As global temperatures continue to rise and extreme weather events occur with increasing frequency, climate change is becoming an urgent global challenge. Against the backdrop of the shift of global climate governance toward an “implementation-oriented” phase, this study explored synergistic mechanisms between energy transition pathways and watershed ecological governance, drawing on thematic presentations from the China Pavilion side event at the 30th Conference of the Parties to the United Nations Framework Convention on Climate Change (COP30) and practical experience in watershed ecological governance. The findings indicate that: Climate change intensifies the dual challenges of global energy security and watershed ecosystem, making the coordinated advancement of energy transition and ecological governance a core pathway for achieving green and low-carbon development and ensuring ecological security. Reducing energy consumption represents the most economically viable option for energy transition, while multi-energy complementarity provides an innovative direction for watershed energy transition. As a mature renewable energy source, hydropower plays a significant role in peak regulation and in maintaining the stability of power supply. This role is particularly prominent in the Yellow River Basin, characterized by “low water and high sediment”, where the existing and planned water conservancy projects exert substantial influence on basin-wide water-sediment regulation. Under current conditions of reduced sediment inflow, hydropower generation, ecological protection and optimized water resources allocation can deliver even greater benefits. Using turbulence research as an example, a velocity distribution formula derived from the “turbulent eddy model” demonstrates cross-disciplinary applicability in fields such as hydraulic engineering, aeolian sand control and energy consumption optimization in aero-engines, underscoring the critical role of fundamental scientific breakthroughs in supporting applied research and technological implementation. The objectives of energy transition and watershed ecological governance are inherently aligned, and the eco-economy emerging from their integration can leverage capital mechanisms to achieve a win-win outcome between ecological protection and economic development. Finally, successful energy transition requires the establishment of a full-chain collaborative system spanning from fundamental research to integrated governance, strengthening innovation at the source, activating energy efficiency markets, and ensuring policy guarantees to drive the transition of the development paradigm.

To thoroughly implement the strategic deployment of ecological protection and high-quality development in the Yellow River Basin, and to incorporate the significant engine role of new quality productive forces in green technological innovation into the specific process of ecological protection, this paper selected the panel data of the nine provinces in the Yellow River Basin from 2014 to 2023. By using benchmark regression models, mediation effect models, threshold regression models, and combining with robustness tests and endogeneity tests, it empirically verified the influence mechanism of green technological innovation empowered by new quality productive forces on ecological protection in the Yellow River Basin. The results show that: a) New quality productive forces can effectively enhance the ecological protection level in the Yellow River Basin. b) New quality productive forces promote ecological protection in the Yellow River Basin through green technological innovation. c) Green technological innovation plays a significant threshold effect when new quality productive forces influence ecological protection in the Yellow River Basin. d) There is significant heterogeneity in the impact of new quality productive forces on ecological protection in the Yellow River Basin due to different regional development levels. The Yellow River Basin should fully leverage the promoting effect of new quality productive forces on ecological protection, continuously improve policies for promoting the green technological innovation system, scientifically determine the intensity of green technological innovation investment, and implement differentiated development strategies based on regional actual conditions.

The implementation of the major national strategy for ecological conservation and high-quality development in the Yellow River Basin urgently necessitates the synergistic development of environmental protection and industries. Practical experiences from representative river basins both domestically and internationally can provide important real-world references for promoting such coordination in the Yellow River Basin. Through a comparative multi-case analysis approach, this study selected the Mississippi River, Rhine River, Amazon River, Nile River, Yangtze River and Pearl River as comparative basins (cases) to examine the commonalities and differences between the Yellow River Basin and these basins in terms of environmental protection and industrial synergy. Building on the diversified development experiences of these comparative basins, as well as considering the unique characteristics and challenges of the Yellow River Basin, strategies were proposed to further advance the synergistic development of environmental protection and industry. The comparative analysis reveals that: While all reference basins emphasize the importance of policy and regulatory guidance, the application of scientific and technological innovations, and the establishment of digital-intelligent monitoring systems, the Yellow River Basin exhibits significant deficiencies in these areas. Moreover, it faces distinct challenges such as severe water scarcity, a monolithic industrial structure, and a high proportion of water-intensive industries. To promote the synergistic development of environmental protection and industry in the Yellow River Basin, the following strategies are recommended: improving vertical and horizontal coordination and compensation mechanisms for ecological conservation; adhering to ecological priority and technological innovation to drive green development; leveraging big data resources to lead the digital-intelligent transformation of industrial development in the basin; and promoting the efficient growth of green industries to establish a green, low-carbon and circular economic system.

