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.

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.

混凝土坝；变形监测；变形预测；LSTM；TCN

For the landslide disaster in the section from Chada Village to Songba Village in the upper reaches of the Yellow River, a logistic regression model was established to conduct a systematic analysis of its susceptibility. Based on the analysis of the disaster-pregnant environment, eight influencing factors, including Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), slope, aspect, precipitation, temperature, ground temperature and SBAS-InSAR time-series surface deformation data were selected to build an indicator system for landslide susceptibility analysis. Subsequently, the binary logistic regression model was utilized to delve into the quantitative relationships between these influencing factors and the occurrence of landslides. In order to further refine the research, spatial consistency processing was performed on local influencing factors, and 362 landslide grid points, covering a total area of approximately 2.855 km2 were established. The regression coefficients and regression equations for each influencing factor were then determined by using the binary logistic regression model. Finally, the occurrence probability of landslide hazards in the study area was derived by using the probabilistic implicit function. Based on the results obtained from the logistic regression model, the spatial distribution patterns of landslide susceptibility in the study area were discussed. It is found that high-risk areas are mainly concentrated in regions with steep terrain, poor vegetation cover and harsh meteorological conditions.

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.

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 view of the issues existing in the current research on runoff evolution in the Aksu River basin, such as a single time-scale and insufficient consideration of the sensitivity of sequence length, a variety of methods such as Mann-Kendall trend test, Theil-Sen Median trend estimation, Pettitt mutation test and wavelet analysis were adopted to systematically study the runoff evolution laws of the main and tributary streams in this basin at different time scales such as inter-annual, intra-annual and flood events. The results show that the annual runoff of the Taushgan River, Aksu New River and Tailan River increase gradually, while the change of the Kumarak River is not obvious. The periodic oscillation of the four rivers is the most obvious on the 35-year, 55-year, 35-year and 55-year timescale, and the corresponding periods are 21-23 years, 32-35 years, 19-25 years and 36 years respectively. The annual distribution of runoff in the Aksu River basin is uneven, the runoff is larger in summer and autumn, and smaller in winter and spring, and the concentrated period is from June to August. The annual maximum flood peak discharge of the mainstream and tributaries fluctuate greatly, mainly concentrate in July and August. The key periods for flood control are July-September, April-August, July-August and July respectively. The tendency, mutability and periodicity analysis results of the runoff in the Aksu River watershed are all affected by the length of time series.

The evaluation of landslide susceptibility is of great significance for regional disaster prevention and mitigation. In view of the issues that the single classifier in the landslide susceptibility evaluation using machine learning algorithms had poor precision, and the selection of negative samples of landslides was relatively arbitrary, a landslide susceptibility evaluation model was proposed, which combined the selection method of negative samples of landslides based on the information quantity method with machine learning integration algorithms. Taking the section from Lijiaxia to Gongboxia in the upper reaches of the Yellow River as the study area, 13 evaluation factors such as elevation, slope gradient and precipitation were selected as the evaluation factors for landslide occurrence. Three selection methods for negative samples of landslides, namely buffer zone, low slope gradient and information quantity were adopted. By building the landslide susceptibility evaluation models of the classification and regression tree (CART) and three integrated learning algorithms (Bagging, Boosting, and random forest), the performance of the evaluation models under different integrated learning algorithms and different selection methods for negative samples of landslides was analyzed. The results show that the integrated learning algorithm can significantly improve the performance of the single base classifier, and the improvement effect of the Boosting algorithm is the most prominent. The selection method of negative samples based on the information quantity takes most of the evaluation factors into full consideration, and the reliability of the model is higher.

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.

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.

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.

