TN and TOC in river water are strongly correlated with the relative abundance of microorganisms, among which the microorganisms are  the most affected by NO-3-N, NH+4-N and TDS. The upstream is affected by the rehydration of reclaimed water from Gushu Sewage Plant, and the bacteria mainly removes organic matter, while the sewage and wastewater in the middle and downstream provide a good living environment for denitrifiers in the water body, resulting in lower nitrate concentration and increased NH+4-N concentration. In the rainy season, the Pearl River estuary is easily supported by the tidal tide of the Lingdingyang Sea area, which is suitable for the growth of Marivita.sp.

After the operation of reservoir impounding and sediment retention, it will cause the downstream river channel to be eroded and cut down, and even change the river pattern, which will have a certain impact on the safety of water-related projects. In order to master the rules of river sediment erosion downstream the reservoir, based on the water and sediment as well as cross-sectional observation data since the application of the Xiaolangdi Reservoir for sediment control, this paper analyzed the cumulative scouring volume, average scouring depth, the maximum water depth and the morphological changes of longitudinal and cross-sectional sections of the lower reaches of the river, and studied the influence of water and sediment and boundary conditions on scouring efficiency. The results show that from October 1999 to October 2022, the downstream river channel has accumulated a total of 2.276 billion m3 of erosion, showing the characteristics of “more erosion at the upper and less at the lower and uneven distribution along the course”. Among them, 70% occurs above Gaocun and 30% below Gaocun. The longitudinal gradient of the river channel increases in the upper and lower sections, while it decreases in the middle section, making the entire longitudinal profile more concave. The river channel has undergone significant widening and downward cutting. The increase in river width is larger at the top and smaller at the bottom, while the increase in water depth is smaller at the top and larger at the bottom. The riverbed has coarsened, with the median particle size increasing by 6% to 79%, and the scouring efficiency of the river channel has significantly decreased. The channel erosion efficiency below the reservoir is closely related to the average flood flow and the cumulative erosion volume in the preceding period. When the accumulative erosion volume above Huayuankou reach reaches 0.6 billion t and the discharge is 2 000 m3/s, the future erosion efficiency will be reduced to -2.9 kg/m3.

This study investigated the spatial-temporal correlation characteristics between groundwater level changes and the intensity of human activities in the western plain of Changji Prefecture from 2000 to 2020. Groundwater depth interpolation was conducted using the ordinary Kriging method, and a Human Activity Intensity (HAI) model was built by integrating multiple indicators such as population density, regional gross domestic product, electricity consumption, and the proportion of construction and cultivated land area. The study employed the centroid migration model and bivariate local spatial autocorrelation analysis to analyze the distribution characteristics and mutual relationships of groundwater depth and human activity intensity in space and time. The results indicate that the overall groundwater level in the study area shows a trend of initial decline follows by an increase. Specifically, the area with groundwater depth greater than 60 meters is accounted for 6.96% in 2000, is increases to 15.51% in 2015, and then decreased to 12.22% iny 2020. The proportion of areas with high human activity intensity increases from 1.03% in 2000 to 3.41% in 2020, and the proportion of areas with medium to high intensity increased from 35.36%  in 2000 to 50.72% in 2020. The correlation analysis reveals a significant positive spatial agglomeration between the spatial-temporal changes of groundwater levels and human activity intensity, especially in the southern region where there is a high concentration of population and economic activities, exhibiting a high-high agglomeration characteristic. Additionally, the centroids of both groundwater depth distribution and human activity intensity are generally migrated from the southeast to the northwest direction.

