Dam deformation is the visual representation of the state of a dam under the influence of internal and external loads during its operation. Building a high-precision deformation prediction model is of significant importance for dam safety monitoring and operational assessment. Existing dam displacement models have long training times， and their prediction accuracy and generalization capabilities are often suboptimal， making them unsuitable for accurate short- to medium-term displacement predictions. This paper coupled Long Short-Term Memory （LSTM） with the Transformer framework and introduced an improved Invasive Particle Swarm Optimization （IPSO） algorithm for optimization to create the IPSO-LSTM-Transformer （ILT） dam deformation prediction model. Using data from measurement point 11-1 on the vertical axis of the Jinshuitan arch dam as an example， this paper analyzed and predicted deformation time series data from 6，150 data sets. The research results demonstrate that the model's prediction accuracy decreases to a certain extent as the prediction horizon increases， but it maintains good predictive capabilities within a prediction step of 10. Compared to traditional Particle Swarm Optimization algorithms， the ILT model significantly improves the model's optimization precision and convergence speed. When compared to the single-step and multi-step prediction results of RNN， LSTM， and IPSO-Transformer neural network models， the ILT model exhibits higher accuracy and better stability， even when trained with limited data. The research findings provide new technical means for the precise short-to-medium-term prediction of dam displacement during operation.

In recent years， the frequent occurrence of urban waterlogging disasters caused by extreme rainfall due to climate change has posed a threat to urban water safety and sustainable development in China. Accurately grasping the public opinion and emotions in the disaster-stricken areas is of great importance for improving the situational awareness capabilities of emergency management departments in dealing with waterlogging disasters. In today's era of intelligent networks， the increasing importance of social media as a platform for people to voice their problems and suggestions has made it a major carrier of public sentiment and societal opinion， providing a new avenue for obtaining information about natural disasters. A key technical challenge that needs to be urgently addressed is how to quickly extract urban flood disaster information from social media， and how to perform thematic categorization and sentiment analysis of natural disaster information to accurately grasp the thematic categories of regional disaster situations and public opinion trends. Taking Sina Weibo as an example， this article elaborates on the methods of collecting and pre-processing flood disaster data， and constructs a thematic classification and sentiment analysis model of urban flood disaster information based on FastText to accurately capture the thematic categories and public opinion orientations of disaster-stricken areas. The research results， using the “7.20” heavy rain and flood disaster in Zhengzhou in 2021 as an example show that the methods proposed in this article achieve intelligent extraction and analysis of urban flood disaster data on social media. The theme classification model achieves an F1 score of over 0.80 for the classification prediction of the eight predefined categories， and the sentiment analysis model is generally able to accurately predict data labelled as “negative” in sentiment， which indicates that the FastText-based urban flood disaster information theme classification and sentiment analysis model constructed in this article can meet the needs of urban emergency management departments to dynamically grasp the development of flood disasters and public emotions. It holds significant guiding importance for flood prevention and disaster mitigation planning， calming public emotions， and pinpointing rescue efforts in real time.

Utilizing satellite remote sensing technology for the retrieval of water depth is a crucial method for quickly acquiring water depth information over large areas. However， traditional optical satellite images often suffer from low inversion accuracy due to various factors， including the presence of sediments， suspended particles， and chlorophyll in the water. These constituents alter the color and turbidity of the water， making it challenging to accurately estimate water depth through remote sensing techniques. To mitigate the influence of these water color materials on the accuracy of water depth inversion， a novel approach was proposed， centered around a mathematical model and the use of Sentinel-2 remote sensing imagery. This approach involves analyzing the mathematical relationships between water depth and the surface area of different types of water channels. This method deduces the river surface depth model by generalizing and classifying the river section. It combines the Sentinel-2 remote sensing images to determine the relevant parameters in the mathematical model， and derives an expression that can directly calculate the water depth from the water surface area， which can minimize the influence of water color on water depth retrieval from remote sensing images. To validate the effectiveness of this approach， it was applied to the Xiaoqing River， and the results were compared with ground-truth data. The average absolute error in water depth inversion at two hydrographic stations， Huangtaiqiao and Shicun， was found to be 0.03 meters and 0.23 meters， respectively. These results demonstrate a significant improvement in the accuracy of water depth estimation. The method proves to be more reliable in areas with varying water color and turbidity. It can quickly obtain water body depth information based on satellite remote sensing technology and has promotion and application value.

Evapotranspiration （ET） is a crucial link in water circulation. It plays an important role in the global water cycle and surface energy balance， and has significant impacts on climate， ecosystems， and water resources management. Therefore， the quality of evapotranspiration data is crucial for the precise management of global water resources. This study conducted accuracy validation and spatiotemporal comparison of three ET products in the Northern Hemisphere， selecting the ET products that is more suitable for the Northern Hemisphere， providing suggestions for strengthening the combination of remote sensing and ground observation research. Using the monthly average measured data from FluxNet2015 flux sites to verify three ET products， it was found that PML_ V2 product has the highest accuracy in the Northern Hemisphere， followed by GLDAS， and finally MOD16A2， with correlation coefficients R of 0.66， 0.57， and 0.56， respectively； The root mean square error （RMSE） is 2.46， 5.68， and 12.42 mm/month， respectively； The average biases are 14.36%， 16.86%， and 35.02%， respectively. The GLDAS ET product has the ability to monitor daily scale ET， and the consistency between the daily average estimated value and the measured value at the flux tower site is high. The correlation coefficient R is 0.74， and the RMSE and Bias are 1.62mm/day and 27.90%， respectively. Overall， on the time scale， all three ET products can simulate the seasonal changes in the Northern Hemisphere， with higher summer evapotranspiration and lower winter evapotranspiration. The three ET products in summer all have overestimation phenomena on different land cover types， and the simulation results in other seasons are better than the ground observation values. Among them， MOD16A2 performs the worst， and the overestimation phenomenon is the most obvious. In addition， during the period from 2001 to 2020， except for arid areas， the spatial distribution of the three ET products was relatively consistent in most regions， with a correlation coefficient R greater than 0.6. This study provides scientific recommendations for selecting suitable ET data sources for conducting evapotranspiration studies in the Northern Hemisphere by evaluating the uncertainty and product quality of different ET products.

The daily peaking capacity is one of the main performance indicators of hydropower station which is of great significance in the grid scheduling of combined water， wind and photovoltaic power. This paper defines the concept of daily peaking capacity of hydropower station from the aspects of peaking magnitude and peaking power， and calculates the daily peaking capacity of hydropower stations based on four typical daily load diagrams of fixed shapes and a generalized typical daily load diagram. For the typical daily load diagrams of fixed shapes， the calculation method of the hydropower stations’ maximum peaking magnitude under the known peaking time is given. For the generalized typical daily load diagram， the calculation method of the maximum peaking power is given. Four reservoirs of hydropower plants with different regulation performance in the midstream and downstream of the Hongshui River were used as research objects. The peaking capacity of each reservoir was calculated at different initial water levels and different average daily inflow under five different typical daily load diagrams. The results show that For hydropower stations with strong regulating capacity such as seasonal and annual regulation， their daily peaking capacity is mainly affected by the reservoir water level， while the average daily inflow has little effect on it. For daily regulated hydropower stations， when the inflow is small， the daily peaking capacity is greatly affected by the daily initial water level and the daily average inflow. When the inflow reaches a certain value， the daily initial water level has less influence on the daily peaking capacity. When the average inflow and initial water level are determined， the maximum peaking amplitude and maximum peaking power of the hydropower station as well as related operation data can be quantitatively derived， which facilitates the optimal effect of the hydropower station to participate in the grid peaking. The concept of daily peaking capacity of hydropower station proposed in this paper is objective and comprehensive. The established method of calculating daily peaking capacity has strong practicality and operability which can provides scientific basis to determine the daily peaking capacity of hydropower station.

