Under the dual pressures of extreme weather and rapid urbanization， urban flooding increasingly threatens city safety management. To overcome the limitations of traditional early warning systems in dynamic response and accuracy， this paper proposes a human-machine collaborative early warning model， integrating multi-source data perception， deep reinforcement learning and expert feedback. Based on urban resilience and situational awareness theories， a multi-level risk assessment system is constructed， applying Deep Q-Network to achieve hierarchical early warning and threshold identification. By incorporating expert feedback， a closed-loop process of “model prediction-expert adjustment-knowledge feedback” is formed. Validated by the Zhengzhou “July 20” extreme rainstorm event， the proposed model presents excellent performance in early warning accuracy， recall rate， and tiered response capability. The study reveals that the integration of artificial intelligence and expert knowledge significantly improves the precision and timeliness of urban flood risk early warning， providing an effective technical pathway for smart city disaster management.

To meet the demand for accurate daily runoff forecasting of the Qingjiang Cascade Reservoirs， address the insufficient accuracy of traditional forecasting methods under complex hydrological conditions， and further provide a scientific basis for the coordinated scheduling of reservoir flood control， power generation， and ecology， this study carried out research on the construction and application of a daily runoff forecasting model for cascade reservoirs. In view of the complexity of runoff-influencing factors， the combination of forward feature selection method and correlation analysis was adopted to screen forecasting factors. The Shuibuya Hydropower Station was taken as the target object for model optimization， and its historical hydrometeorological data were used to complete model parameter tuning and structural optimization. Based on the differences in hydrological characteristics of the three cascade reservoirs （Shuibuya， Geheyan， and Gaobazhou）， an innovative hybrid forecasting framework （Transformer-GRU） based on time series-covariates was constructed. This framework can not only accurately capture long-term complex patterns in time series data by virtue of Transformer， but also deeply explore the interaction relationships between features through GRU. At the same time， it combines dynamic learning rate （to adjust the training step size in real time） and customized loss function （to enhance the forecasting accuracy of key runoff periods）， so as to further improve the comprehensive performance of the model. For the objective evaluation of the model effect， a multi-dimensional evaluation system was established based on the Nash-Sutcliffe Efficiency Coefficient （NSE） and Kling-Gupta Efficiency Coefficient （KGE）. Data from 2011 to 2021 were used for model training， and data from 2022 to 2024 were used for independent verification. The results show that the average NSE values of the training sets for the three reservoirs reach 0.898， 0.902 and 0.875 respectively， and the average KGE values reach 0.859， 0.913 and 0.894 respectively. In addition， the relative errors of the forecasted total runoff are all controlled within 7%， which verifies the reliability and applicability of the model. At present， the research results have been successfully applied to the practical daily runoff forecasting business of the Qingjiang Cascade Reservoirs， which effectively improves the forecasting accuracy and timeliness， and provides important technical support for the scientific formulation of reservoir scheduling plans.

The inherent nonlinearity， non-stationarity， and stochasticity of runoff sequences pose significant challenges for achieving high-precision forecasting. The "decomposition-integration" strategy is a crucial approach to enhance medium- to long-term runoff forecasting. To address the insufficient prediction accuracy caused by the difficulty of traditional machine learning in capturing detailed features of non-stationary runoff sequences， this study constructs a deep hybrid intelligent runoff prediction model driven by adaptive bi-decomposition （AD）. The model construction process is as follows. First， the model extracts multi-scale features through AD. Specifically， Variational Mode Decomposition （VMD） is initially applied to decompose non-stationary observed runoff into multiple sub-sequences containing periodic， trend， and random features. Then， the Ordinal Pattern-based Mode Decomposition （OPMD） is further utilized to decompose the high-dimensional random sub-sequences， thoroughly extracting detailed information. Second， based on the inherent characteristics of each sub-sequence， the model fully leverages the Temporal Convolutional Network （TCN）’s strength in long-sequence modeling， the Bidirectional Gated Recurrent Unit （BiGRU）’s ability to capture bidirectional temporal dependencies， and the attention mechanism’s capacity to place weighted emphasis on key features such as peak discharge. On this basis， TCN-BiGRU-Attention （TBA） models are constructed respectively， forming the AD-driven hybrid forecasting model （AD-TBA）. Meanwhile， to avoid uncertainties introduced by manual determining parameters of the decomposition and forecasting models， a chaotic evolution optimization （CEO） algorithm is introduced for automatic parameter optimization. Finally， the final forecasting result is obtained by superimposing and reconstructing the forecasting results of each sub-sequence. The AD-TBA model was applied to the ten-day inflow forecasting of Wudongde Reservoir and Three Gorges Reservoir. The results show that the Nash-Sutcliffe Efficiency （NSE） values for the two reservoirs in the test set are 0.9427 and 0.9128， respectively， both exceeding 0.90. The forecasting accuracy of AD-TBA is significantly superior to that of typical single deep learning models such as Gated Recurrent Unit （GRU） and TCN. Additionally， the model compensates for the deficiency of TCN， the optimal single model， in capturing the peak occurrence time. This study provides a new modeling approach for high-precision medium- and long-term runoff forecasting in river basins

Accurate prediction of reservoir water withdrawal is a critical prerequisite for optimal scheduling of urban water supply systems and scientific allocation of water resources. Affected by multiple factors including meteorological changes， social water use patterns and water supply scheduling， the water withdrawal series exhibits significant non-stationarity and non-linearity， making it hard for traditional single models to balance prediction accuracy and computational efficiency. To address the core defects of existing decomposition-ensemble prediction methods—mode aliasing caused by empirical parameter selection of Variational Mode Decomposition （VMD）， and ignorance of component frequency differences in homogeneous modeling—this study proposes a hybrid prediction framework combining GSA-optimized VMD with a heterogeneous divide-and-conquer strategy. First， with envelope entropy minimization as the objective function， the Gravitational Search Algorithm （GSA） is used to adaptively optimize VMD’s mode number K and penalty factor α globally， which effectively suppresses mode aliasing and over-decomposition， and achieves high-quality decoupling of the original series into stable Intrinsic Mode Functions （IMFs）. Second， a heterogeneous divide-and-conquer strategy is designed based on the frequency and complexity of IMF components： the Autoregressive Integrated Moving Average （ARIMA） model is adopted for linear modeling of low-frequency trend components to reduce overall computational load， while a multi-scale convolution-enhanced Transformer model （CNN-Transformer） is constructed for high-frequency nonlinear fluctuation components to simultaneously capture local micro-fluctuation features and long-term global dependencies of the series. Finally， validation with measured daily water withdrawal data of a reservoir in Anhui Province from 2017 to 2025 shows that， compared with traditional homogeneous decomposition-prediction models， the proposed framework reduces Mean Absolute Error （MAE） by 10.58%， Root Mean Square Error （RMSE） by 8.78%， and improves computational efficiency by 21.49%. This study confirms that the synergistic application of adaptive parameter optimization and heterogeneous modeling strategy significantly enhances the robustness and practicability of reservoir water withdrawal prediction.

