Rural water conservancy and hydropower is an important component of water conservancy work， which is related to national flood control security， water supply security， food security， ecological security and comprehensive rural revitalization. During the 14th Five-Year Plan （2021-2025） period， notable progress was achieved in rural water supply security， modernization of irrigation areas， agricultural water conservation and efficiency enhancement， remediation of key waterlogged areas， and the green transformation of small hydropower. However， challenges remain. In the 15th Five-Year Plan （2026-2030） period， we shall fully implement the spirit of the 20th National Congress of the Communist Party of China and the Plenary Sessions of the 20th Central Committee， and aim at the objectives set forth in the Outline of the 15th Five Year Plan for National Economic and Social Development of the People’s Republic of China. The “3+1” Standardized Construction and Management Mode featuring integrated urban-rural water supply， large scale centralized water supply， standardized small-scale water supply and professional county-wide unified management shall be fully implemented. Efforts will be accelerated to advance the modernization and renovation of large- and medium- sized irrigation districts as well as the regulation of key waterlogged areas， improve the institutional and policy system for agricultural water-saving and efficiency enhancement， further promote the green transformation and high-quality development of small hydropower， strengthen reform and innovation， and optimize institutional mechanisms， so as to strive for the development goals of rural water conservancy and hydropower in the 15th Five-Year Plan period and make new contributions to high quality water conservancy development and national water security guarantee.

After years of development， China has established a relatively complete rural water supply infrastructure system. During the 15th Five-Year Plan （2026-2030） period， the principal challenge facing rural water supply will lie in unbalanced and inadequate development， and achieving decisive progress in rural water supply modernization will be the foremost development task. Given differences in natural conditions， population distribution， levels of economic and social development， and existing development foundations， there is no one-size-fits-all standard or model for rural water supply modernization. The key is to adapt measures to local conditions and changing circumstances. We should fully implement the “3+1” Standardized Construction and Management Mode for rural water supply， so that the layout， scale， and service capacity of rural water supply systems can be aligned with urban rural integration， rural revitalization， and the national water network strategy， while remaining compatible with rural economic and social development and the reasonable water-use needs of rural residents. The “3+1” Standardized Construction and Management Model should be adopted to promote the coordinated improvement in infrastructure and management. Upgrading renovation shall be carried out by tiers based on development foundations， layouts optimized by zones according to regional conditions， and quality and efficiency improved by categories of project types， so as to advance the “three transformations” of rural water supply. Through sustained efforts， the goal of basically achieving rural water supply modernization by 2035 is expected to be achieved。

Large and medium-sized irrigation districts serve as the core carrier and key water conservancy pillar for safeguarding national food security， important nodes in the national modern water network， and an essential foundation for agricultural modernization. Based on the current development status of irrigation districts in China， this paper systematically reviews the basic conditions of large and medium-sized irrigation districts and the history of their rehabilitation， supporting-facility completion， and water-saving renovation. It comprehensively summarizes the achievements attained， articulates the connotation of modern irrigation districts， and puts forward implementation pathways and policy recommendations for the modernization and high?quality development of large and medium-sized irrigation districts during the 15th Five-Year Plan （2026-2030） period from the dimensions of overall advancement， management innovation， digital and intelligent empowerment， institutional improvement， linkage with high-standard farmland development， and long-term mechanism building. This study provides theoretical references and practical evidence for consolidating the foundation of agricultural and rural modernization and food security， and for advancing the high-quality development of rural water conservancy.

Water conservancy in pastoral areas is an important measure for protecting grassland ecosystems and supporting high-quality animal husbandry， and serves as a key instrument for strengthening ecological security barriers and safeguarding water security in pastoral areas. Guided by the orientation toward high-quality water conservancy development during the 15th Five-Year Plan （2026-2030） period， this study reviews the development history and stage-specific characteristics of water conservancy construction in pastoral areas， summarizes the achievements made in the development of forage and feed-crop land， water supply for grazing areas， ecological conservation， and supporting capacity， and identifies practical challenges， including tightening water-resource constraints， imbalances in the coordinated development of water resources， forage， and livestock， inadequate supporting facilities， and lagging modernization of governance. It further analyzes the development opportunities created by national policy directions， the “Greater Food” approach， rural revitalization， sci-tech innovation， and institutional reform， and proposes specific implementation pathways for the high-quality development of water conservancy in pastoral areas during the 15th Five-Year Plan period. The study proposes establishing a modern pastoral water conservancy system encompassing water-resource allocation， secure supplies of irrigated forage， pasture water supply security， ecological conservation and smart restoration， and modern governance and support mechanisms； and fostering a development pattern characterized by “determining forage production based on water availability， determining livestock numbers based on forage availability， and pursuing development on a scientific basis，” thereby providing robust safeguards for the intensive and safe use of water resources and the construction of ecological security barriers.

Small hydropower is internationally recognized as a clean and renewable energy source， and also an important component of China’s rural water conservancy infrastructure system. It has made historic contributions to ensuring rural electricity supply， optimizing the energy structure， improving the ecological environment， supporting poverty alleviation， and enhancing people’s livelihoods. Since the beginning of the 14th Five-Year Plan （2021-2025）， China has thoroughly implemented the Xi Jinping Thought on Ecological Civilization， further advanced the clean-up and rectification of small hydropower stations， continuously strengthened ecological flow and safety supervision， actively guided the exploration of green transformation and upgrading， and achieved remarkable phased results in the green transformation of the industry. Entering the 15th Five-Year Plan （2026-2030） period， China is advancing ecological civilization in depth， accelerating the construction of a new power system， and comprehensively promoting rural revitalization， presenting small hydropower development with new circumstances and requirements. It is imperative to establish and practice a correct view of political achievements， fully， accurately， and thoroughly implement the new development philosophy， and firmly uphold the guiding principle of “ecology first and green oriented development” for small hydropower in the new era. We must balance high-quality development with high-level safety， centering on building modern small hydropower systems that are safe， green， intelligent， and people-centered. This involves optimizing development layouts， implementing intelligent， intensive， and standardized upgrades， improving mechanisms for ecological protection and restoration， enhancing capabilities in safety governance， expanding diversified value functions， strengthening institutional safeguards and policy support， achieving high-quality green transformation， and supporting the comprehensive green transition of China's economy and society.