Wheat drip irrigation technology is the main project of high-standard farmland construction in North China， and it is also an important technical means to deal with the shortage of water resources in the region and ensure national food security. Based on the latest research progresses of drip irrigation technology on wheat at home and abroad and the research results of our group for many years， this paper elaborates the influences of drip irrigation factors such as drip flow， dripping element spacing， drip belt laying distance on soil water distribution， the ways of drip irrigation scheduling on wheat， and the effects of drip irrigation on wheat water consumption characteristics， dry matter accumulations， yield compositions and so on. At the same time， the development trends of wheat drip irrigation in the future are put forward： smart irrigation decision-making and water-fertilizer integration technology that integrates multiple factors such as meteorology， soil and crops， It is hoped these contents would provide theoretical and technical support for accelerating the application and popularization of drip irrigation technology in the main wheat-producing areas in China..

The optimal regulation of multi-energy complementary systems helps to take advantages of different power sources. The growing installed capacity of wind and photovoltaic power has brought about the problems of clean energy consumption and grid peak shaving. A bi-level optimal scheduling model of wind， photovoltaic（PV）， hydropower， thermal power and energy storage is proposed to solve the problems. Firstly， considering that there are many variables and constraints involved in the optimal scheduling of multi-energy complementary， the model is processed hierarchically based on the output characteristics and complementary relationship of multiple energy sources， to reduce the complexity of model solving. Then， according to the demand of source-load coordination， clean energy consumption and power generation economy， the model is divided into the upper-level model of Wind-PV-Hydro-Storage joint optimal scheduling and the lower-level model of thermal power unit optimal scheduling. The optimization objectives of upper-level model are the minimum net load variance and the maximum clean energy generation， and the optimization objective of lower-level model is the minimum operation cost of thermal power units. The net load curve achieved by the upper-level model is used as the constraint of the lower-level model. Finally， taking the IEEE30-bus system as an example， the CPLEX solver of MATLAB platform is used to calculate three pre-defined scheduling scenarios. The results show that the anti-peak regulation of wind power and photovoltaic output will expand the peak-valley difference and variance of net load， increase the pressure of peak shavingand reserve of thermal power， and increase the power generation cost and unit loss. The participation of hydropower in multi-energy complementary systems has a significant improvement on the net load fluctuation of the power grid， which can effectively reduce the peaking pressure of the power system. Energy storage can reduce the conflict between smoothing net load fluctuation and improving clean energy generation to a certain extent. The optimal scheduling model proposed can effectively reduce the fluctuation of source and load， promote the consumption of clean energy and reduce the operation cost of the system， which contribute to the low-carbon and safe operation goals of the grid.

This paper analyzed the pre-stressing mechanism of the steel wire and the damage process of the pipeline， and proposes a method of PCCP wire broken warning and alarm threshold assessment based on mechanical simulation analysis. The PCCP finite element model was established by ABAQUS program， and the complete construction process including laying bedding， installing pipelines， backfilling in steps was simulated， and the stress state of pipeline prestressing steel wires， steel cylinders and pipe core concrete was calculated under nearly 100 working conditions with different depths of burial and different design working pressures. It was proposed to take the maximum permissible number of broken wires of the pipe core concrete cracked under the action of water hammer as the early warning threshold for the bursting of the pipeline， and the maximum permissible number of broken wires in the limit state of the buried bearing capacity of the pipeline as the alarm threshold for pipe bursting， and analyzed the relationship between the PCCP wire broken warning and the alarm threshold under different working pressures and burial depths.

Irrigation is one of the types of land management that has the greatest impact on regional climate， and its impact has been increasing in recent decades. A large number of papers have demonstrated the cooling effect of irrigation， particularly in arid and semi-arid regions. However， the cooling effect in humid regions has been found to be not significant， and the investigation of the cooling effect pertaining to extreme high temperatures has received comparatively less attention.In this study， a sliding window search algorithm is used to investigate the impact of augmenting the proportion of irrigated areas on the occurrence of extreme high temperatures in the Yangtze River Basin based on a global gridded temperature dataset and a historical irrigation distribution dataset. To isolate the climatic effects of various factors in the observed data， the multiple linear regression techniques are employed. Furthermore， a control test is set up by using the WRF model of the coupled irrigation module to perform numerical simulations for verification.The results show that irrigation in the Yangtze River Basin has a cooling effect on extreme high temperatures， and the intensity of the cooling effect depends on the proportion of irrigated area， which becomes more obvious with the increase in irrigated area， but when the proportion of irrigated area exceeds the threshold （0.25~0.30）， the cooling effect gradually diminishes. Numerical simulation experiments verified the results of the analysis based on observed data， but the model would overestimate the cooling effect of irrigation to some extent. Meanwhile， it was found that irrigation could increase soil moisture and latent heat flux and decrease sensible heat flux and soil heat flux， and the cooling effect would be weakened as the proportion of irrigated area increased， the surface moisture increased and the albedo decreased. The results of this research can provide a theoretical basis for the scientific management of agricultural irrigation in the Yangtze River Basin， which is of great scientific significance.

Making full use of modern technological means to improve the accuracy of runoff forecasting plays an important guiding role in basin flood and drought disaster defense and joint scheduling of reservoir groups. However， existing deep learning models have problems such as lack of model transparency and poor physical interpretability. To address the above problems， in this study， a conceptual hydrological model EXP-Hydro is embedded into the P-RNN layer of recurrent neural network， and a deep learning hybrid model Hybrid-DL coupled with physical mechanism is modeled. The hybrid model adopts a differential framework to realize the deep bidirectional fusion of conceptual model and neural network， which is able to train the parameters of conceptual model and neural network at the same time. And an application study is carried out in the upper reaches of Qingjiang River as an example. The results show that compared with RNN， EXP-Hydro， BP and SVM models， the Nash efficiency coefficient （NSE） of the Hybrid-DL model increases by 6.08%， 21.01%， 37.09% and 73.92%， the root-mean-square error （RMSE） decreases by 10.82%、33.73%、54.70% and 95.57%， the KGE efficiency coefficient increases by 4.78%、12.68%、26.79% and 55.74%， and the peak error TPE decreases by 4.96%、13.12%、252.84% and 297.81%. The Hybrid-DL model has good robustness and adaptability， and can provide a reliable theoretical tool for runoff forecasting in the upper reaches of Qingjiang River and even in other basins.

Based on the knowledge and understanding of modernization of the physical irrigation district and digitalization of the analog irrigation district， this paper analyzes and summarizes the “five major functions” of the construction of modernized digital irrigation district including “Identification system of irrigation district”， “Stereoscopic Perception System”， “Precise Control System”， “Information Exchange System” and “Water Allocation System”. In order to meet the requirements of intelligent water projects and efficient operation and management of irrigation district， to efficiently simulate water flow in canal system， the generalized map of irrigation district with “joint flow process” as input and output is systematically sorted out， and the key application structure system of modernized digital irrigation district is studied and proposed. That is to build an interactive overall structure with “database” as the carrier， “mathematical model” as the support layer， “nine professional applications” as the interaction layer including “organization management”， “project management”， “security management”， “pumping station management”， “water saving and water supply management”， “economic management”， “information management” and “public services” on the basis of “one digital map of the irrigation district”. It is expected to effectively improve the safety， equity， reliability and flexibility of irrigation water supply services.