To broaden the research horizon of how the digital economy empowers low-carbon development and to provide decision-making references for ecological protection and high-quality development in the Yellow River Basin, this study constructed an indicator system and quantified the digital-economy development level by using panel data of the nine provinces in the basin from 2012 to 2022. Furthermore, benchmark econometric models, mediation effect models, and moderation effect models were established, with carbon lock-in intensity as the dependent variable, the level of digital economic development as the core explanatory variable, the level of industrial structure upgrading as the mediating variable, and the level of urbanization as the moderating variable. The carbon unlocking effect of the digital economy and its underlying mechanisms were empirically analyzed. The findings indicate that: a) The development of the digital economy in the Yellow River Basin exhibits a robust carbon-unlocking effect. The effect is markedly stronger in the middle-lower reaches, in areas hosting innovation-oriented industrial clusters, and in regions where resource-intensive industries account for a smaller share of output. b) The digital-economy development delivers its carbon-unlocking impact by propelling industrial-structure upgrading, indicating that industrial structure upgrading plays a significant positive mediating role in the carbon unlocking effect of the digital economy in the Yellow River Basin. c) During the sample period, urbanization negatively moderates the carbon-unlocking effect of the digital economy in the Yellow River Basin. Policy implications are proposed: a) Accelerate digital-economy expansion to reinforce its carbon-unlocking capacity. b) Implement region-specific strategies that coordinate digital-economy growth with green low-carbon industries across the Yellow River Basin. c) Further optimize the industrial structure to promote green low-carbon sectors. d) Pursue green urbanization to create low-carbon living spaces.

To explore the path of green innovation coordinated development in major national strategic regions and provide decision-making references for the implementation of the regional coordinated development strategy, based on the panel data of 22 provinces in four major national strategic regions (the Beijing-Tianjin-Hebei region, the Yangtze River Economic Belt, the Yangtze River Delta, and the Yellow River Basin) from 2014 to 2022, this paper used the super-efficiency SBM model, kernel density estimation, Dagum Gini coefficient and its decomposition method to measure the green innovation efficiency of each strategic region and analyze its dynamic evolution and regional differences. The following main conclusions are drawn: a) The overall green innovation efficiency of major national strategic regions shows an upward trend and still has a large room for improvement. The ranking of green innovation efficiency among the four strategic regions is the Yangtze River Delta, the Beijing-Tianjin-Hebei region, the Yangtze River Economic Belt, and the Yellow River Basin. b) From the dynamic evolution of green innovation efficiency in each strategic region, the Beijing-Tianjin-Hebei region has the largest improvement during the research period, while the Yellow River Basin has the smallest. c) The overall differences in green innovation efficiency among major national strategic regions are mainly caused by inter-regional differences, especially the most prominent difference between the Yellow River Basin and the Yangtze River Delta, and the inter-regional differences are increasing. d) From the differences in green innovation efficiency within each strategic region, the Yellow River Basin has the most prominent problem of unbalanced development, followed by the Yangtze River Economic Belt, while the differences within the Beijing-Tianjin-Hebei region and the Yangtze River Delta are relatively small. Based on the research conclusions, the following countermeasures and suggestions are proposed: a) Build a green technology innovation incentive mechanism and enhance green innovation investment. b) Improve the green innovation policy system to stimulate the vitality of multiple subjects. c) Optimize the internal resource allocation of regions to break the “Matthew effect”. d) Strengthen the cross-regional coordination mechanism to narrow the inter-regional differences.

