This study takes Luoma Lake as the research area， utilizing Sentinel-2 satellite imagery， in-situ water quality data， and spectral data to construct remote sensing inversion models for water transparency and suspended sediment concentrations. The models were developed using both statistical methods and machine learning techniques. The performance of both models in water quality inversion was comprehensively compared， and the optimal model was selected for an in-depth analysis of the spatial distribution characteristics of water quality in Luoma Lake. The results indicate that： ① The correlation analysis between Sentinel-2 equivalent remote sensing reflectance and water quality parameters show that the correlation of band combinations is significantly improved compared to single bands. The transparency has the highest correlation with B3/B4 （0.85）， and the suspended matter concentration has the highest correlation with B5/B4 （0.68）； ② Compared with the statistical regression-based inversion model， both water transparency and suspended sediment concentration achieve higher inversion accuracy using machine learning models. The optimal inversion model for transparency （testing set： R2=0.85， RMSE=7.25， MAE=5.25） and suspended sediment concentration （testing set： R2=0.87， RMSE=3.36， MAE=2.49） are both GA-XGBoost models； ③ The inversion results for Luoma Lake indicated that suspended sediment concentrations are higher， and transparency is lower in the northeastern nearshore areas， possibly due to ongoing human activities such as fish farming； ④ A long-term analysis of inversion results from 2018 to 2022 revealed significant seasonal characteristics in the water quality of Luoma Lake. Suspended sediment concentrations were higher， and transparency was lower during the spring and autumn seasons， while suspended sediment concentrations decreased， and transparency increased in winter. During the 2018-2020 period， both suspended sediment concentrations and transparency showed small fluctuations within a certain range， with only a few significant fluctuations driven by seasonal or weather factors. In 2021 and 2022， the removal of embankments and fishing nets in the northern part of Luoma Lake caused sediment disturbance， leading to increased suspended sediment concentrations and decreased transparency.

Efficient and accurate detection and analysis of concrete dam defects images is necessary to ensure the safe operation of the dam. Aiming at the problems of multi-category and uniqueness of surface defect images of concrete dams and the lack of high-quality samples for on-site inspection and inspection of existing dams， this paper first simulates typical defects such as cracks and leakage through self-made concrete slabs to construct a multi-category defect data set of concrete dams， and then uses Yolov8 + U-Net “two-step method” to establish a multi-category surface defect detection and analysis model of concrete dams. Finally， the surface defect images of on-site inspection and inspection of a concrete gravity dam is taken as the test object， and the established detection and analysis model is used for intelligent detection. The results show that the “two-step method” model based on Yolov8 + U-Net algorithm can realize the efficient and accurate detection of seepage and crack defects in concrete dams. The recognition and positioning accuracy rate of the model is 0.84， the recall rate is 0.98， the segmentation accuracy rate is 0.91， and the recall rate is 0.71.

River-crossing bridges can alter the flow structure of river channels， and excessive flow-blocking effects may compromise both flood control safety and the structural safety of the bridges themselves. The blockage ratio， a key parameter quantifying the water resistance of the bridge， deserves thorough investigation. This study evaluates the effects of bridge types， bridge span arrangements， and skewed piers on blockage ratios through data analysis and generalized calculations. Results show that arch bridges and suspension bridges exhibit relatively smaller blockage ratios， followed by cable-stayed bridges， while continuous girder bridges demonstrate comparatively higher blockage ratios. Under identical pier diameters， larger span lengths between piers correlate with reduced blockage ratio. Notably， an increase in the oblique angle between piers and water flow significantly amplifies the blockage ratio， suggesting optimal control of oblique angles within 5° or 10°. The 1D and 2D mathematical model are employed to calculate the backwater height under the design flood condition in Wuhan river-reach of the middle Yangtze River. Results show that when the blockage ratios range between 2% and 20%， 1D modeling indicates a backwater height of 0.02~0.22 m at the cross-section upstream of bridge， 2D modeling indicates local backwater height of 0.12~0.30 m in front of piers and an average backwater height of 0.01~0.14 m at the same cross-section. The discrepancy in computational results between the two models stems from differences in modeling principles and simplification approaches. Based on comprehensive backwater calculations and relevant technical standards， it is recommended to maintain the blockage ratio of river-crossing bridges within 5%， and the blockage ratio requirements for river sections in mountainous canyon reaches without critical flood-protection objects may be appropriately relaxed upon technical confirmation of minimal impact on flood control safety. Research proposes the following measures to reduce the blockage ratio： selecting bridge types with lower blockage ratio， maximizing span lengths， adopting streamlined or circular pier shapes， aligning pier axis parallel to the flow direction， and so on. These recommendations provide a scientific foundation for bridge engineering design， flood impact assessments， and regulatory management of river-related projects.

Steel fiber reinforced concrete has been widely used in engineering practice due to its superior mechanical properties. However， unlike other fibers， steel fibers are susceptible to corrosion in corrosive environments. This paper systematically reviews the research progress and key findings on the corrosion of steel fiber concrete at home and abroad， including the corrosion mechanism， as well as the effects of corrosion on the mechanical and durability properties of concrete. Previous studies have shown that the corrosion resistance of steel fibers is influenced by several main factors， such as the water-cement ratio， fiber type， concrete cracks， and the erosion environment. Besides， steel fibers corrode only within approximately 5 mm of the surface layer in uncracked concrete. The strength of the steel fibers gradually decreases as corrosion advances， while the corrosion products play dominant roles in the filling and compression of pores at different stages. These processes affect the bonding performance between the steel fibers and the cement matrix， thereby altering the mechanical properties of the concrete. Consequently， the mechanical properties of the concrete are closely related to the corrosion degree of steel fibers. Most existing studies have not considered the impact of the corrosion degree on the strength of steel fiber concrete， and the quantitative relationship between the corrosion degree of steel fibers and the degradation of concrete mechanical property has not been established. Finally， this paper discusses the challenges in steel fiber corrosion research and suggests future research trend.

