The Sediment Delivery Ratio （SDR） is an important index reflecting sediment transport characteristics in river and reservoir. Most existing SDR calculate methods fail to consider variations in riverbed boundary conditions. Taking the braided reach from Huayuankou to Gaocun in the lower Yellow River as a case study and selecting the continuous erosion period from 2000 to 2022 after the operation and water-sediment regulation of Xiaolangdi Reservoir as study period， analysis on the SDR variation law shows that the SDR and the incoming sediment coefficient exhibit a good power-law relationship， and there is a weakening effect as the curve of different time period moves downwards with the increase of accumulate scouring amount. In view of the sediment transport feature of “more incoming sediment leads to more deposition and more outflow sediment” in the lower Yellow River， a power-law sediment transport formula containing upstream sediment concentration is adopted. Based on the definition of SDR， a SDR calculate model considering the impact of riverbed boundary condition is established， combining the delayed response model of sediment transport coefficient during the process of river-bed erosion and deposition. Based on the SDR formula， the SDR variation process of the braided reach in the lower Yellow River during the continuous erosion period is simulated using two methods respectively without and with considering the variation of sediment transport coefficient. The results show that both the calculated results agree well with the measured SDR values， with the correlation coefficient R ² of 0.806 and 0.899. The later effect is better than the former， which verifies that that considering the impact of riverbed boundary condition can improve the SDR simulation effect. Comparing the relation of SDR and incoming sediment coefficient of the two methods， the later is a set of power-law curves， but the former is a single power-law curve which only reflects the average state of sediment transport coefficient variation. So for the SDR formula without considering the variation of sediment transport coefficient， the parameter calibration is influenced by the measured data distribution， and has a statistical significance and demonstrates uncertainty.

Based on the measured hydrologic data of Three Gorges Reservoir from 2012 to 2024， this paper systematically analyzes the effect of sediment reduction regulation at the tail of reservoir during the water level fluctuation period over the years， focusing on the influence of daily water level drop in front of dam on the amount of erosion and sedimentation at the tail of reservoir， and simulates the sediment reduction regulation at the tail of reservoir during the water level fluctuation period in 2022 by using mathematical model. The results show that the accumulated sediment reduction benefit of regulation at the tail of reservoir during the water level fluctuation period is remarkable， and the distribution of erosion and sedimentation presents obvious spatial law. Among them， the scouring efficiency of the main urban reach of Chongqing is less affected by the daily average drop of water level in front of dam， while the reach from Tongluoxia to Fuling is more sensitive to this indicator； From the perspective of spatial distribution， the siltation reduction dispatching in Chongqing urban area is mainly reflected in the main stream of Yangtze River， and only a small amount of scouring is shown in Jialing River reach； The scouring efficiency of Tongluoxia to Fuling reaches increases gradually with the increase of amplitude of water level decline， and the downstream reach 580~478 km away from dam is more significantly affected by the daily average drop of water level.

Tongguan Hydrological Station is one of the key control stations in the middle reaches of the Yellow River. Changes in its water–sediment regime directly affect flood-control operations in the middle and lower reaches of the river and indirectly influence the surrounding hydro-ecological environment. Based on the observed daily discharge and sediment-transport rate data from 1960 to 2023， this study identified abrupt change points in water and sediment series at Tongguan Station using three complementary methods： the Mann–Kendall test， the moving t-test， and the cumulative anomaly method. The combined application of these approaches reduces uncertainties associated with relying on a single test method and thus improves the reliability of abrupt change detection. Meanwhile， drawing on the indicator classification framework of the IHA-RVA method， and retaining 32 flow indicators （excluding indicators related to flow interruption）， we established a set of 31 sediment indicators （with the base-flow index not included for sediment）. These indicators were used to systematically analyze changes in water and sediment characteristics before and after the detected abrupt change points， enabling a quantitative assessment of the overall alteration degree of the water–sediment regime in the middle Yellow River. The results show that： ① Both water and sediment fluxes at Tongguan Station exhibit a decreasing trend， with the annual runoff experiencing an abrupt change in 1985 and the annual sediment-transport rate in 1996. ② Among the 32 hydrological indicators， 4 showed a high degree of alteration， 20 exhibited a moderate degree， and 8 showed a low degree of alteration. Among the 31 sediment indicators， 18 showed a high degree of alteration， 10 exhibited a moderate degree， and 3 showed a low degree， indicating that the alteration in sediment transport is generally greater than that in streamflow. ③ Compared with the pre-change period， the overall alteration degrees of discharge and sediment-transport rate after the abrupt change were 52% and 70%， respectively. The hydrological and sediment regimes in the middle Yellow River underwent moderate and high alteration， respectively.