Using the Wujiang River Basin as the study area, this study computed the monthly-scale meteorological drought index SPEI-1 from multi-year meteorological observation data. A spatio-temporal cube model was constructed to systematically characterize the evolution of meteorological drought across three dimensions: time, space and intensity. The results reveal that: The basin exhibits a spatial “drier in the northwest and wetter in the southeast”. The standardized trend scores range from -2.38 to 1.17, with the minimum P-value of the trend significance test being 0.017; Only a few areas reach a statistically significant level. Based on spatio-temporal clustering, the study area is classified into five categories. Among these, small-area types mostly exhibit characteristics of “oscillating hotspots” with significant drought fluctuations, while large-area types predominantly behave as “oscillating coldspots” with weaker and relatively stable drought conditions. The trend intensity values for the 12 meteorological stations range from -1.954 8 to 0.672 5, showing notable differences between stations, which underscores the significant spatiotemporal heterogeneity and periodicity of meteorological drought.

To investigate the spatiotemporal distribution characteristics of the shallow subsurface hydraulic conductivity (K) and its response to changes in the sedimentary environment, the Yellow River Delta was selected as the study area. In July 2024, a total of 96 soil samples were collected from the surface and depths of 10 cm, 30 cm, and 50 cm below ground level in the Yellow River Delta. Particle size analysis was conducted, and empirical formulas were applied to calculate the average hydraulic conductivity at each depth as well as the equivalent shallow subsurface hydraulic conductivity. The calculated results were validated through in-situ standpipe tests. The study found that the shallow subsurface sediments in the delta primarily consist of clay and sand. The mean particle sizes at the surface, 10 cm, 30 cm, and 50 cm depths were 28.8 μm, 26.1 μm, 29.9 μm, and 29.1 μm, respectively. Compared with sampling results from 2015, the standard deviations of particle sizes d10, d50, and d90 decreased by 26.2%, 47.8%, and 30.4%, respectively. The K values ranged from 0.02-3.10 m/d in 2015 to 0.10-1.54 m/d in the current study, indicating improved sediment sorting. Although the mean K value showed little change, spatial variability decreased significantly. In the Diaokou River protected area, the spatial variability of K was relatively low. In the general protected zones of the Yellow River Delta wetlands, K values were generally higher than those in the core protected areas. The horizontal equivalent hydraulic conductivity of shallow subsurface sediments in the protected areas ranged from 0.07 to 1.78 m/d, while the vertical equivalent hydraulic conductivity ranged from 0.03 to 1.51 m/d. The ratio of horizontal to vertical K was 1.18, indicating significant isotropy. These changes reflect the comprehensive influence of factors since 2015, including variations in water and sediment discharge from the Yellow River, wetland water replenishment operations, and conservation measures.

In response to the local flow field changes caused by the layout of bottom-seated continuous underwater structures, the RNG k-ε turbulence model was used to analyze the sediment scouring and silting patterns caused by bottom-seated continuous structures. The results show that: When the longitudinal length of the structure is less than the width of the water flow, vortices form downstream on the lee side. The extent of these vortices decreases as the length of the structure diminishes, and scour pits develop at the lateral upstream stagnation points. The depth of the scouring pit is negatively correlated with the length of the structure. The flow-blocking effect of the structure induces vortices on both the upstream and downstream sides. When the structure is relatively wide, vortices also form on the upper side, leading to sediment deposition, with the deposition volume positively correlated with the width of the structure. When the structure is oriented at an angle to the flow direction, velocity differences arise within the same longitudinal cross-section. The flow tends to move from the obtuse angle on the lee side toward the acute angle, forming an arc-shaped vortex near the acute angle, which poses a potential threat to the stability of the lower side of the structure.