In order to clarify the groundwater quality evolution characteristics and the sources of groundwater chemical components in the saline irrigation area in western Jilin, the groundwater quality monitoring data from 2012-2014 and 2019-2020 were selected, and mathematical statistics, graphical method, entropy-weighted Bayesian, factor analysis, and Absolute Factor Score-Multiple Linear Regression (APCS-MLR) model were used to carry out the research on groundwater chemical characteristics, water quality evaluation and hydrochemical components traceability analysis in western Jilin irrigation area. The results show that:Fe, F, Mn and tri-nitrogen compounds in the irrigation area and its surrounding areas are seriously exceeded. The water chemistry type is mainly weak alkaline water of the HCO^-₃-Na⁺-Ca²⁺ type, affected by the dissolution and filtration of rocks and evaporation-crystallization, and the long-term irrigation and drainage salt-washing improves salinization of the irrigation area and its surrounding areas. During the research period, the salinization of the irrigation area and its surrounding areas diving Ⅳ,Ⅴ water proportion increased by a total of 5.3 percentage points, Ⅳ,Ⅴ water proportion in the pressurized water decreased by a total of 4.0 percentage points. Dissolved filtration-secondary enrichment effect on groundwater quality is the most significant, so that the concentration of soluble ions in the water, TDS, total hardness and other components of the water increased. Saline and alkaline development of paddy field irrigation area leads to increased regional agricultural activities on the impact of the chemical components of groundwater.

As a supporting project of the Xiaolangdi Key Water Control Works, the Xixiayuan Counter-Regulation Reservoir suffers significant loss of effective storage capacity, directly affecting the comprehensive benefits of the reservoir. Accurate calculation of floodplain and main channel evolution is crucial for the operation and management of the reservoir. On the basis of a one-dimensional mathematical model of reservoir water and sediment, this study proposed a calculation method for floodplain and main channel evolution in the Xixiayuan Reservoir, drawing on the calculation method for river floodplain and main channel evolution. The calculation method was applied to the floodplain and main channel evolution of Xixiayuan Reservoir in 2018 and 2020. The reliable results in the reservoir area confirmed the feasibility of the method.

The upstream mainstream dam group of the Yellow River is located in the transitional zone between the Qinghai Tibet Plateau and the Loess Plateau, requiring large-scale landslide hazard identification. Taking the Longyang Gorge-Lijia Gorge dam group section as the research area, SBAS-InSAR method was used to form small baseline interferograms, the amplitude deviation index and coherence coefficient method were used to select PS points(Permanent Scatterer), and the KS statistical test method was used to select DS points（Pistributed Scatterer）. The surface deformation rate and deformation amount in the study area were obtained  through time-series deformation calculation. Results show that the deformation rate in the study area ranges from -80 to 50 mm/a, with most areas having a deformation rate of -10 to 10 mm/a, indicating a relatively stable state. Combining slope and deformation rate analysis, identifies 13 landslide hazard points in the study area. The maximum deformation rate at the rear edge of the landslide below the top of the Guobu slope is 80 mm/a, which is only 1 km away from the right bank reservoir area of the Laxiwa hydropower station, and the deformation at the top of the slope is increasing year by year. The cumulative deformation value from 2021 to 2024 reaches 300 mm.

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.

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 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.

The reservoir-dam group on the main stream of the upper reaches of the Yellow River serves as a critical flood control barrier. While delivering comprehensive benefits in flood mitigation, water supply, power generation and ecological conservation, it faces complex and dynamic natural disaster risks. By analyzing the characteristics of natural disaster risks in this region, the paper discussed the main natural disaster risks faced by reservoir and dam groups and the challenges of risk management and response. The results indicate that natural disasters in the upper reaches of the Yellow River dam-reservoir system exhibit compound and systemic features, where extreme events such as mega-earthquakes, giant landslides, and super-standard floods may trigger disaster chains. Disaster prevention and mitigation are confronted with challenges such as insufficient identification of multi-source dynamic risks, the need to improve the joint regulation and emergency response mechanism, and the lagging adaptability to climate change.

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.