To investigate the breach modes and evolution mechanisms of landslide dams， a generalized physical model was established based on the topography of the Baige landslide dam on the Jinsha River， employing a horizontal scale of 1∶200 and a vertical scale of 1∶150. The influences of upstream inflow rate， dam shape， particle size distribution， and compactness on dam breach process were systematically investigated. The main conclusions are as follows. Three breach modes were identified corresponding to different dam characteristics and hydraulic conditions-overtopping failure， seepage-induced overtopping breaching， and sliding failure. Dams composed of coarse particles， with high compactness and a large width-to-height ratio， under conditions of gentle slope， exhibited the lowest breach risk and a relatively gradual failure process. In contrast， when loose， fine-grained dams were subjected to high inflow rates， the breach was the most rapid and violent， representing the highest disaster risk. The established multivariate nonlinear regression model effectively predicted the peak breach flow（R2 = 0.923）. The research results provide theoretical support for the emergency response of barrier lakes， improving the scientific decision-making level for disaster prevention and the emergency response capability against outburst flood hazards of landslide dams.

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

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

Controlled discharge of cascade reservoirs is the core means to control flood during the flood season. Restricted by insufficient efficiency and accuracy of traditional reservoir gate flow calculation methods， the current decision-making level for reservoirs flood control operation is mostly based on the outflow， failing to refine discharge volume of individual gates. Moreover， gate operation is mostly based on empirical judgment. In response to this issue， the Three Gorges and Gezhouba reservoirs were taken as case study， and a graph database was built based on Neo4j using historical operation data. On this basis a knowledge graph was formed through knowledge mining. The constructed knowledge graph contains 180 nodes covering 161 reservoir gates， and can provide gate control suggestions within 3 to 5 seconds under given inflow conditions. In the August 2020 flood， based on the knowledge graph operation， the average flow deviation between the Three Gorges and Gezhouba reservoirs outflow and the actual outflow was 1 020 and 1 400 m 3/s， respectively. The outflow water deviation was 178 million m3 and 60 million m3， respectively. The NSE of the Three Gorges water level process was 0.886， and the gate decision of the knowledge graph also complied with the application rules of symmetrical gate opening and quantity limitation. This study combines the dual functions of historical data visualization analysis and real-time decision support， which not only facilitates the deep extraction of operation knowledge， but also provides accurate data support for decision makers.