The scientific assessment of water resources carrying capacity （WRCC） in Zhangye city is helpful in resolving regional water scarcity and imbalances between supply and demand， and providing the scientific basis for rational planning and utilization， protection and management of water resources， as well as promoting sustainable socio-economic development and healthy cycle of the ecological environment. In this paper， a comprehensive evaluation index system for water resources carrying capacity of Zhangye city is constructed based on the composite system of water resources， ecological environment， socio-economy and governmental management. The analytic network process， anti-entropy weight method and improved CRITIC method are employed to calculate the indicator weights respectively， and the optimal weight combination is derived using game theory. The GRA-TOPSIS method and normal cloud model are applied to evaluate the dynamic changes of WRCC in Zhangye city from 2018 to 2022. Furthermore， the improved coupling coordination and obstacle degree models are used to analyze the coupling coordination among subsystems and identify the main obstacle factors. The results indicate that the certainty degree of comprehensive WRCC belonging to Level IV decreased from 0.576 to 0.432， while the certainty degree belonging to Level III increased from 0.156 to 0.322 during 2018 to 2022， indicating a general transition from overloading to normal carrying capacity. The coupling coordination degree of water resources， ecological environment， socio-economy and governmental management system improved from 0.188 6 to 0.390 4， with the corresponding coordination state developing from relatively imbalanced to nearly uncoordinated and indicating slow progress in coordination. The obstacle degree of water resources subsystem decreased from 55.00% to 48.20%， with an average annual decline of 3.25%. The obstacle degrees of ecological environment and socio-economic subsystems increased from 20.86% and 14.11% to 32.15% and 18.30%， with average annual growth rates of 11.42% and 6.71% respectively. The obstacle degree of governmental management subsystem decreased from 10.03% to 1.36%， with an average annual decline of 39.37%. The constraints imposed by water resources and governmental management subsystems on water resources carrying capacity have been effectively alleviated and rapidly weakened， while the ecological environment and socio-economic subsystems have become the limiting factors hindering the improvement of regional WRCC. Measures such as strengthening water conservation and reducing wastewater discharge have practical significance for enhancing the WRCC of Zhangye city.

Against the backdrop of intensifying global climate change and frequent flood disasters， storm-induced floods pose a severe threat to socio-economic development and public safety in coastal regions. To enhance the capabilities of storm flood simulation and forecasting， this study selects the Sai River Basin in Fujian Province on the southeast coast of China as the research area. To address the challenges in accurate flood simulation， this research innovatively couples the HEC-HMS hydrological model with a Multilayer Perceptron （MLP） machine learning method. Firstly， a distributed HEC-HMS hydrological model was constructed to simulate 51 flood events from 2006 to 2021. The simulation achieved a qualification rate of 82% and an average Nash-Sutcliffe Efficiency （NSE） of 0.76， meeting the Grade B accuracy standard. To further improve performance， an MLP model was introduced to correct the bias of HEC-HMS outputs. By establishing a "physical mechanism–data-driven" hybrid modeling framework， the MLP was used to capture the nonlinear error patterns between the HEC-HMS simulations and the observed streamflow data. After 3 000 training epochs， the MLP-corrected model showed significant improvement on the validation set： the NSE increased from 0.679 to 0.748， and the Kling-Gupta Efficiency （KGE） rose from 0.724 to 0.747. Notable enhancement was also observed in the test set； for instance， the NSE for Flood Event No. 44 substantially improved from 0.601 to 0.960， with the simulated hydrograph showing a more accurate fit to the peak discharge and its timing， demonstrating the method's strong adaptability to complex flood processes. The results confirm that the hybrid model， integrating the physical-based HEC-HMS with the data-driven MLP， effectively identifies and corrects systematic biases in traditional hydrological models under complex scenarios， significantly enhancing the overall accuracy and reliability of flood simulation. This approach provides a robust technical pathway for high-accuracy storm flood simulation and disaster risk reduction in southeastern coastal areas of China， holding substantial application value and promising potential for broader implementation.

The precise division of the dry season in the Three Gorges Reservoir is a foundational task for drought mitigation and water replenishment scheduling. Existing studies have primarily focused on the flood season， with limited research on the dry season extremes. Additionally， commonly used methods such as the Fisher optimal segmentation method and probability-change-point analysis method often yield inconsistent results due to differences in sampling strategies and objective functions， making it difficult to draw comprehensive conclusions. Given that both the Fisher optimal segmentation method and the probability-change-point analysis method can be transformed into single-objective optimization problems， a five-objective optimization segmentation model integrating both methods was developed by combining sampling methods. After obtaining the Pareto solution set， the optimal segmentation scheme was selected using the TOPSIS method. Finally， the scheme was evaluated based on the proportion of extreme values and the average values of each phase during the main dry season， and its rationality was verified through segmentation frequency analysis. Taking the Three Gorges Reservoir as an example， the final recommended division of the dry season into three periods is： November 1 to January 10 （pre-dry season）， January 11 to April 10 （main dry season）， and April 11 to April 30 （post-dry season）. The phasing scheme proposed in this study provides a scientific basis for the Three Gorges Reservoir′s drought-resistant fine-tuning operations.

The spatial patterns of precipitation in different regions are significantly different. In order to solve the problem that the accuracy of precipitation prediction is limited due to the neglect of spatial synergy of precipitation in the past， combined with the respective advantages of spatio-temporal graph convolutional network （STGCN）， variational mode decomposition （VMD）， bidirectional long-term and short-term memory network （BiLSTM） and attention mechanism （Attention）， the STGCN-VMD-BiLSTM-Attention fusion model was proposed to carry out the multi-year monthly precipitation prediction of 15 meteorological stations in Three-River Source （Sanjiangyuan）. Firstly， a spatial adjacency matrix of precipitation considering the distance and elevation difference is constructed， and spatial correlation features of non-target sites are extracted by STGCN parallel operation graph convolution and time convolution. Secondly， VMD decomposition is performed on the meteorological elements and precipitation sequences screened by the target site， and the obtained IMF components are input into the BiLSTM layer to mine deep time series features. Finally， Attention is introduced to strengthen the key spatio-temporal nodes， and the spatio-temporal features are fused to realize the monthly precipitation prediction of the target site.The results show that： ① Compared with VMD-BiLSTM， VMD-BiLSTM-Attention， BiLSTM， STGCN， SVR and RF， STGCN-VMD-BiLSTM-Attention achieves obvious improvement in MAE， RMSE and R²， and shows excellent robustness at different sites； ② The prediction results of STGCN-VMD-BiLSTM-Attention model are more consistent with the spatial distribution and change trend of actual precipitation. The average precipitation offset of the predicted trend is only 0.50 mm， which is much lower than 1.05 mm of the VMD-BiLSTM-Attention model. It shows that STGCN-VMD-BiLSTM-Attention fully considers the spatial synergy of precipitation and effectively captures the spatial dependence of precipitation between stations. Its multi-scale feature extraction mechanism significantly improves the accuracy of precipitation prediction and has strong spatial generalization ability by effectively integrating the spatial and temporal information of precipitation between stations. The research results solve the problem that traditional methods are difficult to learn the complex spatial relationship of precipitation in the region， and have theoretical significance and practical value for exploring new precipitation prediction models and efficient utilization of water resources in the Sanjiangyuan Basin.