Through applying technologies such as digital twin, artificial intelligence, mobile communication, and the Internet to enable intelligent management of the Yellow River conservation and governance, the construction of digital twin Yellow River can realize the real-time monitoring, precise early warnings, efficient command and dispatch, and scientific decision-making. In order to provide reference for the digital twin Construction in the Yellow River Basin and nationwide, this paper analyzed the “Three Yellow Rivers” construction system of Yellow River Conservancy Commission (YRCC), particularly the progress and achievements in the construction of digital twin Yellow River. Focusing on the improvement of capabilities of flood control project safety and flood defense in the lower reaches of the Yellow River, this paper proposed the key priorities for digital twin flood control project construction: strengthening the integrated “sky-space-earth-water-project” monitoring and sensor network, establishing data aggregation, governance standards, and update mechanisms, improving the data foundation for flood control projects, accelerating the integration of mathematical models with flood control project data, enhancing the “Three Yellow Rivers” linkage and business applications.

In order to scientifically evaluate the level of high-quality development in the Yellow River Basin and explore its spatial differentiation and influencing factors, and then provide a breakthrough point for policy selection to promote high-quality coordinated development in the Yellow River Basin. Based on the concept of strong sustainable development, using the panel data of 81 prefecture-level cities in the Yellow River Basin from 2011 to 2020, measuring the total factor productivity by the the super-efficiency mixed measurement model to represent the level of high-quality development. The Dagum Gini coefficient and Moran index were employed to examine the spatial-temporal differentiation characteristics, and the spatial Durbin model was utilized to explore the influencing factors and spatial spillover effects. The results show that a) the high-quality development level of the Yellow River Basin shows a fluctuating upward trend. The phased deterioration of efficiency change effects hinders high-quality development. Technological progress effect is the main driving force for high-quality development, but it has already shown signs of weakening. b)The spatial differences in the level of high-quality development in the Yellow River Basin are significant and are increasing. The spatial non-equilibrium of total factor productivity within and among the upstream, midstream and downstream regions has shown a synchronous strengthening trend, and the inter-regional differences are the primary cause of the spatial differences in high-quality development in the basin. c)There is a significant spatial correlation in the level of high-quality development among the prefecture-level cities in the Yellow River Basin. d)The influencing factors like environmental regulation, technological innovation, opening up and urbanization are the stable driving factors for high-quality development, and each of these driving factors has different spatial spillover effects.

With the proposal of the major national strategy of ecological protection and high-quality development in the Yellow River Basin, the research on the Yellow River Basin has become increasingly abundant. This paper mainly reviewed the literature on the high-quality development of the Yellow River Basin from the perspectives of its connotation, level measurement and spatial pattern, constraining factors, multi-dimensional studies, development paths and future directions. The results show that a) the connotation of high-quality development in the Yellow River Basin has been continuously enriched, expanding from economic development to the areas such as regional coordination, rural revitalization and cultural-tourism integration. b) By building different evaluation index systems, the measurements of the high-quality development level of the Yellow River Basin indicate that its spatial pattern is closely related to the administrative hierarchy of cities. c) Constraints to high-quality development in the basin include water resources conflicts, difficulties in industrial structure adjustment and transformation, and insufficient support from scientific and technological innovation capabilities. d) Studies on the high-quality development of the Yellow River Basin have been carried out around dimensions such as industrial structure optimization, technological innovation, urban and city cluster development, and cultural-tourism integration. e) The development paths mainly focus on strengthening ecological environment and industrial layout, promoting economic structure transformation and upgrading, and accelerating institutional and mechanism innovation. f) Future research should focus on interdisciplinary studies, multi-dimensional coupling and coordination research, comprehensive integration of multi-source data methods, and refined policy research based on zoning, grading and classification.

In order to reasonably predict the future water demand situation in Henan Province, considering population scale and economic size, this study built a water demand forecasting method based on social characteristics to project the changes in water demand from 2025 to 2035. The results indicate that a decreasing trend in the total water demand for Henan Province in the future, with the total water demands for 2025, 2030, and 2035 being 21.795 billion m³, 21.198 billion m³, and 20.369 billion m³, respectively, representing reductions of 8.11%, 10.63%, and 14.03% compared to 2020. The proportion of water demand across different sectors is ranked as agriculture>domestic>ecological>industrial, with the share of water demand in agriculture and industry showing a decreasing trend, while the share of water demand in domestic use and ecological needs is increasing. The future population change in Henan Province is relatively small, with urban population growth significantly impacting domestic water demand. There is a negative correlation between economic growth and changes in water demand in Henan Province. The increase in water resources utilization efficiency is a primary factor contributing to the reduction in agricultural and industrial water demand, and is also the reason behind the overall decreasing trend in water demand. The rising level of urbanization and improvements in economic and social living standards are important driving forces behind the changes in future water demand in Henan Province.