To achieve efficient utilization of water resources while balancing downstream urban flood control safety and ecological needs of the river， it is of great significance to explore reservoir discharge patterns and their operational mechanisms. Addressing the issue of insufficient physical consistency in existing data-driven models for reservoir operation simulation， this paper proposes a Physics-Informed Long Short-Term Memory （PI-LSTM） neural network model. By jointly training hydrological observation data and physical constraints of the reservoir system （such as water balance and storage level–capacity relationship）， this model embeds hydrological-physical laws into the data-driven framework， thereby enhancing the physical interpretability and reliability of simulation results. Taking the Pubugou Hydropower Station in the Dadu River Basin as a case study， daily-scale simulation experiments were conducted， covering inflow， outflow， reservoir water level， and storage capacity. Comparative analyses were performed with traditional LSTM and TCCRM models. The results show that PI-LSTM achieves high accuracy in simulating inflow， providing a solid foundation for outflow simulation. In daily-scale outflow simulation， the Nash–Sutcliffe Efficiency （NSE） and Kling–Gupta Efficiency （KGE） reached 0.887 and 0.856， respectively， representing improvements of 15.2% and 9.6% compared to the traditional LSTM model. In terms of storage capacity simulation， the KGE values for PI-LSTM， TCCRM， and LSTM were 0.77， 0.46 and -2.1， respectively， with PI-LSTM significantly outperforming both TCCRM and LSTM， showing a 67% improvement over TCCRM. Additionally， the simulated water level process closely matched the observed values， accurately reproducing the seasonal dynamics of reservoir storage. This study validates the effectiveness of integrating physical information in enhancing the performance of data-driven models for complex reservoir operation scenarios， providing methodological support and practical references for the refined and intelligent operation of cascade reservoir systems in river basins.

To address the current ambiguity in predicting the comprehensive benefits of ecological flow power generation unit， this study takes the Fushui Reservoir as the research object. Based on calculated ecological flow release standards， the power generation discharge capacity of the unit is determined. Utilizing the reservoir’s actual operational records in 2017， we construct an ecologically constrained optimal operation model that guarantees ecological flow releases. This model is solved using the Discrete Differential Dynamic Programming （DDDP） algorithm， and a three-dimensional state transition matrix is constructed to optimize the calculation process under two scenarios： with and without the ecological flow power generation unit. By comparing the comprehensive benefits encompassing flood control， ecological protection， and power generation of these optimized schemes against actual operational outcomes， we systematically analyze the anticipated comprehensive benefits of deploying ecological flow power generation units. The results demonstrate that after installing the ecological flow power generation unit， the Fushui Reservoir achieves： ①19.3% increase in peak flood reduction rate； ②13.5% enhancement in power generation output； ③100% ecological flow assurance rate. This research provides a theoretical basis for reservoirs in China to achieve three-dimensional synergistic benefits in flood control， power generation， and ecological conservation.

Addressing the challenges of dynamically balancing multi-objective trade-offs under complex hydrological variability in reservoir flood control operation， this paper proposes an adaptive dispatching method with dynamically adjusted weights based on real-time hydrological state perception. A complete fuzzy inference system—comprising a fuzzification interface， an inference mechanism， and a defuzzification module—is constructed to establish a dynamic perception mechanism for reservoir water level and flow trend. This system transforms quantitative hydrological features into fuzzy rules with continuous transitions， thereby enabling intelligent， real-time adjustment of multi-objective weights. The proposed method innovatively integrates the fuzzy inference system with the Progressive Optimality Algorithm （POA） to form a closed-loop optimization framework of perception-inference-decision. By continuously sensing the reservoir’s operational state and the basin’s hydrological regime， the proposed framework dynamically adjusts the relative priorities of competing flood control objectives， achieving holistic and adaptive optimization throughout the entire flood control process. To validate the feasibility and effectiveness of the method， a case study was conducted using the “20200814” flood event in the Shuhe River Basin， with several fixed-weight schemes serving as benchmarks for comparison. The results demonstrate that the developed dynamic weight dispatching model not only ensures effective flood peak attenuation but also significantly shortens the duration of high-risk discharge rates. It successfully implements a stage-aware strategy that prioritizes reservoir safety during high-water-level periods and shifts focus to downstream protection during the recession phase， thereby effectively balancing the differentiated demands of competing objectives across different flood control stages. Compared to traditional fixed-weight methods， the perception-based fuzzy inference system considerably enhances the adaptability and scientific rigor of the decision-making process in flood control operations， showing superior performance across multiple comprehensive evaluation metrics. This approach provides more proactive and adaptive scientific decision support for reservoir systems facing complex and variable flood scenarios， offering significant theoretical value and practical implications for advancing basin-wide flood risk management.