The plain river network area has a flat terrain and slow flow velocity， resulting in a limited ability to resist pollution. Therefore， enhancing the connectivity of the water system is crucial for improving the quality of the water environment. This article focuses on the Yuejin polders which has poor water system connectivity in Shengze Town as the subject of research. By conducting water diversion experiments and constructing a hydrodynamic model of the river network in the polders area， the study explores the scheduling and operation rules of water conservancy work， methods of connecting the river network， and the corresponding changes in the water environment. The study utilizes water age as a time scale parameter to measure the connectivity of the water system. It establishes a river network model for hydrodynamics， water quality， and water age in the Yuejin polders. The simulation evaluates the connectivity and water quality improvement in the polders area under five water diversion schemes. Additionally， an evaluation system of pressure， state and response is constructed to quantitatively analyze changes in water system connectivity and the level of water environmental improvement resulting from water diversion. The results reveal that from the perspective of the length of water exchange， the exchange time of water bodies in the main river channels of the research area is within 5 days. The greater the connectivity of these water bodies， the shorter the water age becomes. From the perspective of changes in water quality indicators， during the water diversion experiment， the most noticeable change in the water environment inside the polders is the level of ammonia nitrogen. It shows a strong response to water diversion， and simulation results indicate that water age generally aligns with the concentration of ammonia nitrogen. Enhanced connectivity of water bodies leads to a shorter water age， which aids in reducing ammonia nitrogen levels. From the perspective of water diversion scheme， the uni-source water diversion and uni-region drainage scheme has limited effectiveness in improving the connectivity of the water system within polders， especially in areas with weak water flow in the river network. This scheme also increases variations in water flow within the polders， resulting in spatial differences in connectivity and water quality in the river network. Consequently， local stagnant water areas or dead water areas may be formed. Therefore， the combination of multiple sources of water diversion and multi-regional drainage scheme can enhance the connectivity of the water system and improve water environmental quality.

As the core equipment of hydroelectric power plants， hydroelectric generator units inevitably generate vibration and pressure pulsation problems during long-term operation. Long term vibration can accelerate the wear and fatigue of mechanical parts， reduce the lifespan and reliability of the unit， and may also lead to unstable operation of the unit， and even cause serious accidents， posing a threat to the safety of the power plant. These vibration and pulsation problems stem from multiple factors， including the dynamics of water flow， the design and manufacturing quality of impellers， and the complex interactions between the unit and the hydraulic system. These vibration and pressure pulsation issues have a negative impact on the performance， reliability， and safety of hydroelectric generators. Therefore， it is particularly important to conduct in-depth research on the vibration and pressure pulsation problems of hydroelectric generator units during operation and find effective solutions. This article analyzes the experimental data of Unit 3 of Ansha Hydropower Station and delves into the operational stability issues caused by pressure pulsation and vibration of hydroelectric generator units. By using the Wilcoxon rank-sum test and Pearson correlation coefficient to analyze the pressure pulsation， vibration， and swing signals during the operation of water turbine units， it was found that there is a strong correlation between the overall signals of hydraulic turbine units， especially in the swing signal. Finally， methods and strategies to reduce pressure pulsation and vibration issues generated during the operation of hydroelectric generator units were explored for the design， operation， and maintenance stages. By taking these measures， we can effectively reduce the negative impact of vibration and pressure pulsation， ensure the safe and stable operation of hydroelectric generator units， and contribute to the development of sustainable energy.

Pump storage is of great significance to the development of renewable energy and the construction of a new energy system， and help to achieve the “dual carbon” goal. Fully understand the functions and functions of pumping storage， sort out the policy evolution and development process in the process of modernization of China's pumping storage， analyze the current status of Chinese pumping storage， and look forward to its future. The inherent relationship between the modernization of the entire country is conducive to promoting the high-quality development of the new stages of pumping in the new stage， and it is expected to accelerate and promote the formation of a modern industry with strong international competitiveness in my country.

In order to improve the spatio-temporal accuracy of rainfall and evaporation distribution in the Grid-XAJ model， strengthen the hydrological cycle， and provide a foundation for further coupling of the WRF model with the Grid-XAJ model， we constructed the WRF-driven Grid-XAJ model. Firstly， we used the successive correction method to obtain the WRF-merged rainfall by merging WRF forecast rainfall with gauged rainfall. Then， the WRF forecast meteorological data were input into the Grid-XAJ model， and the hourly evaporation capacity of each grid was calculated by Penman-Monteith method （PE_PM ）. Finally， the Grid-XAJ model was driven by WRF-merged rainfall and PE_PM to simulate floods in the humid Tunxi catchment. The results showed that： ① The WRF-merged rainfall exhibited high accuracy and a fine spatial distribution. Compared with WRF forecast rainfall， the WRF-merged rainfall had a higher correlation （RR≥0.99） and a better fitting degree（NSE≥0.98） to actual rainfall， and errors of rainfall peaks （-8.1%~3.5%） and cumulative rainfall （-2.0%~6.7%） were significantly reduced. In terms of spatial distribution， the WRF-merged rainfall displayed more complex spatial information compared to rainfall interpolated from gauged rainfall， as indicated by Shannon Entropy （SE） significantly increased （30.4%~48.2%）. Additionally， the WRF-merged rainfall can incorporate rainfall centers from both the WRF forecast rainfall and the rainfall interpolated by gauged rainfall. ② The PE_PM exhibited hourly variations and showed a close correlation with rainfall. In terms of spatial distribution， PE_PM was closely related to elevation. Regions with higher elevations displayed a relatively lower mean PE_PM values， while areas with moderate elevations exhibited the highest mean PE_PM values， and slightly lower values were observed in lower elevation areas. ③ The WRF-driven Grid-XAJ model held significant potential in flood forecasting. The flood forecasting accuracy of the Grid-XAJ model， when driven by WRF-merged rainfall and PE_PM， was significantly enhanced compared to the model driven by WRF forecast rainfall and PE_PM . The NSE values of flood forecasts were all above 0.90， and the qualified rates of flood volumes， flood peaks and peak occurrence times were 100%.

In response to the current problems in water quality prediction models， such as the complexity of the data itself， noise interference in signal processing， and insufficient decomposition depth， which make it difficult for a single decomposition to fully capture the nonlinear features of the signal， this paper proposes a water quality prediction model based on secondary decomposition. This innovative method firstly uses the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise （CEEMDAN） to decompose the raw data. Then， Variational Mode Decomposition （VMD） is adopted to perform a secondary decomposition of the Intrinsic Mode Function （IMF） with the highest entropy value. Finally， the processed time series are put into the TCN-light GBM multi-feature prediction model. At the same time， the Sparrow Search Algorithm （SSA） is used to optimize the prediction model. By taking the water quality of Yufu River in Shandong Province as an example， the Root Mean Square Error （RMSE） of this model is 0.105 3， the Mean Absolute Error （MAE） is 0.081 5， and the coefficient of determination （R ²） is 0.947 1.The predictive metrics of the model are compared with those from popular contemporary deep learning and neural network algorithms， such as the Gated Recurrent Unit （GRU）， Long Short-Term Memory （LSTM）， Light Gradient Boosting Machine （Light GBM）， and Temporal Convolutional Networks （TCN）， among others. The results show that， in terms of R ²， the model achieved improvements of 53.04%， 70.41%， 66.07%， and 65.20% respectively. In terms of RMSE， the model represented reductions of 62.76%， 65.50%， 64.93%， and 64.80% respectively. And in terms of MAE， the model witnessed decreases of 62.76%， 66.24%， 63.80%， and 65.24% respectively. Therefore， it is evident that the model based on CEEMDAN-VMD-TCN-light GBM exhibits superior predictive performance， which can reduce the fluctuation of water quality sequences more effectively and improve the ability to capture nonlinear features of signals.