Strong earthquakes will greatly reduce the stability of the reservoir slope and easily induce landslide geological disasters. The superposition of earthquake and landslide surge load greatly threatens the safety of the dam body. In order to explore the dynamic response and damage evolution of high arch dam under the superposition of earthquake-landslide surge, this paper took a high arch dam as the object, built  a fine finite element model, determined the calculation model of earthquake-landslide surge load, and analyzed the modal variation law, displacement response characteristics and damage evolution trend of arch dam under multiple working conditions. The results show that the hydrodynamic added mass significantly reduces the wet modal frequency of the arch dam by 18%-23%, and the high-order vibration modes change significantly. When the earthquake and surge are superimposed, the peak displacement Rd of the midpoint of the vault is positively correlated with the peak acceleration of the earthquake and the maximum surge height. When the peak acceleration of the earthquake increases from 0.2g to 0.6g, Rd increases by 89.7%. The maximum displacement time under different working conditions is affected by many factors. The damage degree of dam body varies greatly under different working conditions. The damage of upstream surface is sensitive to the change of surge height. When the surge height is different by 40 m, the weighted damage area ratio of upstream surface to RUWA changes by 27.1%. From condition one to condition three, the weighted damage area ratio of cantilever surface is increased from 9.99% to 25.76% compared with RFWA, and the dam body has penetrating cracks. The damage dissipation energy increases sharply with the increase of load strength.

The integrated control of small watersheds in the Yellow River Basin is an important part of implementing the national strategy of ecological protection and high-quality development in the Yellow River Basin. In view of the control issues caused by the stepped landform, the characteristics of the climate-vegetation transition zone and the vulnerability of the cascading ecology within the basin, the core contradictions of the comprehensive management of small watersheds were analyzed from three perspectives. In terms of the natural system dimension, it was reflected in the dual pressures of resources and the environment faced by the upstream, midstream and downstream respectively. In terms of the technical system dimension, it was reflected in the lag between the rigid constraints of the resources background and the adaptability of management technologies. In the dimension of the management system, it was reflected in the fragmentation of cross-departmental rights and responsibilities, weak cross-regional collaborative capabilities, and the imbalance between protection and development goals. Building upon this analysis, this study proposes pathways to address the systemic challenges in integrated small watershed management: establishing a resilience enhancement pathway centered on “soil and water conservation, resources efficiency improvement, ecological restoration and climate adaptation”; developing a dynamic intelligent zoning and digital twin decision support system; devising a cross-domain authority-responsibility integration mechanism and a trilateral compensation system for water quality, quantity and sediment; and creating an eco-industrial value-added chain with ecological credit conversion channels. These proposals provide scientific underpinnings for overcoming the systemic challenges in integrated small watershed management within the Yellow River Basin and exploring pathways to improve its quality and efficiency.

The riparian vegetation buffer is an interlocking aquatic and terrestrial ecosystem, which plays an important role in soil carbon sequestration. Soil samples were collected from five plant communities, such as Imperata cylindrica, Artemisia argyi, Tamarix chinensis, Cynodon dactylon and Alopecurus aequalis in the riparian zone of the lower Yellow River. The soil organic carbon (SOC) of the samples were measured and analyzed. The correlation analysis, principal component analysis, linear regression, and linear regression analysis were used to analyze the correlation between soil SOC content is soil physicochemical properties, and the important factors affecting soil organic carbon content were explored in riparian zones. The results show that the soil SOC content was different in vegetation types, and the range of variation  is 0.25-6.73 g/kg, the mean value is 1.91 g/kg. The soil SOC content of Cynodon dactylon and Alopecurus aequalis at 0-30 cm each soil layer is significantly higher than that of  Imperata cylindrica, Artemisia argyi and Tamarix chinensis. The soil SOC content in the riparian zone is positively correlated with the content of soil water content, soil available potassium, soil available phosphorus, available nitrogen, soil clay and silt, is significantly negatively correlated with soil sand content. It can be seen that soil bulk density, soil moisture content, soil available potassium and available nitrogen contribute 92.3% to the variation of 0-30 cm soil organic carbon content.