A WPT-ECO-TFLN monthly runoff time series prediction model is proposed， and its universality is verified by 10 examples such as Disuo hydrological station and Fengtun hydrological station. Firstly， the monthly runoff time series is divided into training set， validation set and prediction set according to 5∶3∶2， and the "training set+validation set" and "prediction set" are decomposed by using one-layer and two-layer WPT respectively. Secondly， the optimization objective function of TFLN input layer weights and hidden layer thresholds （hyperparameters） is constructed based on the training set， and the ECO optimization objective function is used to optimize the optimal TFLN hyperparameters. The WPT-ECO-TFLN model is established through the optimal hyperparameters to predict decomposition components and realize summation reconstruction. And the WPT-ECO-FLN， WPT-TFLN， WPT-ECO-ELM， WPT-ECO-TSVR， WPT-ECO-RR models and EWT-ECO-TFLN， SVMD-ECO-TFLN， WD-ECO-TFLN models are constructed for comparative verification. Finally， the models are verified by a 10 month runoff time series prediction example. The results show that： ① In the case of one-layer WPT decomposition， the MAPE predicted by WPT-ECO-TFLN model for example 1~10 is ≤9.88%， QR≥84.8%， R ²≥0.999 0； In the case of two-layer WPT decomposition， the predicted MAPE≤1.47% and QR are 100%， R ²≥0.999 9， proving favorable prediction accuracy and generalization performance. ② Different decomposition techniques have obvious differences in the decomposition of monthly runoff time series， and the decomposition effect of WPT is better than EWT， SVMD and WD. Among them， EWT has obvious marginal effect in the process of pair decomposition. ③ The model performance can be effectively improved by optimizing the TFLN hyperparameter through ECO； The accuracy of monthly runoff prediction can be significantly improved by increasing the number of WPT decomposition layers； Under identical decomposition and hyperparameter optimization，TFLN has better performance than ELM，TSVR and RR. ④ The WPT-ECO-TFLN model has good universality， and the proposed model provides reference for the prediction of relevant monthly runoff time series.

Floods rank among the most prevalent natural disasters， with extreme floods causing immense loss of life and property. Conducting flood risk assessments is crucial for mitigating flood-related damages. In flood risk assessment， obtaining foundational flood risk data is constrained， and traditional flood risk analysis methods are cumbersome and time-consuming. Machine learning models offer rapid modeling and high efficiency， but issues such as model selection and dataset quality significantly impact assessment outcomes. This study establishes a machine learning model for flood risk assessment by integrating multi-source data， including remote sensing， based on the 2020 catastrophic flood event in the Poyang Lake basin. The objective is to identify the optimal machine learning model solution for flood risk assessment in the study area， generate a flood risk map， and investigate the distribution patterns of flood risk. Satellite remote sensing data were utilized to extract water bodies during the 2020 Poyang Lake flood， and a flood dataset for the study area was established by integrating actual flood points. Twelve flood impact factors were selected through multi-source data fusion， considering three dimensions： hazard， exposure， and vulnerability. Four machine learning models （SVM， MLP， XGBoost， LightGBM） and two coupled models （stacking model， voting model） were applied to assess flood risk in the Poyang Lake basin. Results indicate that the two coupled models outperform most single machine learning models in overall performance. Among coupled models， the stacked model demonstrated the best performance. The LightGBM model achieved the highest overall performance among single models and could replace the voting model in coupled models. The XGBoost model exhibited the highest accuracy rate of 0.87 across all models. High-risk flood zones in the lake area primarily cluster around Poyang Lake， radiating outward from the lake as the core high-risk center. The central region exhibits high risk， while the periphery predominantly shows low risk， aligning with actual remote sensing imagery. Most models indicate that low-risk areas account for the largest proportion， whereas the XGBoost model identifies the highest proportion of high-risk areas at 25.67%. This research offers new insights for flood risk assessment studies integrating multi-source data centered on SAR remote sensing data， providing technical support for regional flood control and risk management.