In order to explore the influencing factors and spatial differences of the green development of agriculture (GDA) in the Yellow River Basin (YRB), and to provide theoretical support and decision-making basis for the high-quality development of agriculture in the YRB, an evaluation index system for the level of GDA was established, which included three dimensions of high efficiency of development, ecological friendliness and resources conservation. Based on the panel data of 68 prefecture-level cities in the YRB from 2011 to 2021, the global entropy method (GEM) was applied to assess the level of GDA in the YRB, and the Moran index was used to analyze the spatial and temporal patterns of its differentiation. On this basis, six indicators were set up in economic, social and natural aspects, including industrial structure, planting structure, industrialization level, urbanization level, topographic relief and annual precipitation, and a multi-scale geographically weighted regression (MGWR) model was used to empirically analyze the influencing factors of the GDA in the YRB and its spatial differences. The results show that a) the level of GDA in the YRB shows an upward trend from 2011 to 2021, but the overall level at the end of the study period is still low, with the level of GDA in the lower reaches significantly higher than that in the middle and upper reaches, the development of efficient dimensions in the upper reaches and lower reaches showing a clear upward trend, and the ecologically friendly and resources-saving dimensions showing a smaller increase in the study period. b) There is an obvious positive spatial correlation between the level of GDA in the YRB, with a relatively large number of H-H agglomeration and L-L agglomeration prefecture-level cities. c) The spatial heterogeneity of the factors influencing the GDA is significant and the intensity of influence is different, among which the spatial differences in the influence of industrial structure, planting structure, industrialization level and topographic relief are small and the spatial difference in the influence of urbanization and annual precipitation is large. Industrial structure, industrialization level and topographic relief have negative effects, while planting structure, urbanization level and annual precipitation have positive effects. The intensity of influence is topographic relief > industrialization level > urbanization level > industrial structure > planting structure > annual precipitation. Some suggestions are put forward, such as changing the concept of agricultural development, strengthening regional cooperation and implementing differentiated agricultural green development strategies.

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.

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. 

Investigating the impact of vegetation restoration on changes in runoff generation patterns is essential for understanding watershed hydrology and soil erosion processes in a changing environment. This study examined the influence of vegetation restoration to the runoff generation patterns in a typical small watershed in the gullied rolling loess area through stratified insitu doublering infiltration tests and the analysis of the measured flood process before and after vegetation restoration. The results show that a) vegetation restoration has significantly enhanced soil infiltration capacity, resulting in varying degrees of increase in the infiltration rate at different depths (0-60 cm). Vegetation restoration has increased the difference in infiltration capacity between the surface soil and the 10 cm layer. b) Soil infiltration capacity increases significantly with a decrease in soil bulk density and an increase in porosity. Vegetation restoration can affect soil infiltration and runoff generation processes by reducing soil bulk density and increasing porosity. c) After vegetation restoration, the peak flow, runoff depth, runoff coefficient and surface runoff ratio of storm events in the small watershed are decreased, while subsurface runoff is increased. The runoff generation pattern is shifted from excessive infiltration to saturated runoff. These findings indicate that vegetation restoration has an impact on the runoff generation patterns and provide a scientific basis for further studying soil hydrological processes in the Loess Plateau.

In order to explore the development level and regional differences of new-quality-productivity in the nine provinces (regions) of the Yellow River Basin and provide a reference for the high-quality-development of new-quality-productivity in the Yellow River Basin, this paper built an evaluation index system of new-quality-productivity development level from the three dimensions of new workers, new labor objects and new labor materials. Based on the panel data from 2013 to 2022, the entropy value method was used to measure the development level of new-quality-productivity in the nine provinces (regions) of the Yellow River Basin, and the nuclear density estimation, σ convergence test and β convergence test, spatial correlation analysis and diagnosis of main obstacle factors were carried out. The results show that a) from 2013 to 2022, the development level of new-quality-productivity in the nine provinces (regions) of the Yellow River Basin shows an increasing trend year by year, among which Qinghai and Gansu have relatively low development level of new-quality-productivity, while Shandong and Shaanxi have relatively high development level of new-quality-productivity. b) There are obvious differences in the development level of new-quality-productivity in the upper, middle and lower reaches of the Yellow River, among which the lower reaches of the Yellow River is always much higher than that in the upper and middle reaches, and the upper reaches of the Yellow River is always lower than that in the middle and lower reaches, but the difference among provinces (regions) is decreasing, the phenomenon of multi-polarization is weakening, and there is a “catch-up effect” and the development of new-quality-productivity in each province (region) will be affected by neighboring provinces (regions). c) From the three dimensions of the development of new-quality-productivity, the new labor object dimension is the “weak side” that needs to be strengthened in the development of new-quality-productivity in the Yellow River Basin. From the perspective of the evaluation indicators of new-quality-productivity, forest coverage rate, R&D personnel full-time equivalent, the number of authorized domestic invention patents, the added value of tertiary industry, traffic network density, the revenue proportion of software and information technology service industry, industrial structure, and environmental protection efforts are the main factors restricting the development of new-quality-productivity in the nine provinces (regions) of the Yellow River Basin. At the end of the paper, some suggestions are put forward, such as attaching importance to personnel training, strengthening environmental protection, encouraging scientific and technological innovation and accelerating industrial upgrading.