In order to reveal the decoupling effects of carbon emissions in urban agglomerations within the Yellow River Basin (YRB) and identify their key influencing factors, this study aimed to provide a scientific basis for formulating effective carbon reduction policies, and advanced the national strategy of ecological protection and high-quality development in the YRB. In this study, the Tapio decoupling index model and LMDI model were comprehensively adopted to analyze the decoupling status of carbon emissions of urban agglomerations in the YRB and their temporal and spatial changes, identify the key factors affecting the carbon decoupling of urban agglomerations, and propose suggestions for the green and low-carbon development of urban agglomerations. The results show that: a) from 2001 to 2020, the carbon emissions of all urban agglomerations in the YRB are on the rise, and the carbon emissions from high to low are Shandong Peninsula urban agglomerations, Central Plains urban agglomerations, Guanzhong Plain urban agglomerations, Lan-Xi urban agglomerations, Jinzhong urban agglomerations, Hu-Bao-E-Yu urban agglomerations and Ningxia Yanhuang urban agglomerations. b) The type of carbon decoupling in urban agglomerations in the YRB is mainly weak, but the carbon decoupling index generally increases from time to time. Therefore, according to the existing economic development pattern, the economic development of urban agglomerations in the YRB is difficult to get rid of the dependence on fossil energy in the short term, and the road to carbon emission reduction is still a long way to go. c) Energy intensity, per capita GDP, technological level, and the population carrying capacity of the real economy are the main factors influencing the decoupling of carbon emissions in urban agglomerations within the YRB. Improving energy efficiency and technological capability, optimizing industrial structure, and enhancing the population carrying capacity of the real economy can effectively promote an ideal decoupling between economic growth and carbon emissions.

Reservoirs are significant emission sources of methane (CH4), and methanotrophs can mitigate their emissions by oxidizing CH4. In order to comprehend the community characteristics, gene abundance, and assembly processes of methanotrophs in the reservoir sediments of the Huangshui River Basin on the Qinghai-Tibet Plateau, surface sediments from eight reservoirs were collected respectively in May 2023 (dry season) and August 2023 (wet season). Based on real-time fluorescence quantitative PCR and sequencing technology of the functional gene pmoA of methanotrophs, the abundance, community composition, and the diversity of methanotrophs were analyzed, and the assembly processes of methanotrophs were analyzed using the neutral community model. The results show that at the phylum level, the methanotrophs in the Huangshui River Basin are mainly composed of Proteobacteria. At the genus level, they are mainly constituted by Methylocystis and Methylobacter. The α diversity is manifested as being higher in the wet season than in the dry season, while the β diversity is not significant. The abundance of the pmoA gene in the dry season is significantly higher than that in the wet season. The assembly processes of methanotrophs in reservoir sediments are dominated by stochastic processes, among which drift is the most powerful. Temperature, total nitrogen, and pH are the main factors influencing the methanotroph community, and sediment pH is the dominant environmental factor for the abundance of the pmoA gene.