Integrating meteorological forecasts with hydrological models is a key strategy to improve the accuracy of runoff forecasting. However， traditional runoff forecasting relies predominantly on deterministic meteorological forecasts， making it difficult to reflect the uncertainty in hydrological forecast results. To quantify the uncertainty in runoff forecasting and improve the reliability of forecast results， this study proposed a runoff forecasting method based on post-processed meteorological ensemble forecasts. First， bias correction was applied to the raw ensemble meteorological forecast data. The corrected data were then used to drive hydrological models to generate forecast runoff. The method was applied to short-and medium-term runoff forecasting for three hydropower stations—Shuibuya， Geheyan， and Gaobazhou—in the Qingjiang River Basin. The performance of the method is evaluated from three perspectives： deterministic， probabilistic， and flood-event forecasting. Results show a significant improvement in the accuracy of post-processed precipitation and temperature forecasts. For deterministic forecasting， the mean absolute error （MAE） of corrected precipitation forecasts for the three hydropower stations decreased by 0.2~1.0 mm compared to those before the bias correction within 1~10 day of the lead time， while the MAE of corrected temperature forecasts decreased by approximately 1°C. Consequently， the accuracy of the runoff forecasts improved. The MAE of corrected runoff forecasts decreased by approximately 30 m3/s on the 1st day of the lead time， while on the 10th day of the lead time， reductions reached 167 m3/s at Shuibuya， 223 m3/s at Geheyan， and 262 m3/s at Gaobazhou. For probabilistic forecasting， the Brier scores （BS） of corrected precipitation， temperature， and runoff forecasts for the three hydropower stations decreased by 9.0%~10.9%， 25.0%~27.0%， and 30.8%~33.5%， respectively. For flood events forecasting， the runoff forecasts obtained by driving the hydrological model with post-processed meteorological forecast data， after the dynamic correction， can accurately predict the time of flood peaks. Moreover， the flood peak patterns of the runoff forecasts are similar to observations， demonstrating the effectiveness of the proposed method. The proposed method can improve runoff forecast accuracy and extend the lead time， which is of great significance for reservoir optimal operation and water resource management.

With the continuous increase in the size and power generation capacity of hydraulic turbines， the operational characteristics of hydraulic turbines under off-design conditions have gradually attracted attention. This paper conducts unsteady calculations on large Francis turbines under 200、450 and 580 MW conditions at low head， focusing on the flow characteristics and hydraulic excitation characteristics within the flow passage. The unsteady characteristics of pressure pulsation， axial thrust of the runner， radial thrust of the runner， and runner stress are analyzed. The results show that under 200 MW and 450 MW conditions， large-scale vortices with rotational frequencies exist in the draft tube， which cause low-pressure zones and induce low-frequency pressure pulsations within the flow passage， with significant pressure pulsations in the draft tube. The axial thrust is affected by the draft tube vortices， generating high-amplitude low-frequency pulsations. The magnitude of radial thrust is much smaller than the axial thrust， but its fluctuation amplitude is slightly smaller than that of the axial thrust. This asymmetry in the draft tube vortex flow causes unbalanced forces on the runner and hydraulic instability， posing a serious threat to the safe and stable operation of the unit.

The water flow inertia time coefficient T _w is a key parameter that affects the transient process of hydroelectric generating units， and there are many problems in engineering applications. This paper starts from the physical concept of T _w， clarifies the misunderstandings caused by different definition methods， and provides a theoretical correction calculation method. Based on the transfer function with rigid water hammer and taking into account the hydraulic losses， a refined transfer function model from the guide vane opening to the water pressure is established. A method for calculating T _w using transient data， the easily measurable guide vane opening and water pressure， is proposed. Taking the hydraulic transient calculated by the Method of Characteristic-line as the benchmark， a Simulink simulation model is established to study the effectiveness of the proposed T _w calculation method. The error generation mechanism and algorithm adaptability are studied. The results show that the proposed algorithm has good accuracy and stability. The field measurement data of the hydropower station are used for calculation and verification. It is found that due to the influence of the flow coefficient， only the disturbance data under the rated load condition can ensure the calculation accuracy of T _w.

