An objective and accurate assessment of the water-sediment regime of the Yellow River is an important prerequisite for formulating strategies for Yellow River governance and major engineering measures. Based on observed data of runoff, sediment, precipitation, and vegetation since 1934, this study analyzed the variation characteristics of sediment inflow into the Yellow River from the Loess Plateau, rainfall-sediment yield relationship, sediment concentration during flood seasons, sediment inflow coefficient, and sediment particle size. The results show that the sediment inflow into the Yellow River from the Loess Plateau can be generally divided into three periods since 1934: 1934-1969, 1970-2007, and 2008-2025. The period 1934-1969 was generally a natural period, during which the impacts of human-induced land surface degradation and conservation offset each other in the last decade. From 1970 to 2007, although sediment inflow decreased by 52%, the water-sediment relationship during flood seasons remained unfavorable, and sediment reduction in most years was characterized as “unhealthy”. Since 2008, large numbers of early-built check dams and reservoirs have gradually lost their flood retention and sediment trapping capacity. Meanwhile, vegetation restoration and terrace construction have achieved substantial sediment reduction at the source. As a result, the annual sediment inflow into the Yellow River has decreased to 255 million tons, sediment particles have become finer, flood-season sediment concentration has decreased by 82%, and the incoming sediment coefficient has decreased by 63%, indicating that the Loess Plateau has entered a stage of healthy sediment reduction. Notably, sediment yield rebound has occurred in the upper Malian River and upper Beiluo River in recent years due to declining vegetation coverage. This phenomenon warns that sustained efforts are still required to maintain the hard-won favorable state of healthy sediment reduction in the long term.

To assess the green development effect of new quality productive forces and explore its path to promoting the high-quality economic development of cities in the Yellow River Basin, this study employed panel data of 77 prefecture-level cities in the Yellow River Basin from 2011 to 2021. The development level of new quality productive forces was measured using the entropy-TOPSIS method. A Spatial Durbin Model was constructed to empirically analyze the promoting effect of new quality productive forces on the high-quality economic development of cities and its spatial spillover effect. A mechanism test model was constructed to explore the transmission mechanism of new quality productive forces promoting high-quality economic development of cities. The results showed that: a) During the research period, the development of new quality productive forces in each city in the Yellow River Basin not only promoted the high-quality economic development of the city itself, but also had a significant spatial spillover effect on the high-quality economic development of neighboring cities. b) The development of new quality productive forces promoted high-quality economic development of cities in the Yellow River Basin through intermediary channels such as environmental regulation, energy conservation and the advancement of industrial structure. c) The direct and indirect effects of new quality productive forces promoting the high-quality economic development of cities were the strongest in downstream cities, followed by middle reaches cities, and the weakest in upstream cities. The study suggests optimizing and improving the top-level design for the development of new quality productive forces, giving full play to the synergy between new quality productive forces and the “dual carbon” goals, strengthening the research and development of core technologies such as 5G networks, the Internet of Things and artificial intelligence, and enhancing the development momentum of new quality productive forces.

Urbanization is accompanied by rapid growth in population, energy consumption and carbon emissions. To forecast the peak of energy-related carbon emissions in the Yellow River Basin under urbanization and provide a reference for advancing ecological conservation and high-quality development in the basin, this study calculated energy-related carbon emissions based on energy consumption data of eight major energy types across provinces (regions) in the Yellow River Basin from 2000 to 2021. The LMDI model was employed to decompose the factors influencing energy-related carbon emissions in the basin. An extended STIRPAT model was used to forecast peak energy-related carbon emissions under multiple scenarios (current, energy-saving, low-carbon and high-energy-consumption). The results indicated that: a) From 2000 to 2021, total energy carbon emissions in the Yellow River Basin continued to grow, but at a declining rate. Economic development, urbanization and population growth promoted energy carbon emissions, while optimization of energy structure, energy intensity and industrial structure suppressed them. Economic development was the primary driver of emission growth, whereas energy intensity was the main factor suppressing emissions. b) Under the current, energy-saving, and low-carbon development models, the peak range for energy-related carbon emissions in the Yellow River Basin is estimated to be between 1.800 billion and 1.869 billion tons, with the peak occurring between 2030 and 2035. In contrast, under a high energy consumption development model, the peak is unlikely to occur before 2040. c) Regulating carbon emission intensity helps reduce peak energy carbon emissions in the Yellow River Basin, while optimizing the energy structure and improving energy efficiency can facilitate an earlier peak. Recommendations include optimizing urban planning, promoting industrial structure upgrades, strengthening public education on low-carbon awareness, advocating low-carbon lifestyles, expanding renewable energy adoption, and optimizing the energy consumption structure.