In order to explore the realistic path of new quality productivity to empower urban economic resilience in the Yellow River Basin, and provide a reference for the implementation of the major national strategy of ecological protection and high-quality development in the Yellow River Basin, based on the panel data of 99 sample cities in the nine provinces (autonomous regions) of the Yellow River Basin from 2011 to 2022, the entropy method was adopted to measure the level of new quality productivity and the urban economic resilience index. Moreover, the two-way fixed effect model and the mediating mechanism model were used to empirically analyze the impact of new quality productivity on the urban economic resilience of the Yellow River Basin and its mechanism of action. The results show that on the whole, the new quality productivity has a significant role in promoting the urban economic resilience of the Yellow River Basin, and its internal mechanism is to promote the upgrading of industrial structure and improve the level of infrastructure construction. The effect of new quality productivity is heterogeneous, especially in the middle reaches of the Yellow River Basin, small-scale and high-intensity environmental regulation cities are stronger. Some policy suggestions are proposed, such as actively cultivating and developing new quality productive forces, promoting the upgrading of industrial structure in the Yellow River Basin and implementing differentiated regional development strategies.

This paper systematically elaborated on the transformation of Yellow River research from the traditional “Three Yellow Rivers” paradigm (Prototype Yellow River, Model Yellow River, Digital Yellow River) to the new paradigm of “Authentic Yellow River”. Over the past 20 years, the construction of the “Three Yellow Rivers” elevated the scientific research and technological development of the Yellow River to a higher level, playing a crucial role in the governance and protection of the Yellow River. With the breakthrough of artificial intelligence technology, this paper proposed the concept of “Authentic Yellow River”, which took the real Yellow River as the foundation, combined artificial intelligence technology to build dynamic scenarios, achieved instant question-and-answer and precise analysis, and broke through the bottleneck of traditional research paradigms. Through the case of the Beijing-Hangzhou Grand Canal crossing the Yellow River project, it demonstrates the application logic of “Authentic Yellow River” in the governance and protection of the Yellow River, pointing out that the “Authentic Yellow River” provides methodological innovation for Yellow River research through the integration of “artificial intelligence + scenario”.

In order to curb the severe wind erosion in the Great Bend Region of the Yellow River and reduce aeolian sediment input, this study investigated the aerodynamic mechanisms of sand transport and clarified the underlying principles and scientific issues of sand arrestation. Turbulent velocity distribution formulas incorporating basal roughness and concentration distribution models typical of desert environments were applied to analyze the wind speed, sediment concentration, and sand flux during dust events. The validation against field observations and theoretical calculations reveals a clear inverse relationship between sediment flux and surface roughness, indicating that sand-control engineering effectively weakens both wind erosion intensity and sand production under strong winds. Further calculations using threshold velocity formulas that account for drag effects show that the entrainment threshold of sand particles is positively related to surface roughness. Natural desert surfaces with low roughness exhibit lower threshold velocities, intensifying wind erosion, whereas the installation of sand barriers increases surface roughness, which simultaneously raises the entrainment threshold and reduces near-surface wind velocity. Under this dual effect-enhanced threshold and diminished near-ground wind speed-the activity of aeolian sand is significantly suppressed. This demonstrates that the principle of sand-control engineering lies in increasing surface resistance to restrain sand mobility. In contrast, smooth-surfaced control structures hinder particle deposition atop dunes, thereby suppressing dune migration and expansion. Based on the principle of minimum energy dissipation in natural systems, we have proposed aligning sand-control layouts along the most stable dune ridge lines. Meanwhile, the overall configuration of control structures, optimized under the principle of minimum resistance, not only modifies near-surface wind fields but also exhibits multi-directional adaptability through its crest morphology.

As a result of the heavy rainfall, flooding remains in the later stages of the rainfall and may continue to cause harm and impact. In order to accurately predict the depth and duration of urban flooding and waterlogging, the RF-LSTM model was proposed to address the difficulty of simulating floods in the later stages of the heavy rainfall. Based on the SWMM model-simulated flood data in Zhengzhou City, China, the flood depths at three representative flooded points were simulated by using the proposed model, and the flooding process caused by rainfall under different recurrence periods was predicted. The results show that compared to the single LSTM model, the simulation accuracy of the RF-LSTM model has been improved, verifying the applicability of the model in flood simulation. The growth rates of flood duration and the maximum flood depth at flooded points are the highest under the 1-2 a return period, therefore the existing drainage system should be renovated or redesigned.