During reverse operation of a bidirectional axial flow pump， the absence of guide vanes leads to the generation of vortices in the outlet flow passage， adversely affecting operational stability. This study investigates the evolution of vortices in the outlet flow passage under different flow rate conditions during reverse operation， as well as their impact on pressure pulsations and pressure standard deviation， using both experimental research and numerical simulation. The results indicate that as the flow rate decreases， the constraining effect of the fluid’s axial inertial force on the vortices weakens. The vortex structure progressively evolves from a stable vortex rope configuration at high flow rates， to a helical vortex structure at the design flow rate， and ultimately forms a complex structure characterized by interference from multiple helical vortices at low flow rates. The vortices expand as the flow rate decreases， leading to increasingly non-uniform vorticity distribution. Pressure pulsations deteriorate correspondingly： high-amplitude， low-frequency pulsations gradually increase， and widely distributed low-amplitude pulsations， caused by the destabilization of vortex structures， emerge. Analysis of pressure standard deviation further reveals that the intensity of pressure fluctuations decreases axially under high and design flow rates. Conversely， at low flow rates， due to changes in vortex structure and reduced constraining force， the intensity of pressure fluctuations increases axially and remains high near the outlet. The research findings provide a theoretical basis for optimizing the reverse operation performance of bidirectional axial flow pumps.

A reasonable start-up strategy can significantly reduce the wear and tear of components in pumped storage units. Vibration conditions of key components can directly reflect the impact of different start-up methods on the units. Therefore， studying the vibration signal patterns of key components during the start-up process of the units has important practical significance. In the actual process， the start-up of units is greatly affected by changes in flow rate and rotational speed， resulting in various vibration phenomena. Existing feature extraction methods fail to accurately extract the characteristics of the start-up state of units. To overcome the shortcomings of traditional methods， this paper uses Mel spectrograms to evaluate the dynamic stress on the top cover of pumped storage units during start-up， and compares and analyzes the operating characteristics under different start-up methods to find the best start-up method. The results show that， considering the start-up duration and the strain on the top cover of the unit and other key operating indicators comprehensively， the open-loop start-up method with small guide vane acceleration shows the best overall performance. This research provides a basis for the selection and optimization of start-up methods for pumped storage units.

To investigate the influence of pitch circle modulus （m=D/d） on the operational stability and efficiency of impulse turbines， this study established a three-dimensional model of a single-nozzle impulse turbine based on the parameters of a specific hydropower station. Simulations were conducted under fixed nozzle opening of 15 mmand unit rotational speed of 38.580 75 r/min. Unsteady numerical simulations were performed using the VOF multiphase flow model and the SST k-ω turbulence model. The focus was on analyzing the force characteristics on the runner （axial force， radial force， tangential force） and the pressure pulsation characteristics （pressure pulsation coefficient Kp， relative amplitude F） at key monitoring points on the runner surface under five different pitch circle moduli （m=11.6， 11.8， 12， 12.4， 13）. The results show that the pitch circle modulus significantly affects axial force balance. Axial force fluctuation is minimal and unit operation is most stable at m=12. When m deviates from 12 （especially increasing to 13）， axial force fluctuation increases sharply， significantly raising resonance risk. Radial force slightly decreases with increasing m. Tangential force is slightly higher at m=11.6~11.8 compared to m=12， corresponding to an efficiency increase of approximately 1.15%~1.59%. The overall pressure pulsation level is optimal at m=12， with the bucket root forming a stable， strong negative pressure zone. Increasing m leads to intensified negative pressure inside the buckets， potential positive pressure impact at the tip notch， and aggravated pressure pulsation at the splitter root. If m is too small， jet interference with the bucket backside can cause local high pressure. Considering both stability and efficiency， m=12 is identified as the optimal pitch circle modulus design parameter for this turbine under the given operating conditions.

