Continuous strong human activities have changed the mutual feedback relationship between water resources， economic society and ecological environment， and affected regional sustainable development. In order to explore the coordinated development of water resources， economic society and ecological environment in the Yellow River Basin of Henan Province and identify the drivers of coordinated development， this study improved the traditional coupling coordination degree model based on synergetics and random forest model， and calculated the comprehensive development index and coupling coordination degree of water resources， economic society and ecological environment in the Yellow River Basin of Henan Province from 2011 to 2022. The spatial and temporal evolution characteristics are revealed by kernel density estimation， and the driving factors of the coordinated development are analyzed by geographic detector model. The results showed that during the study period， the comprehensive development index of each system showed a different degree of growth trend， but there was a certain degree of heterogeneity. The water resources， economic society and ecological environment system showed a spatial disequilibrium pattern of “high in the west， low in the east， high in the south and low in the north”， “the center leads the periphery， the edge develops slowly”， “the upstream is superior to the downstream， the southwest is superior to the northeast”. The coordinated development level of water resources， economic society and ecological environment showed a significant positive trend， and the spatial heterogeneity among cities was obvious， showing the evolution characteristics of “regional rise， high in the south and low in the north， high in the west and low in the east”. Natural endowment， economic foundation and human settlement environment are the main driving factors for coordinated development， and the relationship between each factor is becoming increasingly close， and the interaction type shows a trend of transformation to “nonlinear enhancement”. This study innovatively puts forward a new method of coordinated development evaluation， and verifies its scientific nature， which provides a powerful tool for in-depth analysis of the coordinated development of water resources， economic society and ecological environment in the Yellow River Basin of Henan Province， and has certain practical guiding significance for promoting regional sustainable development.

The external water pressure of lining is of great significance to the determination of lining thickness and drainage scheme of water-rich deep buried tunnel. The existing research often ignores the analysis of the influence of macroscopic geological conditions on the seepage field， which leads to the lack of reliability evaluation of the external water pressure estimation results and lining design schemes of deep buried tunnels. Based on the Qinling Water Conveyance Tunnel Project of Hanjiang-to-Weihe River Diversion Project， this paper generalizes the deep-buried tunnel in the water-rich area into three representative macro-geological environment models under the watershed， the mountainside area and the river， and studies the influence law and mechanism of geological conditions and engineering measures on the seepage field of surrounding rock and the external water pressure of lining. The results show that： ① The seepage direction of groundwater around the watershed tunnel is mainly vertical under the condition of the same height of groundwater at the top of the tunnel and impermeable lining. The seepage direction of groundwater around the tunnel under the river is mainly horizontal. The external water pressure reduction coefficient of the watershed tunnel， the tunnel near the mountain area and the tunnel under the river increases in turn. The reduction coefficient of external water pressure of tunnel under watershed increases with the increase of buried depth， which is 0.72，0.77 and 0.84 respectively. The reduction coefficients of the external water pressure of the mountain tunnel and the river tunnel are almost unchanged， which are 0.91， 0.91， 0.93 and 0.95， 0.96， 0.96， respectively. ② After the same drainage measures are taken， with the increase of buried depth， the reduction coefficients of external water pressure of watershed tunnel are 0.15，0.42，0.64 respectively， and the pressure reduction effect is the best. The external water pressure reduction coefficients of the tunnel near the mountain and the tunnel under the river are 0.48，0.67，0.77 and 0.63，0.80，0.83， respectively， and the pressure reduction effect gradually deteriorates. ③ When the buried depth of the tunnel is 1 200 m， the reduction coefficients of external water pressure of the watershed， mountainside and river tunnel are 0.43~0.73， 0.67~0.89 and 0.80~0.95 respectively with the increase of the permeability coefficient of the surrounding rock， which shows the regularity that the external water pressure increases with the increase of the buried depth of the tunnel. The research results are universal， and the rationality of water pressure resistance design of tunnel lining in water-rich area can be evaluated from the regularity of macroscopic hydrogeological conditions of tunnel and reduction coefficient of external water pressure.

In the practical processes at water purification plants， the water quality is complex and variable， and there are instances of missing water quality parameters due to malfunctioning detection instruments， thus bringing challenges to determining the dosage of coagulants. Traditional methods， such as beaker stirring tests or manual control of excessive dosage， can easily bring water quality risks such as excessive metal ions， posing health risks to users and reducing economic efficiency. To address these issues， a predictive model combining Genetic Algorithm optimized Fuzzy C-Means clustering （GA-FCM） and Adaptive Neuro-fuzzy Inference System （ANFIS） was developed. This model leverages the inference capability of fuzzy rules and the adaptive capability of neural networks to accurately predict coagulant dosage under unstable water quality conditions.Pearson correlation coefficient method was used to filter input water quality indicators， enhancing model efficiency and generalization capability. The genetic algorithm was employed to determine the optimal parameters for FCM clustering， improving clustering outcomes and generating more accurate fuzzy rules. A hybrid method combining backpropagation and ridge regression was used to adjust network parameters， addressing nonlinear components while optimizing the weights of the consequent network. A water purification plant in Northeast China was used as a case study. Experimental results indicated that compared to traditional linear regression and machine learning models， GA-FCM-ANFIS exhibited superior predictive performance under unstable water quality conditions. Furthermore， in the absence of certain parameters， GA-FCM-ANFIS achieved significantly higher predictive accuracy than other coupled models. The prediction evaluation metrics R ²， RMSE， MAE， and MAPE under sudden water quality changes were 0.93， 2.79， 2.33， and 3.71%， respectively； under missing parameters， the metrics were 0.89， 3.07， 2.57， and 4.06%， respectively. The proposed model significantly improves prediction accuracy and serves as a reliable method for determining coagulant dosage.

