In order to reveal the dynamic splitting mechanical properties and failure characteristics of steel fiber reinforced concrete （SFRC）， a mesoscopic model of SFRC including polyhedral aggregate， mortar matrix， interfacial transition zone （ITZ） and steel fiber was established based on the joint simulation method of MATLAB programming and LS-DYNA software. Considering the bond stress-slip behavior of steel fibers， the numerical simulation of the dynamic splitting process of split Hopkinson pressure bar （SHPB） under different impact velocities was further performed. The results show that the mesoscopic model constructed in this paper can effectively simulate the dynamic splitting performance and failure characteristics of SFRC， and the applicability of the model and the accuracy of material parameters are verified. As the impact velocity increases， the area of the wedge-shaped failure zone at the loading end of the specimen increases， and the width of the main crack at the center of the specimen increases significantly. Steel fiber inhibits crack propagation through bridging effect， slows down damage evolution， and significantly improves the splitting toughness of concrete. The internal energy time history curves of aggregate， mortar matrix and ITZ increase first and then decrease， and the peak internal energy of mortar is the highest， followed by ITZ and aggregate. The impact energy absorbed by the specimen is mostly used for crack initiation and propagation， and the kinetic energy accounts for about 5%. The peak internal energy and kinetic energy of aggregate， mortar matrix and ITZ in SFRC specimens are lower than those in PC specimens. Steel fiber changes the energy absorption and dissipation mode of concrete， and converts part of the failure energy into fiber pull-out energy. The results demonstrate that the interfacial transition zone is the weakest link for crack initiation and propagation under dynamic loading， and the uniformity of fiber distribution significantly influences its reinforcing effect， for which the optimization of fiber dispersion processes should be emphasized in engineering practice.

To study the impact of optimizing operating water level on sediment discharge of Xiluodu Reservoir， a one-dimensional unsteady flow and sediment mathematical model of Xiluodu Reservoir was established based on measured data after water storage， and the model was validated. Then， the impact of different operating water levels on sediment discharge in Xiluodu Reservoir was calculated. The calculation results show that the sediment discharge conditions of Xiluodu Reservoir are poor from July to September. During the flood season from July to August， the reservoir only possesses limited sediment discharge capacity when the inflow is large. During the storage period in September， the sediment discharge capacity of the reservoir is relatively small. In July and August， when there is a large influx of water， the sedimentation amount and proportion in the variable return water area of Xiluodu Reservoir are very small when the water level is below 590 m. After the water level is above 590 m， the sedimentation proportion in the variable return water area begins to increase， but the sedimentation amount is still very small. When the incoming water is small， the change pattern is similar， but the corresponding critical reservoir water level decreases to 585 m. In September， when the initial storage water level is below 580m， the sedimentation amount and proportion in the variable return water area are relatively small. When the initial storage water level exceeds 580m， the sedimentation proportion in the variable return water area begins to further increase， but the sedimentation amount in the variable return water area is still very small. This study can provide reference for optimizing the operating water level of Xiluodu Reservoir after its construction.

The turbulence characteristics of continuously curved water flows are significantly influenced not only by the channel shape but also by the widely distributed clustered vegetation within the channel. This vegetation alters the hydraulic properties of the flow， thereby affecting sediment transport and riverbed evolution. To investigate this phenomenon in depth， this study systematically measured the three-dimensional velocity field in a controlled flume experiment， focusing on the distribution patterns of time-averaged flow velocity， Reynolds stress， quadrant analysis， turbulent kinetic energy， and turbulent power spectral density before and after the presence of tufted vegetation. The results reveal that clustered vegetation significantly expands the high flow velocity regions while reducing the low flow velocity areas， leading to increased inhomogeneity in the flow velocity distribution and disrupting the vertical profile of the longitudinal flow velocity， causing it to deviate from the logarithmic distribution. Additionally， vegetation enhances the overall turbulent kinetic energy， particularly at the bend apex and downstream section， where high-energy vortex clusters form. Changes in flume curvature dominates the dominant quadrant flow regime， with vegetation exacerbating these changes to a limited extent. The turbulent power spectral density remains invariant in the absence of vegetation but varies with water depth when vegetation is present， with overall levels significantly lower than those without vegetation. These findings underscore the important role of clustered vegetation in river dynamics， suggesting that optimized vegetation configurations can regulate flow regimes， protect riverbeds， enhance ecological functions， and support sustainable river ecosystem health.

The accuracy of runoff forecasting is of great significance for basin flood warning， water resource management， and ecological conservation. However， due to factors such as climate change and human activities， as well as the notable nonlinearity and temporal dependencies among different hydrological processes， traditional hydrological models often lack sufficient accuracy in handling complex time-series data and non-stationary characteristics. To enhance model applicability in humid regions， this study developed a Long Short-Term Memory Sequence-to-Sequence model integrated with an attention mechanism （LSTM-Seq2Seq-Attention）， using the Gaotianshui Basin in the Pearl River system as a case study. The research systematically optimized the model from three aspects： model structure， input variables， and time step configuration. In terms of model structure， by comparing the performance of LSTM， Seq2Seq， and the attention-enhanced LSTM-Seq2Seq models during the validation period， it was found that the attention mechanism effectively improves the model’s ability to capture key hydrological events. For input variable selection， seven categories of variable combinations were designed based on hydrological process mechanisms to assess the influence of different variables on runoff simulation. Regarding time step optimization， ten input sequence lengths ranging from 3 to 180 days were tested to evaluate the impact of different time windows on model predictive capability and to analyze the memory characteristics of the basin′s hydrological processes. The results demonstrate that the attention mechanism enables the model to more effectively focus on critical periods such as heavy precipitation and sharp runoff increases when processing long sequence information， thereby significantly improving prediction performance. Compared to models without the attention mechanism， the LSTM-Seq2Seq-Attention model achieved a Nash-Sutcliffe Efficiency （NSE） of 0.982 and a Root Mean Square Error （RMSE） of 1.039 mm during the validation period， indicating its superior ability to capture nonlinear hydrological responses. Input variable comparisons revealed that using only the core variables—precipitation， air temperature， and potential evapotranspiration—yielded the highest accuracy （NSE=0.986）， outperforming the full-variable combination that included 18 variables. This suggests that in meteorologically dominated humid basins， excessive variables may introduce noise and impair model performance. Furthermore， the study on time steps revealed a "memory window effect" in the basin. The model achieved optimal overall performance with an input sequence length of 90 days， simultaneously capturing rapid runoff responses and slow groundwater recharge processes， with an NSE of 0.998 and an RMSE of 0.165 mm. In summary， the LSTM-Seq2Seq-Attention model proposed in this study provides a high-accuracy solution for runoff forecasting in humid regions through systematic optimization of model structure， input variables， and time steps. The findings not only validate the effectiveness of the attention mechanism in hydrological modeling but also offer theoretical support and practical references for the application of deep learning in hydrology.