The total area of waterlogged regions nationwide is approximately 894 million mu. By the end of the 14th Five-Year Plan （2021-2025） period， the cumulative area that has been drained and improved exceeds 380 million mu， accounting for about 43% of the nation’s total waterlogging-prone area. As for the untreated areas， prominent challenges remain， including inadequate drainage engineering systems， insufficient capacity alignment， imperfect operation and maintenance mechanisms， and the need for enhanced coordination between regional drainage and basin-scale flood control. During the 15th Five-Year Plan （2026-2030） period， extreme weather events， high-quality economic and social development， the national food security strategy， comprehensive rural revitalization， and high-quality water conservancy development have all created urgent demands for waterlogging control. Emerging technologies such as digital twins provide important support for enhancing waterlogging management capabilities. Given the new situation and new requirements， it is recommended that key projects be taken as the starting point， and efforts be promoted in a coordinated manner across four dimensions： clarifying the current status， project layout， measure selection， and management/maintenance scheduling. Systematic investigations and assessments of waterlogged areas should be carried out， the scope of management and waterlogging control standards should be scientifically defined， project layouts and control measures should be optimized according to local conditions， and the construction of backbone projects， field works， and digital/intelligent infrastructure should be advanced simultaneously. Furthermore， a long-term operation and maintenance mechanism should be improved， so as to comprehensively enhance the overall capacity for agricultural disaster prevention and mitigation， thereby providing solid water conservancy support for safeguarding national food security and promoting high-quality regional development.

To address the limited adaptability of traditional conceptual hydrological models in simulating complex runoff processes， this study proposes a hybrid modeling framework， termed GR4J-DL， which integrates the GR4J model with deep learning architectures. The proposed framework retains the runoff generation structure of GR4J while employing three neural network architectures—LSTM， TCN， and TKAN—to represent nonlinear flow routing processes， thereby enhancing the model’s ability to capture temporal dynamics and spatial structural information. The Enshi hydrological station in the upper Qingjiang River basin is selected as a representative case， and daily meteorological and runoff data from 2010 to 2018 are used for model training and validation to evaluate the performance of different hybrid configurations. The results indicate that， except for the GR4J-LSTM configuration， the GR4J-DL hybrid models achieve consistent improvements in overall simulation accuracy. Among them， the TKAN-based model performs best， with an NSE of 0.757 3， RMSE of 43.67 m³/s， KGE of 0.800 and TPE of 0.212， demonstrating a stronger capability in representing nonlinear runoff behavior. Further feature contribution analysis based on the SHAP method reveals that the intermediate state variables generated by the GR4J runoff generation module primarily act as physical constraints and auxiliary information， which contributes to improving model accuracy. Overall， this study broadens the applicability of deep learning in runoff modeling and provides a promising pathway for enhancing the simulation of complex runoff processes.

Rainfall is a critical input to hydrological models， and its data quality directly impacts the accuracy and reliability of runoff simulation. Investigating the influence of rainfall input errors on runoff simulation is essential for identifying the sources of hydrological simulation errors and improving simulation precision. This study systematically analyzes the comprehensive impact of multiple types of rainfall errors on runoff simulation using the Xin′anjiang （XAJ） model， integrated with explainable machine learning methods. A total of 12 typical flood events were selected， and a multi-dimensional rainfall error perturbation scheme—including systematic bias， intensity bias， and spatiotemporal distribution errors—was designed. Twelve rainfall error features were extracted， and XGBoost was applied to explore the relationship between rainfall errors and runoff simulation， thereby constructing a predictive model for the simulation accuracy （Nash–Sutcliffe efficiency， NSE， and Relative Error， RE） of the XAJ model. SHAP values were further used to quantitatively analyze the influence mechanisms of rainfall error features on runoff simulation accuracy， and to evaluate the global importance， directional effects， and interactions of each rainfall error feature. The results show that： The XGBoost model effectively predicts runoff simulation accuracy （NSE and RE）， with an average coefficient of determination （ R 2） exceeding 0.96 on the test set. Relative Bias （RB） of rainfall is the dominant factor affecting runoff simulation accuracy， with heavy rainfall bias and Root Mean Square Error （RMSE） also being important influencing factors. RB exhibits a strong linear relationship with RE， but a nonlinear relationship with NSE， accompanied by a threshold effect： when RB exceeds 15%， NSE decreases significantly， and overestimated rainfall has a more pronounced impact on NSE than underestimated rainfall； When the rainfall RMSE exceeds 0.2 mm/h， it interacts with RB to jointly intensify runoff simulation uncertainty. The findings provide a new analytical framework and quantitative basis for controlling rainfall input errors in runoff simulation and improving the accuracy of flood forecasting.

Water-supply reservoirs， as important infrastructure that supports both industrial water supply and domestic water supply， are characterized by frequent fluctuations in water level； consequently， it is necessary to establish an accurate relationship between water level and storage capacity in order to realize refined scheduling， to further clarify the applicability of different calculation methods in the measurement of storage capacity for water-supply reservoirs， and to achieve effective management of such reservoirs as well. Taking the Zhaobishan Reservoir， which withdraws water from the Shapotou North Main Canal in the upper reaches of the Yellow River， as the example， this study obtains above-water and underwater topographic data through the combined use of low-altitude UAV aerial photogrammetry and an unmanned surface vessel-mounted echo sounder. The ArcMap module within ArcGIS software is used to realize spatial integration of both the above-water terrain data and the underwater reservoir-bed elevation data， thereby generating a high-precision digital elevation model （DEM） that fully covers the entire reservoir area. Three methods， namely the DEM method， the contour volume method （CVM）， and the cross-section method （CSM）， are applied to calculate the reservoir storage capacity under different water-level conditions and plot storage capacity curves. For the storage capacities computed by the above three methods for partitioned segments between the dead water level and the check flood level of the Zhaobishan Reservoir， verification and rechecking are carried out with South CASS software. The results of the study indicate that CVM attains the highest accuracy together with the best stability across the full water-level range， remaining stably within 0.21% to 0.23% at the key levels of normal storage level 1 311.20 m， design flood level 1 311.60 m， and check flood level 1 312.00 m， and in particular it is therefore recommended as the preferred method for the calculation of storage capacity in water-supply reservoirs. The DEM method performs well within the high water-level range， achieving relative error levels between 0.05% and 1.78% and thus serving as an alternative method under specific water-level conditions. The CSM exhibits relative errors between 3.54% and 12.89% across the full water-level range and is not suitable for the storage-capacity calculation of water-supply reservoirs. Finally， on the basis of calculation data derived from CVM， a shifted power function water level-water surface area relationship model and a cubic polynomial water level-storage capacity relationship model are established， thereby achieving the transformation from discrete data to continuous functional representations. The technical workflow constructed in this study， consisting of the sequential steps of data acquisition， method comparison， error evaluation， and model establishment， provides a reliable basis for refined calculation of reservoir storage capacity and reservoir scheduling of water-supply reservoirs.