The maximum height of the upstream earth-rock cofferdam of a hydropower station is 62 m， and the height of the slope formed by the cofferdam and foundation pit is 135 m. The earth-rock cofferdam is located on deep overburden layer with a maximum thickness of 71 m. The overburden layer has the characteristics of “low bearing capacity， small permeability coefficient， low shear strength and high compressibility”. In order to ensure the safety and stability of the cofferdam and slope， vibro-replacement stone column is used in design to reinforce the deep overburden layer of the cofferdam foundation. The depth of the designed stone column of a hydropower station far exceeds the existing technology level. It is the deepest stone column applied ever in engineering practice in China on a large scale. In order to solve the problem of ultra-deep vibro-replacement stone column construction and verify the effect of stone column reinforcement and drainage， the ultra-deep vibro-replacement stone column construction equipment and technology are determined through on-site productive tests， and the physical and mechanics parameters such as shear strength， bearing capacity and compression modulus of stone column body and soil between column， permeability coefficient of stone column， etc. are obtained. A set of control methods which can be applied to large-scale construction， quality inspection of vibro-replacement stone column in cofferdam foundation are summarized. This research results can be used as a reference for the design， construction and acceptance of foundation treatment of hydropower engineering projects.

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.

As global climate change intensifies， the hazards of different types of droughts have further increased. The causes of droughts are complex， involving a blend of natural and human factors， which makes it challenging to fully assess drought conditions with a single index. To more accurately monitor and evaluate droughts， this study uses the Copula function combined with precipitation data and GRACE satellite-derived terrestrial water storage to develop a comprehensive drought index （saCMDI） that comprehensively considers natural factors and human activity elements. Applied to mainland China from 2002 to 2021， saCMDI demonstrates strong correlations with commonly used indices such as SPEI3， WSDI， and scPDSI. It effectively detects droughts caused by both precipitation deficits and terrestrial water storage shortfalls， capturing the onset and end of drought events， and the identified drought events are highly consistent with historical records in China， indicating that the index has a high reliability for evaluating large-scale droughts. The results reveal significant spatial variability in drought characteristics across mainland China. Southern， northeastern， and some western regions experience longer drought durations （2.5~3.5 months） compared to central and eastern regions （1.0~2.5 months）. Drought frequencies are notably higher in arid and semi-arid areas （13%~18%） than in humid and semi-humid regions （7%~13%）. Drought intensity generally increases from northwest to southeast， with the southern， northeastern， and certain western regions exhibiting higher drought intensities due to the cumulative effects of prolonged drought periods. Reduced precipitation and soil moisture are the main contributors to droughts in most of mainland China， although groundwater extraction and surface water supply issues also play significant roles in specific river basins. This study provides essential insights for improving drought risk management and guiding water resource planning across different regions of China.

In light of the current challenges in managing flash flood disasters in small and medium-sized basins， it is imperative that improvements to hydrological models take into account the vegetation cover of the underlying surface. further clarify the runoff generation and routing characteristics of the watershed， thereby enhancing the accuracy of forecasting and early warning systems. This study builds upon the traditional TOPMODEL， selecting the Gaotian River Basin and Pajiang River Basin in Qingyuan City， Guangdong Province as the research areas. It selects 15 flood events from the Gaotian River Basin （1975-2013） and 15 flood events from the Pajiang River Basin （1971-2002） for simulation. The coefficient of determination is employed as the objective function， and the SCE-UA optimization algorithm is utilized for parameter calibration and validation. This analysis explores the feasibility of incorporating the curve number （CN value） and generalized unit hydrograph theory to enhance the model's runoff generation and routing module. The results indicate that the average coefficients of determination for the calibration and validation periods improved from 0.74 and 0.75 to 0.82 and 0.80 for the Gaotian and Pajiang River Basins， respectively， demonstrating enhanced simulation accuracy compared to the traditional TOPMODEL. According to the "Hydrological Information Forecasting Specification GB/T 22482-2008，" the model achieved pass rates of 80% and 100% for the two study areas， indicating its capability to accurately capture peak flow rates and their occurrence times， along with a high reliability in simulating total runoff for the selected flood events. Moreover， the generalized unit hydrograph， as a model not yet widely utilized， requires further validation in additional practical applications. The multi-event flood simulation results presented in this study not only contribute to the validation of the model but also provide important data support and theoretical foundations for investigating the relationship between generalized unit hydrograph parameters and the characteristics of watershed underlying surfaces， thus promoting its widespread application in the field of hydrological analysis.

There are a lot of crisscrossing waterways in the plain area， with slow and unpredictable water flow. There are numerous gate stations in the plain basin， resulting in extremely complex water and engineering conditions. Once a flood occurs， it will lead to a large-scale flood disaster， posing a significant threat to the safety of people's lives and property. In order to accurately simulate the runoff generation and convergence process of flood in the plain areas， this paper combines the advantages of the SCS model and MIKEFLOOD model to construct a hydrological and hydrodynamic model， and fully considers the different types of underlying surface runoff processes and complex gate station scheduling regulations， which can effectively improve simulation accuracy. The Tongshun River Basin located in the hinterland of the Jianghan Plain in Hubei Province was selected as the research area. The model was calibrated and validated by measured data. The impact of different rainfall conditions on the region was analyze based on the hydrological and hydrodynamic model. The results showed that ① The model was calibrated and validated by using the measured water levels in 2003， 2010， and 2016. The simulated correlation coefficients were all above 0.8， with an error of less than 2%； This result indicates that the simulation effect of the model is good and can be used for simulating the runoff of the Tongshun River Basin. ②The Tongshun River Basin was divided into 11 sub-drainage areas. According to the calculation results of the SCS model， the Pai hu drainage area was the highest water production area； The current drainage capacity of 7 drainage areas， such as Xingfu and Tongbei， is less than that of the 3-day rainstorm with a 10-year return period. ③ When the 5 to 20-year return period flood occurs， the water level of the river gradually decreases when the drainage time of the watershed reaches 10 to 12 days. When the 30-year return period flood occurs， the water level in the basin first increases and then tends to stabilize although the drainage time of the watershed reaches 10 to 12 days. This result indicates that the current drainage capacity within the Tongshun river basin cannot meet the requirements of a 30-year return period flood. This study can provide reference for the development of drainage measures in complex plain river network areas.

Pumping station technology upgrading is an important part of building a national water network， realizing the goal of “dual carbon”， and integrating water resources， ecology and energy security. Overseas pumping stations have made significant progress in hydraulic design， material application， informatization and energy synergy， which is manifested in the enhancement of adaptability to extreme working conditions， improvement of the level of intelligent operation and maintenance， improvement of the ecological mode of operation and the development of green energy supply system. However， under complex conditions such as high sand content and strong corrosion， there are still problems such as insufficient design accuracy， imperfect failure prediction， unclear ecological response mechanism and limited multi-energy synergy optimization. In this paper， the latest foreign research results and engineering practices are sorted out， and key technologies such as inverse problem and machine learning fusion design， digital twin technology， fish-friendly flow channel and ecological protection measures， and wind-solar storage and complementary evaluation are summarized. It also proposes technical paths for the national water network and the follow-up project of South-to-North Water Diversion， which will provide reference for the modernization of water conservancy infrastructure and the construction of intelligent water conservancy during the 15th Five-Year Plan period.