Reservoir flood control optimization is a core component of watershed flood management systems. Current research primarily focuses on five aspects： scheduling principles， constraints， objective functions， optimization algorithms， and application studies. However， existing studies often lack clarity on the dynamic regulation mechanisms of flood discharge facilities and fail to provide optimization methods that effectively balance flood safety with operational practicality. To address these gaps， this paper proposes a multi-objective optimization model for reservoir flood control that incorporates gate operation rules. The model aims to minimize the number of gate operations， maximize the flood peak reduction rate， and control the maximum upstream water level， while strictly adhering to complex constraints such as water balance， water level limits， outflow boundaries， and ecological flow requirements. To solve the model， an Improved Whale Optimization Algorithm （IWOA） is designed. The algorithm enhances population diversity by introducing Logistic-Tent chaotic mapping and improves global search capabilities and convergence efficiency through a dynamic adaptive parameter adjustment mechanism and a simulated annealing strategy. The model was validated through empirical analysis， taking a typical bimodal flood event in June 1955 at the Xin’anjiang Reservoir as a case study. Simulation results demonstrate the superior performance of the proposed approach： compared to conventional scheduling methods and the standard Whale Optimization Algorithm （WOA）， IWOA generates scheduling strategies in significantly shorter computational time while achieving better outcomes in key metrics such as gate operation frequency， flood peak attenuation， and control of the maximum reservoir water level. In summary， this study bridges the gap between theoretical optimization and practical application by developing a flood control scheduling model coupled with gate operation rules and an efficient solving algorithm （IWOA）. The proposed framework offers a computationally efficient， operationally feasible， and decision-reliable method for reservoir flood control， while also providing valuable insights for addressing optimization challenges in other complex water resource systems.

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.

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.

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.

Taking the Xiangjiang River Basin as the research area， this study coupled the physically-based SWAT hydrological model with the data-driven XGBoost machine learning model. Using simulation evaluation metrics， a comparative analysis was conducted on the monthly runoff simulation performance of the SWAT model， the XGBoost model， and the coupled SWAT-XGBoost model at the Xiangtan Station from 2000 to 2020. The main findings are as follows： ① For the SWAT hydrological model， the Nash-Sutcliffe efficiency coefficient （NSE） of runoff simulation was 0.946 during the training period （2000-2015） and 0.930 during the validation period （2016-2020）. Although the overall trend of the flow simulation accuracy was relatively good， there was a underestimation of the extreme flow during the wet season. ② For the XGBoost model， the NSE values were 0.880 and 0.658 during the training and validation periods， respectively. Its significantly lower performance compared to SWAT is attributed to the model’s lack of physical mechanisms and insufficient coverage of training samples， limiting its ability to capture dynamic runoff responses and implicit patterns in complex hydrological processes. ③ The coupled SWAT-XGBoost model achieved NSE values of 0.987 and 0.940 during the training and validation periods， respectively. The coupled model demonstrated relatively superior overall performance and showed improvements in simulating extreme runoff during high and low flow periods compared to the standalone SWAT or XGBoost models. Therefore， by integrating the strengths of physical mechanisms and data-driven approaches， the SWAT-XGBoost coupled model enhances the accuracy of runoff simulation in the Xiangjiang River Basin to some extent， and can provide a valuable reference for water resource management， runoff simulation， and prediction in the Xiangjiang River or similar basins.

With the large-scale integration of renewable energy sources into the power grid， hydropower plants frequently undertake unit start-stop operations to satisfy deep peak-shaving requirements of the power grid. However， factors such as additional water consumption， equipment depreciation， maintenance expenses， and operational risks associated with these frequent start-stop operations are often overlooked， and plants must also bear potential penalty risks arising from start-stop failures. To address this issue， this study proposes an in-plant economic dispatch model that minimizes total generation flow while maintaining a fixed number of operating units. The model is solved via a dynamic programming algorithm， yielding an optimized zero start-stop peak-shaving scheme as an alternative to the frequent start-stop strategy used in practice. By comprehensively accounting for water consumption， equipment depreciation， maintenance costs， and operational risk， an economic-loss assessment method for a single start-stop cycle is established. Comparative analyses based on real operational scenarios quantify the economic-loss differences between the conventional start-stop and the proposed zero start-stop schemes. On this basis， an operational strategy that minimises economic losses is put forward， providing theoretical support and practical guidance for the economical， safe， and efficient operation of large-scale hydropower units under deep peak-shaving conditions.

This study aims to explore the spatiotemporal patterns of soil salinity migration and hydrochemistry and their influencing factors in typical areas of the Hetao Irrigation District， and to provide a theoretical basis for controling soil salinization in the irrigation district. In 2020， a monitoring test was conducted in the 2 667 hm2 branch canal control area of the Yichang irrigation zone in the Hetao Irrigation District. Statistical analysis， correlation analysis， and other methods were employed to investigate the annual spatiotemporal distribution patterns of soil salts， the hydrochemical characteristics， and the main influencing factors. The study revealed that： ① The spatial distribution of total salt， Cl^-， SO₄ ^2-， Ca²⁺， Mg²⁺， and K⁺+Na⁺ in the root zone were uneven， and the spatial coefficients of variation were all greater than 50%. The temporal variation patterns of total salinity and the main salt ions （Cl^-， SO₄ ^2-， K⁺+Na⁺） were similar. During the crop growth period， the content and variability of soil salinity continuously increased. Autumn irrigation reduces the content and variability of soil salinity. ② During the growth period， there was a significant accumulation of salts in the root zone， with a total salt change of 8.8%， primarily caused by the accumulation of SO₄ ^2- and Ca²⁺. The correlation coefficients between total salinity and the change rates of SO₄ ^2-and Ca²⁺ were 0.96 and 0.87， respectively. The effect of autumn irrigation on different ions was significantly different， with effective leaching depths of 100， 140， 140， 100， 30 and 60 cm for total salt， Cl^-， K⁺+Na⁺， SO₄ ^2-， Ca²⁺ and Mg²⁺， respectively. ③ The amount of soil salinity change in the root zone during the growth period was correlated with the degree and type of salinity， depth of groundwater， groundwater mineralization degree， and crop species， and the soil salinity at the beginning of the growth period. There was a clear pattern of salinity changes in the root zone during the reproductive period under the effect of different factors. Research indicates that the spatial and temporal distribution of different salt ions and the dynamics of growth season and autumn irrigation periods are significantly different. In the future， we should focus on the mechanism of salinity ions' influence on crop growth and development under different influencing factors， in order to facilitate the sustainable utilization of saline-alkali land and enhance yields in irrigation areas.

Granite， as a typical rock mass of hot dry rock reservoirs， is often subjected to different refrigerant media during exploitation using Enhanced Geothermal System （EGS）. In this study， a PFC-GBM simulation model was established by combining indoor experiments and simulation， and the evolution of microcracks distribution， number and formation mechanism of the specimens during the cooling process of air， water and liquid nitrogen were analyzed in depth. Furthermore， the changes in the mechanical properties and damage modes of high-temperature granite under different cooling rates were further explored. The results show that： with the increase of cooling rate， the total number of microcracks increased significantly， showing the trend of liquid nitrogen cooling>water cooling>natural cooling， concentrated in the boundary of the mineral particles and quartz grains； under high temperature conditions， intergranular tensile microcracks account for more than 60%， and intragranular tensile microcracks in quartz account for about 20%. The formation of microcrack network is mainly controlled by three minerals： quartz volume change dominates the network formation， feldspar nodal surface controls the expansion path， and mica structure regulates the stress. The cooling rate significantly affects the mechanical properties and damage mode of high-temperature granite. The higher the cooling rate， the greater the reduction range of compressive strength and modulus of elasticity， and the post-peak behavior is gradually shifted from brittleness to ductility， and the plastic deformation is enhanced， with the damage mode mainly characterized by complex multi-cracks and block-like structures. When the temperature is lower than 450 ℃， the damage mode and mechanical property degradation of granite under liquid nitrogen cooling are more obvious； while at higher temperatures， water cooling provides a more economical and practical alternative. The results of this study provide a reference for the effective exploitation of geothermal energy from dry-heat rocks.