This paper proposes a Transformer-based local timestep two-dimensional hydrodynamic simulation method aimed at optimizing the speed and accuracy of flood simulations to meet the urgent needs of flood prevention and mitigation. By incorporating the self-attention mechanism and sequence prediction capabilities of the Transformer model， combined with local timestep techniques， this method dynamically adjusts the local timestep in the hydrodynamic system. Using the real flood process in the Mengwa flood storage area as an example， the simulation results demonstrate that， compared to remote sensing inundation imagery and traditional numerical methods， this approach shows higher consistency and reliability in the precise prediction of inundation extent and depth， maintaining a relative error within 4%. Compared to traditional global timestep methods， this method significantly reduces computational time while ensuring simulation stability and accuracy， achieving a 70.27% improvement in computational efficiency at a 150 m grid resolution.

Clarifying the specific strategies for enhancing the resilience of the “water-energy-grain-carbon” system in the context of high-quality development in the Yellow River Basin is of great value for achieving ecological protection and high-quality development strategies in the Yellow River Basin. By comprehensively utilizing social network models， interpretive structural models， entropy weight methods based on game theory， and obstacle degree models， this study explores the action path of influencing factors on the resilience of the “water-energy-grain-carbon” system in the Yellow River Basin. Based on the analysis of the spatial pattern of resilience in the “water-energy-grain-carbon” system in the Yellow River Basin in 2021， the key obstacles to system resilience are diagnosed， and corresponding resilience improvement strategies are proposed by combining the factor pathways and causal attributes of the influencing factors. The research has found that： ① The main obstacles to the resilience of the “water-energy-grain-carbon” system in the Yellow River Basin are the total power of agricultural machinery， the total capacity of built reservoirs， per capita water resources， recycled water production capacity， per capita energy production， and comprehensive groundwater production capacity. Except for per capita water resources， other factors exist as causal factors in the system. The five main obstacle factors belong to the bottom and middle layers in the explanatory structural model， which play a driving role in the overall resilience level of the “water-energy-grain-carbon” system； ② During the research period， the water resource subsystem had the highest degree of obstacles， and most of the obstacles that hindered the resilience development of the “water-energy-grain-carbon” system in the Yellow River Basin belonged to the water resource subsystem. The obstacle level of the water resource subsystem in the middle reaches was higher than that in the upstream and downstream areas， and the obstacle level of the energy and food subsystems in the upstream was greater than that in the middle and downstream areas. The difference in the degree of carbon subsystem barriers is relatively small， and the rate in the middle reaches is higher than the average level.

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.

With the economic and social development of the Han River basin， the pressure on the water environment and water ecology has increased， and the implementation of the water diversion project from the Yangtze river to the Han River may lead to further changes in the quality and quantity of water in the Han River. Therefore， it is of great significance to carry out simulation analysis and prediction of water quality and water quantity in the middle and lower reaches of the Han River before and after the water diversion project， in order to control the pollution of water bodies in the basin. In this paper， we predicted the water quantity and quality response of Huangjiagang-Wangfuzhou section after the diversion of water by using the segmented Muskingum method and one-dimensional and two-dimensional water quality models. The results show that： ① After the river diversion project adopts the year-round uninterrupted water diversion method to divert water， the flow of the Huangjiagang-Wangfuzhou river section increases， and the increase of flow in the summer flood is higher than that in the fall flood， and the increase of flow in the Wangfuzhou section is slightly higher than that in the Shenwan section； ② Before and after the water transfer， the average monthly total phosphorus concentration in Shenwan section reached the water quality standard of Class II， and the average monthly total nitrogen concentration was within the standard of Class IV-V. The growth rate of various water quality indicators under Scenario 2 was 2-3 times that of Scenario 1. Compared with before the water transfer， the total phosphorus concentration in Scenario 1 increased by a maximum of 4.68%， and the total phosphorus concentration in Scenario 2 increased by a maximum of 13.45%. This was followed by total nitrogen， with a maximum increase of 1.15% for scenario 1 and 3.44% for scenario 2； ③ Monthly total phosphorus concentration in the flood season at Wangfuzhou section before and after the water transfer reached the water quality standard of Class II， and the total phosphorus concentration increased in dry water years after the water diversion while the concentration decreased slightly in abundant water years. Overall， the variation of water quality in dry water years is greater than that in abundant water years， and the variation of each water quality indicator is not much different in the two scenarios.

In order to explore the drag reduction characteristics of different jet parameters in the bulb through pump， the bionic design was carried out for the shark gills， and the model of the bulb through the pump with a jet port at the front end of the bulb body was established， and the influence mechanism of the placement position， jet velocity and number of jet ports on the flow field of the bulb through the pump was studied by CFX. The results show that the near-wall main stream collides with the jet， and the axial momentum transmitted by the main stream to the jet weakens the axial velocity of the near-wall main stream. The radial momentum of the jet to the main stream increases the radial velocity of the near-wall mainstream， and lifts the near-wall high-velocity zone away from the bulb body， so that the near-wall medium and low velocity bands are widened， the high-velocity zone is contracted， and the wall shear stress is reduced. The calculation predicts that the drag reduction effect is up to 23.069%， the head is increased by 0.083 m， and the efficiency is increased by 1.729%， which can provide guidance for the optimal design and efficient operation of the bulb through-flow pump.