With the improvement of river regulation works and the operation of the Xiaolangdi Reservoir, the Lower Yellow River is becoming a channel under strong artificial constraints. Understanding the riverbed evolution under these conditions provides a theoretical basis for effective river regulation and flood control. The wandering reach of the Lower Yellow River was the focus of this study. Based on hydrological and sediment data and cross-sectional topography from Huayuankou (1950-2022), and records of regulation project construction (1949-2019), the riverbed evolution process in the wandering reach under strong artificial constraints was investigated. The results indicate that: The increased density of regulation works significantly enhances the confinement of the wandering channel. The swing range of the main stream noticeably narrows, while the length of projects adjacent to the flow substantially increases, effectively stabilizing the main flow. Under the regulated flow and sediment discharges from the Xiaolangdi Reservoir, the main channel experiences continuous scouring for many years. The median size of bed sediment exhibits a spatial-temporal evolution pattern, coarsening over time and fining along the course. The coefficient of sinuosity generally shows an increasing trend, altering the formerly rapid and straight characteristics of the wandering channel, imparting some traits of a meandering river. The coefficient of fluvial facies generally decreases, improving the cross-sectional form towards a narrower and deeper shape.

To explore the efficient utilization of water resources and the coordinated development of multi-systems in Ningxia, this study took “Basing Four Aspects on Water Resources” as the theoretical framework to examine the coupling coordination status of the Ningxia “Water-Ecology-Energy-Food” (WEEF) system under different scenarios. Parameters related to “Basing Four Aspects on Water Resources” were selected to construct a system dynamics model of the WEEF system in Ningxia. Three scenarios, namely socioeconomic development, water use efficiency improvement and comprehensive development, were designed to predict the changes in the WEEF system and calculate the system coupling coordination degree. The results indicate that: a) The established system dynamics model of the Ningxia WEEF system exhibits high reliability. By adjusting different parameters, it can effectively predict the connections and dynamic changes among the four subsystems (water resources, ecology, energy and food) in Ningxia from 2023 to 2030. b) Under the socioeconomic development scenario, Ningxia is in a stage of economic growth and urbanization, with the projected total population, regional Gross Domestic Product (GDP), green space area of urban built-up areas and electricity generation all showing an upward trend. Under the water use efficiency improvement scenario, the growth rate of indicators such as total population and regional GDP slows down; The total water consumption decreases significantly, the utilization of unconventional water continues to increase, the growth of irrigated cultivated land area is moderate, and the proportion of agricultural water consumption drops remarkably. Under the comprehensive development scenario, the economic development momentum is lower than that in the socioeconomic development scenario but higher than that in the water use efficiency improvement scenario, while the water use condition is better than the socioeconomic development scenario but worse than the water use efficiency improvement scenario, which basically meets the requirements of sustainable development in Ningxia. c) The overall coupling coordination status in Ningxia is good. The development of the WEEF system under the three development scenarios follows the evolutionary path of “gradual coupling-progressive coordination”, and the water use efficiency improvement scenario achieves the optimal coupling coordination of the WEEF system.