Current assessments of aquatic ecosystem health in the middle and lower Yangtze River largely focus on single-factor indicators， limiting their ability to capture cumulative disturbance effects. To address this gap， this study develops a basin-scale framework for evaluating physical integrity. Five core functions—hydrological connectivity， flood regulation， physical habitat structure， water-quality purification， and ecological continuity—were identified. Seven categories of anthropogenic stressors were spatially mapped and weighted to construct an Index of Physical Integrity （IPI）， accompanied by high-resolution mapping. A Human Activity Index （HAI） was also calculated to quantify disturbance intensity， enabling a quantitative analysis of IPI sensitivity to human pressures and offering a new tool for large-scale integrity assessment. Results show a persistent spatial gradient of “low in urban areas， moderate in plains， and high in hills.” From 2015 to 2020， the area of extremely low IPI patches expanded by nearly 70%. The IPI–HAI relationship follows a four-phase nonlinear pattern： （i） a collapse threshold at HAI = 0.35； （ii） a recovery zone in the range of 0.35<HAI≤0.50； （iii） a high-resilience window at 0.50<HAI≤0.70； and （iv） marginal decline when HAI>0.75. Seasonal differences were evident： wet-season IPI values exceeded dry-season levels by 0.10~0.30 on average， with differences around lake clusters reaching 0.20. About 93% of the region exhibited declining seasonal integrity， primarily driven by flood–vegetation interactions. Based on these findings， a zoned regulation strategy is proposed： “push beyond 0.35—cross 0.50—hold 0.75.” This framework provides an operational， quantitative reference for water-ecological management and restoration， while offering a transferable pathway for assessing physical integrity in other large river systems.

This study focuses on the application of reservoir operation regulations to guide reservoir management， addressing the challenges of efficient and accurate retrieval and intelligent reasoning when dealing with complex operational guidelines. Traditional retrieval approaches， such as expert regulation libraries and knowledge graphs， exhibit limitations in handling complex regulations， including insufficient retrieval accuracy， weak reasoning capabilities， and the lack of natural language interaction， which fail to meet the requirements of modern reservoir operation decision-making. To address these issues， this paper develops a retrieval-augmented generation （RAG） system and decision-making framework for complex reservoir operation regulations based on large language models （LLMs）. By leveraging high-dimensional vector processing techniques， an efficient vectorization method for handling reservoir regulations is proposed， establishing a domain-specific knowledge base. Prompt engineering tailored to regulatory characteristics is designed， and logical reasoning capabilities are enhanced through chain-of-thought （CoT） and code-first strategies. A knowledge base system is implemented using the open-source ChatGLM4 model， employing efficient retrieval and information-matching mechanisms. Through the integration of vector databases and prompt engineering， deep injection of regulatory knowledge is achieved， significantly improving retrieval accuracy， efficiency， and reasoning performance. Experimental results demonstrate that， compared with traditional methods， the LLM-based retrieval-augmented approach achieves superior performance across multiple evaluation metrics， with an average answer similarity score of 0.94， answer relevance of 0.90， answer correctness of 0.75， and contextual precision of 0.92， providing an intelligent and high-precision pathway for applying reservoir operation regulations in practical reservoir management.

In actual engineering projects， dynamic changes in the head difference between the upstream and downstream of embankment reservoirs （including amplitude changes and reciprocating fluctuations） exacerbate the risk of filter layer soil permeability instability. The particle transport mechanism requires further in-depth study. Using a self-developed large-scale permeameter， non-cohesive yellow sand was used as the base material and river pebbles as the filter material. Under the premise of keeping the composition of the filter and base materials unchanged， the effects of different pressure amplitudes and varying pressurization methods on the transport behavior of fine particles in the filter layer were investigated. The results indicate that as the amplitude of the head difference increases， seepage failure occurs in advance， and the anti-seepage capacity of the protected soil decreases. The critical hydraulic gradient and failure hydraulic gradient decrease， with their boundary becoming blurred， leading to a weakened anti-seepage capacity of the system. The larger the head difference， the lower the hydraulic gradient required for particle initiation and transport， making seepage failure more likely to occur. As the amplitude of the cyclic head difference increases， the system stability decreases， and particles are more likely to initiate and transport outside the filter layer. Even if no failure occurs under a steady head， a larger cyclic head amplitude may still induce instability and accelerate particle transport. The larger the amplitude， the shorter the seepage failure process， the more sudden the failure， and the greater the difficulty in prevention. Sudden rises or drops， or significant fluctuations in water level hinder the dissipation of water pressure， leading to the accumulation of excess pore water pressure， which weakens the effective stress of the soil and reduces system stability. Under the same head difference amplitude， cyclic head effects further weaken the anti-seepage capacity of the base-filter system， with the impact intensifying as the amplitude increases， thereby raising the risk of system instability.

The reasonable selection of kernel functions and kernel function parameters is of great significance for improving the performance of Twin Support Vector Regression （TWSVR）. To improve the prediction accuracy of monthly runoff time series and compare and verify the effectiveness of TWSVR with different kernel function mappings， Wavelet Packet Transform（WPT）， 18 kernel functions（such as linear kernel function）， Superb Fairy-wren Optimization Algorithm （SFOA）， and TWSVR were used to propose the WPT-SFOA-TWSVR model with 18 kernel function mappings. Five common kernel function mappings of WPT-SFOA-SVR models were constructed for comparative analysis. A total of 23 models were validated through monthly runoff prediction examples at the Dishui， Nandong， Mengda， and Nankanghe hydrological stations in Yunnan Province. Firstly， WPT is used to decompose and process the monthly runoff time-series data of the instance， dividing it into a training set and a validation set. Then， SFOA is applied to optimize the TWSVR/SVR hyperparameters of different kernel function mappings. Finally， using the optimal hyperparameters， a WPT-SFOA-TWSVR/SVR model with different kernel function mappings was established to train， predict， and reconstruct each component of monthly runoff for the four instances. The results show that： ① The WPT-SFOA-TWSVR model based on linear kernel function， Gaussian kernel function， polynomial kernel function， wavelet kernel function， Sigmoid kernel function， and neural kernel function mapping has the smallest prediction error and the best performance. The WPT-SFOA-TWSVR model based on ANOVA kernel function， Bessel kernel function， logarithmic kernel function， multiple quadratic kernel function， and power-law kernel function mapping follows closely. The WPT-SFOA-TWSVR model based on T-Student kernel function， Cauchy kernel function， and rational quadratic kernel function mapping has relatively larger prediction errors； The WPT-SFOA-TWSVR model based on Laplace kernel function， Fourier kernel function， chi square kernel function， and spherical kernel function mapping has the largest prediction error. ② Under the same WPT decomposition and SFOA optimization conditions， the TWSVR model performs significantly better than SVR. ③ Optimizing TWSVR hyperparameters using SFOA can significantly improve model performance and computational efficiency. ④ The WPT-SFOA-TWSVR model with different kernel function mappings has good universality， providing reference and inspiration for the selection and optimization of TWSVR kernel functions.