The acceleration of urbanization and the increasing frequency of extreme rainfall events have posed prominent problems such as urban flooding and overflow pollution to the urban drainage system. Particularly in areas with severe misconnections between stormwater and wastewater pipelines， where strong rainfall can easily trigger sewage manhole overflow. To address this issue， this study focuses on a China urban area with known storm–sewage misconnections and overflow risks. By investigating the misconnection conditions， installing water-level monitoring devices， and deploying self-developed intelligent pipeline control gates， combined with an SWMM-based hydraulic simulation model， the study reconstructs the hydrodynamic characteristics of the drainage system. The model achieves a Nash–Sutcliffe efficiency coefficient of ≥0.75 at key monitoring points， accurately reproducing water-level variations under both dry and wet weather conditions， thereby providing a reliable foundation for scheduling decisions. Under both typical design storms and actual rainfall events， the study evaluates the overflow-control effectiveness of different gate operation combinations. Results indicate that closing a greater number of gates leads to a more pronounced reduction in the peak water level at the overflow-prone point WS7. During the rainfall event on May 22-23， 2025， with a cumulative precipitation of 55.8 mm， closing all six gates controlled the peak water level at WS7 to 2.55 m， effectively preventing approximately one hour of overflow while maintaining operational safety margins. Further simulations show that the system with the existing six gates can withstand a similar 12-hour rainfall event with a total precipitation of 95.7 mm. Water-quality monitoring and time-series gate-operation analysis reveal that timely gate closure after a noticeable rise in water level can effectively intercept highly polluted first-flush runoff while reducing the inflow of low-pollution stormwater into the wastewater system， achieving dual benefits in overflow mitigation and water-quality protection. The study verifies the feasibility of a drainage scheduling framework that integrates model-based decision support， intelligent control facilities， and a smart water-management platform. Since its routine deployment in May 2025， the system has successfully prevented overflow events during multiple heavy rainfalls in the study area， demonstrating strong engineering applicability and significant potential for broader implementation.

To improve the accuracy of flood forecasting in reservoir-influenced basins， the Shangyou River Basin was selected as the study area. According to differences in reservoir scale and the intensity of human activities， the study area was classified into three typical subregions： natural basins， small-reservoir-influenced basins， and large- and medium-size-reservoir-influenced basins. The simulation performance of the Xin’anjiang （XAJ） model and the Long Short-Term Memory neural network model （LSTM） was compared across these different subregions， and the effectiveness of the coupled XAJ-LSTM model in improving hydrological forecasting accuracy for reservoir-influenced basins was further investigated. The results indicate that： ① In natural basins， the XAJ model outperformed the LSTM model in overall simulation performance， with an NSE of 0.82 and a Bias of -3.48%， compared with an NSE of 0.76 and a Bias of -4.12% for the LSTM model. ② In reservoir-influenced basins， the LSTM model exhibits stronger applicability than the XAJ model， with NSE values higher than 0.66. ③ By effectively integrating the advantages of both models， the coupled XAJ-LSTM model further improved flood forecasting accuracy and outperformed the single models in reservoir-influenced areas， with NSE values above 0.7 and relative flood volume errors within ±5%.

Whether the transfer of agricultural water rights can drive high-quality development in the Yellow River Basin， whether the driving effect is sustainable over time and space， and whether it will lead to uncoordinated regional development are core arguments at economic level surrounding the spatial allocation of water resources in the basin. To empirically examine the effects of agricultural water rights transfer and address the above debates， a spatial difference-in-differences （SDID） model is constructed using panel data from 22 cities in the basin spanning 1999-2023. The results show that the transfer of agricultural water rights significantly drives high-quality urban development in the basin. Temporally， the driving effect persists for three years， increasing the latter by 1.68%， and exhibits heterogeneity： it peaks in the 2^nd to 3^rd year after implementation， and then weakens， and disappears in the 4^th year， highlighting risks to long-term sustainability and providing policy validity warnings. Spatially， the policy generates positive spillover effects that drive high-quality development in neighboring cities， thereby contributing to regional coordinated development. However， the spatial spillover effect follows an inverted U-shaped spatial attenuation characteristic： peaking at 300 km， and reaching the spillover boundary at 1 100 km. Mechanism analysis reveals that the transfer of agricultural water rights drives urban high-quality development through pathways such as transferee optimization， scale-driven effects and coordinated and shared development， but fails to effectively promote quality-driven factors， such as efficiency， innovation， and green development.

Addressing the conflicts between excessive water resource development and multi-sectoral demands in northern headwater regions， this study integrates river and lake health assessment with Nature-based Solutions （NbS） principles to develop a deeply coupled System Dynamics （SD） and Multi-Objective Optimization （MOO） model. The proposed framework explores refined allocation pathways to enhance aquatic ecosystem resilience and solve dynamic regulation challenges under environmental changes. Within this framework， the SD module simulates the long-term evolution of the water resource system and provides dynamic scenario inputs for the MOO module， which synergistically optimizes socio-economic and ecological water use objectives to achieve refined and resilient allocation. Taking Liaoyuan City as a case study， the optimization scheme significantly enhances integrated benefits： reserved ecological water volumes are projected to increase to 0.80×10? m3 in 2025 and 0.85×10? m3 in 2035， substantially improving the satisfaction degree of ecological water requirements. This research provides a scientific basis for dynamic regulation of water resources and the enhancement of ecological resilience in northern headwater regions facing the dual pressures of climate change and human activities.