In order to improve the accuracy of dam displacement prediction， this paper proposes a novel comprehensive prediction method based on deep learning. Initially， a multi-level data denoising technology based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise（CEEMDAN） and Singular Spectrum Analysis （SSA） is introduced. This effectively eliminates the noise and outliers in the monitoring data， improving data quality， and providing more reasonable dam deformation data for subsequent predictions. Subsequently， a deep learning model based on Convolutional Neural Networks （CNN） and Gated Recurrent Units （GRU） is constructed. The CNN is used to extract rich features from the monitoring data， and the GRU is utilized to capture and process the long-term dependencies in the time series data. To enhance the model’s performance， a self-attention mechanism is introduced to help the model better handle and recognize complex patterns in the data. Compared with other prediction methods， experimental results show that this method significantly improves the accuracy and stability of dam displacement predictions， providing a new approach for the field of dam deformation monitoring.

To effectively solve the high-dimensional problem of water resources carrying capacity classification and evaluation， and reveal the dynamic changes in water resources carrying capacity， an improved projection pursuit clustering model is proposed. For the improved projection pursuit clustering model and the projection pursuit clustering model based on the maximum information entropy principle， the reasonable range and optimal value of density window width were derived by analyzing the negative entropy change law of projection values； When the density window width is the optimal value， the improved projection pursuit clustering model performs better in classification evaluation than the projection pursuit clustering model based on the maximum information entropy principle. This article uses an improved projection pursuit clustering model to dynamically evaluate the water resources carrying capacity status in Jiangsu Province. The water resources carrying capacity level from 2009 to 2020 and 2022 was level III， and the water resources carrying capacity level in 2021 was level II. A grey GM （1，1） model was established to predict the water resources carrying capacity status of the province as level II from 2023 to 2030. The improved projection pursuit clustering model more effectively extracts the structural feature information of high-dimensional data for water resources carrying capacity evaluation indicators， further enhancing the accuracy of the water resources carrying capacity classification evaluation model and making the evaluation results more objective and reasonable. Through comparative analysis of the contribution rates of indicators in 2022 and 2009， the measures taken by the province to save water in industry and agriculture， accelerate socio-economic development， and protect water resources have promoted the continuous improvement of water resources carrying capacity. Based on the contribution rate of evaluation indicators to the water resources carrying capacity status and evaluation standards in Jiangsu Province， this paper deeply analyzes the shortcomings of the water resources carrying capacity in the province， and puts forward relevant suggestions to improve the water resources carrying capacity status， ensuring that the water resources system carrying capacity status in Jiangsu Province reaches level I as soon as possible and achieving sustainable utilization of water resources.

In order to explore the theoretical technology and method system for the formulation of ecological water replenishment schemes for seasonal rivers， this paper proposes an ecological water replenishment mutual feedback method combining ecological flow calculation and hydrodynamic model.Based on the diagnosis of hydrological sequence variation， this method lays a foundation for the formulation of ecological water replenishment scheme by calculating the ecological flow process in the river channel.Then， according to the actual process of ecological water replenishment， a hydrodynamic model of river ecological water replenishment is established and solved， which provides a model support for the study of ecological water replenishment evolution. Finally， the hydrodynamic model is used to analyze the water replenishment evolution of different water replenishment schemes， so as to obtain the best water replenishment scheme under the given target. By taking a seasonal river DY River in the north as an example， this paper carries out an application analysis. The results show that： ① The variation point of TG reservoir inflow runoff sequence is 1980. On this basis， the runoff sequence can be divided into two parts： natural stage and human disturbance stage. ② The ecological water replenishment scheme obtained by the ecological water replenishment mutual feedback method can not only meet the ecological flow demand in the river channel， but also fully consider the characteristics of the annual variation of the river runoff process， which is of important scientific significance for understanding the ecological flow restoration process of seasonal rivers in China； ③ The ecological water replenishment mutual feedback method is applied to the ecological water replenishment project of DY River， and the effect is good. According to the requirements of the ecological water replenishment target， the ecological water replenishment scheme that meets the water supply of the whole line in DY River is obtained， that is， when the ecological water replenishment of DY River is in spring， the optimal ecological water replenishment flow from March to May is 49.82， 27.17 and 8.67 m³/s. This method can be used for the subsequent DY River to formulate corresponding ecological water replenishment schemes for other water replenishment targets， and can also provide new methods and new ideas for the formulation of ecological water replenishment schemes for seasonal rivers in other basins.

It is one of the necessary conditions to obtain the measured discharge for hydrological frequency analysis and water resources management. It plays a pivotal role in gaining insights into the variations in discharge， forecasting flood risks， and optimizing the allocation of water resources. The installation of the conventional devices is often complex and low-efficient while the non-contact devices （e.g.， video discharge measurement） is easily set and can cut costs. However， for non-contact devices， the error analysis of the discharge measurement results is insufficient， resulting in risks associated with applications in hydrological analysis and water resources management. In order to analyze the error of video discharge measurement， based on the video image method to calculate the cross-section water level and the space-time image velocimetry （STIV） method to calculate the cross-section flow velocity， this study assumes the error distribution of a single measurement， and deduces the water level and flow velocity measurement errors， flow measurement errors and their primary sources. Also， the error average bandwidth （EAB） is defined to quantitatively evaluate the size of the error. Taking the discharge measurement in Yangzigou watershed of Luanchuan County as a case study， the results indicate that the range of the discharge measurement error is small during the stage of the rapid rises in water level or flow velocity. The error average bandwidth （EAB） is 4.98%， 10.83% and 16.08% if there are only the water level error， only the velocity error， and the total errors of the water level and velocity， respectively. The contribution of velocity measurement error for discharge measurement is greater than that of water level measurement error. The video discharge measurement that considers the effects of the error distributions of water level and velocity is beneficial for improving the accuracy of discharge measurement and providing reliable data for its application in hydrological frequency analysis and water resource management， particularly when the need arises for monitoring and responding to dynamically changing hydrological conditions. Additionally， a more profound understanding of measurement errors serves to guide improvements in video-based flow measurement techniques， with the ultimate goal of minimizing errors and enhancing the method’s reliability in practical applications.