Based on one-dimensional water hammer theory and combined with an engineering example， the selection of the flow characteristics of the end control valve of gravity flow system with wide-range flow variation is numerically studied from three aspects： the influence of flow characteristics of control valve on its steady-state opening-degree， the determination of the most unfavorable flow conditions of valve-closing water hammer， and the influence of flow characteristics of contrive valve on valve-closing water hammer. And the flow characteristics of linear type， parabola type and equal percentage type are compared. The results show that： ① The equal percentage flow characteristic is more conducive to the stable operation of the control valve under the low flow condition. Under this condition， the steady-state opening-degree of the equal percentage type control valve is larger， which can avoid cavitation and vibration caused by too small opening. ② The most unfavorable flow condition of the valve-closing water hammer should consider the minimum initial flow condition， under which the water hammer pressure of the input point of the control valve is largest because of the largest initial pressure and the shortest closing time. ③ The equal percentage flow characteristic is more conducive to reducing the control valve-closing water hammer pressure. Under the same initial flow rate and valve closing rate， the control valve with equal percentage type flow characteristics has a larger initial opening-degree， a longer actual closing time， and a smaller change gradient of flow coefficient in the small opening-degree range， so the extreme value of the valve-closing water hammer pressure is smaller， which is more conducive to pipeline safety.

Reclaimed water reuse is an important way to solve the water shortage， which is widely used in industrial production and urban greening. However， it contains a large number of conditionally pathogenic bacteria， which pose a potential threat to the environment and human health.. The study proposes to use Granular Activated Carbon （GAC）， Biological Activated Carbon （BAC） and Nanofiltration （NF） as a combination of filtration units for secondary effluent from wastewater plants to achieve the purpose of removing three types of conditionally pathogenic bacteria （Legionella， Pseudomonas aeruginosa， and Pseudomonas aeruginosa）， Escherichia coli and organic matter in water. The removal effect was analysed using three treatment processes， namely direct NF， GAC-NF and BAC-NF. The results showed that under the conditions of 5 cm/h filtration rate for GAC filtration and 0.4 MPa filtration pressure for NF， both direct NF and its combination process could achieve the complete removal of conditionally pathogenic bacteria from secondary effluent water； compared with direct NF and GAC-NF， BAC-NF process has the highest removal rate of DOC， UV254， microbial metabolic by-products and humic acid organic matter in water， which are 85.2%， 74.5%， 76.2% and 82.0%， respectively； there is a significant correlation between the conditionally pathogenic bacteria in membrane effluent of different combination processes with E. coli， DOC. By strengthening the removal of E. coli and organic matter in the water， we can achieve the purpose of improving the reduction of conditionally pathogenic bacteria by different combination processes； in the biofilm on the surface of BAC， Pseudomonas aeruginosa， Aeromonas aeruginosa， Nitrospiraea and Fusobacterium sp. in the bacterial under the gate level accounted for the highest proportion， and the above bacteria played an inhibitory role in the growth and reproduction of conditionally pathogenic bacteria. The highest percentage of biofilm eukaryotic microorganisms were nematodes and rotifers， which were able to achieve the reduction of conditionally pathogenic bacteria in water by trapping.

Hebei is a major agricultural province in China， and has always been an agricultural drought prone area， which is caused by the serious shortage of regional water resources on the one hand， and the waste of water resources caused by extensive irrigation on the other hand. The key to solve the above problems is to improve the efficiency of irrigation water. To do this work well， it is very important to fully grasp the actual planting structure and irrigation information of crops. In this paper， Qiu County of Handan City was selected as the study area. Based on Landsat 8， GF-1 image data and UAV data， combined with the phenological characteristics of winter wheat， the winter wheat growing season time series data set was constructed， and winter wheat information was extracted by Support Vector Machine（SVM） classification method. On this basis， the land surface temperature （LST）， vegetation water supply index （VSWI） and temperature vegetation drought index （TVDI） were retrieved according to the changes of remote sensing index before and after irrigation， and the regional crop irrigation information extraction model experiment was carried out. The results show that： ① The SVM classification method of remote sensing image time series data has high information extraction accuracy， and the Kappa coefficient is 0.92； ② The comparison of the results of LST， VSWI and TVDI irrigated area extraction in Landsat 8 images shows that the three indexes accounted for more than 60% of the winter wheat area， which has a good consistency. The investigation confirms that the inversion of irrigated area based on VSWI is the best. ③ The superposition analysis of the extraction results of the three indexes obtains an area of 128.357 km²， in which the overlap rate between VSWI index and this area reaches 88.48%. The above research methods can accurately identify the planting area and irrigation information of winter wheat， which can be used as theoretical support and decision-making reference for regional water resource scheduling， planting structure adjustment， and drought prevention and control.

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.