As a new type of ecological agricultural model， crayfish-rice co-culture has gradually occupied an important position in China's agricultural development. Understanding the variation characteristics of the rice growth environment is of great significance for increasing the grain yield in crayfish-rice fields and ensuring the achievement of double harvests from one water source in crayfish-rice co-culture system. Based on this， this paper reviews the current development status， industrial advantages， and still existing theoretical and technical problems in actual production of crayfish-rice co-culture system in China. Combining with existing literature and research foundations， this article summarizes the research progress on the characteristics of the crayfish-rice symbiotic system and the variation characteristics of the soil environment， field water layer control methods， and biological - ecological environment brought about by the changes in co-cultivation management methods. It also summarizes the impacts of the above characteristic changes on rice growth. Finally， it outlines future research directions from three aspects， including the coupling mechanism of the crayfish-rice co-culture system， the collaborative regulation technology for water conservation， emission reduction， and carbon reduction， and model development， aiming to provide a scientific basis for stabilizing grain production and increasing income in paddy fields under integrated rice-fishery farming and for the efficient utilization of agricultural resources.

Accurate and timely soil moisture inversion is a crucial foundation for implementing precision irrigation management in farmlands， significantly enhancing irrigation water resource utilization efficiency. This study developed enhanced Thermal Optical Trapezoid Model （TOTRAM） and Optical Trapezoid Model （OPTRAM） by incorporating nonlinear dry/wet boundaries and phenological characteristics， using multi-source remote sensing data from Landsat-8/9 and Sentinel-2A/B. The OPTRAM model further accounts for the influence of phenological stages.The accuracy of the enhanced models in soil moisture inversion for arid farmland was then evaluated using measured soil moisture data.The findings of the study demonstrated that the original TOTRAM model exhibited superior performance in comparison to the basic OPTRAM model， with root mean square errors （RMSE） of 0.183 cm3/cm3 and 0.141 cm3/cm3， respectively. The implementation of non-linear dry/wet boundaries resulted in a substantial enhancement in the performance of both models， achieving comparable reductions in RMSE to 0.145 cm3/cm3 （TOTRAM） and 0.126 cm3/cm3 （OPTRAM）. The phenology-integrated OPTRAM model demonstrated optimal accuracy， with a root mean square error （RMSE） of 0.096 cm3/cm3. In summary， the enhanced OPTRAM model exhibited superior precision in the retrieval of soil moisture in arid agricultural soil compared to TOTRAM， while concurrently maintaining the higher spatiotemporal resolution advantages afforded by optical satellite data. This advancement provides critical technical support for precision irrigation management systems.

Accurate groundwater level prediction is of great significance for groundwater resources management in irrigation areas. However， due to the difficulty of processing unstructured well position data in irrigation areas， traditional deep learning methods do not consider the spatial positional relationship of each monitoring well and the own attributes of each well in irrigation areas， resulting in a serious lack of physical interpretability of deep learning models. This study fully considers the spatial positional relationship of each monitoring well and the static attributes of well points to construct a graph structure with sufficient physical meaning. A GWN model based on graph neural networks is proposed. Taking the Yichang irrigation area in the Hetao irrigation area as an example， combined with the EEMD algorithm to preprocess the groundwater level data， the model groundwater level prediction results are compared with the two baseline models LSTM and GRU to verify the rationality of the model. The results show that the GWN model with EEMD algorithm is better than the baseline model， and the statistical indicators such as MAE， RMSE， and NSE are better than the baseline model. At the same time， compared with the single well prediction of the baseline model， the GWN model can predict the water levels of all monitoring wells at the same time， and the prediction efficiency is significantly improved. In addition， the graph structure containing physical meaning is adaptive and iteratively updated to reproduce the groundwater network with physical background in the irrigation area. The relationship and reasons between the wells can be intuitively interpreted from the adjacency matrix combined with the static attribute distribution map. In short， the model has good groundwater level prediction effect and interpretability， which can provide a reference for the groundwater level management of the Yichang irrigation area in the Hetao irrigation area of Inner Mongolia.

Large deformation or landslide of dam slope seriously threatens the long-term operation safety of the reservoir area. The mainstream traditional slope deformation prediction models fails to fully considered the temporal and spatial characteristics of deformation. This article introduces four representative deep learning methods， namely Transformer， Spatio-Temporal Graph Convolutional Neural Network （STGCN）， Temporal Convolutional Network （TCN）， and Graph Convolutional Neural Network （GCN）， and proposes a spatiotemporal prediction method for slope inclinometer deformation based on deep learning models. The deformation monitoring data of a certain hydropower slope inclinometer hole are used， and the monitoring data are systematically analyzed. The prediction results indicate that all four models， GCN， TCN， STGCN， and Transformer， are suitable for slope spatiotemporal prediction. Among them， the TCN model shows higher prediction accuracy and reliability compared to the other three spatiotemporal prediction models， with evaluation indicators MAE、MSE、RMSE、MAPE and R ² of 1.007、2.208 2、1.486、102.40% and 0.988 4， respectively. In addition， the prediction results of the four models on different dates have an error distribution of 0-4mm compared to the actual measurement， which verifies the accuracy and effectiveness of the four models in predicting the deformation of slope inclinometers in time and space. The research results provide new ideas for short-term spatiotemporal prediction of slope deformation in the reservoir area.