Analyzing the composition of nitrate sources in the Yellow River is an essential theoretical basis for understanding the nitrogen pollution control and discharge strategies in the basin. Over a period of five consecutive years （2020–2024）， the characteristics of water chemical indicators and nitrate at various sampling points during the irrigation and non-irrigation seasons of the Yellow River were analyzed. Through monitoring and research on the water chemical characteristics， the nitrogen and oxygen isotope composition of nitrate， and the nitrogen and oxygen isotope composition of various sources （precipitation， domestic sewage， fertilizers， sediments， and surrounding soils）， the Bayesian mixture model MixSIAR was used to quantitatively identify the sources of nitrate in the river. The results showed that the water of the Yellow River was weakly alkaline， with nitrate being the main form of nitrogen. The seasonal variation characteristics of Cl? and SO₄ ²? were consistent. By analyzing the regression relationship between the NO₃?/Cl? ratio and the Cl? concentration， combined with the actual situation of land use and industrial and agricultural production in the Yellow River region， it was revealed that nitrate in the Yellow River mainly originates from domestic sewage， livestock and poultry breeding， and chemical nitrogen fertilizers. In both the irrigation and non-irrigation seasons， the water in the Yellow River showed a low NO₃?/Cl? ratio and high Cl? concentration， indicating that domestic sewage and livestock manure are the primary nitrate sources. The calculation results of the MixSIAR model showed that the nitrate load in the water of the Yellow River at various sampling points from 2020 to 2024 mainly come from soil nitrate， followed by domestic sewage and atmospheric nitrogen deposition. Overall， the results provided a scientific basis for the prevention and control of non-point source pollution in the Yellow River Basin.

Balancing social development with river health and quantifying channel compensation control thresholds is crucial for achieving sustainable watershed development. Aiming at the threshold identifying and judgment of the coordination between river ecological flow and river health， this study introduces the near-natural concept and develops a near-natural river health assessment system through a combination of subjective and objective weighting methods and matter-element analysis. This system is then coupled with various hydrological ecological flow calculation methods to further construct a quantification model for in-channel water compensation control thresholds. Based on this model， a method for determining in-channel water compensation control thresholds is proposed， relying on the “relationship between river health grade and in-channel compensation control threshold response.” The Chishui River （upstream of the Maotai section） is used as a case study to determine the in-channel compensation control thresholds using the constructed model. The results indicate that the Chishui River （upstream of the Maotai section） is classified as “healthy，” and the minimum ecological flow process control line is recommended as the in-channel compensation control threshold. This study clarifies the relationship between in-channel compensation control thresholds and river health threshold identification， providing valuable references and new approaches for defining and quantifying in-channel compensation control thresholds.

This study investigates the spatiotemporal evolution of fractional vegetation cover （FVC） in the Shule River Basin and quantifies the driving effects of climatic， topographic， land-use， and socio-economic factors， so as to provide a scientific basis for ecological restoration and water resources management in arid oasis–desert ecotones. FVC was retrieved from MODIS-NDVI data via the pixel dichotomy model on the Google Earth Engine platform. Theil–Sen trend analysis， Mann-Kendall test， and Hurst index were applied to identify trends and persistence， spatial transition matrices to track changes among FVC classes， and the geographic detector model to assess factor contributions and interactions. Results show that： ① From 2000 to 2020， the vegetation cover of the basin improved significantly （annual growth 0.27%·a?1， P0.01）， with the fastest increase in the upper-reach Sunan County （0.60%·a?1） and slower， more variable growth in the downstream oasis. ② In terms of spatial pattern， the spatial succession among vegetation cover grades shifted from the upgrading of low and moderately low coverage （low→moderately low， moderately low→moderate） during 2000-2010 to the expansion of high-grade coverage （moderate→moderately high， moderately high→high） during 2010-2020. Areas with high improvement potential are concentrated in middle- and lower-reach nature reserves and along the northern Qilian-Altun foothills， while localized degradation risk exists downstream and in scattered northern areas. ③ Land use was the dominant driver， followed by precipitation and temperature， with the strongest explanatory power arising from interactions between land use and climatic or topographic factors. Findings indicate that vegetation restoration has been driven by the synergy of climate amelioration and ecological engineering. Future strategies should optimize “water–vegetation–land use” coupling， target degraded areas， and strengthen oasis ecological security to sustain vegetation recovery and support high-quality basin development.

Watershed water resources regulation is a crucial measure to achieve rational utilization of water resources and alleviate the spatial-temporal imbalance of water resources. Evaluating watershed water resources regulation work is an important support for improving water use efficiency and ensuring water supply security in the watershed. To promote the high-quality development of water resources in the Lijiang River Basin and evaluate the guarantee effect and potential of upstream reservoir groups on ecological landscape flow， a water resources regulation model for the upper reaches of the Lijiang River was constructed based on the WEAP software. By formulating combinations of different engineering configuration systems and water project operation modes， five simulation scenarios were established to compare and evaluate the differences in the effectiveness of supplementing ecological and landscape flows in the Lijiang River under different scenarios. Additionally， the reasons for the ineffective water supplement in the basin were analyzed， and the guarantee potential of the joint optimal operation of the reservoir group in the basin was explored. The results show that： ① The current water resources regulation work in the Lijiang River Basin has significantly improved the guarantee rate of the ecological and landscape target flows， but there is still room for further improvement. The main factor restricting the ineffective water supplement in the basin is that the reservoirs cannot store water to a high level at the end of the flood season. ② The effect of joint optimal operation of reservoir groups on ensuring the ecological and landscape flow of a river basin is superior to that of empirical operation and independent operation of single reservoir. Based on the current status of water projects in the Lijiang River Basin， the joint optimal operation of the existing reservoir groups in the basin can raise the guarantee effect of the basin's ecological and landscape flow to more than 90%. The research results can provide theoretical support and practical guidance for improving the guarantee effect of the ecological and landscape flow of the Lijiang River and promoting the rational utilization of water resources in the Lijiang River Basin.