To explore whether the digital economy can achieve coordinated governance of pollution reduction and carbon mitigation in the Yellow River Basin, an empirical analysis was conducted using panel data from 65 prefecture-level cities within the basin for the period 2003-2021. A fixed effects model was constructed for empirical analysis. Robustness tests were performed by altering the explained variable, employing instrumental variable methods and conducting quantile regression analyses. This study conducted a mechanism analysis on industrial structure optimization, government regulation and green technology innovation. Additionally, the regional heterogeneity of the impact of the digital economy on the coordinated governance of pollution reduction and carbon mitigation was analyzed. The results indicate that: The digital economy significantly contributes to the coordinated governance of pollution reduction and carbon mitigation in the Yellow River Basin. Optimization of the industrial structure, government regulation and green technological innovation are key mechanisms through which the digital economy promotes the collaborative governance of pollution reduction and carbon mitigation. The influence of the digital economy on the coordinated governance of pollution reduction and carbon mitigation varies significantly in the upper, middle and lower reaches of the Yellow River Basin, and none of them have significantly promoted such collaborative governance in any of the three reaches. Therefore, it is imperative to establish a robust digital infrastructure to narrow the development gap in the digital economy among the upper, middle and lower reaches. Governments should play an active role in ensuring flexible and efficient regulation, promoting the free flow of elements such as data. By capitalizing on the comparative advantages of factor endowments in each region, the digital economy can empower the synergistic governance of pollution reduction and carbon mitigation across the Yellow River Basin.

The planning and construction of the central economic zone and urban agglomerations pose certain challenges and pressures on the ecological environment of the Yellow River Basin (in Henan Province). It is of great significance to evaluate the spatial and temporal evolution of the ecological environment quality and explore the key influencing factors to promote the sustainable development of the region. This paper used multi-category of remote sensing data and social statistical data as data sources, and used Ecological Index (EI) and Remote Sensing-based Ecological Index (RSEI) to analyze the spatial and temporal evolution of ecological environment quality and driving factors in the study area from 2010 to 2021, and analyze the trend of ecological environment quality change in each prefecture-level city. The results show that: The ecological environment quality in the study area has shown an upward and downward trend in 12 years, with overall control and relative stability; Spatially, the ecological environment quality shows the characteristics of high in the west and northwest and low in other areas. The combined effect of multiple factors on ecological environment quality is higher than the influence of any single factor, and the key factors for controlling ecological environment quality in the study area are vegetation coverage and land use. All prefecture-level cities show a trend of improving ecological environment quality. Among them, Zhengzhou City and Xinxiang City are relatively stable and the trend of improving ecological environment quality is not obvious. The trend of improvement in Jiyuan City, Luoyang City and Sanmenxia City is obvious.

Promoting the coordinated and coupled development of industrial intelligence and ecological efficiency is an essential pathway for implementing the major national strategy of ecological protection and high-quality development in the Yellow River Basin. In order to provide support for formulating differentiated coordinated development policies in the Yellow River Basin, this study analyzed the coupling and coordination mechanism between industrial intelligence and ecological efficiency. Utilizing data from 60 cities in the Yellow River Basin from 2007 to 2021, the coupling coordination degree model was applied to measure the coupling coordination degree (CCD) between the two. Additionally, the Gini coefficient and Gaussian kernel density model were employed to explore the spatial disparities and dynamic evolution characteristics respectively. The results show that: a) The CCD between industrial intelligence and ecological efficiency in the Yellow River Basin exhibits a persistent upward trajectory. It progresses from barely coordinated to favorably coordinated, exhibiting a spatial gradient pattern of “low in the upper reaches, high in the lower reaches”. All three sub-regions (upper, middle and lower reaches) maintain sustained and rapid growth. The CCD between industrial intelligence and ecological efficiency in prefecture-level cities exhibits a distinct characteristic of “coexistence of overall improvement and internal disparities”. b) The overall disparity in the CCD between the two exhibits a declining trend; Nevertheless, spatial heterogeneity remains pronounced. Specifically, the between-group gap between the upper and lower reaches is the dominant source. c) The non-equilibrium of the CCD is diminishing over time, and no tendency toward polarization is observed.