Soil moisture is a crucial parameter for the exchange of matter and energy at the land-atmosphere interface. Timely and accurate acquisition of soil moisture information is of paramount importance for drought monitoring， water resource management， and crop yield estimation. In this study， utilizing Sentinel-1 SAR remote sensing data and Sentinel-2 optical remote sensing data， the relationship between various optical vegetation indices and measured vegetation water content was systematically analyzed. The Fusion Vegetation Index （FVI） was preferentially selected to establish vegetation water content estimation model，which was combined with the vegetation microwave scattering model—Water Cloud Model （WCM） to correct the impact of vegetation layer on SAR backscattering signals. On this basis， a surface microwave scattering model—Oh model was used to construct the backscattering coefficient simulation database， and soil moisture retrieval for the summer maize-covered surface under both VV and VH polarizations was achieved through the application of the Look-Up Table （LUT） algorithm. The results indicate that， for surfaces covered by dense vegetation like summer maize， vegetation water content characteristics can be better reflected by FVI， enabling the accurate correction of the impact of vegetation layers on SAR backscattering coefficients. The vegetation water content inversion model based on FVI achieved an R ² of 0.693 and an RMSE of 0.303 kg/m². After vegetation correction， the correlation between soil moisture and SAR backscattering coefficients increased by 21.6% and 27.9% for VV and VH polarizations， respectively. Compared to VH polarization， VV polarization is found to be more suitable for soil moisture retrieval， with an R ² of 0.672 and an RMSE of 0.048 m³/m³ between retrieved and measured soil moisture values. The findings of this study provide robust support for the remote sensing observation of soil moisture information in densely vegetated surfaces.

This study has carried out the three-point bending test with the digital image correlation technology （DIC） to explore the mechanism of size effect on the tensile properties of clay， and the tensile crack failure process of compacted clay beam under the condition of geometric similarity and non-geometric similarity （the change of sample height h， thickness b） is analyzed from macro and micro perspective. The results showed that： The increasing of the size of geometrically similar and non-geometrically similar specimens prompted the increasing of the peak load of the specimens. The tensile strength σ _t of soil is significantly affected by the size effect and σ _t of geometrically similar soil beam linear decreases with the increase of sample size. When only increasing the soil beam h， σ _t increases at first and then decreases. When the soil beam b is changed， σ _t decreases first and then increases with the increase of b， in which the change of b has less influence on σ _t. By introducing the meso-evaluation index： Local strain degree ε _a， it is concluded that the ε _a of geometrically similar specimens increased with the increase of size， while the ε _a of non-geometrically similar samples increased at first and then decreased with the increasing of h or b.

Under the background of “dual carbon”， in order to promote the intensive operation and maintenance management of small hydropower and ensure its high-quality development， this paper constructs a tripartite game model among small hydropower owners， governments and operation and maintenance service enterprises， based on the evolutionary game theory. And Matlab software are used for numerical simulation analysis to explore the evolutionary path of tripartite strategy selection for intensive operation and maintenance management of small hydropower. The results show that： the change of initial strategy has significant influence on the convergence speed of the tripartite evolution； under certain conditions， the game system will evolve stably in an ideal equilibrium state （1，0，1）； The main factors affecting the implementation of intensive operation and maintenance management of small hydropower are cost efficiency， government reward and punishment， intelligence degree of operation and maintenance platform and scheme charge coefficient. The research conclusions can provide certain theoretical guidance for small hydropower owners to participate in intensive operation and maintenance management and for the government to formulate relevant policies.

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.

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.

Prestressed concrete cylinder pipe （ PCCP ） plays an important role in China 's water diversion projects. The safety of PCCP operation is very important to the allocation of water resources and is related to the national economy and people 's livelihood. When harmful cracks occur on the inner surface of the core concrete， the inner core concrete will be damaged under the action of water pressure， resulting in the corrosion of the steel cylinder and the formation of safety hazards. Therefore， research on the prevention and control of concrete cracks on the inner wall of the core is of great significance to the rational utilization of water resources. In order to prevent and reduce harmful cracks on the inner wall of PCCP， according to the research results of scholars at home and abroad， the types and forms of concrete cracks on the inner wall of PCCP core are summarized， including longitudinal cracks， spiral cracks and socket cracks. The relationship between factors such as drying shrinkage， settlement shrinkage， temperature shrinkage， welding seam， winding stress， hoisting and transportation， and concrete cracks on the inner wall of PCCP is summarized. The prevention and control methods of cracks in the inner wall of PCCP core are summarized from the aspects of PCCP design， raw materials， mix ratio， maintenance system， weld， steel cylinder， socket and crack self-healing， fiber reinforced materials and so on. Finally， the control and prevention of cracks in the inner wall of PCCP core concrete are prospected， which provides ideas for further research.