Understanding the characteristics of extreme climate change is an important part of groundwater resources management. To investigate the impact of extreme climate on groundwater resources， this paper takes the Dagu River groundwater resources area as an example and based on the meteorological data for over 40 years， 14 extreme climate indices are selected to analyze the trend， mutation and periodicity of meteorological changes in the study area by using methods such as linear trend method， Mann-Kendall mutation test and wavelet analysis. And based on extreme climate index and hydrological frequency analysis， the representative extreme climate years of the research area are determined， and the groundwater balance under extreme climate year is estimated by using water balance analysis method. Meanwhile， the influence of extreme climate index on groundwater depth is evaluated by correlation coefficient and periodicity analysis. The results are listed as follows： ① From 1979 to 2021， the extreme climate indexes representing high temperature and precipitation show an increasing trend， while the extreme climate indexes representing low temperature show a decreasing trend. The representative years of extreme wet years are 2007 and 2020， and the representative years of extreme dry years are 1981 and 1986. ② The extreme climate indexes mutate in the 1990s and the early 21st century， and then the trend of high temperature becomes more significant. ③The mean period corresponding to the first main period of the extreme temperature index have three main variation ranges： 20~24， 12~14 and 5~6 a. The average cycles of most extreme rainfall indices are 11~16 a. ④ Extreme precipitation index inhibits the increase in groundwater level depth. Extreme precipitation （R95p） and simple daily precipitation intensity index （SDII） are the main precipitation indexes affecting the groundwater level depth in Dagu River water source area. The period corresponding to the first main cycle of the groundwater level depth is 13~15 a， which is basically consistent with the cycle characteristics of the extreme precipitation index. This means that the groundwater level depth is significantly affected by the extreme precipitation index. ⑤ In the extreme wet year （2007）， the total groundwater recharge and discharge are about 319.4 mm and 204.8 mm， respectively， with an equilibrium difference of about 114.6 mm. In the extreme dry year （1981）， the total groundwater recharge and discharge are about 104.2 mm and 141.1 mm， respectively， with an equilibrium difference of about -36.9 mm.

To study the rainfall runoff characteristics of green roofs in the northern semi humid area， this papar explores the impact of structural parameter changes on their rainwater storage effect， and provide data support and guidance for the green New Infrastructure of Sponge city. Based on measured rainfall runoff， we established comparative experiments on three types of roofs： green roofs （gradient of 0°）， ordinary roofs （gradient of 0°）， and sloping roofs （gradient of 5°）， as well as five sets of green roof experiments based on adjustment of matrix layer thickness， matrix layer， drainage layer， and drainage method structural parameters. Research shows that during the monitoring period， the average runoff coefficients of comprehensive rainfall on green roofs， ordinary roofs， and sloping roofs were 0.27， 0.50， and 0.77， respectively. The reduction effect of rainfall runoff on green roofs was significant. As the intensity of rainfall increases， the probability of green roof runoff generation gradually increases， and the total runoff control rate gradually decreases. Green roofs have a significant effect on the storage of rainfall at light to heavy rain levels. During the monitoring period， the runoff coefficient of green roof rainfall is significantly positively correlated with the total rainfall， but weakly negatively correlated with the number of days between previous rainfall intervals and previous rainfall； The green roof significantly reduces the efficiency of rainfall runoff control， corresponding to a critical value of 30 mm of rainfall intensity. The significant factors affecting the rainwater storage effect of green roofs in descending order are： substrate layer （ultra light substrate layer）， drainage method （5 cm drainage from the bottom）， substrate layer thickness （20 cm）， and drainage layer （ceramic layer）. The rainfall runoff characteristics and rainwater storage effect of green roofs are influenced by multiple internal and external factors. Based on long-term rainfall monitoring periods， further research should be conducted on the ecological effects of sponge cities with different structures and scales of green roofs.

Against the backdrop of global warming， the frequency， intensity and range of compound dry-hot events， that is drought and extreme high temperature event occurring simultaneously are increasing and causing serious natural disasters and socio-economic losses. However， in a region with complex climate and terrain conditions like China， the evolutionary characteristics of compound dry-hot events are still unclear， and the potential driving mechanisms require further study. In order to understand and deal with these events， the spatiotemporal variation of summer compound dry-hot events in China from 1961 to 2015 are studied based on the Standardized Dry and Hot Index （SDHI）. The frequency and intensity changes of compound dry-hot events in different regions are compared and analyzed. Additionally， the relationship between these events and large-scale climate modes are explored by wavelet coherence， multiple linear regression and other correlation analysis method. The results show that： The frequency and intensity of the summer compound dry-hot events in China have increased， and the increase of temperature leads to the increase of the severity of compound dry-hot events； The El Ni?o-Southern Oscillation （ENSO） and Atlantic Multidecadal Oscillation （AMO） are negatively correlated with SDHI， while the Pacific Decadal Oscillation （PDO） and North Atlantic Oscillation （NAO） are positively correlated with SDHI； The AMO has a significant influence on compound dry-hot events in the northeastern， southern， and southwestern regions， whereas the NAO primarily impacts compound dry-hot events in the northwestern and northern regions； Except for East and Central China， the synergistic contribution of the four climate modes to the compound dry-hot events was more than 15%. The findings of this study offer valuable insights and scientific basis for addressing climate change， enhancing disaster resilience， and mitigating risk losses.

Clarifying the pollution status and quantitatively identifying the pollution sources are an important foundation for effective control of surface water pollution. This paper proposes a data preprocessing method by comprehensively uses mathematical statistics， GIS spatial analysis， and principal component analysis-absolute principal component score-multiple linear regression （PCA-APCS-MLR） model and other methods. Using this data preprocessing method， a quantitative analysis of pollution sources and their contribution rates was conducted on 23 key water quality indicators at 49 sampling points in the surface water system of Yan'an City from 2018 to 2020. The results indicate： ① The main sources of surface water pollution are nutrient pollution （54.02%） and heavy metal pollution （17.97%）. ② The average contribution rate of nutrient pollution to various water quality indicators （52.70%） is greater than that of heavy metal pollution （47.30%）. ③ Nutrient pollution has a greater impact on the northeastern part of Yan'an City， and heavy metal pollution has a greater impact on the upper reaches of Yan River and Xiuyan River. Compared with the traditional data preprocessing method， the analysis results of the data preprocessing method proposed in this paper are more regionally representative which can provide a data support for the treatment of surface water pollution in Yan′an City and a scientific reference for the designation of surface water quality protection policies.

To tackle the problem that the amount of hydropower unit condition monitoring data is gradually increasing and the data quality is poor， this paper proposes a cleaning method of hydropower unit condition monitoring data based on improved K-Dimensional Tree （ KD-Tree ） and Density-Based Spatial Clustering of Applications with Noise （ DBSCAN ）. Firstly， KD-Tree is established for the input data. Then DBSCAN is used to scan the nearest neighbor samples to complete the clustering. After the clustering is completed， the noise points will be separated， and the noise points will be removed to complete the cleaning of the condition monitoring data of the hydropower unit. 1 088 data of swing of upper guide on the condition monitoring system of a hydropower station are selected， and 100 random data are inserted at the same time interval. The clustering performance and time performance are compared with the conventional DBScan， K-means and OCSVM algorithms. The method proposed in this paper has the highest recognition accuracy of 97.78%， and the least consumption time of 0.007 732 s. It has the best data cleaning effect， and can significantly reduce the calculation time.