Integrated hydro-wind-photovoltaic development in river basins represents a crucial initiative for implementing low-carbon and green development principles. However, intensified climate change currently leads to increased volatility in renewable energy outputs, heightened uncertainty regarding future power generation potential, and challenges in accurately predicting the eco-environmental benefits of integrated energy bases. These factors pose significant obstacles to the efficient utilization of basin clean energy resources and ecological conservation. Therefore, the Cihaxia integrated water-wind-photovoltaic base in the upper reaches of the Yellow River was taken as the research object. The quantile mapping method, CNN-LSTM-Attention deep learning prediction model and improved theoretical output calculation method of water-wind-photovoltaic were used to screen high-precision future climate model data applicable to the study area. The daily average output of water-wind-photovoltaic power and its multi-time scale complementarity during the planned operation period of the integrated base (2035-2065) were predicted, and the ecological and environmental benefits of the integrated base were estimated. The key findings are: a) From 2035 to 2065, the predicted average annual power generation is 10.13 billion kW·h for hydropower, 1.187 billion kW·h for wind power, and 43.785 billion kW·h for photovoltaic power. Under four SSP scenarios, the average daily inflow to the Cihaxia Hydropower Station is increased by 0.97, 1.74, 1.25, and 1.99 m3/s respectively. The average daily theoretical hydropower output is increased by an average of 2.23 MW, while wind and photovoltaic outputs experience slight average decreases of 0.29 MW and 0.80 MW respectively. b) The annual power generation correlation coefficients are -0.22 for hydro-wind, 0.18 for hydro-photovoltaic, -0.10 for wind-photovoltaic and -0.03 for hydro-wind-photovoltaic combined. The hydro-wind combination exhibits stronger inter-annual complementarity, with the strongest intra-annual complementarity occurring during winter. c) The annual power generation from the integrated base can potentially replace 55.102 billion kW·h of coal-fired power, reducing carbon emissions by 48 million tonnes per year. Furthermore, replacing coal power with wind and photovoltaic generation saves approximately 108 million m3 of water annually. Additionally, the base is projected to reduce the average annual potential evapotranspiration of the underlying surface by 284.56 mm and increase the average annual Net Primary Productivity (NPP) of the ecosystem by 97.04 gC/m2.

In order to accurately identify the leakage risks of Tanshan Reservoir under complex conditions, this study proposed a multi-method integrated leakage detection framework. It combined various detection techniques, including the simulated flow field method, geophysical methods (such as high-density resistivity, transient electromagnetic, and ground-penetrating radar), and tracer techniques, along with geological surveys for comprehensive analysis. The study results indicate that the leakage mainly occurs through fractures in the dam foundation rock from the reservoir interior to the downstream, eventually emerging at the stone masonry sidewall of the energy dissipation basin. The main leakage channel lies within the completely weathered limestone layer beneath the dam body. The integrated leakage detection approach proposed in this study-revealing the overall leakage distribution through the simulated flow field method, identifying water-rich and high-moisture zones using geophysical methods, and determining leakage sources and paths with tracer techniques-can provide a reference for detecting similar complex leakages and offers technical support for emergency response and reinforcement efforts.

Currently, the frequency and intensity of extreme climate events have significantly increased, making the safety risks of reservoir overtopping and dam breaches more prominent. This study investigated the overtopping-induced breach process through a combination of generalized flume experiments and mathematical theoretical analysis. The results indicate that compared to coarser-grained dam materials, fine-grained dam bodies exhibit slower seepage rates and weaker permeability. As the upstream water level rises, the dam material gradually becomes saturated, with the saturation rate accelerating as the water level rises faster. The initiation of surface particle movement is identified as the key factor triggering erosion damage, while the upstream retreat of the scarp is critical to the occurrence of a breach. Based on the breach process, a geometric generalization model for the longitudinal and lateral development of the breach is proposed. Furthermore, by applying calculus principles, the flood discharge process during overtopping failure is analyzed, demonstrating that the flood release process can be regarded as the cumulative effect of various breach-influencing factors. Mathematical expressions for breach development and discharge variation at different stages are also derived. 