In response to the common problem of “water quality-based water shortage” faced by megacities in humid regions， this study quantifies the mechanism by which water quality constraints restrict available water resources， building upon existing research on the coupling of water quantity and water quality. The aim is to reveal the nonlinear constraint mechanism of water quality constraints on the water resources carrying capacity （WRCC） and identify the optimal development path. Taking Suzhou City as a case study， this research couples the fuzzy comprehensive evaluation method （FCE） with system dynamics （SD） to construct a comprehensive simulation and evaluation model of WRCC. An index system is established from four dimensions： water quality， water resources， social economy， and ecological environment. The cooperative game theory is used to integrate the analytic hierarchy process （AHP） and entropy weight method for the comprehensive weighting of subsystems. Based on the Vensim-PLE platform， an SD model is constructed to simulate the dynamic changes of WRCC in Suzhou City from 2024 to 2035 under five scenarios： the status quo continuation type， water-saving type， industrial structure adjustment type， water quality pollution control type， and comprehensive development type. The research results show that： ① water quality constraints are the rigid bottleneck of WRCC in megacities in humid regions， further deepening the applicability of the water quantity–water quality coupling carrying capacity theory in humid regions. ② Scenario simulation shows that by 2035， the WRCC score under the status quo continuation scenario drops to 0.526， which is a medium carrying capacity level， while the water quality pollution control type and comprehensive development type scenarios can increase the WRCC score to 0.802 and 0.819 respectively， entering a strong carrying capacity level， indicating that water quality improvement can directly alleviate the water quality-based water shortage problem in Suzhou. ③ The comprehensive development type achieves the best effect through the positive feedback effect of the multi-dimensional collaborative path of “water quality improvement–industrial optimization–water conservation coordination”. This study provides a feasible solution for water resources management in Suzhou City and theoretical support and operational paths for Suzhou and similar regions to break through the water quality-based water shortage dilemma.

To investigate the effects of different soil conditioners on soil pH and the remediation for bioavailable cadmium in rice fields， three field experiments were conducted under traditional flooding conditions. The experiments included a blank control group （Control Field A）， an iron-based biochar conditioner treatment group （Experimental Field B） and a modified zeolite conditioner treatment group （Experimental Field C）. The application rates of iron-based biochar conditioner and modified zeolite conditioner were 50 kg/hm2 and 100 kg/hm2 for Experimental Fields B and C， respectively. Comparative studies were performed on the characteristics of soil pH and bioavailable cadmium content changes at depths of 0–10 cm， 10–20 cm， and 20–30 cm below soil surface. The results indicated that both iron-based biochar and modified zeolite conditioners alleviated soil acidification which reduced the content of bioavailable cadmium in the soil. Compared to the Control Field A， the soil pH values at 0–10 cm， 10–20 cm， and 20–30 cm depths in Experimental Fields B and C increased by 14.8%， 15.7%， 3.1% and 6.3%， 8.1%， 1.4%， respectively. Concurrently， the corresponding available bioavailable cadmium content in Experimental Fields B and C decreased by 58.3%， 53.8%， 17.8% and 43.1%， 35.5%， 10.0%， respectively. Iron-based biochar demonstrates a more significant effect in alleviating rice soil acidification and reducing bioavailable cadmium content. This finding provides significant theoretical and practical references for addressing both soil acidification and cadmium contamination in rice fields.

In order to maximize the net economic benefits of agricultural production systems while meeting water environmental protection requirements， and to scientifically characterize the complex uncertainties involving both randomness and fuzziness within the agricultural non-point source pollution management system， this paper proposes an optimization model for agricultural non-point source pollution control decision-making based on a novel fuzzy-boundary left-hand-side chance-constrained programming method. With the objective of maximizing the agricultural system’s net economic benefits， the model takes crop planting area， livestock and poultry breeding scale， and rate of fertilizer application as decision variables， subject to constraints including achieving watershed water quality standards and ensuring that agricultural irrigation water demand does not exceed the region’s available water resources. To address the multiple uncertainties arising from variable natural conditions and estimation errors in pollution management parameters， as well as the inherent “risk of constraint violation” due to the practical impossibility of achieving 100% compliance with management targets under agricultural activities， a novel fuzzy-boundary left-hand-side chance-constrained programming method is developed by integrating left-hand-side chance-constrained programming with triangular fuzzy numbers and α-cut sets. This framework generates optimal decision schemes under various scenario combinations of environmental management levels （confidence levels） and risk tolerance levels （allowable violation probabilities）.The results indicate that as the confidence level increases， the environmental management requirements of the agricultural system become more stringent， leading to a narrowing of the allowable fluctuation ranges for total crop planting area， livestock breeding scale， and fertilizer application rate. The decision space is reduced by 15% to 20%， and the system’s net economic benefit decreases by 3% to 5%. Conversely， as the allowable violation probability increases， the environmental management standards are relaxed， resulting in an expansion of total crop planting area and livestock breeding scale， and an increase in the system's net economic benefit by 5% to 8%. The study demonstrates that this method can effectively formalize the decision-making needs for agricultural non-point source pollution control into a mathematical model， successfully addressing the impacts of data uncertainty and the possibility of environmental management target violations on environmental decision-making. It thereby provides optimal decision-making schemes for agricultural non-point source pollution control under different environmental management levels.