The He-Long Region (Hekouzhen to Longmen) of the middle reaches of the Yellow River is a major source of sediment for the entire river. Understanding the spatiotemporal abrupt changes in sediment transport and the underlying driving factors is crucial for optimizing water resource utilization and developing a scientific water-sediment regulation system for the middle reaches of the Yellow River. Based on sediment transport data from 22 sub-basins in the He-Long Region between 1955 and 2016, this study analyzed change trends and time of abrupt changes in sediment transport during the study period, and further explored the contribution rates of climate change and human activities to sediment reduction. The results showed that: 21 sub-basins in the study showed a significant decreasing trend in annual sediment transport, with abrupt changes occurring primarily in the 1980s and 1990s. The spatial distribution characteristics of annual sediment load in the He-Long reach were fundamentally consistent between the baseline period (P1) before the change point and the human-activity-impacted period (P2) after it, with a significant reduction occurring in P2. The primary sediment-producing areas were concentrated in the Wuding River and Kuye River Basins. The average contribution rates of precipitation and human activities to sediment reduction of 22 tributaries were 24.42% and 75.58%, respectively, with large-scale soil and water conservation project constructions being the primary driver of sediment reduction.

The simulation and prediction of river runoff are of great significance for controlling basin water volume and ensuring optimal allocation of basin water resources. However, due to the influence of abnormal climate and human activities, the instability of medium- and long-term runoff sequences has increased the difficulty of runoff prediction. To improve prediction accuracy, a coupled deep learning model framework based on variational mode decomposition (VMD), mutual information (MI), and bidirectional long short-term memory (Bi-LSTM) networks, called the VMD-Bi-LSTM model, was established. First, VMD was used to decompose the original runoff data into intrinsic mode components; Then, Bi-LSTM was applied to each component to build prediction models, with the input lag time determined by the mutual information method; Finally, the prediction results of each subsequence were superimposed to obtain the final prediction result. The paper explored the performance of the proposed model in predicting the monthly runoff at Tiantang hydrological station in the Datong River Basin and compared it with other models. The results show that: Compared to other models, this model exhibits significant advantages in both point and interval predictions. The Nash-Sutcliffe efficiency coefficient (NSE) of the prediction results reaches 0.95, and the coverage rates of interval predictions are 0.92 and 0.85 at the 95% and 90% confidence intervals, respectively.

Flood monitoring is crucial for disaster prevention, reduction and urban sustainability. Taking the “23·7” Baoding City center flood event as an example, this study utilized multi-source data, such as volunteer geographic information (VGI), remote sensing images and precipitation products. Methods such as Python web scraping and random forest classification were employed to extract and comprehensively analyze multi-source information of flood disasters at different temporal scales. The results show that: a) The number of VGI waterlogging points decreases over time, with a primary distribution in the densely populated central urban area. Spatially, these points are distributed from high-density to low-density areas and from the urban center to the periphery, primarily along roads and waterways. This distribution reflects the public’s increased concern for transportation and safety, while also indicating a delayed response from the public. b) The flooded areas extracted from SAR images are sparsely distributed, clearly visible around large water bodies. Different degrees of flooding can be observed in the hotspot areas of waterlogging points, highlighting the collaborative role of remote sensing data and social media data in flood information extraction. c) Cumulative precipitation has a certain impact on the extent of urban flooding, while factors such as water engineering operations also affect flood responses. d) The significantly affected land types by flooding are crops, built areas and expanding water bodies.