To enhance the adaptability of the “Water-Energy-Food” （WEF） symbiotic system in the Yellow River Basin, promote the synergy of water, energy and food resources, and facilitate ecological protection and high-quality development in the Yellow River Basin, this study took the nine provinces (regions) in the Yellow River Basin as the research area. Based on symbiosis theory, an evaluation index system for the adaptability of the WEF system was constructed. The TOPSIS method was used to calculate the adaptability degree, and an obstacle degree model was applied to discover key constraints to improve the adaptability degree in each province (region). The development and changes in the adaptability of the WEF symbiotic system in the Yellow River Basin from 2012 to 2020 were analyzed. The results show that: a) The adaptability of Ningxia’s WEF symbiotic system shows a slight fluctuating upward trend; The adaptability of Qinghai, Sichuan and Gansu shows a fluctuating downward trend, but has begun to rebound in recent years; The adaptability of five provinces, including Inner Mongolia, Shanxi, Shaanxi, Henan and Shandong, fluctuates within certain ranges. b) According to the standard deviation grading method, Qinghai is a high adaptability zone; Sichuan, Inner Mongolia and Shaanxi are comparatively high adaptability zones; Shanxi, Gansu, Henan and Shandong are medium adaptability zones, while Ningxia is a low adaptability zone. c) Per capita water resources and water resources utilization ratio are the main constraints to the adaptability of the WEF symbiotic system across nine provinces (regions) in the Yellow River Basin. Finally, corresponding suggestions are put forward according to the main obstacle factors for each province (region).

In order to accurately predict the water demand of resource-based water-scarce regions and reveal the driving mechanisms, this paper took the typical resource-based water-scarce  Taiyuan as the research object, integrating Principal Component Analysis （PCA）, eXtreme Gradient Boosting （XGBoost） and Long Short-Term Memory （LSTM） network to construct a three-stage prediction framework of “feature dimension reduction-feature selection-time series correction”. Firstly, PCA was used to reduce the dimension of 11 initial indicators, extracting three types of principal components: economy-industry-urbanization, agriculture-efficiency and nature-ecology. Then, the XGBoost algorithm was adopted to rank the importance of PCA factors and conduct preliminary predictions. Finally, the preliminary prediction values of PCA and XGBoost were jointly input into the LSTM network to deeply explore the long-term dependence and nonlinear dynamic characteristics of the water demand sequence. The results show that: a) The PCA-XGBoost-LSTM model has an average absolute error E_a of 0.003, a root mean square error E_r of 0.024, a goodness of fit R² of 0.970, and a Nash coefficient η of 0.930 on the test set. Its prediction accuracy and stability are significantly better than those of single models such as XGBoost and LSTM. b) The cumulative variance contribution rate of the first three principal components (PC1, PC2, PC3) reaches 86.3%, reflecting the water demand characteristics of resource-based  water scarity in Taiyuan City. c) Sensitivity analysis shows that the water demand elasticity coefficient of GDP is 0.76 (<1), indicating that the dependence of economic growth on water resources is decreasing marginally; The elasticity coefficient of agricultural irrigation water use (-0.85) has the largest absolute value, indicating that improving water use efficiency is the key approach to alleviate the imbalance between water supply and demand.

In order to support water resources precision management in water-scarce northern regions, a water resources carrying capacity evaluation index system was constructed based on the DPSIR (Driving Forces-Pressures-States-Impacts-Responses) framework. Utilizing the Game Theory Composite Weighting Method and the Extension Cloud Model, it assessed the water resources carrying capacity of Changyuan City from 2013 to 2022. The results revealed that: a) During the study period, the water resources carrying capacity level of Changyuan City increased significantly during the study period, rising from Grade V (serious overload) in 2013 to Grade I (bearable capacity) in 2022. b) Key factors influencing the water resources carrying capacity levels of Changyuan City were the green coverage rate, ecological environment status index, water production modulus and urban sewage treatment volume. c) A sudden decline in the water resource carrying capacity levels was observed in Changyuan City between 2018 and 2019, with a return to normalcy after 2020. This was primarily attributed to a decrease in total water resources, water wastage and low efficiency in urban sewage treatment. By analyzing the impact of various indicators on water resource carrying capacity, this study provides references for formulating measures to enhance the water resources carrying capacity in Changyuan City.