Bioretention facilities are one of the main types of source emisssion reduction facilities in sponge cities. Currently， there have been many studies on the hydrological effects of bioretention facilities， but the understanding and simulation of the migration rules and water-holding capacity of various water sources remain insufficiently in-depth. This paper， through experiments and stable isotope techniques， studied the water source composition of the outflow process of two bioretention columns with different ratios （60% sandy soil + 40% silty loam soil and 40% sandy soil + 60% silty loam soil）， and obtained the variation rules of the proportion of “old water” and “new water” during the outflow process of the bioretention column experiments. It was found that the duration of the rainless interval between two experimental water injection events （simulated rainfall events） had a significant impact on the proportion of soil water （old water） in the bioretention facilities. A discharge simulation model based on Physics Informed Neural Networks （PINN） was constructed to quantitatively analyze the water-holding capacity of the bioretention columns from aspects such as water-holding capacity， water-holding time， and rainless period. The results indicate that the filling material with a higher proportion of sandy soil showed a stronger water storage potential due to its large pore advantage， especially after a long rainless period， the internal water was more thoroughly drained， thus providing a larger water storage space for subsequent rainfall events. This indicates that the differentiating design of bioretention facilities needs to comprehensively consider factors such as particle size composition， the application of amendments， water-holding capacity， and the local dry period. The research results of this paper have reference value for in-depth understanding of the water and quality regulation mechanism of bioretention facilities， achieving intelligent real-time regulation of bioretention facilities， and precise design and operation and maintenance management.

To assess the impact of cascade reservoir development on the structure and function of river ecosystems， this study conducted a systematic investigation of zooplankton communities at 22 sampling sites across four cascade reservoirs in the middle and lower reaches of the Jialing River in August 2024 （flood season）. The study found that the zooplankton community exhibited clear miniaturization， with 75 species identified. The taxonomic composition was dominated by Rotifera （33 species， accounting for 44.0%） and Protozoa （21 species， accounting for 28.0%）. The average community density was 672.23 ind./L， and the average biomass was 0.18 mg/L. Influenced by both tributary inflows and hydraulic stagnation caused by dam impoundment， high-abundance areas were concentrated at tributary confluences and slow-flowing zones upstream of dams. Based on functional trait analysis， the zooplankton functional groups were dominated by filter-feeding taxa， with Rotifer filter-feeders （RF） and Protozoan filter-feeders （PF） being the primary dominant functional groups. Redundancy Analysis （RDA） results indicated that the key environmental variables driving the spatial differentiation of zooplankton functional groups were permanganate index （COD_Mn） and water temperature （P < 0.01）. The study indicates that the synergistic effect of prolonged hydraulic residence time and organic load accumulation induced by cascade development has driven the evolution of the energy flow path from the traditional grazing food chain to a microbial loop dominated by bacteria-protozoa/rotifers. This study provides a scientific basis for river ecological conservation under cascade development.

To address the complex optical composition of water bodies， wide concentration range of suspended particulate matter （SPM）， and strong spatiotemporal variability in river-reservoir transition zones， a semi-analytical remote sensing inversion method for SPM concentration based on inherent optical properties （IOPs） was developed. By optimizing the reference-band selection in the quasi-analytical algorithm （QAA） and integrating a spectral-angle weighting strategy， this proposed method achieves unified inversion of SPM across different optical water types. The results showed that， under low-to-moderately turbid conditions， the MQR655 algorithm substantially outperformed the QAA-RGB algorithm， with RMSLE decreasing from 0.72 to 0.05 and MAPE decreasing from 98.20% to 6.70%. Under highly and extremely turbid conditions， the z859 method based on a near-infrared reference band exhibited better stability. Application to the Three Gorges Reservoir indicated that SPM concentrations before and after dam flushing periods increased significantly， accompanied by enhanced spatial gradients. The proposed method can effectively characterize the spatiotemporal distribution of SPM in river-reservoir composite waters and provides technical support for remote sensing monitoring in highly dynamic sediment environments.

To enhance the capacity for non-point source pollution control in the Chuantang River Basin， field monitoring data were used to calibrate and validate the runoff， total nitrogen （TN）， and total phosphorus （TP） parameters of the SWAT model. The nitrogen and phosphorus pollution status and the mitigation effects of Best Management Practices （BMPs） were systematically evaluated. The results indicate： ① The SWAT model demonstrated good applicability in simulating the Chuantang River Basin， with both the coefficient of determination （R² ） and Nash-Sutcliffe efficiency coefficient （NSE） of all indicators exceeding 0.61 during the calibration and validation periods. ② From 2022 to 2024， the average annual TN and TP loads in the basin were 2 481 t/a and 177 t/a， respectively. TN and TP outputs were predominantly concentrated in the wet season （March-September）， accounting for 87% and 85% of the annual loads. Spatially， TN and TP loads exhibited a pattern of higher values in the east and lower values in the west， mainly distributed in sub-basins with higher proportions of cropland and urban land. ③ Nitrogen and phosphorus pollution in the basin were jointly influenced by rainfall and land use. Chemical fertilizer reduction， crop residue cover， and vegetative buffer strips all effectively reduced TN and TP outputs. Among single BMPs， the 10-meter vegetative buffer strip achieved the highest reduction rates for TN and TP， at 25.92% and 31.78%， respectively. Among combined BMPs， the combination of a 10-meter vegetative buffer strip with crop residue cover performed best， achieving TN and TP reduction rates of 36.40% and 38.07%， respectively.The findings provide scientific support for controlling nitrogen and phosphorus loads in the Chuantang River Basin and offer decision-making references for the precise management of non-point source pollution and the promotion of green development.

In order to optimize the hydraulic characteristics of a double-sided vertical slot fishway， the RNG k-ε turbulence model was used to conduct three-dimensional numerical simulations of the double-sided vertical slit fishway. The effects of three types of obstacles， namely prisms， cylinders， and semi cylinders， on the hydraulic characteristics of the pool chamber were studied. The study used a double-sided vertical slot fishway with no additional structure as the control group （A0）， and set up three obstacle configurations： fishway pools arranged with prism （A1）， cylinder （A2）， and semi cylinder （A3）， to systematically analyze the hydraulic characteristics of the flow velocity distribution， turbulent energy， unit water energy dissipation rate， and total hydraulic strain of the 0.5 h water depth section in the middle chamber of the fishway. The results show that the placement of obstacles in fishway ponds can significantly alter the main flow path， forming multiple streams of diversion and new backflow areas， providing fish with more upstream path options； The arrangement of obstacles can significantly reduce the flow velocity inside the pool and at the vertical joints. Among them， the arrangement of prismatic obstacles in the pool reduces the maximum flow velocity in the vertical slots from the prototype 1.33 to 1.12 m/s （a decrease of 15.8%）， with an average flow velocity attenuation rate of 19.4%. The average flow velocity attenuation rates of cylindrical obstacles and semi cylindrical obstacles in the pool are 6.4% and 14.3%， respectively； Turbulent kinetic energy analysis shows that both the prismatic and cylindrical obstacles can maintain the maximum turbulent energy to be less than 0.05 m2/s2， while the average turbulent kinetic energy of semi cylindrical obstacles in the pool increases by 18%； The energy dissipation rate per unit of water in the pool room under all obstacle configurations is far below the range of 150~200 W/m3； The total hydraulic strain decreased with both prismatic obstacles and cylindrical obstacles， while the maximum total hydraulic strain of the semi cylindrical obstacles is similar to that of the fishway without additional structures.