Under the dual impact of climate warming and drying as well as intensified human activities， the water surface area of Hongjiannao Lake has been continuously shrinking， accompanied by deteriorating water quality. There is an urgent need for scientifically informed and precisely targeted water supplementation and management strategies to maintain its ecological functionality. Based on multi-source remote sensing data from 2016 to 2023， this study applied the Normalized Difference Water Index （NDWI） combined with a mean-threshold segmentation method to achieve high-accuracy monthly water surface extraction （R2 = 0.96）. By integrating the entropy weight–exceedance multiple coupling weighting method， a WQI-P water quality evaluation model was constructed. In addition， a TIN-DEM-based three-dimensional hydrological modeling approach was employed to establish a multi-dimensional assessment framework of “quantity-quality-level” for the lake. The results reveal that the surface area of Hongjiannao Lake exhibited three distinct stages during 2016-2023： an expansion phase from 2016 to 2017 （2.28 km2/year）， a slow growth phase from 2018 to 2020 （0.50 km2/year）， and a contraction phase from 2021 to 2023 （-1.06 km2/year）. The reconstructed water level series further indicated a trend of rising followed by falling， with an increase of 2.27 meters between 2016 and 2020 and a subsequent decline of 0.53 meters from 2020 to 2023. According to the WQI-P evaluation results， water quality significantly improved after 2016， reaching Class III， and even achieving Class I standards on multiple occasions in 2021. However， under the influence of extreme climatic conditions， water quality temporarily deteriorated to Class III again during 2022–2023. Based on the established relationship model among water level， water volume， and water quality， the threshold values of water level and storage capacity required to sustain comprehensive Class I and Class II water quality were identified as 1 220.01 m-1.853×10? m3 and 1 219.07 m-1.218×10? m3， respectively. Under the condition of unchanged external water replenishment， and compared with the observed water level in December 2023， achieving comprehensive Class I and Class II water quality standards would require additional water supplementation of 0.486×10? m3 and 0.113×10? m3， respectively. This research provides a precise quantitative strategy for water supplementation and regulation to restore the ecological functionality of Hongjiannao Lake， and provides important technical support for integrated cross-regional water resource management in arid and semi-arid areas.

Cotton is a waterlogging-sensitive crop， and its yields are significantly affected by waterlogging disasters. Although substantial research has focused on cotton waterlogging， there remains a lack of systematic reviews that organize existing prevention and mitigation measures from the perspective of hazard system theory. Based on the three core risk dimensions of the hazard system， i.e.， hazard-affected bodies， hazard-formative environment， and hazard-inducing factors， this work comprehensively reviewed research progress on cotton waterlogging disaster prevention and mitigation measures. Currently， mitigation strategies primarily focused on the risk dimension of hazard-affected bodies， including reducing cotton vulnerability （e.g.， breeding waterlogging-tolerant varieties， applying exogenous regulatory substances， and optimizing agronomic management） and developing “cotton yield–waterlogging index” vulnerability models， as well as optimizing planting layouts through regional waterlogging risk assessments to reduce cotton exposure to waterlogging. In the hazard-formative environment dimension， key measures involved establishing field drainage criteria based on the “cotton yield–waterlogging index” vulnerability model， improving soil conditions （e.g.， ridge tillage and rational fertilization）， and enhancing field drainage systems. Measures targeting the hazard-inducing factors dimension are relatively limited， mainly consisting of emergency drainage during disasters. Furthermore， climate change has increased the frequency of compound disasters such as waterlogging–heat and waterlogging–drought compounds， posing new challenges to previous cotton waterlogging control strategies. Future research should strengthen mechanistic studies on compound waterlogging-relevant disasters and the development of adaptive drainage strategies， promote the application of artificial intelligence and meteorological–remote sensing data in cotton waterlogging monitoring and risk assessment， develop smart drainage systems integrated with weather forecasts and waterlogging disaster-forecasting models， and establish a low-cost integrated prevention and control technology system for cotton waterlogging.

To investigate the drainage discharge and water quality output characteristics of different forms of subsurface drainage pipes in farmland， four types of subsurface drainage systems were tested in situ under different burial depths （50 cm， 80 cm） and installation methods （siphon pipes， filter cloth）. The results showed the deep-buried subsurface pipe D-1 had a 53.46% increase in drainage discharge compared to the shallow-buried S-1， the Subsurface siphon pipe S-2 had a 26.40% increase in cumulative drainage discharge compared to S-1， and the drainage discharge of the added cloth D-2 was 7.18% higher than that of D-1 treatment. The loss form of total nitrogen （TN） in subsurface pipe drainage was mainly NO₃ ^--N （for 77.78%）， and the loss form of total phosphorus （TP） was mainly PO₄ ^--P （82.51%）. Increasing the burial depth of underground pipe， adding a siphon and a permeable filter cloth could reduce the effluent concentration of drainage NH₄ ^--N， PO₄ ^--P， TP and COD_Mn， but it increased the effluent concentrations of NO₃ ^--N and TN. Compared to the shallow-buried subsurface pipe S-1， the loss load of NH₄ ^--N， NO₃ ^--N and TN in the drainage water of the deep-buried subsurface pipe D-1 increased by 37.93%， 67.99% and 82.12%， respectively， and the PO₄ ^--P， TP and COD_Mn increased by 15.25%， 1.27% and 13.15%. The addition of siphon outlets and permeable filter cloth in underground pipes increased the nitrogen loss load while decreasing the loss loads of phosphorus and COD_Mn. The research results can provide a theoretical basis for the technology of efficient drainage and nutrient loss synergistic regulation of subsurface pipes in farmland.

Due to their small size relative to the entire hydropower hub， fishways areoften referred to as “needle-eye projects.” The flow condition at the fishway entrance directly affects the success of the fishway construction. To enhance the attraction effect at the fishway entrance， this study used Mylopharyngodon piceus as the test species and combined physical model experiments with numerical simulations to investigate the effects of three intersection angles between the fishway outflow axis and the main river flow axis（0°， 30°， 60°）， as well as supplementary flow discharge， on fish upstream behavior. The results showed that： ①Based on the upstream swimming behavior characteristics of 138 Mylopharyngodon piceus under different hydraulic conditions， all movement trajectories were classified into three typical categories： swimming in low-velocity zones， swimming across high-to-low velocity transition zones， and swimming in high-velocity zones.The low-velocity zone path is the most preferred route for fish. ②Uunder experimental conditions， regions with flow velocities between 0.05 and 0.38 m/s， turbulent kinetic energy less than 0.003 5 m2/s2， and turbulence intensity ranging from 0.026 to 0.047 are conducive to fish attraction， indicating that Mylopharyngodon piceus prefer hydraulic environments with low flow velocity， low turbulent kinetic energy and low turbulence intensity during upstream movement. ③ During upstream migration， Mylopharyngodon piceus tended to select routes with lower cumulative and per-unit-distance energy consumption. Although the installation of supplementary flow facilities increased the flow velocity at the fishway entrance and， to some extent， the swimming resistance for fish， fish could locate the entrance more quickly by adjusting their swimming routes. At an entrance angle of 0°， the migration time was effectively reduced by 44.47%， and the energy expenditure per unit distance was generally decreased. This study provides a novel perspective on upstream behavior and energy expenditure， offering theoretical support for the optimization of fishway entrance design.