In order to effectively integrate raster-based remote sensing monitoring results of soil erosion with plot-based (land use patches) soil and water conservation management, and to enhance the application of dynamic soil erosion monitoring outcomes in conservation practices, this study proposed a method and standard for categorizing land use patches based on practical experience in remote sensing image interpretation. Using land use patches derived from dynamic soil erosion monitoring as the basic evaluation unit and raster-based monitoring results as the foundation, land use patches were classified into five types of non-urgent treatment, desirable treatment, preventive protection, industry management, and non-soil erosion. The categorization criteria were determined based on topographic slope, vegetation cover, proportion of soil erosion area, and whether the responsible entity for soil erosion prevention and control was clearly defined. In Yunyang County, where hydraulic erosion dominates, the application results align with local conditions and meet the requirements of soil and water conservation management.For regions with mixed wind and hydraulic erosion, areas dominated by other erosion types, or locations with special conservation management needs, the categorization methods and standards can be adjusted accordingly during land use patch classification.

Due to its complex terrain and special climate, Qinghai Province is prone to sudden and destructive mountain floods. In order to provide a basis for the monitoring and prevention of mountain floods in the region, taking the Longyangxia-Jishixia section of the upper reaches of the Yellow River in Qinghai Province, where mountain flood disasters were relatively severe, as the study area, 10 influencing factors of mountain flood disasters were initially selected. Based on the data of 115 historical flood disaster points in the study area from 1958 to 2000, four factors with strong correlations were eliminated through Pearson correlation test. The remaining six influencing factors were classified. GIS spatial analysis technology was used to obtain the classified data of the six influencing factors. The entropy index method was adopted to calculate the weights of each factor and identify the main disaster-causing factors. The research results show that elevation, annual precipitation, terrain roughness, NDVI, distance from the river course, and aspect are the disaster-causing factors of mountain floods in the study area (with the weights of 0.571 6, 0.144 8, 0.107 9, 0.094 8, 0.071 9 and 0.009 0 respectively), among which, elevation, annual precipitation and terrain roughness are the main disaster-causing factors. According to the classification of the main disaster-causing factors, 91.30% of the historical mountain flood disasters in the study area occur in the areas with an elevation lower than 3 091 m, 99.14% occur in the areas with an annual precipitation greater than 317 mm, and 98.26% occur in the areas with a terrain roughness less than 1.10.

In order to explore the impact of green technology innovation in the cities of the Yellow River Basin on carbon emission intensity and its spatial spillover effects, and to provide references for promoting ecological protection and high-quality green low-carbon transformation in the Yellow River Basin, based on the panel data from 79 prefecture-level cities in the Yellow River Basin from 2005 to 2021, the study measured the level of green technology innovation and carbon emission intensity in these cities, applied a spatial Durbin model for regression analysis and robustness testing, and analyzed the impact of green technology innovation on carbon emission intensity along with its spatial spillover effects, regional heterogeneity, and resources endowment heterogeneity. The results indicate that a) the level of green technology innovation in the cities of the Yellow River Basin has steadily improved while carbon emission intensity has significantly decreased during the study period, with both showing significant spatial inequity. b) Urban green technology innovation can effectively reduce carbon emission intensity, and the indirect carbon reduction effect on neighboring cities is greater than the direct carbon reduction effect in the city itself. c) The carbon reduction effects of urban green technology innovation exhibit regional heterogeneity and resources endowment heterogeneity, with downstream cities showing significant spatial spillover effects from green technology innovation while upstream cities have not yet formed significant spatial spillover effects. Resources-based cities have a greater direct effect of green technology innovation, while non-resources-based cities have a larger indirect effect. d) The spatial spillover effect of urban green technology innovation on carbon emission intensity shows an N-shaped variation with increasing geographical distance, with the optimal geographical distance being 600 km. Recommendations: a) Continue to increase support for green technology innovation in the Yellow River Basin, enhancing the training and introduction of talents for green technology innovation to improve innovation capabilities. b) Build a collaborative green technology innovation system across multiple cities and improve the mechanisms for green technology diffusion, etc., to fully leverage the spatial spillover effects of green technology innovation from central cities, downstream cities, and non-resources-based cities, enabling green technology innovation to play a greater role in overall carbon reduction and emission reduction in the basin.