This study aims to explore the feasibility of estimating the content of water-soluble soil ions by combining multispectral remote sensing technology with soil physicochemical properties. The research area is situated in the saline soil regions of southern Xinjiang， where the concentrations of major water-soluble cations and anions （K⁺、Na⁺、Ca²⁺、Mg²⁺、HCO₃ ^-、Cl^-、SO₄ ^2-） were measured. Machine learning algorithms such as Random Forest （RF）， Gradient Boosting Regression （GBR）， and Extreme Gradient Boosting （XGBoost） were employed to construct soil ion content inversion models based on remote sensing spectral features and soil information. Additionally， the study compared the estimation accuracy of models incorporating soil variables with those that did not. Results indicate that when only multispectral remote sensing data were used as input variables， all three models could only differentiate between high and low levels of soil ion content， with limited ability to accurately estimate the concentrations of individual ions. Incorporation of soil variables into the models significantly enhanced estimation accuracy. Among the methods used， the RF model exhibited the highest prediction accuracy， followed by XGBoost， and GBR had the lowest accuracy. Regarding the estimation of specific ions， the concentrations of Mg²⁺， Ca²⁺， and Na⁺ were predicted with relatively high precision and model performance was stable； SO₄ ^2-， Cl^-， and K⁺ showed moderate performance with quantitative prediction capabilities； whereas HCO₃ ^- content estimation was only feasible to a certain extent with the GBR model. Optimal models varied for different ions， with the RF model providing the best inversion results for K⁺， Mg²⁺， and Cl^-； the XGBoost model excelling in the inversion of Ca²⁺， Na⁺， and SO₄ ^2-； and the GBR model performing well for HCO₃ ^- inversion. Notably， the optimal relative analysis errors for Mg²⁺， Ca²⁺， and Na⁺ content estimation were 2.829， 1.951， and 1.870， respectively， indicating that these models are highly reliable for estimating the concentrations of these ions. The findings of this study provide a scientific reference for the regional-scale estimation of major ion concentrations in soil salinity within arid regions.

Dyes are widely used in production and daily life， leading to frequent dye pollution in the environment， especially in water bodies， which poses potential risks to ecosystem and human health. Biochar has a wide range of sources and meets the requirements of “dual carbon”， and is widely used in environmental pollution control. However， due to its poor adsorption capacity and lack of selectivity， modification is the most commonly used method to improve its sorption capacity. In order to improve the adsorption of crystal violet （CV） by biochar， magnetic biochar modified with cobalt-zinc ferrite （Co_0.5Zn_0.5Fe₂O₄@BC） was prepared from orange peel by coprecipitation-pyrolysis method. X-ray diffraction （XRD）， scanning electron microscope （SEM）， Fourier transform infrared spectroscopy （FT-IR）， specific surface area and pore size analyzer （BET） and Zeta potentiometer were used to characterize the biochar before and after modification. The experimental results showed that Co_0.5Zn_0.5Fe₂O₄@BC produced mesoporous structure， which significantly increased the specific surface area of the material. Moreover， the surface positive charge of the material is increased， and the pH adaptability of the material is improved. Thus， it promoted the adsorption of CV by biochar. The effects of different experimental conditions on the adsorption of CV by modified biochar were investigated through batch experiments. The adsorption kinetics and isotherm were fitted using different models. It was found that the adsorption capacity of Co_0.5Zn_0.5Fe₂O₄@BC for CV was significantly improved， and the removal rate reached 99.64%， which was significantly improved compared with the raw biochar （41.94%） and Co_0.5Zn_0.5Fe₂O₄ （26.78%）. The first-order kinetics and Langmuir model better fit the adsorption process and adsorption isotherm. The adsorption process was monolayer adsorption， mainly physical adsorption， and the maximum adsorption capacity was 227.59 mg/g. The modified biochar could effectively adsorb CV at pH 5~11. Common co-existing cations and anions slightly promoted the adsorption of CV by modified biochar. The adsorption performace was better in tap water and Yangtze River water than pure water， which shows that the material has a good anti-interference ability. After 5 cycles， the removal rate of CV can still reach 69.75%. This study provides a potential material for the treatment of CV polluted water. It also provides a new approach for the adsorption of dye wastewater by biochar modification.

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.

Automated precise identification of rivers in high-resolution remote sensing images holds significant importance and research value in river and lake environmental monitoring， as well as watershed change studies. However， due to the relatively small area occupied by rivers in the images， it can lead to an imbalance between positive and negative samples in the dataset. Additionally， the morphological variability and complex scale transformations inherent in rivers contribute to challenges in river identification， resulting in issues such as discontinuous boundaries and grid effects. In response to these challenges， this paper proposes a cross-scale river precise identification method with fusion of global multilevel features. The method can be divided into three main parts. Firstly， we construct a multi-feature river dataset by selecting globally distinctive meandering and braided rivers to enhance data diversity. Secondly， we construct the R-Seg model， utilizing the lightweight semantic segmentation model Segformer as the backbone network. We design the Global and Adaptive Scale Pyramid Pooling （GASPP） module for extracting multi-scale features. This module， coupled with Transformers， facilitates the extraction of multi-scale features， enabling the model to capture contextual information in river images， reduce information loss， and amplify global dimension interaction features. Lastly， we propose a cross-scale river image prediction method based on mask-weighted voting. By employing sliding window cropping on large-scale river images， we obtain sub-prediction results by multiplying each unit prediction block with a specific mask weight. These results are then sequentially concatenated through overlapping voting， achieving precise identification of river images at different scales. The experiments demonstrate that， in the constructed multi-feature dataset encompassing meandering and braided rivers， a comparative analysis with other methods reveals the following： qualitatively， the overall structure of the R-Seg network ensures high identification accuracy for main rivers and effectively mitigates interruptions in smaller river flows， smoothing river boundaries with good robustness for 500×500 small-scale river image identification. Moreover， the use of mask-weighted voting method significantly reduces the edge loss problem caused by grid effects in unit blocks， making full use of unit block prediction results， improving river prediction accuracy for larger scenes， and achieving accurate identification of river images of different scales. From a quantitative perspective， the method achieves an overall accuracy of 99.49% with optimal performance across various accuracy evaluation metrics. Also， the single-image identification time is less than 1 second， meeting the efficiency requirements of most practical applications. Furthermore， the mask-weighted voting strategy exhibits an overall higher river identification accuracy of approximately 0.28% to 6.93% compared to a pure overlap prediction strategy. By adjusting the overlap parameter， it is observed that accuracy and overlap are not positively correlated； an accuracy of approximately 12.5% achieves relative optimization. This approach， through the design of the R-Seg network model and the introduction of the mask-weighted voting prediction method， effectively alleviates issues such as discontinuity in river boundary recognition and grid effects. It significantly enhances the accuracy of river identification in remote sensing images across diverse scenarios， demonstrating strong robustness and visual performance. The identification outcomes hold crucial application value in geological exploration of rivers and studies on watershed changes.