In order to investigate the status of water resources security and its influencing factors in the Dagong Irrigation District under multiple pressures such as climate change, water-use competition and utilization efficiency, an evaluation index system was constructed covering four subsystems: water resources, engineering construction, ecology and socioeconomics. A combined weighting method integrating the Order Relation Analysis Method （G1） method and the CRITIC method was adopted, and the TOPSIS model was used for evaluation. In addition, an obstacle degree model was applied to identify the major obstacle factors affecting the safety status. The results showed that: From 2010 to 2020, the closeness coefficient of water resources security in the irrigation district fluctuated upward from 0.418 to 0.613, and the security level gradually improved from “critical safety” to “relatively safe”. The dominant factors constraining water resources security evolved across different stages. In the early stage, the main obstacles were concentrated in the engineering construction subsystem; In the middle stage, the focus shifted to water resources allocation and water-use efficiency; Recently, groundwater over-exploitation emerged as the most significant obstacle factor. Based on these findings, recommendations are proposed including strict control of groundwater over-exploitation, optimization of water allocation among domestic, production and ecological uses, and the enhancement of engineering-based water-saving renovations and refined water management. These suggestions aim to provide references for the sustainable utilization and management of water resources in irrigation districts.

To enhance the rational utilization of resources and ecological conservation, and to obtain a high-resolution understanding of terrestrial water storage changes in the Upper Yellow River, this study used topographic data and hydrometeorological variables. These variables included elevation, slope gradient, aspect, precipitation, air temperature, soil moisture, normalized difference vegetation index, runoff and GLDAS surface water changes. Three machine learning models (CNN-LSTM, Random Forest and XGBoost) were employed to enhance the spatial resolution of terrestrial water storage changes derived from GRACE data from 0.25° to 1 km. The CNN-LSTM model demonstrated better downscaling performance, with a root mean square error of 0.33 cm. Comparative validation with measured data from groundwater observation wells yielded a goodness-of-fit R2 of 0.51. Regarding terrestrial water storage changes in the Upper Yellow River: Spatially, the southwestern region (centred on Maqu to Longyangxia) exhibited an increasing trend of approximately 0.2 cm/a, while the northeastern region (centred on the internal drainage basin) showed a decreasing trend of approximately -0.6 cm/a. Temporally, terrestrial water storage underwent an abrupt change in 2009, shifting from an upward to a downward trend. The overall slope of the fitting straight line for terrestrial water storage between 2002 and 2022 was -0.15 cm/a.

In order to explore the intrinsic link between digital economy development and industrial water resources utilization efficiency in the Yellow River Basin, based on the panel data of 55 cities （prefecture-level） in the Yellow River Basin from 2013 to 2022. Industrial water use efficiency was measured using the Super-SBM model that accounts for undesirable outputs. The entropy method was used to measure the level of digital economy development. The fixed effect model was constructed to empirically analyze the influence of digital economy development on industrial water resources utilization efficiency. The results show that: a) The digital economy significantly improves industrial water resources utilization efficiency, and further releases the improvement effect under the role of industrial structure upgrading. b) The digital economy has an obvious effect on the improvement of industrial water resources utilization efficiency in non-resource cities and the humid and semi-humid ecological zones of the northeastern part of the country. c) The development of the digital economy has a significant spatial spillover effect, which is able to improve the industrial water resources utilization efficiency of neighboring cities. Therefore, it is recommended to accelerate the development of the digital economy, establish a modernized industrial system, and leverage the digital economy to enhance water resources utilization efficiency according to local conditions.

The Yellow River Basin faces the severe challenge of water shortage in energy production. A comprehensive assessment of water resource vulnerability in energy production is crucial to promote the coordinated development of resources in the basin. Based on the input-output analysis method and considering regional water resources pressure, this study developed the scarce water footprint measurement model and scarce virtual water trade measurement model to comprehensively assess water scarcity for energy production in nine provinces （regions） of the Yellow River Basin. The results show that: Although the water use for energy production in provinces （regions） like Ningxia is not large, its energy production faces a serious water shortage problem due to high water pressure. To effectively alleviate the vulnerability of water resources in energy production in the water-scarce regions, at the regional level, attention should be focused on scarce virtual water transfer from net outflow areas like Ningxia to net inflow areas like Shaanxi. At the sectoral level, attention should be focused on scarce virtual water transfer from the energy industries and agriculture sectors to industries like petroleum, coking and nuclear fuel processing. This study calls for incorporating water scarcity into energy production planning in the Yellow River Basin and strengthening cooperation between energy and water management departments to promote sustainable development of energy and water resources in the Yellow River Basin.