Groundwater is an important water supply source in Jinan City. In order to achieve sustainable development and utilization of groundwater resources， it is necessary to carry out groundwater storage changes analysis and groundwater resources assessment in Jinan. This paper is based on the GRACE gravity satellite and GLDAS global land surface assimilation system data， and uses singular spectrum analysis method to reconstruct terrestrial water storage change （ΔTWS） data. A multiscale geographical weighted regression （MGWR） model was constructed， using evapotranspiration （ET）， precipitation （PRE）， land surface temperature （LST）， and vegetation index （NDVI） as regression variables to downscale groundwater storage change （ΔGWS） data in Jinan， and the spatiotemporal change analysis of ΔGWS and groundwater resource evaluation were conducted. From April 2002 to December 2023， ΔTWS and ΔGWS showed a downward trend in Jinan. During the year， ΔGWS was the smallest in June and the largest in December. The lag effect of ΔGWS on precipitation changes is obvious. The MGWR model enhanced the resolution of ΔGWS data in Jinan from 0.25° to 1km. The trend changes and spatial distribution of ΔGWS exhibited a high degree of consistency before and after downscaling， indicating that the MGWR model is effectively applicable for downscaling ΔGWS data.The BEAST algorithm is used to deconstruct ΔGWS time series in Jinan. ΔGWS is mainly controlled by the trend term， and there are two mutation points with a probability greater than 0.5. There are obvious differences in the spatial distribution of ΔGWS in Jinan. Laiwu District and Gangcheng District have relatively large groundwater storages， while Pingyin County and Changqing District have lower groundwater storage. The ΔGWS center of gravity is mainly located in Licheng District. The water amount in the rising section of ΔGWS is used to characterize the amount of groundwater resources in Jinan. From 2003 to 2019， the average amount of groundwater resources is 2.290 billion^{m3， and the amount of groundwater resources in extraordinary dry years， dry years， and normal flow years are 1.657、2.722 and 2.955 billionm3 respectively in Jinan. The research results provide scientific reference for the rational development and utilization of groundwater resource in Jinan.}

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.

Piping is a typical form of seepage failure in earth-rock dams， and its evolution process is accompanied by internal physical field changes within the earth-rock dam structure. Investigating the relationship between the variations in the internal physical field of earth-rock dams and the evolution of piping is of significant importance for identifying the state of piping progression. In view of this， a multi-field information fusion analysis framework for piping evolution is first constructed based on the multiple physical field information obtained during the operation of earth-rock dams. Building on this foundation， the seepage pressure， temperature， electrical potential， and flow velocity fields during the piping evolution process in earth-rock dams are integrated. By applying normalization and dimensionality reduction techniques， a matrix decomposition fusion method and principal component analysis （PCA） are employed to perform fusion analysis of the multi-field fitting functions. Subsequently， a quantitative characterization function with established correlations is constructed to analyze the logical symbiotic relationships among seepage pressure， temperature， electrical potential， and flow velocity fields. Finally， by analyzing the characteristics of different stages in the evolution of piping， the normalized timeline of piping evolution in earth-rock dams is divided into three stages： （0， 0.481）， （0.481， 0.663）， and （0.663， 1）. These stages correspond to the initial phase， expansion phase， and critical phase of piping evolution， respectively. The proposed method for multi-field information fusion and state identification in the piping evolution process of earth-rock dams offers a valuable reference for seepage safety monitoring and early warning in such structures.

Based on in-depth research， this study systematically reviews the development history， current status， and existing problems of rural water supply and standardization. It thoroughly analyzes the demand for standardized development in rural water supply and proposes several considerations and recommendations to accelerate progress. The standardization of rural water supply should adhere to a problem-oriented and demand-driven approach， emphasize technological innovation， strengthen deep integration of industry， academia， research， and application， foster and develop new quality productive forces， and leverage the guiding role of standards to unleash innovation potential. Following the principle of balancing development and safety， a full-lifecycle technical standard system should be established， covering planning and design， construction， operation and maintenance， water quality safety， and management services. This system will consist of "2+N+M" standard system. In terms of engineering planning， it is essential to effectively align with strategic plans such as the National Water Network， urban-rural integration， rural revitalization， and rural construction to optimize the overall layout of rural water supply. For engineering construction， adherence to standards must be prioritized to ensure standardized construction of rural water supply projects， establishing and proving rural water supply system that achieves overall optimal performance. Regarding operation and management， upgrading standards is crucial to transition rural water supply toward a modernized management model characterized by specialization， equalization， and degitization， thereby promoting high-quality development in rural water supply.

Meandering rivers exist widely in nature， and understanding their complex evolution process and mechanisms is important. Many studies have been conducted to investigate the movement of water and sediment， as well as the evolution of riverbeds in meandering rivers， using river model tests. This paper reviews the research results of existing meandering river models and systematically summarizes the development process， characteristics， and application scope of idealized river models and natural river models. The main results are as follows： the idealized river models make a high degree of generalization of natural rivers. It can be categorized into three types of meandering river models based on different design approaches： arc and straight line combination， sine-derived curve， and Kinoshita-derived curve. These idealized river models are more applicable in fundamental theoretical studies of river sediment dynamics. The natural river model is based on an actual river and is designed according to a certain similarity principle. It mainly includes the scale model and the natural evolution model. The scale model is a physical model of the prototype river scaled according to a certain geometric scale that ensures the similarity of water movement and sediment movement. The scale model is suitable for the inversion and simulation of water and sediment transport， channel siltation and degradation， and riverbed evolution of the prototype river channel. The natural evolution model is a model river similar to the prototype in riverbed morphology， which is shaped by controlling the flow conditions and river boundary conditions such as riverbed composition and riverbed gradient. The natural evolution model is more applicable in studies related to the evolution of river types. Both types of natural river models bear similarities to the prototype river and can provide a direct reference for practical engineering applications. In summary， idealized river model and natural river model have their own advantages and disadvantages in basic and applied research， and different model types can be selected according to the research needs.

To achieve the safe and stable operation of reservoirs and the efficient utilization of water resources in the basin， the key lies in clarifying the operation rules and dispatching strategies of reservoirs. To this end， aiming at the problems that traditional neural networks have when simulating reservoir dispatching operation strategies， such as vanishing gradients， getting stuck in local optimal solutions， and difficulty in determining hyperparameters， an improved deep neural network model （TSD） combining the Stochastic Gradient Descent with Momentum （SGDM） and the Tree-structured Parzen Estimator （TPE） is proposed. It has enhanced the accuracy and applicability of the deep neural network model by improving the training method of network parameters and automatically optimizing hyperparameters. Meanwhile， considering the numerous factors affecting reservoir dispatching decisions， the F-test method （FR） and the Mutual Information method （MIR） have been adopted respectively to achieve the optimization of input factors. Further， six simulation methods for reservoir dispatching strategies were constructed based on the Decision Tree model （DT）， Support Vector Machine model （SVM）， and improved deep neural network model： FR-DT， MIR-DT， FR-SVM， MIR-SVM， FR-TSD and MIR-TSD. And case studies were carried out with four reservoirs， namely Ahai， Jin’anqiao， Ertan and Pubugou. The research results show that compared with other methods， the FR-TSD and MIR-TSD methods have larger deterministic coefficients and smaller root mean square errors and mean absolute errors when simulating the dispatching strategies on various reservoirs. This indicates that the simulation effects of the FR-TSD and MIR-TSD methods are better and the accuracy is higher， and they can simulate the dispatching operation strategies of reservoirs more accurately， thus providing decision support for the actual operation of reservoir dispatching. Furthermore， an analysis of the simulation results of different reservoirs under the same method reveals that the simulation effect of the model is influenced by the regulating capacity of the reservoir and the allowable range of water level variation. Reservoirs with smaller regulating capacity and allowable range of water level variation have poorer simulation effects， while those with larger ones have better effects.