In order to clarify the internal flow law of the outlet conduit of the slanted axial-flow pump device under different flow conditions， the SST C-C turbulence model is used to numerically calculate the internal flow field in the whole flow channel of the slanted axial-flow pump device. The inlet surface and internal flow structure of slanted outlet conduit under different flow conditions are analyzed emphatically. The results show that with the increase of flow rate， the average velocity circulation at the guide vane outlet of the slanted axial-flow pump device decreases first and then increases. The turbulent kinetic energy intensity at the outlet of the guide vane is the largest at low flow rate， accompanied by significant mixing and dissipation effects. There are obvious differences in the velocity distribution on both sides of the pier of the slanted outlet conduit under different flow conditions. The flow rate on the left side of the outlet centerline is obviously larger than that on the right side， presenting prominent flow deviation. Under the condition of small flow rate， the water flow on the right side of the pier shows obvious spiral winding shape. The volume ratio of the vortex structure under design and large flow conditions is much lower than that under small flow conditions. As the flow rate increases， the main precession frequency of spiral flow decreases first and then increases.

Accurate calculation of transient processes in long distance water transmission systems is crucial for ensuring the safe and stable operation of engineering projects. The hydraulically complex overflow-type elevated outlet tank is installed downstream of the pump in a large-scale long-distance conveyance system. The use of the one-dimensional method of characteristics for dynamic water level computation is prevented by this configuration， and the internal flow characteristics remain unclear. To address this issue， a coupled “overflow weir–elevated tank” model applicable to one-dimensional analysis was derived， and macroscopic parameters under pump startup and pump power failure conditions were computed. Furthermore， the 1D+3D coupled approach was employed to perform simulation analyses， through which the evolution of flow patterns during transient processes was revealed. The results show that the water level in the tank rises continuously and finally stabilizes under pump startup conditions. The strong water level fluctuations are generated due to the contraction structure under the flow spills over the overflow weir. The water level in the tank is observed to fluctuate and reach the crest of the overflow weir during the initial stage of pump power failure， after which repeated oscillations occur until the level becomes equalized with that of the downstream reservoir. Once the overflow weir height is reached， complex internal flow patterns are induced by the outlet tank structure and increased head loss is caused. The findings are expected to provide technical support for the safe operation of pumping stations.

To investigate the influence of blade sweep on the transient internal flow behavior of an axial-flow pump， a combined approach of numerical simulation and model testing was adopted. The internal flow fields of both the original and swept blade configurations were solved using computational fluid dynamics （CFD） methods， and their transient flow characteristics were comparatively analyzed. Numerical results show that the swept blade model yields a more uniform distribution of axial velocity and total pressure at the impeller outlet， with the high-efficiency zone expanded by approximately 21%. At the design flow rate （1.0Q_d ）， the hydraulic efficiency increases by 3.4%， and the average head rises by about 0.41 m. At the impeller inlet， a relatively large deviation angle is observed near the blade leading edge， while at the impeller outlet， the deviation angle increases gradually from hub to shroud. The distribution of deviation angle in the swept blade configuration is more sensitive to the position variation between the impeller and guide vanes. In the guide vane passage， the vortical structures in the swept blade model gradually decrease in size but increase in number from inlet to outlet， with a significant reduction in the resultant velocity. The pressure fluctuation patterns of both models are generally similar； however， the swept blade model exhibits more complex pressure fluctuations， with a richer frequency spectrum at the guide vane outlet， due to stronger rotor–stator interaction. The findings provide a physical basis for the judicious application of blade-sweep technology in axial-flow pump design.