A scientific assessment of the green water resource utilization performance in the urban agglomerations of the Yellow River Basin was conducted, with an in-depth analysis of its spatial differences. Based on the global-reference slack-based measure directional distance function, this study constructed a green water resource utilization performance index for urban agglomerations in the Yellow River Basin. It analyzed the spatial differences of the performance index using the Dagum Gini coefficient. The findings indicate that: The green water resource utilization performance of urban agglomerations in the Yellow River Basin is showing a steady upward trend, yet the overall level remains relatively low, leaving substantial room for improvement. The variation in the performance differences among urban agglomerations in the Yellow River Basin exhibits a divergent-converging characteristic. The inter-agglomeration differences contribute the most to the total Gini coefficient.

The distribution characteristics of microplastics in rivers are notably influenced by human activities. To investigate the occurrence characteristics and ecological risks of microplastics in water and sediment of the Jialu River (Zhengzhou section), 12 sampling sites were established for sample collection. The distribution characteristics of microplastics in the samples were analyzed through laboratory testing. The results indicate that: The average abundance of microplastics in the water of the Jialu River (Zhengzhou section) is (5.08 ± 3.53) per liter, and the average abundance of microplastics in the sediment is (246.75 ± 169.09) per kilogram (dry weight). Microplastics between 0.1-0.5 mm in size constitute the highest proportion. In water and sediments, the dominant colors are yellow and transparent, respectively. Fragments are the predominant shape in both media. Polyethylene and polypropylene occupy a relatively large proportion in water and sediments. The calculation results of the pollution load index, the hazard index and the environmental status index （ESI） indicate that: The water and sediments are at slight and moderate pollution levels, respectively. The hazard levels for water and sediment are Ⅲ and Ⅱ, respectively. The impact of microplastics in water and sediments on biological health is at a moderate level. 

Water conservation function is one of the most important functions of ecosystem services, and calculating the spillover value of water conservation can promote balanced regional development. Based on the water yield module and the modified model of the InVEST model, the water conservation volume in Heilongjiang Province was obtained. This was used to assess the province’s water conservation supply value. Simultaneously, the water footprint was applied to derive the water conservation demand value. Considering the spillover value and economic development level, the priority of ecological compensation in different regions of Heilongjiang Province was determined. The results showed that: In 2020, the water conservation supply in Heilongjiang Province was 25.856 billion m³; the water conservation of forest land was the highest, accounting for 74.38% of the total supply. The spillover value of water conservation function in Heilongjiang Province was 117.840 billion yuan. Among them, except for Suihua, Jiamusi, Daqing and Qiqihar, which exhibited a supply-demand deficit, all other regions showed a surplus, indicating outward spillover of water conservation value and qualifying for ecological compensation. The order of ecological compensation priority, from highest to lowest, is: Greater Khingan Mountains ＞ Yichun ＞ Heihe ＞ Mudanjiang ＞ Qitaihe ＞ Jixi ＞ Shuangyashan ＞ Hegang ＞ Harbin. 

The Yellow River water and shallow groundwater are vital resources for domestic use, industrial and agricultural production in the Lower Yellow River floodplain (Henan section). Since the implementation of the water and sediment regulation scheme at the Xiaolangdi Reservoir, the recharge-discharge relationship between river water and groundwater has undergone significant changes. By comprehensively employing hydrochemical and isotopic methods, this study analyzed the hydrochemical and hydrogen and oxygen isotopes characteristics of the Yellow River water and groundwater in the Lower Yellow River floodplain (Henan section), and explored their formation mechanisms and environmental implications. The results show that: HCO₃^- is the dominant anion in both the Yellow River water and groundwater. Regarding dominant cations, Na+ is dominant in the Yellow River water, while Ca²⁺ and Na⁺ are dominant in the groundwater. The hydrochemical type of the Yellow River water is mainly mixed type, while that of the groundwater is mainly HCO₃^- type. Along the direction of lateral seepage flow, the Yellow River water transforms into groundwater, with the hydrochemical type gradually evolving from mixed type to HCO₃^- type. The ion sources of the Yellow River water and groundwater are primarily controlled by the dissolution effect induced by rock weathering. Specifically, the ionic composition of the Yellow River water is mainly derived from the weathering of silicate rocks, whereas the ionic composition of the groundwater is primarily derived from the weathering of both silicate and carbonate rocks. The hydrogen and oxygen isotopic compositions of the Yellow River water are relatively more enriched than those of the groundwater, showing a gradual depletion trend in δD and δ18O from the Yellow River water to the groundwater. Groundwater is recharged by both atmospheric precipitation and Yellow River water. Although the Yellow River water serves as an important source of groundwater recharge in the study area, atmospheric precipitation still plays a dominant role.