This study takes the Chaohu Lake Basin as the research area. Based on high-resolution remote sensing images from 2014 and 2024， a river system connectivity evaluation framework was constructed. By integrating morphological parameters such as river length， width， and sinuosity， the changes in river system connectivity and the morphology of typical main rivers in the Chaohu Lake Basin were systematically analyzed. The results show that from 2014 to 2024， the water circuit connectivity index （α）， node connection ratio （β）， and network connectivity index （γ） in the Chaohu Lake Basin increased by 14.09%， 1.70% and 1.71%， respectively， indicating an improvement in river system connectivity， though the overall level remains relatively low. The morphology of typical main rivers is significantly influenced by human activities. Rivers such as the Shiwuli River， Pai River， Xuxiao River and Zhegao River show a trend of “channel straightening through bend cutting and channel widening，” with synchronous and consistent changes over time. The channel widths of the Baishitian River and Zhao River increased significantly， while the morphologies of the Yuxi River， Hangbu River， and Nanfei River remained stable. This research can provide a scientific basis and decision-making support for optimizing the river system structure， ecological restoration， and sustainable water management in the basin.

Aiming at the control problem of assembly torque for the impeller nut in a high-flow pump， a novel nonlinear control strategy based on a smooth sliding mode disturbance estimator was proposed in this paper. Firstly， a mathematical model of the impeller nut torque control system was established and simplified through a rational order reduction process. Subsequently， a novel smooth sliding mode disturbance estimator was designed， which ingeniously incorporates the sign function into an integrator. This structure not only enables accurate estimation of system disturbances but also yields smoother disturbance estimates. Furthermore， a nonlinear controller was developed based on the designed sliding mode disturbance estimator. By introducing a hyperbolic tangent nonlinear function into the controller， both the convergence rate of torque tracking error and the disturbance rejection capability are improved， and the underlying mechanism was rigorously analyzed using Lyapunov stability theory. Finally， a simulation platform for the impeller nut torque control system was built using Simscape software. On this platform， the proposed control method was compared and validated against a PID controller and the proposed controller without the introduced nonlinear function term. The simulation results demonstrate that when a step torque reference of 250 N·m was applied， an overshoot-free response is achieved by the proposed method， with a settling time of only 0.9 s. When a step disturbance of 30 N·m is introduced， a maximum torque error is merely 4 N·m， and a recovery time of 0.3 s to the reference value of 250 N·m was obtained. Moreover， under highly complex operating conditions where a sinusoidal time-varying disturbance with an amplitude of 30 N·m and a frequency of 1 Hz was applied， along with sinusoidal parameter perturbations at 0.5 times the nominal value and a frequency of 1 Hz， a maximum steady-state torque error of only 3.5 N·m was observed. Therefore， the proposed method was shown to effectively enhance the control performance of impeller nut assembly torque， and strong robustness was exhibited against both parametric uncertainties and external disturbances in the system.

As a core power equipment in water conservancy projects， the operation safety of the pump motor in the pumping station is directly related to the stability and reliability of the water supply system. As an important component of motors， bearings are prone to abnormal temperature rise under long-term high load and complex working conditions， which may cause malfunctions or even shutdowns. Therefore， it is of great significance to carry out temperature rise prediction and fault diagnosis of motor bearings. In view of the deficiencies of traditional methods in processing non-stationary signals， feature selection and parameter optimization， this paper proposes an EMD-LASSO-SVM hybrid model based on EGWO optimization， which integrates EMD feature decomposition， LASSO feature screening and SVM prediction mechanism. Firstly， empirical mode decomposition （EMD） is utilized to perform multi-scale decomposition on non-stationary temperature rise signals and extract multi-level dynamic features. Secondly， the LASSO method is introduced to achieve feature sparsity and dimension reduction， and to reduce redundant parameters. Finally， the key parameters of the Support Vector Machine （SVM） are globally optimized through the Enhanced Grey Wolf Optimizer （EGWO） to improve the prediction accuracy and model robustness. The experimental results show that the proposed model exhibits high accuracy on both the training set and the test set. The MAE of the test set is 0.427 ℃， the RMSE is 0.648 ℃， the MAPE is 1.45%， and the R2 reaches 0.996. Compared with the model with unprocessed data， the prediction error is reduced by more than 50%. The research results verified the effectiveness of the proposed method in non-stationary signal processing， feature selection and parameter optimization， which can provide reliable technical support for the early warning and intelligent operation and maintenance of temperature rise faults of motor bearings in pumping stations.

Large-capacity and high-head double suction centrifugal pumps are commonly installed in pump stations along the Yellow River. The pumps operate under complex conditions of water and sediment two-phase flow for a long time， resulting in serious wear and tear of the impeller. However， existing research on the wear laws of impellers under various combined operating conditions is not clear， and there is a lack of pump station operation control modes to reduce the wear of pump units. This article aims to address the engineering requirements for evaluating and controlling the wear of centrifugal pumps in the Yellow River Water Diversion Project. A calculation method is established to predict the sediment wear of centrifugal pumps. Taking the double suction centrifugal pump of the seventh pumping station in Jingtaichuan Electric Power Irrigation Project in Gansu Province as an example， the sediment wear characteristics of the pump station are analyzed. The wear risk of the pump station during an irrigation season is evaluated， and technical measures are proposed to reduce pump sediment wear by optimizing the operation mode of the pump unit. The results indicate that the main wear areas of the impeller blades， which are the core overcurrent components， are in the leading edge and trailing edge regions of the blades. With the increase of operating flow rate and sediment concentration， the area of the main wear areas expands and the wear rate increases. Within one irrigation season， the maximum cumulative wear depth of centrifugal pump blades can reach 2.15 mm. After optimizing the operation mode of the pump station， the average cumulative wear depth of centrifugal pumps decreased by 11.34%， and the maximum cumulative wear depth decreased by 25.37%. The research results can provide engineering guidance for the design of pumping stations and safe operation of pump units along the banks of sediment-laden rivers.