Focusing on the "dual carbon" strategy and the recycling of waste resources， avoiding traditional breakwaters made of cement， rocks or concrete， fully utilizing the advantages of anti scouring， anti buffering performance of waste tires as well as good bending resistance of bamboo poles， a new type of ecological breakwater structure is proposed， which is light in texture， low in engineering cost， and easy to construct and dismantle， using bamboo poles as the main reinforcement to fix and stack waste tires. It is referred to as the "permeable breakwater composed of bamboo poles and waste tire strings". In order to obtain a new type of permeable breakwater structure with good wave dissipation， weak flow， and shore protection performance， a water flow erosion model test was conducted to consider the effects of waste tire opening ratio， opening direction， symmetry of front and rear holes， arrangement of permeable breakwater， and water depth. The variation laws of soil pressure， pore water pressure， and wave reduction coefficient before and after the permeable breakwater were analyzed under different working conditions. The inclination angle before and after the permeable breakwater scouring and the amount of mud and sand loss of the embankment were compared under different working conditions. The wave dissipation， current weakening， and shore protection performance under each working condition were analyzed， and compared with the amount of mud and sand loss without a breakwater. According to the physical experiment scale， it is found that when the median particle size of sandy coast in the actual engineering is 0.375~0.75 mm and the water depth is 3.5m， the "permeable breakwater composed of bamboo poles and tire strings" with a tire opening rate of 30% has the best anti current and revetment protection performance； When the direction of the waste tire opening on the permeable breakwater is consistent with the direction of water flow， this new type of permeable breakwater is more suitable for flow dissipation and shore protection； When the front and rear holes of the waste tire of the permeable breakwater are asymmetric， the new type of permeable breakwater has better flow dissipation performance； When the spacing between rows of the permeable breakwater is equal to the diameter of the waste tire of the new permeable breakwater， the new type of permeable breakwater has better flow dissipation performance； When the permeable breakwater is arranged in two rows， the comprehensive benefits of this new type of permeable breakwater project are the highest.

Reservoir dam risk standard and risk acceptability determination are important bases for risk management. This paper comprehensively considers three aspects including standard rigor， risk probability accuracy and risk level difference， and explores the risk acceptability determination based on existing risk standards. Firstly， risk standards at home and abroad are compared to explore the strictness of our standards. Secondly， the application of the reliability method in calculating the risk probability is investigated. Then， the difference between risk level and acceptability are discussed. Finally， the feasibility and applicability of this paper are verified by combining with the risk assessment process of a certain gravity dam. The results show that the gravity dam has a reliability index β=4.42， an annualized probability of failure F=2.5×10^-8， and a loss of life N=535.The risk level under Chinese standard is classified as "risk that should be reduced as far as possible"， while it is an “acceptable risk” under foreign standards， which indicates the strictness of Chinese standard. The risk level is general risk， which combined with the acceptability determination indicates that the risk level of the gravity dam is within the tolerable range， and it is necessary to strengthen the risk monitoring and take appropriate safety measures to reduce the risk. The research content of this paper can provide reference for the risk management of reservoir dams in China.

After the operation of Lianghekou， Wudongde， and Baihetan Reservoirs in the upper Yangtze River， the inflow discharge and sediment of Three Gorges Reservoir （TGR） has significant changed. As a result， the current operation scheme of TGR can no longer meet the new demands. Based on rainfall data from the upper Yangtze River basin （1961-2022） and flow data from the Yichang Hydrological Station （1882-2022）， the flood season is divided by using several mathematical statistical methods. Furthermore， the temporal evolution patterns of Meiyu in the middle and lower reaches of the Yangtze River and the Autumn Rain in west China is analyzed， the TGR observed and restored inflow peak discharges and flood volumes are compared， and the necessity and feasibility of dynamically adjusting seasonal division and operational water levels are also discussed. The main flood season of the TGR is determined to be from June 21 to September 10 based on statistical analysis of rainfall and flow data series. The Meiyu is mainly occurred in late June and July， while the Autumn Rain begins in late August. Most reservoirs in the upper Yangtze River enter the refill period and TGR inflow peak discharges are reduced significantly. In summary， it is suggested to divide the TGR flood season into four periods： i.e.， drawdown period （May 1-June 20）， Meiyu flood period （June 21-July 31）， transition period （Aug. 1-Sept. 10）， and Autumn Rain refill period （Sept. 11-Oct. 31）. In practical operation scheduling， the phased transition points can be determined based on the forecasted dates of Meiyu onset and retreat. Following the Meiyu retreat， the flood control capacity reserved by the TGR may be gradually released， allowing a controlled rise in operational water levels. During the Autumn Rain period in West China， opportunities should be seized to initiate early impoundment. Dynamic control of operational water levels is implemented based on 1~5 day flood forecasting processes， balancing flood-drought mitigation and efficient water resource utilization. This integrated approach significantly enhances the comprehensive benefits of the Three Gorges Reservoir.

Revealing the intrinsic relationships between driving factors and water quality evolution within a river basin can support environmental authorities in implementing targeted water pollution control measures. However， existing studies predominantly conduct spatial analyses based on Euclidean distances from monitoring stations and often focus on either anthropogenic activities or climate change in isolation， lacking a comprehensive assessment of their combined effects. To address this gap， this study integrates multiple datasets from the Yangtze River Basin， including GDP distribution， population density， land use patterns， precipitation， temperature， flow discharge， and water quality monitoring data. Four distinct study zones were delineated based on flow accumulation length， Euclidean distance， county-level administrative boundaries， and city-level administrative boundaries. A comparative analysis was performed to examine how different spatial scales influence the correlations between various driving factors and water quality trends at key monitoring stations along the Yangtze mainstem， thereby identifying critical influencing zones. The results demonstrate that for permanganate index （COD_Mn）， ammonia nitrogen （NH₃-N）， and total phosphorus （TP）， the area within a flow accumulation length of 200 km constitutes the key influencing zone for Yangtze water quality. Moreover， three principal components were identified as the dominant drivers across all monitoring stations， collectively explaining an average of 86.7% of the total variance. Specifically， the first principal component includes GDP， population， per capita GDP， cropland area， and residential land area. The second principal component consists of precipitation， grassland area， water body area， and forestland area. The third principal component includes temperature and cross-sectional flow.

To mitigate the subjectivity of rust annotation and enhance annotation efficiency， this paper proposes a rust detection method based on a YOLOv8 cascaded architecture to rapidly implement deep learning-based corrosion detection for hydraulic engineering metal structures. To enhance accurate detection of rust target， the MobileViTv3 module is integrated into YOLOv8n， resulting in a modified model named YOLOv8-vit. Based on YOLOv8-vit， we further propose YOLOv8-vit-cls for rust grade learning and classification. This network leverages the pretrained parameters of YOLOv8-vit to efficiently learn features of different rust grades. Finally， a cascaded architecture combining YOLOv8-vit and YOLOv8-vit-cls is constructed to perform both rust detection and grade classification of hydraulic metal structures.

The Pinglu Canal construction involves extensive cutoff engineering， which creates semi-enclosed river segments with reduced flow velocity， thereby limiting hydrodynamic connectivity and adversely affecting wetland habitats and biodiversity. To optimize water exchange efficiency， this study employs three-dimensional numerical simulations to analyze the rectification scheme of gravel spur dikes in a representative cutoff section of the Pinglu Canal. Key parameters including water exchange time and spatial distribution of low-velocity zones are investigated to evaluate how channel narrowing rates and weir spacing affect water exchange efficiency. Further optimization is achieved through perforation strategies at dike roots and heads. Results demonstrate that under operating parameters of 175m dike spacing and 15% narrowing rate，the straightened section exhibits the shortest water exchange time and the smallest proportion of low-velocity zones at typical cross-sections. Water exchange time is reduced by 29.7% to 35.6% compared to pre-remediation conditions， with low-velocity zones in characteristic cross-sections decreasing to 11.3%、8.9% and 19.5%. Additionally， incorporating openings in the dike bodies further improved flow velocity distribution characteristics between dikes， with perforations at dike roots exhibiting superior performance in flow redistribution compared to head modifications. This research provides a scientifically validated approach for hydrodynamic optimization and ecological restoration in navigational channel engineering， offering practical solutions to mitigate environmental impacts of river regulation projects.