To improve the prediction accuracy of groundwater levels, this study proposed Multi-level Empirical Wavelet Transform (MEWT) method. A groundwater level prediction model of MEWT-HTS/CFA/MDWA/TEO/CryStAl/FLA/EVO/ROA/PSA/AROA-HKELM was proposed. The model integrated the MEWT, Hybrid Kernel Extreme Learning Machine (HKELM) predictor, and ten “physical” algorithms including Heat Transfer Search (HTS), Coulomb-Franklin Algorithm (CFA), Moving Damped Wave Algorithm (MDWA), Thermal Exchange Optimization (TEO) algorithm, Crystal Structure Algorithm (CryStAl), Fick’s Law Algorithm (FLA), Energy Valley Optimization (EVO) algorithm, Rime Optimization Algorithm (ROA), Propagation Search Algorithm (PSA), and Attraction-Repulsion Optimization Algorithm (AROA). The models were validated using groundwater level time series data from Chenguan, Lin’an and Dongcheng hydrological stations in Yunnan Province. Firstly, the MEWT was used to perform dipole decomposition on the groundwater level time series. Secondly, based on the training sets of decomposed components, an objective function for HKELM hyperparameter optimization was constructed. The ten “physical” algorithms were employed to search for the extreme value optimization of this objective function. Finally, a MEWT-HTS/CFA/MDWA/TEO/CryStAl/FLA/EVO/ROA/PSA/AROA-HKELM model was constructed to predict and reconstruct the various components of the groundwater level time series. The results show that: HTS/CFA/CryStAl/FLA/EVO/ROA/PSA/AROA has better optimization ability than MDWA and significantly better than TEO. The MEWT-HTS/CFA/CryStAl/FLA/EVO/ROA/PSA/AROA-HKELM model has Mean Absolute Percentage Errors (MAPE) values ranging from 0.486 2% to 0.341 2%, 0.266 9% to 0.222 4%, and 0.048 6% to 0.044 8% for predicting Chenguan, Lin’an and Dongcheng, respectively. The Determination Coefficient (DC) value is above 0.999 9, indicating ideal prediction performance. The optimization performance of the ten “physics” algorithms is consistent with the prediction accuracy of the ten models. Overall, the stronger the optimization performance of the algorithms, the better the hyperparameters obtained for HKELM, and the higher the prediction accuracy of the constructed models. MEWT has the advantages of good decomposition effect and fewer decomposition components, making it a concise and efficient decomposition method.

The gap between water supply and demand in Gansu Province continues to widen, accompanied by the superposition of supply-demand conflicts and ecological degradation. This study selected Gansu Province as the research area. From the perspective of the supply-demand trade-off of water conservation services, a water conservation supply assessment model and a water demand assessment model were used to quantitatively evaluate changes in the water conservation supply-demand pattern. The results showed that: From 2000 to 2020, the spatial distribution of water conservation volume in Gansu Province exhibited a pattern of high in the south, low in the north, low in the west and high in the east. High water conservation volume was concentrated in the Qilian Mountains and the Qinling Mountains. Meanwhile, water conservation volume showed a fluctuating trend of increase, decrease, and then increase. The maximum water conservation deficit increased from 303.701×10⁴ m³ in 2000 to 648.525×10⁴ m³ in 2020. The deficit areas shifted from a scattered northwest-southeast and southwest-northeast distribution pattern in 2000 to an agglomeration around the Lanzhou metropolitan area. The distribution pattern of cold and hot spots of water conservation supply-demand surplus changed. Hot spots migrated from Gannan to the eastern part of the Qilian Mountains, while the cold spot agglomeration area continued to expand, spreading from the junction of Jiuquan, Jiayuguan, and Zhangye cities along the Hexi Corridor into Zhangye City.