In order to study the influence of solid particle properties parameters in sediment-laden water flow on pressure pulsation inside an axial flow pump device， numerical simulations were carried out by adopting Euler multiphase flow model， RNG k - ε turbulence model and SIMPLEC algorithm. The pressure pulsation characteristics at key sections of the pump device were analyzed in detail， and the influence of different particle diameters and concentrations on the pressure pulsation law of the axial flow pump device was compared and analyzed. The research results show that the pressure pulsation at the inlet and outlet of the impeller exhibits obvious periodicity， decreasing first and then increasing from the rim to the hub， with the main frequency being the blade frequenc. The pressure pulsation in the middle of the guide vane periodically decreases， decreasing first and then increasing from the rim to the hub， with a main frequency of 0.125 times the blade frequency. The pressure pulsation at the outlet of the guide vane shows no obvious pattern， increasing and then decreasing from the wheel rim to the hub， with a main frequency of 0.25 times the blade frequency. The smaller pressure pulsation at the middle of the guide vane indicates that the guide vane has a damping effect on pressure pulsation. As the diameter of solid particles increases， the influence on the pressure pulsation at the impeller inlet and outlet is relatively small， while the pressure pulsation at the middle and outlet of the guide vanes first increases and then decreases. As the concentration of solid particles increases， the pressure pulsation at each monitoring section gradually increases， with the greatest impact variation occurring at the middle of the guide vane. Compared to the diameter of solid-phase particles， the concentration of solid-phase particles has a more dominant impact on the pressure pulsation of the pump device.

In response to the high sediment content in the Yellow River， to enhance the performance of double-suction pumps in transporting solid-liquid two-phase media， a single-stage double-suction pump of model 1200S56G used in the second phase of Jingdian Project was taken as the research object. A new impeller design concept was innovated， and a hydraulic model based on multiphase flow theory was established. Combined with the full-flow numerical simulation method， through four progressive corrections， the influence of different correction coefficients k on the pump performance was systematically analyzed. The research found that as the correction coefficient increased， the pump performance significantly improved. When k=1.12， the average flow rate increased from the initial 3.13 m3/s to 3.92 m3/s， the vibration decreased from 3.8 cm/s to 2.3 cm/s， and the noise decreased from 85 dB to 83 dB. However， when k=1.130 5， the average flow rate increased， but the noise and vibration also rose. Considering all factors， the optimal correction coefficient was determined to be k=1.12. This study effectively improved the hydraulic performance of existing double-suction centrifugal pumps in dealing with high sediment content water conditions of the Yellow River by optimizing the impeller structure parameters.

This study takes the circular intake forebay of the pumping station in the second phase of the Jingtaichuan Electric Irrigation Project in Gansu Province as the research object. It designs circular intake forebays under different working conditions， adopts Fluent software combined with the Realizable k-ε turbulence model and Mixture multiphase flow model for simulation calculations， obtains the sediment volume distribution characteristics in the circular intake forebay under different working conditions，and explores the influence of the second water exchange coefficient on the water-sediment movement law of the forebay. The results show that when the second water exchange coefficient of the circular intake forebay is 110， the vortex scale at the bottom of the forebay is small and the water flow field structure remains stable. Along the direction perpendicular to the water flow， the sediment volume distribution in the forebay shows a decreasing trend from the center to both sides， and the circular intake forebay under the working condition of the second water exchange coefficient K=110 has the best sediment prevention performance； along the water flow direction，the sediment volume in the forebay increases with the water depth， and shows a relatively reasonable distribution under the working conditions of the second water exchange coefficient K=100、110 and 120， which helps to improve the sediment deposition problem on both sides of the forebay. Along the water depth direction， the sediment content in the forebay decreases continuously from the bottom to the surface， and the circular intake forebay under the working condition of the second water exchange coefficient K=110 has outstanding sediment prevention and reduction performance. Comprehensive analysis shows that the sediment volume in the circular intake forebay under the working condition of K=110 is only 0.018 m3， which is 0.579 m3 less than that in the forebay of the prototype pumping station， with a reduction of 97%. The research results have important engineering reference value for reducing sediment deposition in the circular intake forebay of pumping stations and can provide a basis for the optimal design of forebays of the same type of pumping stations.

To reveal the sediment deposition patterns in intake conduits of sediment-laden pumping stations and ensure the safe and stable operation of pumping units， this study adopts a partitioned intake conduit design and establishes a numerical simulation framework based on the Mixture multiphase model. A single-unit intake conduit was selected as the research object to systematically analyze sediment deposition characteristics under varying sediment properties， inflow conditions， and geometric control parameters. Additionally， the influence of sediment-laden flow on the inlet flow regime of the pump was investigated. Results indicated that increased sediment concentration significantly elevated the sediment volume fraction within the intake conduit. Coarse sediment particles predominantly deposited along the downstream （rear） wall， while fine particles primarily remained suspended. Increased discharge effectively suppressed sediment deposition and modified the vortex structures inside the intake conduit. When the characteristic width exceeded 2.6 D?， sediment deposition metrics increased rapidly， whereas hydraulic performance at the conduit outlet deteriorated sharply below 2.6 D?. It is recommended that the characteristic width be controlled between 2.6 D? and 2.8 D?. Furthermore， an inlet slope below 1/6 markedly enhances the outlet velocity uniformity. To reduce sediment deposition while maintaining hydraulic performance， an optimal slope range of 1/8 to 1/6 is suggested.

Pump stations drawing water from sediment-laden rivers often suffer from sediment deposition during operation， which significantly impairs their operational performance. To mitigate the sediment accumulation problem， this study focuses on optimizing the design parameters of the lateral intake forebay of a pump station project in Ningxia Hui Autonomous Region， employing Fluent-based numerical simulation of water-sediment two-phase flow to examine how forebay size， bottom slope， and intake pond length affect sediment deposition in the forebay. The results reveal that sediment deposition in the pump station is primarily concentrated in the diversion channel section and the bottom of the intake pond， with more severe accumulation on the inner side of the intake pond. Vertically， the sediment volume fraction increases gradually from the surface to the bottom. Under the same flow rate， narrowing the forebay， increasing its bottom slope， and shortening the intake pond length all help reduce sediment deposition， with a more pronounced effect when these three measures are combined. These findings hold positive significance for enhancing the operational efficiency of pump station projects and can provide theoretical basis and technical guidance for the design and retrofitting of pump stations in sediment-laden river regions.

Traditional open-canal hydraulic control technologies struggle to handle scenarios where neither the inflow nor the outflow of the canal system is regulated by the automatic controller， failing to meet the safety operation requirements of the water conveyance project under inflow-outflow mismatch conditions. Therefore， this study attempted to investigate the Proportional-Integral-Derivative （PID） feedback control performance using the water-level difference control method， which achieved synchronous changes of water levels in all canal pools solely through the regulation of internal check gate groups. In view of the fact that existing water-level difference control methods fail to incorporate water-level safety considerations in the control logic， this paper proposed an improved water-level difference control method based on the soft-constraint control strategy. Using a typical mild-slope open canal proposed by the American Society of Civil Engineers as the test case， PID controllers were designed based on various water-level difference control methods， and the control performance under inflow-outflow mismatch conditions was evaluated. The results demonstrated that： ① The classical water-level difference control method could achieve synchronous water-level variations across all canal pools. However， the hydraulic response exhibited increasing time delays as the canal pools were located farther from the hydraulic disturbance source. ② The accelerated water-level difference control method was unsuitable for PID control due to its insufficient responsiveness to water-level deviation in the most downstream canal pool. ③ The weighted water-level difference control method could effectively reduce water-level deviation in the target canal pool and enhance the water supply stability. ④ The secure water-level difference control method proposed in this study could effectively suppress water-level exceeding tendencies and mitigate accident risks caused by delayed monitoring or emergency response. This study conclusively demonstrates the hydraulic regulation capability of water-level difference control methods for the water delivery canal systems operating under inflow-outflow mismatch conditions. The proposed secure water-level difference control method can provide technical support for ensuring the safe operation of the open canal water conveyance project.