Dissolved oxygen is a core element regulating the biochemical processes in river ecosystems， while nitrogen and phosphorus are key factors influencing the nutrient status of water bodies and the productivity of phytoplankton. Accurate prediction of these indicators can provide scientific basis and decision support for water environment management in river basins. This study， based on the Beijing–Hangzhou Grand Canal Basin， developed a spatio-temporal coupling model MKIE11-STG-Transformer that integrates the physical mechanism of the MIKE11 hydrodynamic module and the spatio-temporal graph structure of the STG-Transformer deep learning architecture. By comprehensively utilizing meteorological， hydrological and water quality observation data， it achieved high-precision predictions of key water quality parameters such as dissolved oxygen， nitrogen and phosphorus. The results show that STG-Transformer can effectively learn the hydrological response characteristics output by MIKE11 and outperforms the baseline model in predictions at all stations. Meanwhile， water quality in the basin shows obvious spatio-temporal correlations. By inputting the constructed spatio-temporal graph into the Graph Convolutional Network （GCN） and Transformer model， the spatio-temporal dependencies of water quality factors can be effectively captured. In the design of the loss function， the hydrodynamic equation on which MIKE11 is based is introduced as a physical constraint， and combined with the Stochastic Gradient Descent （SGD） algorithm for optimization， which significantly reduces the uncertainty of the deep learning black box model and improves the reliability of the prediction results. This study provides a new approach that integrates physical mechanisms and data-driven methods for water quality prediction in complex river basin environments.

Revealing the effect of the daily regulation process of the Xiangjiaba Hydropower Station on the propagation of unsteady flow in the downstream river channel has great significance for reservoir operation and downstream waterway maintenance. Based on the hourly discharge and water level time series observed at the Xiangjiaba Hydrological Station from 2018 to 2024， this study analyzed the characteristics of unsteady flow released by the Xiangjiaba Hydropower Station. The results indicate that the average annual discharge of the Xiangjiaba Hydropower Station during 2018-2024 was 4 102.34 m3/s， with the average daily variations in flow and water level being 1 123.82 m3/s and 1.19 m， respectively. A significant linear correlation was observed between these two parameters. Under three typical daily regulation scenarios of the Xiangjiaba Hydropower Station （flow increments of 2 000， 1 600 and 1 000 m3/s）， water level observations were conducted along the downstream river channel. The results show that the amplitude of water level fluctuations caused by unsteady flow generally exhibits an attenuation trend along the river course. Specifically， under the flow increment conditions of 2 000， 1 600 and 1 000 m3/s， the water level fluctuation amplitudes decreased from 2.59， 1.56 and 2.19 m at the Xiangjiaba Hydrological Station to 1.30， 1.26 and 1.50 m in the Yibin section， respectively. Further analysis indicates that the influence of unsteady flow on water level fluctuations is predominantly concentrated in the reach upstream of Pu’an Station. The inflow from the Minjiang River exerts a significant inhibitory effect on the propagation of unsteady flow and the resulting water level fluctuations. In addition， a theoretical formula describing the relationship between the amplitude of water level fluctuations and the propagation distance of unsteady flow was derived in this study， and its validity was verified using the aforementioned observational data. The coefficient of determination （R2） for linear fitting ranges from 0.67 to 0.93， which confirms the rationality and reliability of the proposed theoretical formula. The research findings provide a robust scientific basis for the optimal regulation of hydropower stations and the safety guarantee of downstream waterway navigation and operation.

In this study， a collaborative optimization method for preventing boundary violations in variable-speed pumped storage units （VSPSUs） is proposed to enhance their operational stability at boundary operating points. Firstly， an adaptive adjustable time-delay link is introduced into the AC excitation system. Secondly， based on control requirements， a multi-objective particle swarm optimization （MOPSO） algorithm is employed. The PID parameters of the governor and the delay time are selected as decision variables. A multi-objective optimization framework is established by incorporating the operating characteristics of the unit at different operating points within the stable operating region. Finally， the Pareto optimal solution set is obtained through optimization. A well-balanced solution is selected and compared with fixed PID parameters via simulation. The results demonstrate that the optimized solution effectively coordinates the trade-off between the fast response requirement and the risk of water pressure， rotational speed， or active power exceeding boundaries under different control modes. This study provides valuable technical guidance for ensuring the safe and stable operation of VSPSUs during load variation and primary frequency regulation processes.

The geometric shape and hydraulic characteristics of side inlet/outlet of pumped storage power station significantly affect the operation efficiency of the water hub and system stability. Aiming at the lower reservoir side inlet/outlet of the LB pumped storage power station project， the characteristic body shape parameters （adjustment section length L_a， side pier rearward distance {\mathrm{d}}_{side}， middle pier rearward distance {\mathrm{d}}_{\mathrm{m}\mathrm{i}\mathrm{d}}， and width ratio of middle hole to side hole m） are nondimensionalized. Physical model tests and numerical simulations are used to study their influence laws on the hydraulic characteristics of the side inlet/outlet. The results show that： increasing L_a can reduce the maximum velocity non-uniformity coefficient in the outflow condition； increasing d_{side} can reduce the flow non-uniformity degree of each flow channel in the inflow condition； increasing d_{mid} can reduce the flow non-uniformity degree of each flow channel in the inflow condition； decreasing \mathrm{m} can increase the flow in the side hole and decrease the flow in the middle hole. On this basis， the appropriate value ranges of characteristic body shape parameters are recommended， and the response surface method is used to optimize the body shape of the lower reservoir side test inlet/outlet of the LB pumped storage power station project. Verified by physical model tests， the body shape optimization achieves satisfactory results. The research results can provide references for the design of similar projects.