To support the high-quality development of the Yellow River Basin, this study evaluated the green, coordination and efficiency indices of high-efficiency water resource utilization in the basin based on a mixed weighting approach. Using a Difference-in-Differences (DID) model, this study examined the policy shock, multidimensional impacts and policy integration effects following the establishment of the national and Yellow River Basin water rights trading platforms in 2016. The results indicated that: Water rights trading significantly improved the green index of high-efficiency water resource utilization in the Yellow River Basin, with a specific increase of 6.540; The coordination and efficiency indices increased by 1.243 and 1.972, respectively. From the perspective of policy integration, the integration of water-saving policies, afforestation policies and water rights trading policies significantly enhanced the green index of high-efficiency water resource utilization in the basin. Furthermore, the integration of transfer payment policies for key ecological function zones and water rights trading policies contributed to some extent in promoting the coordination index of regional high-efficiency water resource utilization.

Water resources are the core lifeline for ecological conservation and high-quality development in the Yellow River Basin. Studying the Pressure-State-Response (PSR) coordination of the water environment in the Yellow River Basin is crucial for promoting ecological conservation and high-quality development. Using the PSR framework model, this study systematically analyzed the PSR of the water environment and its coordinated development degree in the Yellow River Basin. A panel data model was applied to explore the impact of green development in cities, land use, population and industry—under the guidance of “Basing Four Aspects on Water Resources”—on the PSR coordination of the water environment. The results showed that: From 2011 to 2020, the coordinated development of the water environment PSR in the Yellow River Basin showed a positive trend of improvement. The water environment pressure was higher in the middle and lower reaches than in the upper reaches. In the lower reaches, response measures implemented were particularly effective, leading to a marked rise in the PSR coordinated development degree. The comprehensive green development index of the Yellow River Basin increased significantly and had a significant positive impact on the coordinated development degree of the regional water environment PSR. Under the “Basing Four Aspects on Water Resources” principle, the constraining and forcing effect of water environmental conditions led to a “stress optimization” effect in urban and industrial development; However, the positive effects of land and population were not significant and need further enhancement.

The Yellow River Basin is the region with the most severe soil and water loss and has the most fragile ecological environment. The water and soil conservation measures in the Yellow River Basin have significant ecological, economic and social effects. To provide theoretical support for enhancing the functional value of soil and water conservation, realizing its exchange value and amplifying its financial value, and to offer references for accelerating the formation of a pattern for realizing the value of ecological products in soil and water conservation in the Yellow River Basin and promoting the high-quality development of soil and water conservation in the Yellow River Basin, this study, from the perspective of sustainable development, revealed three value forms of soil and water conservation effects: ecological products embodying functional value, ecological commodities embodying exchange value, and ecological financial products embodying financial value. Furthermore, it clarified the value conversion process of soil and water conservation effects through four stages: valorization, productization, commercialization and financialization. It also expounded on five mechanisms of value transformation, including clarification of property rights, value accounting, value pricing, market absorption and financial innovation. Countermeasures for improving the value transformation mechanism are proposed, including establishing the common ownership of water resources in the basin, refining the value accounting models and methods for soil and water conservation, and implementing a differentiated value pricing mechanism.

To achieve visualization of construction management and precise operational decision-making for long-distance water supply projects under the EPC model, a digital twin smart construction system was developed, integrating engineering design and construction. This system was based on an architecture comprising an infrastructure layer-digital twin platform-smart application layer. It developed an engineering design-construction integration digital twin-based smart construction system. The system incorporated design-construction integrated parametric forward design technology, design-construction model conversion and rendering technology, knowledge extraction technology, and a digital twin system for project progress. It was applied to the “Northern Three Counties” water supply project in Langfang City. Application results indicate that: Based on a deep semantic mapping protocol with IFC standards and data dictionaries, the conversion from design BIM models to construction BIM models is achieved. Five key metrics of the exported model (including geometric accuracy and material texture fidelity) all exceed 90%. Rendering the model using a combined optimization technique of dynamic LOD and GPU instancing rendering, improves the frame rate and Video Random Access Memory saving rate of the optimized models. Establishing a project-level localized knowledge base achieves a knowledge retrieval accuracy rate of 92%. The system effectively addresses the current challenges in construction and management of EPC water supply projects, enabling efficient and smart project operations.