To address the the complexity of wind resource simulation for mountainous wind farms， this study takes a complex mountainous wind farm as an example. Based on existing anemometer tower data near the wind farm， and considering the relative relationship between the terrain shape and the prevailing wind direction， the computational fluid dynamics （CFD） software Windsim 12.0 was employed to establish two digital terrain models with different boundary ranges of the wind farm. Numerical simulations of the flow fields in the wind farm area were simulated， and the power generation results from the two models were compared with the actual power output of the wind farm. The results indicate that， to accurately assess wind resources in complex mountainous wind farm areas， it is essential to avoid insufficient development of numerical terrain flow field simulations and the adverse effects of boundary conditions on internal flow fields during modeling. This study highlights the critical role of digital terrain model construction in wind resource assessment for complex mountainous wind farms， providing a reference for wind resource evaluation and rational utilization in such terrains， as well as technical support and empirical insights for wind energy development in similar environments.

The calculation of TBM tunnel segment lining structure is an important link in lining design. This paper combines an engineering example in South China to establish the lining structure calculation model using the modified usage method， beam-spring method， and three-dimensional finite element method（3D FEM）， and compares the internal forces and deformations of the lining structure calculated by each method. At the same time， the paper studies the influence of the staggered assembly angle on the lining structure forces. The results show that the maximum axial force and shear force of the structure calculated by each method are close to each other. The order of the maximum bending moment and deformation of the whole structure is as follows： modified conventional method > beam-spring method > three-dimensional finite element method. According to the results， the modified conventional method yields results with high safety reserve， simple calculation， making it suitable for preliminary design of engineering. The three-dimensional finite element method is suitable for accurate calculation and preliminary design check of segments， and can show the mechanical characteristics of the detailed structure of segments. The calculation result of beam-spring method is between other two methods， which is relatively economical and can reflect the stiffness contribution of the joint of segment more accurately than the modified conventional method， and the model is more user-friendly than the three-dimensional finite element calculation model which is suitable for engineering design. At the same time， the beam-spring method has a more obvious staggered assembly effect than the 3D FEM in calculating the maximum bending moment.

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.

The wind-photovoltaic-hydro complementary energy is an effective strategy to enhance the consumption of renewable energy. In the context of short-term operational scheduling， the multi-energy complementary dispatch of cascade reservoir systems necessitates the coordination of power generation and load balancing across various reservoirs and diverse renewable energy sources. Furthermore， the scheduling process must consider the time delay associated with water flow through the cascade reservoirs， resulting in a lag effect on power discharge during the scheduling period. This scenario presents a multi-objective optimization challenge， requiring a balance between the benefits of power generation both within and outside the scheduling period， as well as the benefits of peak-shaving during the scheduling period. This study develops a short-term multi-objective optimization model for the wind-photovoltaic-hydro complementary energy in cascade reservoir systems. The model generates a Pareto-optimal set of solutions， encompassing power generation benefits， peak-shaving benefits， and potential power outside the scheduling period. Additionally， it investigates the conflicting and synergistic relationships among these multiple objectives and quantitatively assesses the comprehensive benefits of complementary dispatch through comparative analysis. Using the cascade hydropower station group in the Yuanshui River Basin as a case study， model simulations for typical dry and wet seasons yield the following key findings： ① There exists a conflict between long-term and short-term scheduling benefits. As the energy storage capacity and lagged power within the cascade reservoirs increase， the power generation benefits within the scheduling period diminish. ② A trade-off exists between power generation and peak-shaving objectives， with this trade-off becoming less significant as the potential power decreases. ③ Hydro power compensation for wind and photovoltaic generation effectively enhances the total system output， with additional generation during the dry and wet periods amounting to 8.08 million and 5.2 million kWh， respectively. In peak-shaving mode， the maximum residual load is reduced by 880，000 and 60，000 kW during the dry and wet seasons， respectively. The multi-objective optimization scheduling model for wind-photovoltaic-hydro complementary energy in cascade reservoirs provides valuable insights for advancing the integration and consumption of renewable energy.

Studying the future precipitation and aridity characteristics of the Weihe River Basin under the background of climate change provides a theoretical basis for policy formulation and decision-making in water resources management and flood-drought disaster prevention in the basin. Based on the measured precipitation， average temperature， sunshine duration， average relative humidity and average wind speed from 18 meteorological stations in the Weihe River Basin， the potential evapotranspiration of these stations were calculated by the Penman-Monteith method， the four scenarios of SSP1-1.9、SSP2-4.5、SSP3-7.0 and SSP5-8.5 from the CanESM5 model in CMIP6 were selected， and the SDSM was used to predict the precipitation， potential evapotranspiration and aridity index in the Weihe River Basin from 2031 to 2090. The results show that SDSM can simulate potential evapotranspiration and precipitation in the Weihe River Basin well， with better simulation effects on potential evapotranspiration compared to precipitation. Climate change has a significant impact on precipitation and aridity index in the Weihe River Basin， with varying degrees of influence under different climate scenarios and time periods. Temporally， the future precipitation in the Wei River Basin will increase， with the interdecadal trend rates of precipitation under four scenarios are 2.34、13.3、32.5 and 25.1 mm/10 a respectively； In particular， the precipitation in the SSP3-7.0 scenario from 2071 to 2090 will increase by 35.2% relative to the baseline period； Except for the SSP1-1.9 scenario， potential evapotranspiration of the other scenarios shows an increasing trend； The aridity index decreases by an average of 0.145. Spatially， precipitation shows an increasing trend from northwest to southeast in the basin， with significant increases in precipitation above the Beiluohe Zhuangtou station as the forcing scenarios enhance； Potential evapotranspiration increases from west to east， and higher forcing scenarios result in greater increases in the same region； The aridity index decreases from northwest to southeast， and the climate of the basin becomes humid overall. In the future， the Weihe River Basin should enhance its ability to prevent and control flood and drought disasters by strengthening risk monitoring， allocating water resources scientifically and establishing a water and drought disaster early warning mechanism， so as to effectively respond to risks and challenges brought by climate change.