In order to study the clogging mechanism of the drip irrigation system of Yellow River diversion in the field， and to reduce the clogging of the irrigation system in the field， based on the characteristics of sediment in Zuncun irrigation district of Yuncheng， Shanxi Province，SolidWorks is used to model and design the flow path of emitters. The simulation parameters were set according to the actual engineering conditions of drip irrigation， and Ansys Fluent simulation software is used to simulate the flow passage of different types of emitters. The turbulent characteristics such as pressure， velocity and turbulent kinetic energy of the emitters， as well as the particle trajectories are studied to reveal the internal flow mechanism of the emitters. The pressure of the fluid basin inside the emitters decreases linearly with the flow channel units， and the pressure drop gradient is linearly related to the number of flow channel units. The fluid in the mainstream area is updated quickly， and is not prone to sedimentation. The vortex area of the flow channel and the chamfered parts of the storage tank are the main parts where sedimentation occurs.The turbulent kinetic energy is lower in the vortex region of the channel and the chamfering part of the water storage tank than in the other regions of the channel， so the turbulent flow can not be formed or maintained， which is not conducive to sediment transport， thus forming a plug. The simulation results show that， the analysis of flow velocity， turbulent kinetic energy and streamline in the inner structure of the emitters shows that the low velocity zone and the chamfer and side wall of the storage tank are the main locations of sediment deposition in the emitters.

The Three Gorges Hydropower Station has 34 units with a total installed capacity of 22 500 MW. It is a key power source for the backbone power grid of State Grid Corporation of China′s “West-to-East Power Transmission” project and “North-South Interconnection” project， supporting the safe and stable operation of the grid. However， the annual maintenance work at the Three Gorges Hydropower Station is complex and highly uncertain due to the large number of equipment， diverse unit models， and varied demands for equipment technological upgrades. Additionally， the workload for manual operations is significant. To address these challenges， a maintenance planning platform has been developed based on the SpringBoot microservices architecture. The platform includes the Three Gorges Hydropower Station Operation Scheduling and Optimal Maintenance Arrangement System. It provides a hierarchical operational service framework for decision-makers， managers， implementers， and operators at the power plant. This platform enables comprehensive digital management and control of the yearly maintenance planning， execution， feedback， and adjustments for the equipment at the Three Gorges Hydropower Plant. Furthermore， considering different maintenance requirements and prioritizing safety and stability during the maintenance process， multiple maintenance plan options have been proposed and optimized. Currently， this platform effectively serves the routine maintenance scheduling work at the Three Gorges Hydropower Station.

Water distribution is an important mode of the pipe network operation. In order to explore the basic water hydraulic characteristics near the right-angled branch of the circular channel， DN315 PVC round pipes were used to carry out tests under 5 flow rates （30， 35， 40， 45， and 50 L/s） and 5 split ratios （0.1， 0.2， 0.3， 0.4， and 0.5） in this paper. Measurements of the water depth and three-dimensional instantaneous velocity on typical sections were recorded， using the Acoustic Doppler Velocimeter （ADV）， and the mean velocity distribution and the head loss near the unpressurized pipe branch were performed. The research shows that： the surface line of the main pipe near the right-angled branch generally decreased first and then increased. High plugged water level of the large flow （45 L/s） requires attention to the headroom. The split ratio mainly affected the water level in the mouth part. The longitudinal flow velocity through the branch decreased， while the transverse flow velocity through the branch increased. The vertical and transverse flow velocity of the upper and lower the branch were distributed symmetrically， and the velocity near the right-angled branch side was larger in the area of the bleeder. The longitudinal velocity was distributed in a parabolic vertical direction， and the longitudinal velocity appeared negative at the middle axial section of the bleeder， that is， there was a reflux phenomenon. There was no negative velocity of the transverse velocity， that is， there was no lateral circulation， and the transverse velocity was distributed in a concave vertical direction. The diverter widths of horizontal flow field increased with the increase of the split ratios， and the diverter widths of middle section were larger than that of other layers. Head losses were different under different flow rates and spilt ratios， and the coefficient of total head loss was the smallest when the diversion ratio was 0.4 and the flow rate was 50 L/s. This study is of great significance to guide the design and operation of pipe networks.

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.

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.