In order to solve the problems of low efficiency, large error and difficult audit of traditional water conservancy project maintenance and repair funds calculation, this study introduced digital twin technology, and constructed an intelligent calculation model based on the product matrix of project quantities, quota standards and adjustment coefficients. The model collected the basic engineering data through the digital twin platform, constructed the rule base according to the calculation standard, realized the automatic matching of the adjustment coefficient, and used matrix operations to improve the calculation efficiency. At the same time, a set of multi-dimensional dynamic audit mechanism was designed, which integrated state perception, spatial feature review, business rule verification and blockchain certificate deposit technology to realize multi-level and whole-process audit of fund declaration data. The budget management system for water conservancy project maintenance funds, developed based on the above model and mechanism, was put into use in 79 water management units of the Yellow River  Conservancy Commission. It increased the work efficiency of multi-level manual filling, review and summary by three times. The calculation results were verified to be accurate and reliable, achieving a two-way breakthrough in speed and accuracy, and providing a scalable digital application solution for quota-based fund management in the water conservancy industry.

Scientifically evaluating the value of data assets for cascade hydropower stations in river basins is crucial for optimising resource allocation and enhancing data management efficiency. This paper proposed a comprehensive lifecycle assessment methodology for data asset value tailored to cascade hydropower stations in river basins. Leveraging knowledge graph and data lake technologies, this approach integrated multi-source heterogeneous data from cascade hydropower stations in river basins. Machine learning and fine-tuned large models were employed to automatically generate data asset catalogues. The HydroDV-CPAR model quantified the value of data assets across nine dimensions: intrinsic value, cost value, indexing value, schedule value, quality value, safety value, production value, environmental value and decommissioning value. Building upon this, a data asset management platform was developed. The effectiveness of the proposed methodology was validated using safety risk data, borehole trajectory data and dispatching data from cascade hydropower stations in river basins. The verification results demonstrate that: The method can automatically generate corresponding data asset catalogues and quantify data asset value. The platform successfully implements data collection, equipment monitoring, data warehouse construction, expert-level question-answering, and data asset value assessment functions.

An efficient canal system to optimize water distribution is essential to improve irrigation water use efficiency in irrigation districts. Aiming at the current situation of Xinjiang Wujiaqu Irrigation District, which has many channels and large leakage losses, a water distribution model was established with the optimization objectives of minimizing the leakage losses of the main and branch channels and minimizing the time difference of water distribution in the rotational irrigation groups. The beetle swarm optimization algorithm was employed to solve the model, and the results were compared and analyzed with those obtained by the particle swarm optimization algorithm. The rotational irrigation groups obtained by the beetle swarm optimization algorithm are more practical, and the water distribution time is shortened from 15.0 days to 8.7 days, which is 42.00% shorter; The total leakage loss is reduced from 11.06×10⁵ m³ to 6.10×10⁵ m³, which is 44.85% less. The improved beetle swarm optimization algorithm has more significant advantages than the particle swarm optimization algorithm, and can better meet the water distribution needs of the irrigation district.

To solve the problem of traditional methods failing to capture dynamic market price fluctuations in predicting material unit prices for water conservancy project maintenance and repair quota standards, this paper proposed a Long Short-Term Memory-Random Forest (LSTM-RF) hybrid prediction model based on Bayesian Optimization (BO). The model integrated time-series analysis and multi-factor nonlinear relationship processing capacity, significantly improving prediction accuracy. Using time-series data from 32 units (2 560 periods) for training and validation, the model incorporated 11 influencing factors: input regional GDP, total construction industry output, consumer price index, ambient air quality indicators, etc. It accurately predicted mortar unit prices for the next 20 periods, providing a scientific basis for quota pricing. The results show that: The LSTM-RF hybrid model based on BO greatly enhances the scientificity, rationality and reliability of material unit price determination.