Considering the highly complex and nonlinear characteristics of hydroelectric unit vibration signals， the fault feature extraction and fault type identification will be encountered with serious challenge. For this， a fault diagnosis method integrating improved complete ensemble empirical mode decomposition with adaptive noise （ICEEMDAN）， scale-adaptive composite fuzzy entropy （SACFE）， and strengthened whale optimization algorithm optimized XGBoost （SWOA-XGBoost） is proposed in this paper. Firstly， the raw vibration signals are firstly decomposed by the ICEEMDAN method. Subsequently， fault features are extracted using SACFE based on its advantage of scale self-adaption. Finally， the fault feature vectors are classified by a XGBoost model optimized by the SWOA algorithm. Experimental results demonstrate 100% recognition accuracy for four fault types in raw signals and also maintain the accuracy of 97.58% under noisy conditions. The developed method shows significant superiority and contributes to valuable theoretical insights and technical enhancements in the fields of hydroelectric unit fault diagnosis.

Aiming at the nonlinear and hydro-electro-mechanical coupling dynamic characteristics of pumped-storage unit regulation systems， an intelligent identification approach is proposed. By integrating the method-of-characteristics （MOC） model of the penstock with the discrete-time state-space representations of the pump-turbine， motor-generator and governor， a comprehensive nonlinear model of the entire regulation loop is established. An improved tree-seed algorithm （ITSA） is introduced as the heuristic optimizer； it synergizes the standard tree-seed framework with opposition-based learning， chaotic local search and an elastic wall mechanism to enhance global exploration. The ITSA is subsequently employed to identify the model parameters. Numerical simulations under a typical load-rejection conditions are carried out with multiple intelligent identification algorithms for comparison， The results demonstrate that compared with conventional intelligent identification schemes， the ITSA-based strategy significantly improves the matching accuracy of both rotational speed and key-pressure responses for the pumped-storage regulation system.

To investigate the chain-like transmission of construction schedule risks in underground cavern group projects for hydropower engineering， this study employs risk chain theory to analyze risk propagation mechanisms through a multi-level interactive framework integrating risk association networks， process networks， and risk consequences. An integrated Monte Carlo–System Dynamics （MC–SD） schedule risk assessment model is developed. MC quantifies the random distribution characteristics of multi-source risk factors—including geological， technical， environmental， and managerial aspects—while SD depicts the dynamic feedback and non-linear cumulative effects within risk chains. The proposed model enables stochastic quantification， transmission path identification， and key node localization of schedule risk. Through case analysis of a practical project， six risk chains are identified by the proposed model. The impact of each risk chain on total construction duration is accurately quantified， and the critical risk transfer paths are located. This study expands the dimension of construction schedule risk analysis from a methodological perspective， and provide methodological support for construction schedule control and risk management in underground cavern group projects.

To address the challenge of controlling sudden displacement in the high sidewalls of deep-buried underground powerhouses characterized by alternating soft and hard rock layers during excavation and unloading， this study investigates the mechanism of discordant deformation and failure evolution under transient unloading. Relying on the Gongyi Pumped Storage Power Station as a case study， a combined approach of theoretical analysis and numerical simulation is employed. A dynamic model for transient unloading in alternating soft and hard rock masses was constructed. The study elucidates the sensitivity mechanism of the dynamic response to lateral constraints and unloading dimensions， and proposes an engineering strategy based on “energy-stress” dual control. Governed by stiffness disparities， the high-rate release of strain energy in hard rock significantly promotes deformation in soft rock. In the final state of deformation， the deformation of soft rock exceeds that of hard rock， creating a mutual constraint effect. This discordant deformation tends to induce interlayer shear dislocation， rendering the soft-hard interface a preferential zone for failure development. As the lateral pressure coefficient （k） and unloading height （h） increase， the transient dynamic relaxation displacement exhibits explosive growth. This indicates that the coupling of “strong lateral pressure and large-scale unloading” represents a typical condition leading to instability. It is recommended to adopt “small-bench layered” excavation to fragment the process （literally "break up the whole into parts"） and control transient energy release. Additionally， a “central trenching first” scheme is suggested to release high in-situ stress， thereby reducing the lateral pressure effect on sidewalls and the risk of interlayer dislocation. This study provides a reference for the excavation design of underground engineering projects under similar complex geological conditions.

Engineering analogy is a crucial method for determining the permeability coefficients of fractured rocks in hydropower projects. However， traditional similarity assessments are highly dependent on subjective experiences and lack quantitative standards， which may introduce uncertainty and safety risks to engineering seepage control design. A data-driven method was proposed for the similarity analysis of rock’s permeability. The method utilizes a confidence ellipse-based similarity index to achieve quantitative assessment of the correlation between permeability characteristics of rock masses across different dam sites. Based on in-situ packer test data from 9 large-scale dam sites in Southwestern China， the joint distribution of permeability coefficient and burial depth was analyzed. The results indicate that the permeability of dam sites A and B， which share similar geological background， exhibits a high overall similarity， with a similarity index of 1.38. Further similarity analysis on different classified subsets reveals that the overall similarity may conceal significant differences or correlations between the subsets under specific conditions. For example， the similarity index between dam sites A and B is only 0.20 in the subset of weakly-unloaded zone， whereas dam site I， which has a low overall similarity index of 0.41 with dam site A， shows a high similarity of 1.58 in the subset of moderately-weathered zone. This study not only provides a general method for quantitative similarity assessment of site-specific rock mass parameters， but also significantly enhances the reliability of parameter determination via analogy.