Accurate groundwater modeling is essential for the scientific management and decision-making of groundwater resources， as it involves hydraulic conductivity， a key hydrogeological parameter. To fully understand and effectively utilize groundwater， we not only need to accurately estimate the spatial distribution of hydraulic conductivity but also need to quantify the uncertainty of the parameter to evaluate its credibility. In this study， parameter inversion and uncertainty analysis of hydraulic conductivity were investigated using the Bayesian Convolutional Neural Network （BCNN）. To test the validity of the method， a synthetic numerical experiment of a two-dimensional steady-state hydraulic tomography pumping test was conducted. The baseline model is a convolutional neural network with an encoder-decoder structure， which builds an inverse mapping that estimates the parameter field directly from the head fields obtained by spatial interpolation. Based on this deterministic model， we trained the Bayesian Convolutional Neural Network. The results show that the BCNN outperforms the deterministic model in accuracy under various training data sizes， with a more significant advantage when the data is scarce. By analyzing the test set samples， we observe that the models exhibit different levels of confidence for their estimates across different regions. A well-trained BCNN can faithfully capture the approximate pattern of the hydraulic conductivity distribution. Moreover， the BCNN also excels in estimating the more challenging multimodal non-Gaussian logarithmic hydraulic conductivity field compared to the generative model， which indicates the wide applicability of the BCNN under diverse geological media conditions. The use of Bayesian Convolutional Neural Networks enables accurate inversion of hydraulic conductivity and evaluating uncertainty， providing a solid basis for subsequent physical processes such as groundwater flow simulation.

Based on fractal geometry， the description of soil pore size distribution is established， and a suction model of unsaturated soil matrix considering the fractal characteristic of pores is proposed in combination with Young-Laplace theory. For the loess in Shaanxi region， the soil-water characteristic curves of different dry densities were collected， and the effects of dry density on pore distribution characteristics and matrix suction were analyzed. The results show that the loess pores have obvious fractal properties， which can be well described by the Menger Sponge fractal model. The pore fractal model parameter analysis shows that with the increase of dry density， the fractal dimension D increases approximately linearly， while the maximum aperture L decreases as a power function. Through function fitting， the characteristic relationship between loess pore size distribution and dry density is further established， and the suction capacity of loess matrix suction under different dry densities is quickly predicted.

Soil water content is one of the important factors affecting the growth of crops， and plays an important role in crop yield estimation and drought monitoring. In soil water content calculation， multiple spectral variables are generally extracted for inversion， but the spectral information contained between the variables may have redundancy and overlap. In order to extract effective feature variables and make them independent of each other， the thesis selects the feature variable screening method and verifies the applicability in soil water content inversion. Based on the UAV multispectral images， 12 types of vegetation indices such as Normalized Difference Vegetation Index （NDVI） are calculated， combined with UAV thermal infrared（TIR）data to calculate the Land Surface Temperature （LST） and the corresponding Temperature Vegetation Dryness Index （TVDI）， as well as four backscattering coefficients obtained from miniSAR data processing. XGBoost feature variables and the Best Subset Selection （BSS） algorithm were used to screen the optimal variable combinations， and then Partial Least Squares Regression （PLSR） and Random Forest Regression （RFR） algorithm was used to invert， the soil water content at the tasseling stage of winter wheat in the experimental area. The research results show that： ① The inversion results of 0~20 cm depth are better than those of 0~10 cm depth； ②Comparing the four soil moisture inversion models of XGBoost-PLSR， XGBoost-RFR， BSS-PLSR and BSS-RFR， the inversion accuracy of the RFR model at different depths is the highest； ③The inversion accuracy of the XGBoost-PLSR model is better than that of XGBoost-RFR at a soil depth of 0~10 cm， but the inversion accuracy is the opposite at a depth of 0~20 cm， where the inversion accuracy of the BSS-RFR model is higher than that of BSS-PLSR. The research results can provide theoretical and technical support for UAV multispectral remote sensing inversion of soil water content， and provide test basis for satellite remote sensing large-scale soil moisture monitoring.

Urban surface pollution is the second largest non-point water pollution following the agricultural surface pollution and an important cause of the deterioration of urban water ecosystems. With the rapid urbanization in recent years， the increasing area of urban surface has led to an increase in flood flow and surface runoff， thus bringing more pollutants from the city into the urban pipe network and causing a more serious pollution to urban water bodies. In this paper， the pollution level of rainfall runoff， the change characteristics of runoff water quality and the initial scouring effect under different underlay conditions are investigated by using the monitoring data of four typical urban underlying surfaces， namely， asphalt roofs， brick pavements， linoleum roofs and green spaces. The results show that： ① the quality concentration of total nitrogen in rainfall runoff from the four types of underlying surfaces is inferior to Class V water； the degree of total phosphorus pollution of the four types of underlay is relatively light， and the frequency of rainfall runoff total phosphorus quality concentration of asphalt roofing， brick pavements， linoleum roofing and green spaces that is better than Class IV water is 80%， 16%， 88.46% and 5.56%， respectively. The distribution ranges of rainfall runoff mass concentrations of total suspended solids for asphalt roofing， brick pavements， linoleum roofing and green spaces are 7~798， 15~569， 10~871 and 44~378 mg/L， respectively. ② The runoff pollution levels of total nitrogen and total phosphorus are ranked as follows： brick pavements > green spaces > asphalt roofing > linoleum roofing； and the runoff pollution levels of total suspended solids were ranked as follows： green spaces > brick pavements > linoleum roofing > asphalt roofing. ③ The trends of the different pollution indicators varied greatly among the underlying surfaces under the medium rainfall scenario， while the concentration of the pollution indicators is higher in the underlying surfaces that are greatly affected by anthropogenic activities under the light rainfall scenario. ④ The initial flushing intensity of pollutants on each underlay is ranked as follows： total suspended solids （TSS） > total phosphorus （TP） > total nitrogen （TN）； and the initial flushing intensity of each underlay is ranked as follows： asphalt roofing > linoleum roofing > brick pavements >green spaces. ⑤ Total suspended solids and total phosphorus in rainfall runoff from asphalt and linoleum roofs has good homology， while total phosphorus in rainfall runoff from brick pavements has good homology with both total nitrogen and total suspended solids. In summary， the average field rainfall runoff concentrations of total nitrogen and total suspended solids in the various underlying surfaces far exceed the water quality standard for surface water Class V， which would pollute urban water bodies， and corresponding measures can be taken to reduce the pollutant concentrations according to the characteristics of different underlying surfaces.

This paper reviews the technical background and development progress of digital twin technology， and points out that digital irrigation district is a fundamental part for the digital twin irrigation district establishment. Modernized digital irrigation district is an important stage of development to promote the construction of digital twin irrigation district. Based on a functional understanding of the mathematical model and within the framework of digital twin irrigation district， it is firstly proposed to divide the mathematical model into three levels， namely basic platform model， targeted application model and friendly interaction model. Based on the definition of digital twin irrigation district， it also analyzes that the digital twin model has the essential features with real-time dynamics and closed-loop feedback. There exsists a process of synchronous simulation， virtual-real interaction and iterative optimization between physical irrigation district and twin irrigation district. They influence and promote each other throughout the life cycle of the irrigation district. The twin irrigation district not only changes in real-time with the change of physical irrigation district， but also the analysis and decisions based on the twin irrigation district will feed back to the physical irrigation district in real-time which results the response of the physical irrigation district. Therefore， in practice， problems should be identified based on the real needs of physical irrigation modernization， and the principles of real need， water-saving efficiency and sustainability should be followed. Digital twin physical object of irrigation district should be accurately selected and focused， mathematical models need to be optimized and innovated. Key advanced technologies and properly major application should be developed and practiced. Network security and data safety should be strengthened. It is expected to establish a digital twin irrigation district more orderly， accurately and efficiently.