Regional integration is a crucial driving force for regional economic development. Based on the land use and socioeconomic data, this study adopted methods including equivalent factor correction and Pearson correlation analysis, and employed the coupled GMOP-PLUS-InVEST model to conduct quantitative assessment and multi-scenario simulation of land use changes and ecosystem services (ESs) (water yield, carbon storage, habitat quality and soil conservation) in the Xi’an and Xianyang area from 1985 to 2030. In addition, the trade-off and synergy relationships among different ecosystem services were explored. The results showed that: During the study period, the main land use change was a continuous decrease in cropland, which was mainly converted into forest land, grassland and construction land. Carbon storage and habitat quality exhibited a decreasing trend, while water yield showed a trend of decreasing first and then increasing. Ecosystem services in the Xi’an and Xianyang area were dominated by synergistic relationships, among which the pairs of water yield-carbon storage and water yield-habitat quality shifted to trade-off relationships in 2010. The comprehensive development scenario can effectively balance the expansion of construction land and ecological protection, which is more conducive to the green and sustainable development of the Xi’an and Xianyang area.

The problem of distortion in remote sensing vegetation indices due to terrain effects is more prominent in the loess hilly and ridge area of the Loess Plateau with intense terrain changes. To obtain the true information about vegetation growth, it is necessary to eliminate the terrain effects in remote sensing images through terrain correction. Taking the complete ratio-type, non-complete ratio-type and non-ratio-type vegetation indices (NDVI, EVI and GVI) in the loess hilly area as the research objects, the SCS+C correction model was used to perform terrain correction for the terrain effects caused by the local solar incidence angle, the spectral variations of the same land type caused by the terrain effects, and the abnormality of vegetation indices caused by the aspect. The improvement effect of terrain correction on the three vegetation indices was investigated. The correlation between various vegetation indices and both topographic factors (elevation, slope, aspect, etc.) and measured leaf area index (LAI) was analyzed before and after terrain correction. The results show that: a) Among the three vegetation indices, GVI is less affected by terrain effects, while EVI and NDVI are relatively more affected by terrain effects. When using EVI and NDVI in related studies, terrain correction must be performed. b) After terrain correction, the regularity of the three vegetation indices changing with terrain factors and the correlation with the measured leaf area index have both been enhanced. c) Using the SCS+C correction model for terrain correction is feasible and effective. The terrain correction effect of the three vegetation indices is as follows: EVI > NDVI > GVI.

In complex terrain areas, there is a severe issue of spectral characteristics confusion among ground objects in remote sensing imagery. Taking the Qinling Mountains region in Shaanxi as the study area, a land cover classification method combining Support Vector Machine (SVM) and hierarchical segmentation was developed. First, various ground-measured sample data were integrated to construct training and validation sample sets with high spectral consistency and accuracy. SVM was used to obtain coarse classification results (of cropland, forestland, grassland, artificial surfaces, bare land, and water bodies). The detailed expression characteristics of large-scale classification product data were fully utilized, and the classification of cropland, forestland, and grassland types was further refined by Fractional Vegetation Cover (FVC) estimates. Comparison with the classification results of the Random Forest method shows that the overall accuracy of this method reaches 88.74%, an improvement of 14.06 percentage points compared to the Random Forest method, with a Kappa coefficient of 0.82. The classification results exhibit complete patches and clear boundaries, effectively representing the continuous spatial distribution characteristics of actual land classes in the study area and revealing the “ecology-dominated, agricultural infiltration, controllable construction” human-land system features of the region.

To address the limitations of existing flow measurement equipment in achieving rapid and accurate flow measurements during flood periods and meeting the high standards of modern hydrological monitoring, emergency flow measurement equipment for amphibious UAV was developed. The equipment adopted an integrated flight-drift flow measurement method. It utilized a high-precision differential positioning system to acquire buoy displacement and temporal data in real time, and an echo sounder to obtain water depth measurements. A video transmission system and a data transmission system enable the real-time relay of first-person view video, water depth and flow velocity. Key algorithms including barometric imbalance altitude compensation and magnetic interference correction were developed to ensure stable operation of the equipment. Field comparative tests demonstrated the feasibility and accuracy of deriving surface flow velocity using the drift method with the amphibious UAV emergency flow measurement equipment. The equipment exhibits outstanding applicability in emergency response operations at the Madu Dangerous Project, Liuyuankou Dangerous Project and the Heyang floating bridge in Shaanxi Province, providing critical data support for hazard warning and emergency management.