Water supply projects， as critical infrastructure， play a vital role in optimizing regional water resource allocation， enhancing the scientific basis of project investment decisions， and ensuring sustainable water supply through their long-term operational effectiveness and comprehensive impacts. To address the systematic deviation between actual operational outcomes and design expectations in water supply projects， this paper establishes an ex-post evaluation framework， encompassing five secondary indicators—engineering design capability effectiveness， water resource development and utilization， social benefits， economic benefits， and ecological benefits—and 22 tertiary indicators. For weight determination， the AHPGA method—an Analytic Hierarchy Process （AHP） approach optimized by genetic algorithms—calculates subjective weights. Objective weights are derived using the Entropy Weight Method （EWM）， CRITIC method， and Coefficient of Variation （COV） method. Three combined weighting approaches—AHPGA-EWM， AHPGA-CRITIC， and AHPGA-COV—are constructed based on game theory principles. Sensitivity analysis comparisons confirmed AHPGA-EWM as the optimal weighting method， effectively integrating subjective judgment with data-driven insights. To address ambiguity and randomness in the evaluation process， cloud model theory was introduced to enable bidirectional conversion between qualitative concepts and quantitative assessments. Visualization of evaluation outcomes was achieved through cloud diagrams. Empirical research on Qiandao Lake Water Diversion Project， the second water source of Hangzhou City， demonstrates that the proposed evaluation system and methodology accurately reflect the project’s actual operational status. Specifically， the project’s design capacity achieves high effectiveness； water resource development and utilization levels are high； social and ecological benefits receive excellent ratings； economic benefits are favorable； and the comprehensive evaluation grade is Level I， indicating significant implementation effectiveness. The case analysis further demonstrates that the evaluation method based on combined weighting and cloud models can be effectively applied to the post-evaluation of water supply project implementation outcomes， providing a new approach for accurately assessing the actual effectiveness and comprehensive impacts of projects.

In recent years， driven by the engineering demands for safety， efficiency， environmental protection， and energy conservation， non-explosive low-impact underwater excavation technology has achieved leapfrog development from single fragmentation to systematic operation， gradually forming a complete technical system with reef crushing technology as the core， crushed stone cleaning technology as the collaboration， and suspended matter and vibration control technology as the guarantee. In this work， the technical principles and application points of various types of non-explosive low-impact underwater excavation technologies were elucidated， and corresponding development and application process were summarized. By discussing their applicability and limitations under different engineering conditions， the future development direction of non-explosive low-impact underwater excavation technology is prospected in view of the current technical bottlenecks and challenges faced by non-explosive low-impact underwater excavation technology. In order to meet the major strategic needs of near-earth coast and underwater engineering， non-explosive low-impact underwater excavation technology urgently requires theoretical innovations and technological breakthroughs， as well as iterative upgrading of equipment. It aims to achieve economic， intelligent， safe and efficient crushing of hard rock mass and timely and controllable gravel clearing and suspended matter in complex geological environments.

Concrete freeze-thaw damage is the most common disaster in hydraulic engineering. In order to fundamentally solve the problem of concrete freeze-thaw damage， a double-microcapsule containing repair agent and curing agent was implanted into hydraulic concrete， and a microcapsule concrete freeze-thaw test with different waterbinder ratios and different dosages was carried out to monitor the internal morphological changes of concrete and test its mass loss rate and dynamic elastic modulus. The repair characteristics of microcaps concrete in the freeze-thaw cycle process were studied. The results showed that after the addition of double-microcapsules， the frost resistance of concrete with different mix proportions was significantly improved， with a maximum increase of 53.8%， and the optimal dosage threshold of microcapsules is 4%. The enhancement mechanism is mainly attributed to the low-temperature shrinkage of microcapsules in the elastic stage， as well as the synergistic repairing effect generated by the timely rupture of microcapsules and the release of repairing agent and curing agent when concrete was damaged. In addition， the double-microcaps exhibit a superior filling effect in concrete with higher water-binder ratio. Under the same dosage， the greater the water-binder ratio of concrete， the more obvious the repair effect， and vice versa. Finite element calculation simulation verified the freeze-thaw cycle damage process of microcapsule concrete， which confirmed the self-repair ability of microcapsule. When applying in hydraulic engineering， its self-repair characteristics should be fully utilized. The more severe the environment， the more serious the concrete freeze-thaw damage， the more suitable the application of double-microcapsules for self-repair to achieve the purpose of prolonging the service life of hydraulic concrete.

To address the century-scale time-dependent large deformation of Jurassic red-bed soft rock in the Huanbei Nafeng Water Conveyance Tunnel， triaxial step-loading creep tests were conducted to reveal the time-dependent mechanical degradation mechanism of red-bed soft rock. A stress-level-dependent nonlinear Burgers-Mohr constitutive model was established， and numerical calculation was conducted using FLAC3D. Coupled with an RBF neural network， an integrated platform of “monitoring–inversion–validation” was constructed. Four key creep parameters were identified， reducing the errors of crown settlement and peripheral convergence to 5.62% and 4.07%， respectively. Century-scale computational results from a 150 m × 70 m × 100 m three-dimensional model show that the maximum vertical stress in the surrounding rock increased from 4.26 MPa to 10.97 MPa， with crown settlement of 7.38 mm and a minimum principal stress of 12.16 MPa in the secondary lining. Under uncontrolled conditions， the safety factor decreased to 1.84. After installing a 20 cm-thick flexible compressible layer made of 0.5 GPa foam concrete， the safety factor increased to 2.05， meeting regulatory requirements and achieving economic rationality. This study achieves a unified description of the complete “elastic–viscoelastic–viscoplastic–stable creep” process of red-bed soft rock， providing a systematic solution for the life-cycle safe operation and maintenance of deep-buried soft rock tunnels.