In order to solve the issue of inconsistent spatial-temporal correlation of multiple disaster causing factors, a method for building a knowledge graph of multiple disaster types in the upper reaches of the Yellow River based on multi-source information fusion was proposed. Through data cleaning, entity recognition and relationship extraction, structured and unstructured data such as remote sensing images, meteorological data and geological data are integrated to build the core concept of ontology definition and define six types of semantic associations such as causal relationship, spatial-temporal association and participation relationship. Knowledge storage and dynamic reasoning were carried out by using the Neo4j graph database, and a multi-source data mapping mechanism was designed to support multi-dimensional queries of entities such as disaster events, trigger factors and derivative disasters, as well as their relationships. Combining Bayesian network probabilistic reasoning and graph topology analysis algorithms, the propagation paths of disaster chains were quantitatively evaluated. Based on the landslide event in a certain area of the upper reaches of the Yellow River in 2023, the potential relationship chain between landslides and debris flows can be discovered through the inference function of the graph database. Compared to traditional databases, knowledge graphs have faster and more accurate information queries.

Concrete face rockfill dams (CFRDs) are widely utilized in water conservancy and hydropower engineering due to their excellent durability, strong impermeability, and cost-effectiveness. However, their seismic safety during service has raised significant concerns. In this study, a concrete face rockfill dam located on the upper reaches of the Yellow River was selected as the research object. Based on the structural characteristics of the dam and geological conditions, a three-dimensional numerical model was established to simulate the dam’s dynamic response under seismic action by integrating the Duncan-Chang model and the concrete damage plasticity constitutive model. Through this model, the dynamic responses of the dam under varying seismic intensities were analyzed, revealing the evolution of stress distribution and structural deformation characteristics. The results indicate that the critical zones prone to transverse damage under seismic action are located at two-fifths of the total height of the concrete face and the toe slab. Furthermore, based on the computational results, the correlation between failure variables and structural damage patterns under seismic action was investigated, and vulnerability curves under different seismic intensity levels were plotted. The findings demonstrate that with the increase of  seismic intensity, the vulnerability curves shows a rightward shift trend, and the failure probability of the dam body rises significantly.

In order to explore the water-saving efficiency of floating balls on large water bodies in the arid regions of northwest China, this study was based on a water surface shading experiment conducted in Kunyu City of Hetian Prefecture. The experiment utilized two Φ20 standard evaporation pans, six 20 m² evaporation ponds, and one 3 000 m² evaporation pond as evaporation devices, with the covering material being HDPE black weighted floating balls with a diameter of 10 cm. The study mainly analyzed the differences in annual evaporation between the Φ20 standard evaporation pans and the 20 m² evaporation ponds and calculated the evaporation conversion coefficient between the two. Additionally, by analyzing the stability of the floating balls in the 3 000 m² evaporation pond under different wind speed conditions, the relationship between floating ball coverage area and water-saving rate was explored. The study primarily reveals that the main cause of the evaporation differences between the Φ20 standard evaporation pans and the 20 m² evaporation ponds lies in the variation in internal water temperature and the heat supply differences caused by the wall effects. The evaporation conversion coefficients are 0.587 during the non-freezing period and 0.282 during the freezing period. When the wind speed exceeds 4 m/s, the blank water area under the floating ball coverage shows a nonlinear positive correlation with wind speed, expressed by the equation y=0.070 68x1.913 69, the floating ball coverage rate and water-saving rate decrease as wind speed increases.