This study aims to optimize the water-saving, salt-control and high-yield planting strategy for maize in the Hetao Irrigation District, and to elucidate the synergistic regulation effects of different drip tape burial depths and mulching methods on soil water-salt distribution and maize yield under subsurface drip irrigation. Field experiments were conducted using mulched surface drip irrigation (0 cm) as the control group. A total of five treatments were established by combining drip tape burial depths (8 cm and 25 cm) with mulching and non-mulching conditions. Soil moisture and salt dynamics in the 0-60 cm soil layer, along with final maize yield, were measured throughout the entire growing season to analyze the water-salt distribution patterns and their relationship with yield under different treatments. The results indicated that: The mulched deep burial treatment achieved the optimal water-salt regulation effect. Throughout the growing season, the relative soil water content in each layer remained generally above 60%, characterized by uniform salt distribution and no significant surface accumulation. In contrast, the non-mulched shallow burial treatment experienced strong evaporation, severe water deficit in deep soil layers and significant salt accumulation. Regarding yield, the mulched shallow burial treatment was the highest, while the non-mulched shallow burial treatment was the lowest. The average yield of mulched treatments increased by 8.8% compared to non-mulched treatments. Notably, the deep burial treatment maintained a relatively high yield even under non-mulched conditions, demonstrating stronger environmental adaptability. Under the silty clay soil conditions of the Hetao Irrigation District, the “mulching + shallow burial” mode is recommended to achieve synergistic water-salt regulation and high yield. In the absence of mulching, the “deep burial” mode should be selected to mitigate water-salt stress, while the “non-mulched + shallow burial” combination should be avoided.

To ensure the quality of water conservancy engineering construction, the optimal quality control strategy of the owner in the three-stage supply chain composed of the owner, contractor, and supervisor was explored. By using the quality control level of the contracting party and the supervising party as parameters, and the quality control level of the owner, the amount of quality guarantee deposit withheld, and the penalty for the supervising party’s inaction as decision variables, derived the optimal quality control strategy for the owner under symmetric information based on the maximum principle, and the optimal quality control strategy for the owner under incomplete information when the probability density functions of the quality control levels of the contractor and supervisor follow uniform and triangular distributions, respectively. Combined with simulation calculations, the optimal quality control strategy of the owner under different information conditions were analyzed. The results indicate that: Under symmetric information, the quality control strategy of the owner should be organically combined with the quality control level of the contractor and the supervisor, and appropriate quality control strategies should be selected. Under incomplete information conditions, changes in the upper limit of the contractor’s quality control level have a significant impact on the owner’s expected returns. The owner can lower their requirements for the quality control level of the supervisor to a certain extent, prioritize the contractor’s quality control level, and make appropriate decisions.

Research on the spatiotemporal changes in carbon storage is an important prerequisite for achieving regional low-carbon and high-quality development based on the “dual carbon” goals. This study employed a method combining supervised classification with field surveys to extract land use information of Bayannur City using high-resolution satellite imagery data. The study quantified the spatiotemporal changes in carbon storage in Bayannur City from 2015 to 2021 based on the InVEST model. The results showed that: a) Carbon storage in Bayannur City increased by a total of 97.55 million tons from 2015 to 2021, with high-value areas concentrated in Dengkou County, Hangjin Banner, Linhe District and Wuyuan County, and low-value areas concentrated in Urad Rear Banner and Urad Middle Banner. b) Changes in land use were consistent with changes in carbon storage. Compared to 2015, cultivated land and grassland areas in Bayannur City increased significantly by 1 740.00 km² and 11 099.09 km², respectively, while unused land decreased by 13 143.96 km². The area of water bodies showed an initial increase followed by a decrease over the six years. Significant changes were observed in the conversion of unused land to grassland in Urad Rear Banner and Urad Middle Banner, resulting in carbon storage increases of 36.72 million tons and 58.70 million tons, respectively.