Rock-filled concrete （RFC） exhibits complex mechanical properties due to the large particle size of the rockfill and its skeletal effect. Investigating its uniaxial compression deformation and failure characteristics is essential for understanding the actual bearing capacity of RFC. In this study， uniaxial compression tests were conducted on 300 mm cubic specimens of RFC and self-compacting concrete （SCC）. The relationship between acoustic emission （AE） parameters （ringing counts， cumulative energy） and the stress-strain curve was investigated based on AE monitoring. The characteristics of crack initiation and propagation were interpreted based on the dynamic b-value. Additionally， the types of cracks at different failure stages were distinguished through RA-AF statistical characteristics. The results indicate that： ① AE ringing counts failed to provide early warning for the failure of SCC and RFC， whereas AE energy and b-value characteristics enabled the analysis of crack propagation processes and failure prediction. ② During compressive failure， SCC primarily exhibited tensile-shear damage mode （with tensile and shear cracks accounting for 48.47% and 51.53%， respectively）， while RFC predominantly experienced shear failure （with shear cracks accounting for 57.38%）. ③ Both SCC and RFC displayed an "X"-shaped failure pattern with positive and reverse connections， where cracks initiated from the ends and propagated toward the center of the specimens until through-cracks appeared. RFC exhibited a greater degree of failure compared to SCC.

Due to the influence of uneven settlement， high pressure seepage and other factors， the interface between grouting material and concrete at the crack of reservoir dam is susceptible to shear failure. It is of particular importance to study the shear characteristics of grouting materials and the concrete interface at dam cracks when considering the action of multiple factors. Based on the direct shear test， the influence of immersion duration， normal stress and surface roughness on the shear strength of grouting materials-concrete interface were investigated. The primary and secondary degree of the influence of various factors on the interface was clarified. And a theoretical model of shear strength of grouting materials-concrete interface was established and verified. The results show that the shear strength of epoxy resin-concrete interface is higher， and the influence of normal stress and immersion time on it is more prominent than the cement-sodium silicate-concrete interface. Orthogonal test results further indicate that the factors that most significantly affected the shear strength of epoxy resin-concrete interface versus cement-water glass-concrete interface were normal stress and surface roughness. The proposed theoretical model has high fitting accuracy and can be effectively applied to the prediction of interfacial shear strength.

The repeated expansion and contraction of clay mineral particles （viscous particles） in silty mudstone is one of the main reasons for its deterioration under wet-dry cycle. In this paper， a rock discrete element model based on particle expansion-water-induced strength degradation is established by unloading-wet-dry cycle test. Considering the reduction of mechanical parameters of rock particles during wet-dry cycles and the deterioration of its internal structure by particle expansion and shrinkage， the degradation mechanism of silty mudstone under unloading-wet-dry cycle is analyzed. The results show that with the increase of the number of wet-dry cycles， the porosity of rock sample increases， the cohesion and internal friction angle decrease， and the expansibility increases first and then decreases， and the fourth time is the largest. The effect of unloading damage on the expansibility and cohesion of silty mudstone is greater than that on the internal friction angle. During wet-dry cycles， a large number of micro-cracks are generated inside the silty mudstone. The particle contact force controlled by the cohesive particles is greater than that of the non-cohesive particles， resulting in more cohesive particles being destroyed and rotating at a large angle. The structural damage of the sample caused by wet-dry cycles is much greater than the effect of unloading. The structural deterioration of silty mudstone is controlled by the expansion effect of viscous particles， and the degree of structural deterioration is positively correlated with the expansion. The influence of unloading effect on the structural damage of silty mudstone under wet-dry cycle is greater than that of the strength weakening between particles.

In view of the characteristics of levee emergency reinforcement projects-namely， constraints imposed by seasonal flood periods and high uncertainty of potential hazards-and considering their differences from conventional safety assessment in applicable standards， weighting mechanisms， and data processing， this study establishes a safety assessment model based on the BAFSA-CM-Rosenblueth method. The model develops an index system centered on engineering safety， environmental factors， operation and management， and emergency support. The combined G1-improved CRITIC method and game theory are employed to overcome the limitations of expert subjectivity and the bias of single weighting approaches caused by incomplete monitoring data. Furthermore， the cloud model （CM） is introduced to determine levee safety grades under both stochastic fluctuations of field-measured parameters and the cognitive uncertainty inherent in qualitative expert evaluations. To verify the reliability and stability of the model， the emergency reinforcement project at Tuanzhouyuan in Hunan Province is taken as a case study， and the Rosenblueth method is applied to analyze reliability indices under multivariate high-frequency fluctuations. The results indicate that the expected comprehensive safety score of the project is 0.750 7， with a reliability index of approximately 42.06 and a coefficient of variation of only 0.023 8. The proposed model effectively reflects the overall condition and weak links of the project， providing a scientific quantitative basis for post-disaster routine management and risk prevention of levees.

Based on the Cohesive Zone Model （CZM）， this paper establishes a mesoscopic model of hydraulic asphalt concrete containing matrix， aggregate， pores， and fibers. Through Abaqus/Python joint modeling， the explicit dynamic solver was used to investigate the effects of fiber dosages （0‰， 2‰， 3‰， 4‰） and aggregate shapes （sphere， spheroid， 48-hedron） on the uniaxial compressive performance and damage evolution of hydraulic asphalt concrete. The study shows that the increase of fiber dosage significantly improves the mechanical properties of the material. A 3‰ dosage increased the peak load of asphalt concrete by 10.7% compared to the non-fiber group， with the average SDEG distribution increasing by 26.7%， while suppressing concentrated crack propagation through the bridging effect. Aggregate shape affects damage patterns， where 48-hedron aggregates cause earlier initial damage due to stress concentration at vertices， and fiber incorporation can weaken the shape effect of aggregates. Damage was quantified using the proportion of failed cohesive elements and SDEG box plots， revealing that fibers enhance the overall strength of asphalt concrete by reducing the proportion of interfacial damage units and dispersing matrix damage. This study provides a numerical analysis reference for the mesoscopic damage mechanism of hydraulic asphalt concrete.

In light of the local rockburst risks during the construction of deep-buried water diversion tunnels by TBM， the drilling and blasting pilot tunnel of small diameters is widely adopted before TBM expansion excavation. Because of the flexibility and variability of drilling and blasting pilot tunnel， it is necessary to study the impact of different excavation parameters for pilot tunnels on subsequent TBM expansion excavation， which can provide guidance for on-site drilling and blasting pilot tunnels excavation. In this paper， excavation models with different pilot tunnel sizes， shapes， and distribution were first established to investigate the adjustment laws of strain energy induced by different parameters. Subsequently， the energy release laws of surrounding rock induced by TBM expansion excavation under different parameters were simulated. Finally， the optimal excavation parameters for pilot tunnels were determined through comparative analysis. The research results indicate that the more irregular the shape of the pilot tunnel， and the larger its size， the more conducive it is to energy release， which reduces the risk of rockburst during subsequent TBM expansion excavation， but will increase the risk of rockburst during the excavation of the pilot tunnel. The research findings can provide valuable reference for determining the parameters of drilling and blasting pilot tunnels.