To accelerate the addressing of shortcomings in operation and maintenance， this paper systematically analyses the current status of county-level unified management models， institutional frameworks， maintenance capabilities and operational mechanisms， based on a survey of 230 county-level unified management entities across 221 counties in 10 typical provinces. It summarises and identifies the pain points and bottlenecks that have emerged in the practical implementation of county-level unified management. Data analysis indicates that， whilst the coverage rate of county-level unified management systems for rural water supply in China is generally high at present， there are shortcomings in some areas， such as incomplete detailed standards for unified management， insufficient competence of professional maintenance teams， and a low level of smart management. As a key innovation in the rural water supply management system， county-level unified management requires a clear definition of its precise concept and positioning， full grasp of core elements for its sustainable operation， and comprehensive consideration of regional differences， different development stages of water supply and economic viability. Drawing on typical practical experiences from various regions， this paper proposes systematic optimisation measures， including selecting management models tailored to local conditions， strengthening the development of professional teams， establishing smart management systems， and formulating sound water pricing and fiscal subsidy mechanisms. Whilst ensuring the basic safety of water supply from small-scale and scattered projects， sustained efforts should be made to enhance management and maintenance capabilities and ensure long-term operational sustainability， thereby continuously improving the standard of rural water supply management and operation， and contributing to the high-quality development of rural water supply.

To address the challenges of reduced computational efficiency and convergence speed in Non-dominated Sorting Genetic Algorithms II （NSGA-II） with increasing multi-objective dimensions， as well as the lack of effective local search strategies， this study innovatively proposes the LNS-NSGA-II algorithm. This algorithm integrates the core principles of Large Neighborhood Search （LNS） into NSGA-II， enabling individual-based neighborhood searches to enhance local search capabilities and increase the diversity of feasible solutions. Using the western water supply network construction project in Tiexi District， Shenyang as a case study， and supported by existing research findings， this study explores the optimization design of water supply networks centered on core objectives. A multi-objective optimization model incorporating economic efficiency， water supply reliability， and water quality safety was developed and solved using the improved LNS-NSGA-II algorithm. The improved LNS-NSGA-II algorithm demonstrates convergence of its economic objective function around 200 iterations， compared to approximately 300 iterations for the standard NSGA-II algorithm， with an optimal construction cost of 40 million yuan. The variance of node surplus head is 21.077 1 meters， and the average node water age is 5.275 hours. The improved algorithm outperforms the standard NSGA-II in terms of convergence speed， Pareto front distribution， and optimal solutions， validating the rationality and effectiveness of the multi-objective model based on LNS-NSGA-II in rural water supply network applications. This optimization achieves effective cost control， ensures economic efficiency and reliability， and enhances water quality safety.

This study takes Hetao region of Inner Mongolia as the research area， and seven remote sensing images in recent 40 years are adopted as the data source. The decision tree algorithm and vegetation indices of different phase imaging are used to classify the soil salinization grades. The classification method integrating spectral index and the ground test data are taken as the reference object to estimate the classification accuracy. Variations in salinized soil area and water-salt migration rules are analyzed based on long-term area statistics and groundwater level data. The results show that： ① The total accuracy of the classification method based on the decision tree is 89%. ② These two classification methods have high similarity and the Pearson correlation coefficient is above 0.9. ③ Soil salinization degree is closely correlated with groundwater depth. In 1988 year， the average ground water level reached the highest value of 0.87 meters， but the extent of soil salinization is the most serious， and the saline soil area is only 362 800 hm²， accounting for 32% of the total area. Thanks to water-saving renovation， groundwater level decreased year by year， and the average ground water level in 2012 year drops to 2.29 m. The non-saline soil area is 425 500 hm²， occupying 40% of the total area. Thus it can be seen that the main factor to control the soil salinization is the groundwater depth.This research can provide references for regional water resources management and soil salinization control.