In order to solve the coordination problem between agricultural green development and ecological protection in the Yellow River Basin and identify the reasons behind the contradiction between agricultural production and ecological environment protection, the paper took the nine provinces (regions) in the Yellow River Basin as the research object to build an evaluation index system for agricultural green development and ecological protection was constructed. The entropy method was used to measure the agricultural green development index and ecological protection index of each province (region) from 2006 to 2020. The fusion coordination model was used to calculate the coupling coordination between agricultural green development and ecological protection, and the obstacle degree model and grey correlation degree model were used to diagnose and analyze the obstacles and driving factors of coupling coordination development. The results show that during the research period, the coupling coordination degree between agricultural green development and ecological protection in the Yellow River Basin shows an overall upward trend, with a spatial pattern of “upstream>downstream>midstream”. There is still a significant gap between each province (region) and high-quality coordinated development. The coordinated development of agricultural green development and ecological protection is mainly hindered by the dimensions of output efficiency and resources endowment. The internal driving factors that affect the degree of coupling and coordination between agricultural green development and ecological protection in the Yellow River Basin, in descending order of correlation, are the proportion of nature reserves area, the multiple cropping index of arable land, forest coverage, grain yield per unit area, per capita arable land area and fertilizer application intensity. Therefore, it is necessary to strengthen the overall coordination of agricultural industry development planning in the upper, middle, and lower reaches of the Yellow River Basin, accelerate the flow of factors such as technology, capital, and labor between regions, and formulate differentiated agricultural development and ecological civilization construction strategies.

为了分析混凝土面板挤压式边墙的物理力学性能,结合黄河公伯峡、积石峡水电站挤压墙施工质量检测,通过配合比验证试验和现场检测结果,指出:当挤压墙混凝土湿密度在2.15～2.20 g/cm3范围内,即干密度为2.04～2.09 g/cm3、弹性模量在4 000～6 000 MPa时,能够满足施工要求和坝体渗透要求.

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.

In order to do a good job in the governance and management of digital twin Yellow River data, and consolidate the data foundation of the digital twin Yellow River, this paper summarized and analyzed the current status of Yellow River data storage and management. Addressing the main existing issues, it conducted research on the data governance system, computing power assurance system, security assurance measures and other aspects. According to the principle of “one data from one source by one pathway, and one data for multiple uses”, it established the data governance system for the digital twin Yellow River. In accordance with the principle of “integrating what has been built, coordinating what is under construction, and standardizing new construction”, a “1+X+1” digital twin Yellow River computing power support system was built, and the disaster recovery backup system and data security protection measures were improved. It also intended to improve disaster recovery system and strengthen data security protection. Using the reservoir data about the region of the Yellow River Basin as a case study, data governance practice was carried out and the governance achievement data covering 3 673 reservoirs in the Yellow River Basin was formed, demonstrating the feasibility of data governance for the digital twin Yellow River.

Facing the national strategic requirements of improving the ecological environment quality and high-quality development in the Yellow River Basin, in order to promote the conservation and intensive utilization of water resources, this paper constructed 15 evaluation indexes including water resources system, socio-economic system and ecological environment system. The entropy weight method was used to evaluate the water resources conservation and intensive utilization level of 50 cities in the upper and middle reaches of the Yellow River Basin from 2018 to 2021, and identified the main factors affecting the water resources conservation and intensive utilization level of cities in the study area by using obstacle model. The results show that the overall level of conservation and intensive utilization of water resources in the study area is on the rise, from 0.505 in 2018 to 0.553 in 2021. The utilization rate of reclaimed water, the proportion of agricultural water use and the sewage treatment rate are the main obstacle factors affecting the improvement of the level of conservation and intensive utilization of water resources in the study area. Based on this analysis, the countermeasures for the conservation and intensive utilization of water resources were put forward from four aspects of water saving, social and economic development, ecological environment protection and unconventional water resources utilization.