The establishment of a multi-field coupled model has become an important means of predicting the evolution of concrete properties， and the values of the parameters in the model have a great influence on the calculation results. Aiming at the problem of insufficient simulation accuracy due to the difficulty in determining parameters in the finite element simulation of early-age concrete， this paper proposes a multi-field coupled simulation method of concrete chemistry-heat-humidity （CTH） that integrates physical experiments and BP neural network parameter inversion. Firstly， an optimized chemical affinity function and a modified evaporable water equation are introduced to achieve an accurate simulation of the multi-stage evolution of relative humidity. subsequently， a parameter inversion method based on BP neural network is used to invert and optimize the key parameters of the CTH multi-field coupled model by combining physical experimental data， to construct a high-precision numerical model， which is then applied to the simulation calculations of concrete chunk casting. The training results of the BP neural network model of this method have good stability， and the predicted value of the humidity decline period fits the experimental data by more than 90%. The simulation results show that the constructed CTH multi-field coupling model can accurately simulate the spatial and temporal evolution of the temperature and humidity fields inside the concrete. This research result provides theoretical and methodological support for the parameter calibration and engineering prediction of multi-field coupled model for early-age concrete.

Drought-flood abrupt alternation， a compound extreme climate phenomenon， has caused serious negative impacts on the ecological environment， agriculture production， and social economy. In order to deeply understand the regional characteristics and overall evolutionary patterns of drought-flood abrupt alternation events in summer in China， this study uses observational data and employs two indices， the Long-cycle Drought-Flood Abrupt Alternation Index （LDFAI） and the Short-cycle Drought-Flood Abrupt Alternation Index （SDFAI）， to identify the drought-flood abrupt alternation events in summer across the eight sub-regions in China. It further reveals the spatial and temporal distribution， trends and future persistence of the frequency and intensity of these events， and finally focuses on the characteristics of the short-cycle drought-flood events with different alternation timings and their influencing factors. The results show that： ① Drought-flood abrupt alternation events are primarily concentrated in eastern China， particularly in South China （SC）， Southwest China （SW）， Northeast China （NE）， and Eastern Northwest China （ENW）. The frequency of both long-cycle and short-cycle flood-to-drought events shows a significant increasing trend. Additionally， the future trends in the frequency and intensity of these events across all the sub-regions are consistent with those observed over the past 58 years. ② The short-cycle drought-flood abrupt alternation events with alternation timing between May and June generally exhibit higher intensity and show the characteristics of gradual enhancement. ③ The short-cycle drought-flood abrupt alternation events with different alternation timings primarily concentrate in the mid-latitude regions （20°N~40°N）. In particular， the alternation timing of drought-flood abrupt alternation events corresponded well to the advance of summer rain belt in eastern China. Meanwhile， the occurrence frequency of severe flood-to-drought events with different alternation timings remains relatively stable and consistently high. ④ Comparing to the earlier period （1961-1990）， influenced by the earlier onset of the South China Sea summer monsoon， the drought-to-flood abrupt alternation events with alternation timing between May and June （June and July） increased （decreased） in eastern China during the later period （1991-2018）. Similarly， flood-to-drought events with alternation timing between June and July （May and June， July and August） increased （decreased）.

In order to reveal the co-evolution mechanism of water-energy-food-ecology （WEFE） system security and economic resilience in the Beijing-Tianjin-Hebei urban agglomeration， this study used the panel data of 13 prefecture-level cities in the Beijing-Tianjin-Hebei region from 2007 to 2022 to construct a three-dimensional evaluation framework of pressure-state-response， and systematically analyzed the characteristics and influencing factors of regional coupling spatial network from 2007 to 2022 by comprehensively using the coupling coordination degree model， modified gravity model， social network analysis and secondary assignment procedure. By quantifying the nonlinear correlation between resource constraints and economic resilience， this paper aims to break the limitations of traditional single-factor research and propose a differentiated networked governance path， so as to provide theoretical support and decision-making basis for urban agglomerations to break through the path dependence of "resource consumption for growth" and realize sustainable development of ecological priority. The results show that from 2007 to 2022， the average coupling coordination degree of the WEFE system security and economic resilience in Beijing-Tianjin-Hebei increased from 0.38 to 0.48， but regional differences expanded significantly， and the spatial pattern evolved from a “core-periphery” fragmented structure to a “multipolar network” form. While the number and density of network associations are increasing， the correlation and stability still need to be optimized： Beijing and Tianjin form technology capital spillover through the “siphon-radiation” effect， and intermediary cities such as Shijiazhuang and Baoding play the role of “structural holes” to promote peripheral integration； Geographical proximity significantly strengthens network association， while population quality， government efficiency， technological innovation， openness level and climate risk differences have inhibiting effects on network formation. Based on this， this study proposes to promote the multi-directional circulation of elements by constructing a dynamic monitoring and three-level governance structure， implementing a core node feedback mechanism and reward fund to promote network collaboration， and establishing a cross-domain collaboration， talent sharing and climate adaptation mechanism， so as to realize the differentiated networked governance of the coupling of WEFE system security and economic resilience in Beijing-Tianjin-Hebei.

Reservoirs are critical infrastructure for watershed water resource management. The water storage and release processes of reservoirs directly influence the efficient utilization of water resources. Accurately monitoring changes in reservoir storage is of great significance for understanding operations of reservoirs and their impact on watershed water resources. Synthetic Aperture Radar （SAR） satellites and optical remote sensing satellites provide an effective means for accurately monitoring reservoir storage， due to wide coverage and high accuracy. However， the extraction of water bodies from SAR images is affected by the incidence angle effect. The reservoir storage results vary and the accuracy is reduced due to differences in water body extraction. Thus， this study focuses on the Maoergai and Qiaoqi Reservoirs in the Minjiang River Basin. A regression-based correction model was developed using Sentinel-2 optical imagery to reduce the impact of incidence angle effects on water surface area extraction from Sentinel-1 data. Then， an area-storage model was applied to fit reservoir storage based on area extraction results， using in-situ storage measurements. Finally， a consistency evaluation of the retrieval outcomes was performed. The results indicate that the combination of Sentinel-1 and Sentinel-2 data enhances observation frequency， improving the continuity of inundation area and storage observations， achieving continuous weekly monitoring. Orbital-specific correction reduces inter-orbital discrepancies， and improves the fitting accuracy of the area–storage capacity model. The fitted model accurately represents the relationship between water surface area and reservoir storage. Both reservoirs in the study area have a storage capacity exceeding 100 million cubic meters， consistent with the operational characteristics of multi-year regulation reservoirs. Both reservoirs released water earlier than usual in response to emerging drought conditions in the Yangtze River Basin during the 2022 flood season. This method provides reliable technical support for intelligent management based on remote sensing of reservoirs as well as the monitoring of unregulated reservoirs.