It is very easy to encounter the phenomenon of water and mud gushing during construction and excavation of tunnel through karst area. How to choose appropriate anti-seepage measures to reduce the seepage flow of tunnel has always been a hot issue. In this paper， a three-dimensional finite element model of complex strata in karst area is established based on a water diversion project. Three anti-seepage measures， namely back-filling concrete on the roof of the tunnel， grouting ring around the tunnel and concrete cutoff wall， are mainly considered. The change law of pore water pressure and seepage flow of the tunnel during tunnel excavation is studied， and the influence of permeability coefficient of the cutoff wall and grouting ring on seepage characteristics is analyzed. The results show that the concrete cutoff wall has a great influence on the distribution of pore water pressure in the tunnel， the water level between the cutoff walls decreases obviously， and the grouting ring around the tunnel has the greatest influence on the seepage flow of the tunnel， and the quality of the grouting ring should be ensured during construction. The pore water pressure increases with the increase of the horizontal distance from the tunnel center， and increases first and then decreases with the increase of the vertical distance from the tunnel center. With the increase of the permeability coefficient of the grouting ring or the cutoff wall， the pore pressure growth rate in the area of the grouting ring or the wall decreases gradually， and the seepage flow of the tunnel increases continuously.

In Argentina's Mendoza River Basin （33°S）， glaciers provide an essential supply of freshwater. They act as sensitive climate change indicators and are crucial for comprehending and evaluating changes in local and global climate. Based on Landsat remote sensing imagery from 1990 to 2020， this study aims to understand the distribution and changing patterns of glaciers in the Mendoza Basin and their influence on regional water supplies and ecosystems. It makes use of ratio threshold algorithms and manual interpretation to get glacier boundary data from seven different eras， using the Argentine National Glacier Inventory and the RGI glacier catalog. Furthermore， by integrating climate data from TerraClimate， the study examined the influence of regional climate change on glacier fluctuations and demonstrated the trajectory of climate change in this area during the study period. The findings show that： ① The Mendoza Basin's glacier area was 134.09±11.86 km2 in 2020. The glacier area decreased considerably （p<0.01） between 1990 and 2020， totaling 86.87±21.30 km2 （39.31±10.14%）. The greatest rate of retreat was recorded between 2010 and 2020. ② The majority of glaciers are more than 10 km2， while the least common are less than 0.1 km2. The biggest glaciers are melting the quickest. The majority of the glaciers are found on the southern slopes， with the northwest slopes having the fewest and the southeast and northeast slopes seeing the quickest rate of retreat. Their range of slopes is mostly 5° to 40°， with 50° to 55° being the quickest of retreat. With the greatest retreat rates below 4000 meters， the bulk are located at elevations between 4 200 and 5 400 meters. ③ The Mendoza region has seen a considerable increase in temperature since the late 1950s （p<0.001）， with an average decadal rise in maximum and minimum temperatures of 0.53 ℃ and 0.29 ℃， respectively. In the Mendoza Basin， glacier retreat is mostly caused by long-term increases in temperature； short-term factors include variations in precipitation. This research offers a guide for mitigating the effects of glacier fluctuations on the Mendoza Basin's water resource availability and geological calamities.

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.

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.

Researches on hydro-wind-solar hybrid energy systems have mostly focused on coordinated operation and enhancing the new energy consumption but often overlooked the potential impact on the water levels of reservoirs. In this paper， a long-term optimal operation model for hydro-wind-solar hybrid energy systems， nested with short-term energy curtailment， is established. Firstly， the curtailment loss function was fitted based on actual hydro-wind-solar output data simulating potential curtailment scenarios， which was incorporated into a long-term optimal operation simulation； Secondly， simulations of the reservoir dispatching operation process before and after the integration of wind and solar energy were conducted； Finally， the impacts of wind and solar energy consumption on the annual， sub-annual， and monthly operating water levels of the cascade reservoir group were analyzed. A case study was carried out using the hydro-wind-solar hybrid energy system from Longyangxia to Liujiaxia cascade reservoirs in the upper reaches of the Yellow River. The results show that： ① After wind and solar energy integration， the annual average output of the cascade reservoirs decreases by approximately 0.5% to enhance the consumption of wind and solar energy， increasing the wind-solar power in grid and total system output by approximately 2.2%. ② The wind and solar energy consumption leads to increased annual and monthly water level fluctuations in the cascade reservoir group. The end-of-year drawdown and annual average water level fluctuations ranges of Longyangxia and Liujiaxia reservoirs increase by 2.5%， 101.6%， 0.8% and 78.9%， respectively. Longyangxia experiences no change in monthly water level fluctuation， while Liujiaxia sees a reduction of 14.7% in the lower limit of monthly water levels. ③ In wet years， the wind and solar energy integration leads to a significant decrease in water levels of the cascade reservoirs. In normal years， Longyangxia reservoir experiences a slight decrease in water levels， while water level of Liujiaxia reservoir decreases in pre-flood spring and increases in post-flood autumn. In dry years， wind and solar energy consumption has almost no impact on reservoir water levels. These research findings hold significant practical implications for better understanding the complex relationship between wind and solar energy integration and reservoir operations and for optimizing the operational strategies of hydropower-wind-solar complementary systems.

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.