To enhance the control performance of single-pipe four-turbine hydropower stations， a mathematical model of the water intake system was constructed based on two-port network theory. An integrated model of the regulation system was established， and the Roach Optimization Algorithm （ROA） was introduced to synchronously optimize the PID control parameters of the speed governors for all four turbine units. Simulation verification was conducted on the frequency regulation and valve opening regulation modes of the single-pipe four-turbine hydropower station regulation system. The ROA algorithm was used to optimize and compare the response characteristics of synchronized and asynchronous speed regulation systems， validating the feasibility of the proposed method for optimizing control parameters in such systems. Simulation results demonstrate that after synchronous optimization， system oscillations are reduced by at least 50%， which significantly improves the operation stability of the system.

The 20th National Congress of the Communist Party of China explicitly identified “high-quality development” as the foremost task in comprehensively building a modern socialist country. Rural water supply， as a fundamental project for agricultural and rural modernisation， directly impacts the quality of life for over 800 million rural residents and the revitalisation of the countryside. In 2023， the Ministry of Water Resources issued the “Guiding Opinions on Accelerating High-Quality Development of Rural Water Supply”， which for the first time set the objective of “basically achieving modernisation of rural water supply by 2035”. The “3+1” standardised construction and management model for rural water supply serves as the core mechanism for achieving high-quality development in this sector. Through an engineering approach characterised by “urban-rural water supply integration， scaled centralised water supply， and standardised small-scale water supply projects”， coupled with an operational management mechanism of “county-level unified management and professionalised maintenance”， this model establishes a modernised framework for rural water supply tailored to China’s rural geographical features， population distribution， and socio-economic development levels. Based on the “3+1” model， this paper explores the theoretical framework and implementation pathways for modernising rural water supply.

Aiming at the hydraulic constraints at the bifurcation point and branch back-flow risk during parallel water supply from the Xiajiashan Pumping Station to the Tangkeng and Qiufeng Reservoirs in the Yuedong Water Resources Optimal Allocation Project， an optimization model for coordinated pump-valve operation was developed to provide methodological support for the operation and scheduling of the parallel water-supply system for the two reservoirs. The model used the total head at the bifurcation point as a control variable， converted the branch backflow risk into a water-head safety constraint， and determined the safe operating boundaries of the system. On this basis， the operating points of the pumping station under different bifurcation-point water-head conditions were analyzed， and the feasible operation regions for single-reservoir and dual-reservoir supply were divided. With the minimum unit water-lifting energy consumption as the objective， the unit combination， rotational speed of variable-frequency pumps and opening of inflow regulating valves under different working conditions were optimized. Results show that for single-reservoir supply， one pump unit is preferred for Tangkeng Reservoir， with variable-frequency operation at low water levels and fixed-frequency operation at high water levels， while two variable-frequency units are optimal for Qiufeng Reservoir. For dual-reservoir supply， three to four variable-frequency units are recommended within the feasible operating region， whereas alternating single-reservoir supply should be adopted outside this region. The proposed method can effectively avoid branch backflow risk while ensureing water-supply targets， reduce backflow risk， and support safe and economical operation of similar parallel reservoir supply systems.

Multi-source remote sensing data fusion broke the spatial， spectral， and temporal limitations of single sensors， enabling more accurate and timely crop identification and providing scientific support for precision agriculture， sustainable agricultural management， and food security. To rapidly and effectively obtain the crop planting structure of the Zhaokou Irrigation District in the spring， this study relied on the Google Earth Engine （GEE） platform and multi-source remote sensing images to build a multi-source and multi-temporal feature set. Feature optimization was carried out according to the feature importance and classification accuracy of Random Forest （RF）， and machine learning classifiers including Random Forest were adopted for crop classification followed by result verification. The results show that selecting key single-phase images and combining them with multi-source data yields the best classification accuracy. Multi-source features such as spectral， polarimetric， textural， and terrain features improve crop classification performance. As the number of features increases， the accuracy of the RF classifier first rises， then levels off， and fluctuates slightly. The main crops in Zhaokou Irrigation District maintained the basic pattern from 2019 to 2025， but the internal structure showed slight adjustment. Wheat is mainly distributed in the central and southern parts of the district， while garlic is concentrated in the northern and northeastern areas. Other crops are mostly found along riverbanks， roadsides， and around villages in farmland areas. The research results can provide reference for crop remote sensing monitoring and irrigation area management.

The purpose of this study is to explore the impact of artificially layered soil on soil water and heat transfer during desertification control and conduct a water-heat balance analysis. Taking Medicago sativa （common name： alfalfa） planted in typical desertification control areas of Northwest China as the research object， a desertification control technology of Artificially Layered Soil （ALS） based on the principles of soil physics was proposed. The soil structure was reconstructed by sequentially laying a sandy soil layer and a soil-stone mixture layer on the surface of Field Undisturbed Soil （FUS）， aiming to enhance the soil’s water and heat retention capacity and reduce ineffective evaporation. Based on real-time monitoring data of soil moisture， soil temperature， and meteorological factor data， numerical models for the two treatment methods were established. The SHAW model was used to simulate soil water and heat transfer， and indicators such as the Nash-Sutcliffe Efficiency （NSE） and Average Relative Error （ARE） were adopted to verify the applicability of the model， followed by a water-heat balance analysis.The results showed that： ① The SHAW model had good applicability in simulating soil water and heat transfer in arid areas. The temperature simulation had a high degree of agreement， and although there was a small numerical deviation in the soil water content simulation， the overall variation trend was consistent. ② During the growing season， the soil water storage capacity of ALS was significantly lower than that of FUS， with higher leakage and less water recharge. The water consumption of ALS was 35.3% less than that of FUS， and the water consumption rate was reduced by 35.4%， indicating that ALS could effectively reduce ineffective soil evaporation and improve water use efficiency. ③ During the experiment， the net radiation obtained by ALS was 969.7 W/M2 less than that of FUS， the latent heat flux was only 53.53% of that of FUS， while the soil heat flux was 23.2 W/M2 higher than that of FUS. This indicated that the soil temperature was higher and the heat retention effect was better after ALS treatment. Meanwhile， the growth indicators of Medicago sativa under ALS treatment， such as plant height， stem diameter， and dry weight， were all superior to those under FUS， which verified the application value of this technology in desertification control. This study provides a scientific basis and data support for the improvement and promotion of the artificially layered soil technology and the control of desertification in arid areas.