The conventional reservoir operation diagram， constrained by static regulation frameworks and linear assumptions， demonstrates limited capacity in characterizing dynamic coupling mechanisms and nonlinear constraints within cascade reservoir systems. To address this， based on the theory of system dynamics， this study built a simulation model for early flood-season storage scheduling in mega reservoirs. Taking six cascade reservoirs （Ludila， Guanyinyan， Wudongde， Baihetan， Xiluodu， and Xiangjiaba） in the middle and lower reaches of the Jinsha River as the case study， the dynamic coupling path among water level， reservoir capacity and flow rate was analyzed through system dynamics feedback loops and stock-flow architecture. The study coupled optimization scheduling techniques using LOOKUP functions and IF-THEN-ELSE conditional statements， which characterized nonlinear relationships including water-level-reservoir capacity curves and hydropower unit output constraints， achieving multi-objective dynamic simulation of coordinated reservoir impoundment operations. The Research results indicated that the system dynamics model could accurately depict the dynamic and nonlinear characteristics of the impoundment operations， and its simulation results were highly consistent with those of optimal operation schemes. Under various hydrological scenarios （wet， normal， dry， and extremely dry years）， the Nash-Sutcliffe Efficiency （NSE） values for the reservoir operation water levels exceed 0.99， and the NSE values for the reservoir discharge processes surpass 0.97. The mean absolute error （MAE） values for reservoir operation water levels have not exceeded 0.11 m， while the root mean square error （RMSE） values have not surpassed 0.28 m. For reservoir discharge simulations， the MAE values are below 54 m3/s， and the RMSE values are less than 171 m3/s. Compared with the conventional operation scheme， the system dynamics simulation scheme can increase the multi-year average hydropower output by 2.495 billion kWh/a （an improvement rate of 3.21%）， reduce the spilled water volume by 1.435 billion m3/a （an improvement rate of 15.61%）， and decrease the average computation time for the joint operation of mega reservoirs from 6 hours to 5 seconds. The results not only thoroughly demonstrate the accuracy and reliability of the simulation model， but also provide scientific and technical support for the joint operation of mega reservoirs.

Soil salinization is one of the key factors affecting the sustainable development of agriculture in the Hetao Irrigation District. Accurate prediction of soil salinization and sodification trend is an important means to formulate scientific prevention and control measures. Traditional studies usually use the total amount of soil salinity in the root zone as an indicator to evaluate the evolution trend of salinization， while rarely paying attention to changes in ion composition. Due to the different physical and chemical behaviors and migration capacities of different salt ions， soil salts not only changes in total amount， but also change in composition of salt ions under the influence of external forces such as irrigation leaching， evaporation， freezing and thawing. To conduct in-depth research on the distribution and transport patterns of salt ions in different land use types， this paper selects a typical area in the Yichang Irrigation Area of Inner Mongolia's Hetao Irrigation District as the research area. From 2017 to 2019， observations were conducted on the distribution processes of various ions， analyzing the dynamic processes of water， salt， and ions， as well as the characteristics of ion differentiation.The soil moisture content increases with depth due to water infiltration and water-holding capacity， while variability showes an inverse trend， decreasing with soil depth. The electrical conductivity （EC） profile of abandoned cultivated land decreases with depth， with surface accumulation being the main feature of overall salt distribution. The distribution of salt ions in soils of different land types is primarily characterized by highly differentiated Na⁺， Cl^-， and SO₄ ^2- ions. The HCO₃ ^- content is also significant but is relatively uniformly distributed in the soil profile. The contents of Ca²⁺ and Mg²⁺ are noticeably lower， especially in wasteland where both their composition and proportion are minimal. The mobility differentiation of various salt ions is evident. The migration degree and rate of cations are in the order of Na⁺ > Mg²⁺ > Ca²⁺， which correlates with ion migration rates through soil layers. Among anions， Cl^- shows the strongest differentiation， while HCO^3- exhibits weaker differentiation.

The Jinsha River serves as the main stem of the upper Yangtze River， contributing approximately 50% to the total runoff discharge and 60% to the total sediment load in the upper Yangtze River Basin. Its extensive tributary network and complex sediment sources render its hydrological dynamics highly complex. In recent years， the operation of key cascade reservoirs has significantly altered flow and sediment transport characteristics， necessitating an in-depth analysis of these changes. In this study， hydrological data （1977-2020） from the Xiangjiaba（Pingshan） Hydrological Station in the lower reaches of the Jinsha River were used to investigate sediment transport dynamics during flood events before and after the impoundment of the Xiangjiaba Reservoir and Xiluodu Reservoir. Flood events were extracted from the time series data， and analytical methods， including Mann?Kendall （M?K） analysis， sediment rating curve analysis， and hysteresis pattern analysis methods， were systematically applied. The key findings indicated that a 99% reduction in sediment load occurred during the postimpoundment period， which was primarily attributable to reservoir sediment trapping. The discharge?sediment concentration relationship exhibited fundamental regime shifts. The original robust power-law relationship between water discharge and sediment load was disrupted after reservoir operation. Under moderate and high discharge levels （10 000~13 000 m3/s）， the sediment transport capacity was no longer governed primarily by hydrodynamic forces but was instead constrained by sediment supply limitations. However， this flow range remained the dominant sediment transport range. The sediment rating curves significantly flattened （the coefficient decreased by 87%）， reflecting reservoir-induced flood peak attenuation and changes in the discharge?sediment relationship. Asynchrony analysis of flood events revealed that under natural conditions， sediment source depletion during flood events led to insufficient subsequent supply， causing sand peaks to exhibit predominantly leading asynchrony. After reservoir operation， a notable change in asynchrony characteristics between flood and concentration peaks occurred， with the sediment peak lag pattern becoming dominant， driven by a substantial weakening of hydrodynamic conditions due to elevated reservoir water levels. This study elucidated the mechanistic shifts in water?sediment transport in the lower Jinsha River， thereby providing theoretical foundations for the formulation of reservoir sediment management strategies.

Branching channels are common in nature. After the construction of the upstream reservoir hub， the downstream clear water scouring causes the riverbed armoring of the branching channel to become normal. Therefore， it is of great significance to study the laws related to the riverbed armoring of the branching channel under the action of clear water. Through the flume test， it is found that in the dynamic process of the damage and formation of the armoring layer in the branching river bed caused by clear water scouring， the bed-load transport rate presents a fluctuating change of first increasing and then decreasing. And with the increase of flow， the peak value of the transport rate increases， the occurrence time is advanced， and the formation time of the armoring layer is shortened. The armoring degree of the main branch is higher than that of the branch， and the increase of discharge intensifies the damage of armoring layer， resulting in the overall increase of sediment diameter and the reduction of particle size distribution. In addition， the erosion and deposition of riverbed topography are significant. The main branch erodes violently and the branch deposits， and there is a dynamic balance between the flow and the riverbed topography.

Aiming at the freezing and thawing damage observed in dispersive soil canal slopes with high groundwater levels in seasonal frozen regions， we systematically investigated the frost-heave and thawing deformation characteristics of dispersive soils under varying initial conditions of temperature， moisture， and stress. The results indicated that： ① During the freezing process， as the temperature decreased， the freezing front of the dispersive soil advanced downward， and moisture within the soil migrated toward the freezing front， leading to ice formation. Ice layers and lenses were primarily distributed within the 1/2 to 2/3 depth range of the maximum frozen depth. ② The pore water pressure within the soil exhibited distinct periodic fluctuations in response to temperature changes， accompanied by a significant hysteresis effect. ③ Under identical conditions， as the dry density increased， the frost heave rate increased， the thaw settlement rate decreased， and the cumulative freeze-thaw deformation was reduced. ④ External water supply significantly influenced the frost heave process. Under conditions with external water supply， the frost heave rate of dispersive soil was 3 to 4 times higher than that without external water supply. ⑤ After three freeze-thaw cycles， greater overburden pressure resulted in a lower frost heave rate and a higher thaw settlement rate， indicating that overburden pressure effectively suppressed frost heave deformation. Based on the research findings， it is recommended that the anti-frost design of dispersive soil channel slopes in seasonal frozen regions should prioritize the impact of external water supply. Comprehensive measures， including thermal insulation， soil replacement， drainage， dry density control， and optimization of protective structure thickness， should be adopted to enhance frost resistance.