In order to study the internal flow law of centrifugal pump during cavitation process and optimize its cavitation performance， this paper proposes an intelligent optimization method combining neural network and genetic algorithm on the basis of combining traditional optimization methods. Through the Plackett Burman experimental design， three optimization design variables are selected from 7 design parameters of the centrifugal pump， including impeller inlet and outlet diameter， inlet and outlet placement angle， number of blades， and blade wrap angle. The significance of the three optimized design variables’ impact on cavitation performance from large to small is ranked as follows：blade outlet width＞blade wrap angle ＞impeller inlet and outlet diameter. The Latin hypercube sampling method is used to extract 30 groups of design schemes， and the corresponding NPSH values are obtained by numerical simulation. The neural network model is established， and the optimal design variable combination and the optimal NPSH value are obtained by combining the genetic algorithm to optimize within the specified range. By taking the optimized parameters for numerical simulation calculation， the NPSH of the optimized centrifugal pump decreases by 43.1% under the same working conditions， indicating that the anti-cavitation performance of the optimized centrifugal pump was significantly improved.

Environmental DNA （eDNA） technology is a key tool for river health assessment. It shows great application potential in the biological analysis of aquatic ecosystems. However， the relationship between eDNA and sediments that are widely present in water bodies is complex， and eDNA is also affected by water flow， which seriously restricted the promotion and application of eDNA technology. Therefore， it is necessary to investigate the influence of sediment and water flow on the degradation of eDNA. This study took Ctengodon Idella， an important freshwater cultured fish in China， as the research object to explore the influence of water flow and sediment on the persistence of eDNA of grass carp. The results show that： ① In flowing water bodies， the degradation rate of the eDNA accelerates with the increase of flow rate. ② The presence of sediment accelerates the degradation rate of eDNA in water. Compared with the thickness of sediment laying， the sediment partical size has a greater impact on eDNA degradation. ③ Notably， in still water， the eDNA degradation rate decreases with the increase of sediment partical size. While in flowing water， water interference leads to the opposite eDNA degradation pattern， that is， the degradation rate of eDNA increases with the increase of sediment partical size. The study emphasizes that the impact of the sediment response method is critical in planning or interpreting eDNA studies and provides a valuable reference for the application of eDNA technology in aquatic ecosystems.

To improve the accuracy of multi-step prediction of daily runoff， reduce the computational scale of the model， and enhance the performance of the Coati Optimization Algorithm （COA） and Hybrid Kernel Extreme Learning Machine （HKELM）， a Multi Pole Wavelet Packet Transform （MWPT） - Improved COA（ICOA） algorithm - HKELM daily runoff time series prediction model is proposed. Firstly， using MWPT， the daily runoff time series data is decomposed into 1 low-frequency component and 2 high-frequency components， and a HKELM is constructed by combining local Gaussian radial basis function kernel and global polynomial kernel function； Secondly， the principle of COA algorithm is briefly introduced， and by improving COA based on strategies such as Circle mapping， we propose the ICOA algorithm. The ICOA algorithm is simulated and verified through 8 typical functions， and is compared with the basic COA algorithm， Whale Optimization Algorithm （WOA）， and Grey Wolf Optimization Algorithm （GWO） to verify the optimization performance of the ICOA algorithm； Finally， using ICOA to optimize HKELM hyperparameters （regularization parameters， kernel parameters， weight coefficients）， a MWPT-ICOA-HKELM model is established， and MWPT-COA-HKELM， MWPT-WOA-HKELM， MWPT-GWO-HKELM， Wavelet Packet Transform （WPT） - ICOA-HKELM， Wavelet Transform （WT） - ICOA-HKELM， and MWPT-ICOA-BP models are compared and analyzed. The models are validated through multi-step prediction examples of daily runoff time series from Jingdong and Baobian hydrological stations in Yunnan Province from 2016 to 2020. The results show that： ① ICOA has a good improvement effect， and the simulation accuracy is better than COA， WOA， and GWO algorithms. ② The MWPT-ICOA-HKELM model has better prediction performance than other comparative models， with the best single step prediction performance for instances， better results with 3 and 5 steps ahead， and worse results with 7 steps ahead. The prediction accuracy decreases with the increase of prediction step size. ③ Optimizing HKELM hyperparameters using ICOA can significantly improve HKELM prediction performance， and the hyperparameter optimization effect is better than COA， WOA， and GWO algorithms.

In response to the growing demand of green development in beautiful rural construction， this paper proposes a scheme of hydro-photovoltaic-storage complementary ultra micro power generation lighting system using siphon turbine and photovoltaic panel as power generation devices and battery energy storage. The siphon turbine uses steady-state numerical simulation to calculate output and efficiency， combined with water level data to calculate power generation， photovoltaic panel area method combined with solar radiation data to estimate monthly power generation， and calculates the electricity provided to night lighting and power grid. The construction costs， economic benefits and emission reduction benefits of the system are analyzed， and the results show that the system has practical popularization prospect and emission reduction benefits.

Flood disaster is one of the main natural disasters in Hubei province. Conducting research on the resilience of urban flood disasters in Hubei Province can accelerate the construction of resilient cities in this region and promote the sustainable and healthy development of cities. This paper takes 17 cities in Hubei Province as the research object， selects the period from 2010 to 2021 as the research period， constructs the urban flood disaster resilience evaluation index system from three dimensions， including pressure， state and response， analyzes the spatio-temporal evolution of flood disaster resilience by applying the CRITIC-Entropy Weight Method combination weighting method， and analyzes the spatial agglomeration characteristics of flood disaster resilience by using the spatial autocorrelation method. Then the driving factors are analyzed by the geographic detector model. The results indicate that： First， during the study period， the resilience index of urban flood disasters in Hubei Province showed a wave rising trend， with an increase of 34.82%， Second， The spatial distribution of urban flood disaster resilience is mainly “high in the west and low in the east” pattern， with values decreasing outward from Wuhan City. The spatial agglomeration effect of urban flood disaster resilience is strong. Third， the spatial heterogeneity of urban flood disaster resilience is caused by multiple factors. Wherein， population exposure， terrain fluctuation， surface steepness and medical security capacity being the main driving factor. The research results can provide reference for urban flood control and disaster reduction policies.

Under the goal of “double carbon”， China urgently needs to carry out the transformation of energy structure and increase the proportion of renewable energy in the power system. Regulating hydropower stations and pumped storage power stations have good storage capacity， and can be used as regulating power sources to operate in conjunction with wind and solar power sources， from the front-end slowing down the impact of unstable wind and solar output on the power system， and promoting new energy centralized online consumption. Therefore， making full use of the regulating performance of cascade hydropower stations and pumped storage power stations， and formulating a reasonable integrated scheduling strategy are conducive to the realization of bundled transmission and synergistic consumption of water， wind， and solar storage on the power generation side， which will help realize the goal of “double carbon”. Based on the consideration of the grid-side load demand and the utilization rate of hydropower water resources in the short-term scheduling of the cascade hydro-wind-solar hybrid power generation system， a short-term complementary scheduling model is constructed with the goal of minimizing the difference between the sources and loads， and maximizing the incremental amount of cascade hydropower storage and with the constraints on the safe operation conditions of the conventional hydropower plant， the photovoltaic power plant and the pumped-storage power plant， and the model is solved by using the inner and outer nested algorithms of the PSO-DP coupling. The hydro-wind-photovoltaic-storage complementary power generation system composed of conventional cascade hydropower stations， pumped storage power stations and surrounding wind power and photovoltaic stations in a basin in Southwest China is selected as the research object for simulation. The results show that： ①pumped storage power stations tend to choose water discharge for power generation when the head of the cascade power station is low， and choose to pump water or keep the current water level unchanged when the head of the cascade power station is high； ② the model constructed in this paper effectively improves the matching degree between the total system output curve and the grid load curve， realizes a reasonable distribution of load and water between cascade hydropower stations， reduces the benefit loss of cascade hydropower stations to a certain extent， improves the utilization rate of hydraulic energy， increases the cascade hydropower storage increment， and is conducive to the coordinated operation of the source and grid and the stable operation of cascade power stations.