Precipitation is one of the most important driving factors in hydrological processes， and its data source and accuracy are key factors affecting the accuracy of flood forecasting. Affected by many factors such as the imperfect water and rain condition monitoring system and the small range of the basin， the accuracy of the measurement of precipitation data in the small and medium -sized basin is high， but the distribution is uneven， which will affect the simulation results of hydrological models. In order to improve the accuracy of flood forecasting in the watershed， this study combines the measurement data with satellite product precipitation data by constructing a CNN-LSTM neural network model， and combines the HEC-HMS model to carry out the flood simulation Taking the catchment area above the Jinpen Reservoir in the Heihe River Basin in Shaanxi as an example， the application effect and applicability of satellite-ground fusion precipitation in the flood simulation are discussed. The results show that ① The HEC-HMS model with the site precipitation data as the input has good applicability in the research area， and can achieve Class B accuracy in both the rate period and verification period. ② The correlation coefficients between the two types of IMERG satellite products and measured precipitation are low， which are generally higher than the actual value， and the error is large. After CNN-LSTM data fusion， it is close to the measured precipitation data， and the fusion effect of the IMERG-Early product is better. ③ The HEC-HMS model after fusion of satellite products has a simulation pass rate of 80% for the 10 floods， with an average definitive coefficient of 0.856 9， and the average absolute value of the absolute value of the flood peak is 0.8h， reaching Class A accuracy. ④ The certainty coefficient of the flood simulation results of satellite-ground fusion precipitation increases， and the absolute value of the average flood peak time difference is reduced； the simulation effect of the flood peak flow decreases and is significantly smaller. The results show that integrating the actual measurement precipitation data and appropriate satellite precipitation product data in small and medium -sized basin can improve the accuracy of flood forecast models to a certain extent.

The change of temperature stress in high altitude area has a significant influence on the overall bearing capacity of concrete double-curvature arch dam. In order to analyze the overall bearing capacity of the concrete double-curvature arch dam of ZY hydropower station under temperature effects， and ensure the safety design， construction and operation of the project， a three-dimensional nonlinear finite element analysis method of TFINE program was employed to establish a numerical model of the integrated arch dam-foundation system. Firstly， under normal water level conditions， the displacement， stress and yield state of arch dam under temperature decrease and increase scenarios are analyzed， and unfavorable conditions are obtained. Then， under this working condition， the bearing capacity of arch dam is analyzed by overload method， such as arch dam displacement， unbalanced force， complementary energy norm and yield zone volume. The results show that under the action of temperature stress， the safety of the arch dam meets the requirements under the normal water level condition， and the temperature drop condition is a more unfavorable condition. When the overload ratio is 1.2~1.5， the upstream dam heel cracks； when the overload ratio is 4.0~5.0， the dam body is in nonlinear deformation； when the overload ratio is 7.5~8.5， the dam body loses its bearing capacity.

The increasing nitrogen input load in the Pearl River Delta urban agglomeration has led to a rising trend in total nitrogen （TN） levels in the Pearl River Estuary. To reveal the spatiotemporal variations of different nitrogen forms and identify the sources of nitrate nitrogen （NO??-N） ， the dominant inorganic nitrogen species， surface water sampling was conducted during both flood and ebb tides in the wet and dry seasons of 2023 across the complex river network of the estuary. Based on the analysis of the spatiotemporal distribution patterns of various nitrogen forms， nitrate sources were quantitatively apportioned using nitrate isotopes （δ1?N-NO?? and δ1?O-NO??） and the SIAR model. Results indicated that nitrate was the predominant form of inorganic nitrogen， with a mean concentration of 1.85 mg/L. Except for a few sampling points affected by upstream cross-border inflows， ammonia nitrogen （NH??-N） and nitrite nitrogen （NO??-N） concentrations were generally low at most monitoring sites， typically below 0.50 mg/L and 0.10 mg/L， respectively. Although higher nitrogen concentrations were observed at some locations during the dry season， their contribution to the annual total load was substantially lower than that during the wet season. The isotopic compositions ranged from +3.08‰ to +10.03‰ for δ1?N-NO?? and from +0.51‰ to +5.12‰ for δ1?O-NO??. Quantitative source apportionment revealed that the primary contributors to nitrate pollution were agricultural sources （44.7%）， followed by domestic sewage （36.4%） and industrial wastewater （18.9%）. No significant differences in contribution proportions were observed between flood and ebb tides； during flood tide， the contributions from agricultural， domestic， and industrial sources were 45.6%， 36.2%， and 18.2%， respectively， while during ebb tide， they were 43.4%， 36.5%， and 20.1%. The significant spatial heterogeneity in nitrogen concentrations and fluxes， along with the identified source characteristics， provides a scientific basis for developing targeted total nitrogen pollution control strategies in the Pearl River Estuary.

The bidirectional operation of inlet/outlets of pumped storage power stations must meet the requirements of smooth flow and minimal head loss. So， hydraulic optimization of inlet/outlets is crucial for the safety and efficiency of stations. Some low-head stations adopt layouts with open-air powerhouses， where inlet/outlets are closely linked to the draft tubes. Due to the influence of the elbow section of the draft tube， the problem of uneven flow is obvious， so a new shape needs to be proposed. Taking a certain station as an example， CFD simulations were conducted to carry out research and found that the original inclined side inlet/outlet scheme was connected to draft tubes. Due to the centrifugal forces， the main flow within the diffusion section of the original oblique lateral inlet/outlet deviated， resulting in inadequate flow evaluation parameters. Starting from the principle of advanced flow allocation， a solution involving additional baffles was proposed to improve the flow pattern. To further optimize the flow pattern， an innovative shape combining the advantages of lateral and vertical shapes was proposed， with its sensitivity analyzed. The special shape parameters that ensured uniform flow were given. This shape has reference value for the design of the inlet/outlet of pumped storage power stations with open-air powerhouses.