The uneven distribution of hydrological monitoring networks has resulted in a lack of long-term available streamflow monitoring data in most watersheds worldwide. However， traditional hydrological models and single machine learning models have poor simulation accuracy and generalization capability when simulating runoff in data-scarce watersheds. This study proposes a SWAT-LSTM-Transformer （SLT） coupled modeling framework that integrates physical mechanisms with data-driven approaches， implementing cross-basin knowledge transfer through transfer learning and innovatively introducing a rainfall-based peak flow identification correction method to enhance daily runoff simulation performance in data-scarce watersheds. Using the Hanjiang River Basin （a data-rich basin） as the source domain， a multi-feature input set was constructed， which includes seven hydrological variables simulated by SWAT （actual evapotranspiration， infiltration， surface runoff， etc.） and five meteorological factors （rainfall， temperature， etc.）. The SLT model demonstrated excellent simulation performance in both training and testing periods， with Nash-Sutcliffe efficiency （NSE） coefficients reaching 0.96 and 0.95， respectively. To address the streamflow data scarcity in the target domain of the Wusha River Basin（a data-scarce basin）， a cross-basin transfer learning strategy was designed： based on hydrological similarity analysis， the parameters of the pre-trained SLT model from the Hanjiang River Basin were transferred to the Wusha River Basin's SLT model， incorporating rainfall-based peak flow identification correction for runoff simulation at the Wanli station. Results show that the SLT model performed well in streamflow simulation at the Wanli station， achieving NSE values of 0.86 and 0.73 for the training and testing periods， respectively. Soil infiltration， rainfall， surface runoff， and soil moisture content significantly influenced the SLT model’s simulation performance. The established SWAT-LSTM-Transformer coupled model provides a viable approach for runoff simulation in data-scarce watersheds.

The tidal level in tidal river sections is influenced by multiple factors， including river hydrodynamics， astronomical tides， and meteorological conditions， with complex physical mechanisms that make precise forecasting challenging. This study develops a tidal level forecasting model on integration of mechanism-based and data-driven approaches. The Non-Stationary Harmonic Analysis Model （NS_TIDE） analyzes the seasonal and periodic characteristics of tidal level changes， accounting for the underlying mechanisms. This model provides a baseline for tidal level forecasting， ensuring the rationality of the predicted tidal level trends. The data-driven model， DRSN-LSTM， is constructed using Deep Residual Shrinkage Networks （DRSN） and Long Short-Term Memory Networks （LSTM）. It captures the impacts of multiple influencing factors on tidal levels and generates amplitude values for tidal changes. An Adaptive Genetic Algorithm （AGA） is employed to couple and optimize the forecasting results of the NS_TIDE and DRSN-LSTM models. The proposed model was applied to the entrance of the Qinhuai New River in the Yangtze River basin. By comparing the forecasting performance of NS_TIDE， DRSN-LSTM， and the coupled model under different forecast periods， the results demonstrate that the coupled model not only adheres to the physical laws governing tidal level changes but also simulates the effects of multiple influencing factors on tidal amplitude. For a 24-hour forecast period， the Root Mean Square Error （RMSE） for hourly forecasts in non-flood and flood seasons were 0.15 m and 0.18 m， respectively. During typhoon events， the average accuracy rate for forecasts with errors within ±0.30 m exceeded 80%. The coupled model exhibits high accuracy and practicality， ensuring the scientific validity and precision of hydraulic engineering operations in tidal rivers.

To achieve efficient resource utilization of construction waste residue （CWR） and address the issue of effluent pH reduction during the nitrogen removal process in a pyrite-enhanced bioretention system， a bioretention system was constructed using a composite medium composed of CWR and pyrite. This study investigated the impacts of varying dosing of CWR and different compositing approaches for the mixture medium on pollutant removal efficiency by bioretention system. Moreover， an optimal strategy for composite media was determined by analyzing microbial community composition and evaluating by-products control effects. The results demonstrated that the addition of 40% （V/V） CWR into the traditional medium resulted in an enhancement of 8.70% and 9.69% in the removal rates of NH₄ ⁺-N and TP， respectively. Nevertheless， due to alkaline leaching stress， there was a decrease of 5.27% in COD removal rate and emergence of NO₂ ^--N accumulation phenomenon. The integration of media significantly mitigated the alkaline stress effect caused by CWR， leveraged the strong adsorption capacity of CWR for nitrogen and phosphorus， and enhanced denitrification through the use of pyrite as an electron donor for autotrophic denitrification. Consequently， this strategy not only increased the α-diversity of the microbial community but also elevated the relative abundance of nitrifying genera （e.g.， Nitrospira） and denitrifying genera （e.g.， Denitratisoma and Bacilli）， while simultaneously inhibiting the growth of Thiobacillus and Thauera. As a result， the system's pollution removal efficiency was significantly improved， effectively addressing the issue of NO₂ ^--N accumulation in the effluent and achieving better control over effluent pH、TFe and SO₄ ^2- byproducts. Particularly， incorporating 40% （V/V） CWR in the planting layer along with 20% （V/V） in the submerged layer of bioretention system achieved superior decontamination performance with respective removal rates for COD、TP、NH₄ ⁺-N、NO₃ ^--N and TN being 84.49%、96.94%、91.83%、87.11% and 82.13% respectively. Additionally， all concentration levels for by-products remained below permissible limits.

Aerodynamic resistance represents the mass transport capacity of atmospheric turbulence in water-heat exchange， and its accuracy is important for modelling and estimation of land surface water-heat exchange. However， due to the absence of data of atmospheric stability， the existing studies usually estimate aerodynamic resistance based on the neutral stratification hypothesis， which would result in considerable uncertainties. Therefore， based on data at 187 global stations and the Monin-Obukhov similarity theory， Penman-Monteith equation， Thom model and nonlinear regression methods， this study constructed an aerodynamic resistance estimation model， which considers the effects of atmospheric stability and vegetation characteristics， and investigated the ecohydrological implications of the aerodynamic resistance estimation model based on comparing the estimated potential evapotranspiration using different aerodynamic resistance estimations. Results showed that： ① the mean equivalent aerodynamic resistance at the 187 flux stations was 44.6±24.6 s/m， while those estimated using the traditional method was 125.1±22.7 s/m， which considerably overestimated aerodynamic resistance； ② the land surface factors including canopy height（h_c ）， Leaf Area Index（LAI）， Normalized Difference Vegetation Index（NDVI） and Enhanced Vegetation Index（EVI） could express most of the variabilities in the aerodynamic resistance coefficient （R ²=0.81）， based on which the estimated mean aerodynamic resistance at the 187 stations was 57.8±51.9 s/m， which is significantly improved compared with the traditional method； ③ the potential evapotranspiration （PET） based on traditional aerodynamic resistance methods considerably underestimated PET （y=0.55 x）， while those based on the aerodynamic resistance coefficient model were very close to the truth （y=1.15 x）， especially in land surfaces with high canopy heights， such as deciduous broad-leaved forests， evergreen broad-leaved forests， and mixed forests，where the traditional method overestimated PET dramatically. These findings would be valuable for evaluating the interactions among land surface and atmosphere and for estimating water-heat exchange.