The processes of rising water temperature， photosynthesis， and high dam discharge may all cause the problem of supersaturated dissolved gas， which can affect the biodiversity of fish in rivers and other water bodies. Aeration technology has been proven to significantly promote the release of supersaturated TDG， DO， and DN， and is an important method for mitigating the impact of supersaturated water bodies. This article conducted experiments on the release of supersaturated dissolved gas under different aeration rates and water depths using pinhole aeration discs and microporous aeration pipes， exploring the release laws of supersaturated dissolved gas. The experimental results show that the release processes of supersaturated TDG， DO， and DN under the action of microporous aeration conforms to the first-order dynamic equation. All supersaturated DO under experimental conditions can be released to an equilibrium state， while the release rate of supersaturated DN is slow and cannot reach an equilibrium state for a long time， showing k _DO>k _TDG>k _DN； The release coefficients of supersaturated TDG， DO， and DN increase with the increase of aeration volume， but decrease with the increase of aeration depth. The release effect of supersaturated TDG， DO， and DN using microporous aeration pipes is significantly better than that of pinhole aeration discs under the same aeration volume and aeration depth. A binary linear regression model was used to establish the release coefficient relationships of supersaturated TDG， DO， and DN under microporous aeration， which can effectively predict k _TDG with the correlation coefficient of 0.958. This study reveals the release characteristics of supersaturated dissolved gases under microporous aeration， providing certain reference value for aeration technology to mitigate the impact of supersaturated dissolved gases.

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.

Soil salinization and sodification is one of the key factors affecting the sustainable agricultural development in cold arid irrigated districts. Traditionally， the total soil salt contents were used as the indicator to evaluate the evolution trend of salinization and sodification， but little attention is paid to the change of ion composition. For a further study of the distribution and migration patterns of salt ions among various water in agricultural irrigation and drainage systems， this paper analyzes the distribution and change characteristics of salt and ions in different water in irrigation areas systematically based on the observation data of various water during the irrigation and drainage process. Research has shown that the composition of salt ions in waters in the irrigated districts is different obviously/ Irrigation water is dominated by Ca²⁺ and HCO₃ ^–， while groundwater and drainage are mainly Na⁺， Cl^– and SO₄ ^2–. The quality of irrigation water is relatively good， and it is generally HCO₃ ^–－Ca²⁺ type water. Groundwater shows weak alkalinity， and is generally of Cl^–·SO₄ ^2–－Na⁺ and Cl^–·SO₄ ^2–－Na⁺·Mg²⁺ type water. Drainage is mainly influenced by factors such as regional groundwater and irrigation recession， with Cl^–·SO₄ ^2–－Na⁺ and Cl^–·SO₄ ^2–－Na⁺·Mg²⁺ type.

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.

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.

A multi-strategy fusion improved Golden Jackal Optimization Algorithm （MGJO） is proposed to address the shortcomings of the Golden Jackal Optimization Algorithm in solving complex or high-dimensional optimization problems， such as being prone to local optima， slow convergence speed， and low computational accuracy. Firstly， by introducing a chaotic mapping strategy to initialize the population instead of random parameters， the algorithm can generate initial solutions with good diversity in the search space and avoid the initial population distribution deviating from the optimal value. Secondly， a nonlinear dynamic inertia weight is proposed to make the search process more realistic， effectively balancing the algorithm′s global and local search capabilities. Finally， the position update strategy of Cauchy mutation is introduced to fully utilize the guiding role of the optimal individual to improve population diversity， effectively exploring unknown regions and avoiding the algorithm falling into local optima. In order to verify the optimization accuracy， convergence performance， and stability of the improved Golden Jackal Optimization Algorithm， eight benchmark test functions with different features are selected for experiments. The results show that among the 8 benchmark test functions， the improved Golden Jackal Optimization Algorithm has achieved optimal results in terms of mean， standard deviation， and optimal value. In addition， the results of Wilcoxon’s sign rank test indicate that the improved Golden Jackal Optimization Algorithm is significantly superior in statistics. Through practical applications， it shows that the Golden Jackal Optimization Algorithm based on multi-strategy fusion improvement can effectively estimate the parameters of the Muskingum Model， and the optimization effect is significantly better than the particle swarm optimization algorithm， the sine cosine optimization algorithm， and the Golden Jackal Optimization Algorithm. This further verifies the effectiveness of multi-strategy fusion improvement and the superiority of the improved algorithm in parameter optimization. This provides an effective new method for more accurate estimation of the parameters of the nonlinear Muskingum Model.

In view of the fact that the multi-stage pump type intermediate guide vane designed according to the pump working condition cannot meet the runner’s requirements for flow circulation when used in hydraulic turbine devices， the influence of key structural parameters of pump type equal diameter positive and negative guide vanes on water head loss， efficiency and working head of hydraulic turbine devices is studied by combining the hydraulic turbine design theory and CFD numerical simulation. The results show that appropriately increasing the diameter of the inlet edge of the reverse guide vane， chamfering the edge of the baffle， and chamfering the inner and outer walls of the inter-stage guide vane inlet can reduce the head loss of the inter-stage guide vane and improve the working efficiency of the hydraulic turbine； the shape and number of guide vanes have a significant impact on their outlet water flow velocity circulation； increasing the wrap angle or increasing the number of blades can enhance the forcing effect of the guide vanes on water flow， increase the outlet water circulation， but increase the head loss of the guide vanes. This paper can provide a reference for the design of the same diameter forward and backward guide vanes used in hydraulic turbines.