Although cavitation models are necessary in the study and design of fluid machinery， etc.， researchs on the numerical cavitation models suitable for high altitudes are unavailable. In this paper， firstly， the key input parameters in Zwart-Gerbera-Belamri（ZGB） cavitation model， such as initial radius of bubble and volume fraction of gas nucleus， are analyzed and expressed with the altitudes in order to make the model applicable to high altitudes environments. Secondly， the applicability of the modified ZGB model is verified though the error analysis between the numerical calculated results under different working conditions， and the experimental data in the reference. Finally， numerical calculations for the NACA0009 hydrofoil at 0~5 km altitudes， are carried out by using the modified model. The results show that under the same working conditions， with the increase in altitude， the ambient air pressure decreases， the initial radius of bubble increases， the volume fraction of air nucleus decreases， the maximum pressure coefficient of the pressure surface remains basically unchanged， the minimum pressure coefficient of the suction surface increases slightly， and the cavity length increases significantly. When the altitude is 5 km， the cavity length increases to about 81% of the chord length. The growth rate of cavity length increases with altitude. The growth rate reaches the peak value of 0.17 at 4 km and then starts to decrease.

Ecological water replenishment is an important initiative for the ecological environment management of Baiyangdian， and this paper divides the ecological water replenishment process of Baiyangdian into a temporary water replenishment stage， an emergency water replenishment stage， and a stable water replenishment stage according to the change process of ecological water replenishment mechanism. In order to analyze the spatial and temporal pattern changes of water bodies in Baiyangdian under each ecological recharge stage， this paper extracts water bodies and ice bodies from 701 remote sensing images of Baiyangdian from 1990 to 2022 through the GEE platform， analyzes the spatial and temporal pattern of the water bodies under different stages， as well as specifically analyzes the change of the ice bodies' area in the winter freezing period. The main conclusions are as follows： ① In each ecological recharge stage， the water body area of Baiyangdian is the smallest in summer and the largest in winter and spring， and with the continuous improvement of ecological water replenishment mechanism， Baiyangdian water body area change amplitude is gradually reduced， showing the change rule of “cutting peaks to make up for the low”. The range of monthly average water area in the temporary water replenishment stage is between 0.63~186.03 km²， and that in the stable water replenishment stage is between 17.88~109.15 km²， the magnitude of change reduced by 50.77%； ②The perfection of ecological water replenishment mechanism makes Baiyangdian present the change rule of permanent water body increasing greatly and intermittent water body decreasing greatly， among which the permanent water body area in the temporary water replenishment stage is 7.66 km²， while the permanent water body area in the stable water replenishment stage is 23.62 km²， which is an increase of 208.16%， and the area of the intermittent water body is reduced from 263.50 km² to 143.58 km²， a decrease of 45.51%； ③ From the perspective of the change of the ice body area， with the continuous improvement of ecological water replenishment mechanism， the area of Baiyangdian's ice body decreased continuously， in which the average area of the ice body in January decreased from 72.77 km² to 12.60 km²， which was reduced by 82.69%. The results show that the improvement of the ecological water replenishment mechanism did not lead to the continuous increase of the water body area of Baiyangdian， but made the water body environment of Baiyangdian more fragmented； under the influence of global warming and other factors， the ice area of Baiyangdian was greatly reduced； this tendency to fragmentation of the characteristics of the watershed may not be conducive to the preservation of biodiversity， but given that ecosystems have a lag in their hydrological response， it is necessary to further strengthen the monitoring and research on the process of ecological and hydrological response.

In the stilling pool of the spillway and flushing tunnel， there are often problems such as unstable fluctuation range of hydraulic jump and even overtopping the side wall. In order to obtain a good energy dissipation and erosion prevention effect of the stilling pool， the fluid movement during the flood discharge process of the spillway and flushing tunnel was studied through a combination of numerical simulation and physical model testing， which was beneficial for gaining hydraulic dynamics of the flow field and the downstream riverbed erosion conditions. The hydraulic characteristics of water flow under different stilling pool body shape optimization schemes were analyzed， and those schemes were verified by physical experiments. The results show that the flow pattern is more stable under adding a rectangular tailgate at the stilling pool， the water surface fluctuation is reduced. At the same time， the maximum decrease of inlet and outlet velocity in the stilling pool is 7.66 m/s. The pressure distribution is uniform， no negative pressure is generated， and the energy dissipation rate reaches 80.60%. After the addition of the rectangular tailgate， the riverbed scour pit range of the design and verification conditions are reduced by 12.20 m and 13.30 m compared with the original scheme， and it’s maximum scour pit depth are reduced by 0.48 m and 1.00 m compared with the original scheme， respectively. The decrease of the riverbed scour pit range and the maximum scour pit depth shows that the addition of rectangular tailgate has obvious protective effect on the downstream. The research shows that the stilling pool body shape optimization scheme can effectively improve its energy dissipation. The flow field distribution of the optimized stilling pool body shape is uniform， which have great protection on the downstream river bed. The research results can provide reference for diversion and flood discharge and sand flushing projects.

The installation of porous orifice plates in pipeline systems can play a role in throttling and reducing pressure， but if the pressure difference between the upstream and downstream of the orifice plate is too large， it is easy to cause cavitation in the downstream of the orifice plate. The occurrence of cavitation can cause pipeline vibration， and have adverse effects on pipeline systems. This article experimentally measures the vibration acceleration， corresponding pressure difference， and flow rate generated by porous plates with different porosity in different cavitation stages. The experimental results indicate that as the degree of cavitation continues to intensify， vibration acceleration in the full frequency range significantly increases， and the peak value of vibration acceleration gradually increases. The vibration acceleration exhibits a broadband characteristic. Based on the experimental results， we plotted the vibration intensity curves of orifice plates with different porosity as a function of cavitation number. By varying the slopes of the fitted curves in the cavitation vibration curve， different cavitation stages and three critical cavitation numbers （including incipient cavitation number， constant cavitation number， and choked cavitation number） can be distinguished. The critical cavitation number of orifice plates with a porosity less than 1.8 rapidly decreases with the increase of porosity. When the porosity is greater than 1.8， the incipient cavitation number and constant cavitation number slow down with the increase of porosity， while the choked cavitation number remains basically unchanged. This means that increasing the porosity appropriately is beneficial for delaying the occurrence of cavitation. Based on the experimental results， we plotted the variation curve of flow resistance coefficient with cavitation number for orifice plates with different porosity. From the cavitation flow resistance curve， it can be seen that： ① When cavitation phenomenon has not yet occurred， the flow resistance coefficients of orifice plates remain basically unchanged. ② When cavitation phenomenon just occurred， the flow resistance coefficient of the orifice plate decreases slightly， but thereafter， as the degree of cavitation intensifies， the flow resistance coefficient begins to rapidly increase. ③ The larger the porosity of the orifice plate， the greater the flow resistance， and the greater the influence of cavitation on the flow resistance. Taking into account the vibration and flow resistance characteristics of the orifice plate， it is recommended that the porosity of the porous orifice should be maintained between 1.8 and 2.0.