The Sixth Coupled Model Comparison Program （CMIP6） data is often employed to evaluate potential global-scale changes in climate variables. However， it is less efficacious in projecting changes at the regional level. The Ili River Basin in Xinjiang Uygur Autonomous Region of China （IRBC）， characterized by significant elevation variation and complex terrain， gives rise to considerable discrepancies in data analyses based on limited number of meteorological stations. Consequently， reliable meteorological data are essential for climatic and hydrological research. This study develops CMIP6 data into a new multi-model ensemble dataset by four approaches： reliability ensemble average （REA）， convolutional neural network （CNN）， random forest （RF）， and Bayesian model average （BMA）. Additionally， we employed performance indicators such as the Taylor Diagram， Taylor Skill Score （TSS）， and Kling-Gupta Efficiency （KGE） to identify the method with the best simulation effect on precipitation and temperature in the study area. The results demonstrate that the BMA dataset exhibits the most effective simulation of precipitation patterns across a range of extreme precipitation indices. In calibration period（1961-1999）， the annual total precipitation when daily wet day amount 95th percentile （R95PTOT） is the highest. In the validation period（2000-2014）， BMA's rank sum ratio （RSR） is 1.1， ranking first， indicating that its comprehensive simulation effect is the best. The KGE results of annual maximum consecutive 1-day （Rx1day） and 5 day precipitation （Rx5day）， and R95PTOT are 0.032， 0.39 and 0.52， respectively， which are all higher than the values obtained from other datasets. In the average temperature simulation， the RF dataset performed the best. The standardization deviation （SD）， the center root mean square error （CRMSE）， the correlation coefficient （r） and TSS in the calibration period are 1.005， 0.088， 0.996 and 0.49， respectively. These values represent the most favorable outcomes compared to other datasets. Similarly， the RF also outperformed others during the validation period， with a RSR value of 1.18. The findings of this study assess the efficacy of various approaches in simulating meteorological data within the study area. This can provide a usable method for the analysis of meteorological data in future scenarios and provide a scientific foundation for the management of meteorological disasters and water resources in the IRBC.

Combined supporting structures consisting of steel set and shotcrete are widely used in underground tunnels when the geological conditions are poor. However， there exist many difficulties in conducting nonlinear finite element analyses for the steel set due to its small size and dense arrangement. Therefore， according to the mechanical properties of the steel set， a four-node isoparametric element model of the steel set structure was proposed， and its mechanical action was simulated by stiffness superposition. Based on the Mises yield criterion and the secondary plastic flow model， an elastoplastic constitutive model was established to simulate the yielding and hardening behavior of steel set. The elastic damage constitutive model was used to simulate the damage softening behavior of concrete， and finally， a nonlinear numerical simulation method for the combined supporting structure of steel set and concrete was formed， which is verified to be reasonable by a planar circular tunnel example. This method was used to investigate the force characteristics of the combined supporting structure， and the results show that： when the surrounding rock pressure is small， the concrete is in an elastic state so it has a high bearing capacity and is the main bearing structure； when the surrounding rock pressure is large， the concrete undergoes compression damage， and the excess stress is transferred to the steel set， resulting in a significant increase in the stress of the steel set， and the steel set evolves into the main bearing structure； reducing the spacing between steel sets can to some extent reduce the steel set’s stress， slow down the development of concrete damage， and enhance the supporting effect of the combined supporting structure.

Global climate change has led to significant changes in hydrological cycle processes， resulting in an increased risk of hydrological droughts. Therefore， the study of hydrological drought changes under changing environments is of great significance for early warning of flood and drought and water resources management.Typical basins in north and south China （Xiangjiang river basin and Weihe river basin） are selected as the study areas.The period from 1982 to 2015 is selected as the base period （the historical period） and the period from 2030 to 2060 is selected as the future period. Five Global Climate Models （GCMs） provided by CMIP6 under the three scenarios of SSP126， SSP245 and SSP585 are selected to output the climate data for the future period.The Extreme Gradient Boost （XGBoost） is utilized to downscale the precipitation and temperature from GCMs to drive hydrological models to obtain streamflow in the future period， and then compare and analyze the evolution of hydrological drought characteristics in Xiangjiang river basin and Weihe river basin in different periods.The results indicate that， after downscaling by XGboost， the RMSE of simulated precipitation of Xiangjiang river basin and Weihe river basin are reduced by 57.6% and 51%， respectively， and the correlation coefficient with the measured values are improved by 20% and 32%， respectively. The RMSE of simulated temperature are reduced by 50.9% and 51%， and the correlation coefficients with the measured values are improved by 4.3% and 6.25%， respectively.The increase in precipitation in Xiangjiang river basin in the future period will increase the streamflow in this basin， thus alleviating hydrological drought， and the severity， duration， intensity and peak intensity of hydrological drought are reduced by 6.3%， 4.8%， 10.6%， and 6.5%， on average， compared with those in the historical period.In contrast to Xiangjiang basin， the increase in temperature and potential evapotranspiration decreases the streamflow in Weihe river basin.Therefore， Weihe river basin will face a greater risk of drought in the future period. Among the selected scenarios， the drought is the most severe under the SSP585 scenario. Compared with the historical period， the severity and duration of drought will be increased by 72.7% and 30.8%.

In deep buried water diversion tunnels， there are limited measures for preventing and controlling rock burst risks of TBM construction. Once an extremely strong rock burst occurs， the TBM machinery will be buried. Therefore， it is of great significance to study rock burst risk prevention and control measures suitable for TBM excavation. This paper proposes a method of using drilling and blasting pilot tunnels for stress release in the construction of deep buried water diversion tunnels. The stress adjustment process induced by drilling and blasting pilot tunnel excavation and full section excavation methods （single drilling and blasting or TBM method） was compared. The evolution law of strain energy near the TBM excavation face induced by pilot tunnel excavation was analyzed. The results show that the excavation of pilot tunnel reduces the degree of strain energy accumulation near the TBM excavation face， and the peak accumulation position of strain energy is far away from the tunnel surface， which will make the TBM excavation face in the stress release area and reduce the risk of rock burst. Finally， the strain energy release effect of drilling and blasting pilot tunnel excavation is verified based on case study.

The high energy efficiency of hydraulic machinery has always been the direction pursued by many scholars. Centrifugal pump is a kind of widely used hydraulic machinery， and its efficiency is generally between 70% and 80%. The method of optimizing centrifugal pump and improving its efficiency is worth discussing. In this paper， a vertical single-stage single-suction volute centrifugal pump is used to calculate and analyze the simulated fluid. Based on RNG turbulence model， the fluid simulation calculation of centrifugal pump under seven different flow inlet conditions of 0.5Q_d ~1.3Q_d was carried out， and the hydraulic performance of centrifugal pump under large and small flow conditions was obtained. The main performance was low efficiency and high head under low flow conditions. The opposite was true for large large flow conditions， with high efficiency and low head. After that， the impeller of centrifugal pump was optimized by Bezier spline curve， which mainly shows the change of bending degree of edge profile of blade inlet. Five schemes including the initial model were established. The simulation numerical calculation of different schemes was carried out under rated working conditions， and the changes of efficiency and head with the bending of blade inlet edge profile were analyzed. The difference of blade pressure， velocity vector and outlet pipe flow pattern under different schemes was compared. Finally， the optimal scheme was obtained， with the efficiency slightly increased to 91.2% and the head became closer to the design head.