Unmanned Aerial Vehicle （UAV） remote sensing technology presents a multitude of advantages， including cost-effective deployment， rapid responsiveness， versatile and user-friendly operation， superior image quality， and minimal disruption to the environment. It can be used to monitor multiple water quality indicators， such as total nitrogen， suspended matter， turbidity， total phosphorus and chlorophyll， etc.， which is the development trend of river and lake water environment monitoring technology system. To assess the applicability and reliability of this technology for monitoring river water quality， the Xiushui River—one of the five major rivers in the Poyang Lake Basin—was selected as the research subject. A total of 31 samples were collected， and key water quality parameters including suspended solids （SS）， total phosphorus （TP）， total nitrogen （TN）， turbidity （TUB）， and chlorophyll-a （Chl-a） were quantitatively retrieved using remote sensing data obtained from UAV multispectral imaging.The accuracy of monitoring was evaluated through linear fitting， coefficient of determination （R2）， normalized root mean square error （NRMSE）， and mean relative error （MRE）. Furthermore， a comparative analysis along with an economic assessment demonstrated the applicability of UAV remote sensing technology in this context. The results show that：①The remote sensing technology of UAV demonstrates a high accuracy in monitoring water quality parameters such as SS， TP， TN， TUB， and Chl-a. The coefficient of determination （R2） for the linear fitting curves between the inversion values and the measured values ranges from 0.565 to 0.89， closely aligning with a 1∶1 trend line， thereby meeting the precision requirements for water quality parameter retrieval. Among these parameters， Chl-a exhibits the highest accuracy， while TP shows the lowest； thus， they can be ranked in descending order as follows： Chl-a SS TUB TN TP. ②The normalized root mean square error （NRMSE） values for each index fall between 0.1 and 0.3， indicating a moderate preference for goodness of fit. Furthermore， the mean relative error （MRE） for each index ranges from 13% to 24%， which is generally close to the measured values with deviations remaining within a controllable range. ③Additionally， the costs and construction periods associated with UAV remote sensing monitoring are more than 50% lower than those of manual monitoring， indicating that UAV remote sensing technology is a more economical option. This study innovatively uses UAV remote sensing technology to invert river water quality， and verifies the feasibility of UAV remote sensing technology in river water quality monitoring， which provides a new method for rapid and accurate monitoring of river water quality， and has certain practical guiding significance for promoting the development of water environment monitoring technology.

Intelligent grouting technology plays a critical role in hydraulic engineering and foundation reinforcement. Currently， grouting pressure and unit grouting rate are primarily adjusted manually， leading to low grouting precision and frequent overpressure incidents. This study designs an intelligent grouting pressure control system， integrating a pressure regulation platform， intelligent control unit， and upper-level master control center. The system is capable of controlling pressure and processing real-time data， ensuring the stability and safety of the grouting process. The system's hardware includes key components such as a grouting pump， diverter valve， inflow meter， pressure gauge， outflow meter， and electric control valve. By integrating a PLC with the electric control valve， real-time adjustments to the return pressure are made， ensuring stable and precise pressure control. A data acquisition and pressure stability control logic program for the grouting process are developed， and an integral sliding mode controller is applied for pressure regulation. The controller's performance under dynamic and disturbance conditions is verified， demonstrating its ability to respond quickly to pressure changes and adapt to different grouting requirements. This enhances the automation and intelligence of the grouting operation. This provides a solid foundation for the widespread application of intelligent grouting technology and offers significant potential for future use. This work presents a comprehensive design of an intelligent grouting system， explores the effects of various pressure control algorithms， and confirms the system's performance in real-world applications. The research results show that the intelligent grouting system operates stably and has precise pressure control， which provides a solid foundation for the widespread application of intelligent grouting technology and has broad application prospects.

Water and soil resources are the basic elements of “Production-Living-Ecological Space”. The particularity of water and soil resources in Inner Mongolia Autonomous Region makes the optimization of “Production-Living-Ecological Space” face major challenges. Based on the perspective of “Production-Living-Ecological Space” and PSR model， an evaluation index system of water and soil resources carrying capacity in Inner Mongolia Autonomous Region was constructed. The entropy weight-TOPSIS evaluation model was used to study the spatial-temporal evolution and influencing factors of water and soil resources carrying capacity in Inner Mongolia Autonomous Region from 2013 to 2022. The coupling coordination degree model was used to analyze the coupling coordination degree of “Production-Living-Ecological Space” for the water and soil resources carrying capacity. The results show that： ① The carrying capacity of water and soil resources in each league and city of Inner Mongolia Autonomous Region showed a fluctuating upward trend， and it was mainly at three levels： lower carrying capacity， general carrying capacity and higher carrying capacity， and there is no low carrying capacity level and high carrying capacity level. ② During the study period， the carrying capacity of water and soil resources in each city and league can be divided into two stages： In the first stage （2013-2017）， the carrying capacity of water and soil resources in each city and league showed a slow and fluctuating upward trend， which was mainly at a lower carrying capacity and general carrying capacity level， and the evaluation value of carrying capacity was mainly concentrated in 0.3~0.6； In the second stage （2018-2022）， the carrying capacity of water and soil resources in each city and league showed a steady and upward trend， which was mainly in the general carrying capacity and higher carrying capacity level， and the evaluation value of carrying capacity was mainly concentrated in 0.4~0.7， which has been improved to a certain extent compared with the first stage. ③ The coupling coordination degree of “Production-Living-Ecological Space” for the water and soil resources carrying capacity in Inner Mongolia Autonomous Region generally shows an upward trend. The change trend of the coupling coordination degree of “Production-Living-Ecological Space” was basically consistent with the spatial-temporal evolution trend of water and soil resources carrying capacity in each city and league， which is the result of a steady improvement in the coordination between production， living and ecological space. The research results can provide reference for the sustainable planning， utilization and management of water and soil resources as well as the optimization of “Production-Living-Ecological Space” pattern in Inner Mongolia Autonomous Region.

Vibration monitoring of hydropower units plays a crucial role in evaluating unit health status and providing early warnings of deterioration. Accurate prediction of the trend of vibration deflection of unit components can detect the abnormal operation of the unit in advance， and effectively enhance the safety and stability of the unit operation. However， vibration oscillation signals of hydropower units monitored on-site contain noise， and the current methods for predicting the vibration oscillation of hydropower units mostly predict the single position of the hydropower units， leading to incomplete evaluation of the operational stability of the hydropower units， and the interaction among the vibration oscillation of multiple measurement points is not fully considered. To address these challenges， this paper proposes using Cross Graph Neural Networks （CrossGNN） to predict the vibration oscillation signal of multiple positions of hydropower units. CrossGNN is a graph neural network model with linear complexity. The multi-component vibration signals of hydropower units can be interoperable across scales and variables， thus implicitly revealing the spatio-temporal relationship of vibration signals at multiple positions of hydropower units. In order to effectively deal with the noise in time dimension， the adaptive multi-scale identifier （AMSI） is used to construct a multi-scale time series with noise reduction effect. Then a cross-scale neural network is proposed to accurately extract scales with clear trend and weak noise. At the same time， cross-variable GNN is constructed based on the homogeneity and heterogeneity among different variables. By focusing synchronously on edges with higher significance scores and restricting edges with lower scores， the time and space complexity of CorssGNN can be linearly related to the length L of the input sequence， thus effectively improving the prediction accuracy and generality. Finally， taking a hydropower station unit in China as an example， the effectiveness of the proposed method is verified by predicting the vibration oscillation signal of six positions of the unit， which provides a new idea for the vibration prediction of the hydropower station.

While reservoir dams have made significant contributions to flood control， power generation， navigation， and other areas， they also pose potential safety hazards， such as the risk of dam failure. With the increasing frequency of extreme weather events， the risk of dam failure has grown， making dam breach flood analysis especially important. The simulation of flood evolution in the event of a dam breach is a critical method for risk analysis. Previous studies have generally used the broad-crested weir formula for simulating breach flow， which does not account for the influence of factors such as downstream water depth and the development process of the breach. To fully consider these factors， this study proposes a two-dimensional dam breach flood evolution coupled breach model based on the summary of prior research results. This model conceptualizes the development process of the breach shape and integrates reservoir water balance analysis with two-dimensional hydrodynamic computations to simulate both the breach formation process and the downstream flood evolution. It enables the automatic calculation of breach flow and real-time exchange of data with the downstream flood evolution. The model is validated using empirical formulas and experimental data， with the simulation results showing good agreement with both the empirical formulas and experimental observations， confirming the reliability of the model. The model is then applied to simulate the dam break flood of a specific reservoir. Based on the simulation results， the processes of flood flow variation， reservoir water level changes， flood inundation， and the evolution of the flood at key points and typical cross-sections are analyzed. The results demonstrate that the simulated outcomes of the proposed model are reasonable， consistent with the actual physical processes， and suitable for application in dam break flood studies.

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.

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%.

Central China has a typical temperate and subtropical monsoon climate， 60% of which is mountainous， and the spatial and temporal characteristics of atmospheric precipitation and water vapor sources are very complex. By searching the literature on precipitation hydrogen and oxygen isotopes and water vapor sources in central China （Hunan， Hubei and Henan） in recent years， the isotopic characteristics of precipitation in central China and its related aspects are sorted out and summarized， and the future research on the stable isotopes of hydrogen and oxygen of atmospheric precipitation in central China is prospected. The results show that the precipitation isotope compositions in central China show seasonal variations of low summer and high winter in time， which fully reflect the precipitation effect of the distribution of hydroxide stable isotopes； there is a spatial trend of decreasing from south to north， and the areas of high precipitation hydroxide stable isotope values are mainly located in low-latitude and low-elevation areas such as eastern Hunan and eastern Hubei， and the high-elevation mountainous areas such as western Hubei and western Henan are the areas of low values. The slopes of the local atmospheric precipitation lines are closer to the global and national atmospheric precipitation lines， but there are different degrees of deviations in the intercepts of each region， with imbalanced isotope fractionation in wet and rainy regions in Hunan and Hubei， and strong secondary evaporation under the arid and low rainfall clouds in Henan. Precipitation is the main factor affecting the isotopic composition of atmospheric precipitation in central China， and there are inverse temperature effects and elevation effects in some regions. The water vapor of atmospheric precipitation in central China is mainly influenced by the oceanic air masses carried by the southwest monsoon and southeast monsoon in the summer half of the year， while the atmospheric precipitation air masses in the winter half of the year mainly come from the interior of the Asian-European continent and local evaporative water vapor. The above results provide a theoretical basis for the study of the ecohydrological cycle process in central China， and provide scientific guidance for the rational regulation of regional water resources.

The existing deformation monitoring models of concrete dams rarely consider the dynamic spatiotemporal correlation of multiple measurement points， thus limiting the prediction accuracy of deformation. To overcome this shortcoming， this paper constructs a dynamic prediction model of spatial deformation of concrete dam based on cluster analysis， long short-term memory （LSTM） network and attention mechanism， which can improve the prediction accuracy of deformation of target measurement point by introducing dynamic correlation factors reasonably. Taking the horizontal displacement monitoring data of several measurement points of a gravity dam as an example， the effectiveness of the model and the importance of spatial dynamic correlation factors are verified by comparing with three other LSTM-based models. Furthermore， the contribution degree of multiple factors to the model output is quantified by two interpretable machine learning algorithms for different measurement points， and the ranking results are consistent with the prior knowledge of dam engineering. The research results can provide reference for promoting the engineering application of intelligent dam deformation monitoring model.

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.

The aim of this paper is to explore the spatial distribution of ecosystem services， the trade-offs and synergistic relationships among ecosystem services， and the key factors driving the changes of these services in the Han River Basin. Five ecosystem services， namely， habitat quality （HQ）， water yield （WY）， carbon storage （CF）， soil conservation （SC）， and net primary productivity （NPP）， were selected for analysis， and the synergies and trade-offs among them were assessed using Spearman’s method， and the XGBoost-SHAP model was adopted for the analysis of driving factors. The results showed： ① Each ecosystem service showed significant imbalance in spatial and temporal scales. Temporally， water production and soil retention showed a trend of decreasing and then increasing， habitat quality showed a trend of increasing and then decreasing and then increasing， and net primary productivity and carbon storage showed a trend of continuous increase. Spatially， water production was more affected by rainfall presenting a larger yield in the eastern region， while the other four ecosystem services generally showed smaller values in the east. ② Water yield showed obvious trade-offs with the other four services， especially with carbon storage， while the other four services mainly showed synergistic effects. ③ Rainfall is the main natural factor influencing water yield， while elevation has a significant effect on net primary productivity， habitat quality and carbon storage， and slope is a key determinant of soil conservation.