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

With increasing frequency of extreme drought events， reservoirs have become critical infrastructure for drought mitigation， and the Seasonal Drought-limited Water Levels analysis plays a vital role in developing effective drought operation strategies. This study takes the Three Gorges Reservoir as the study object. P-III distribution is used to fit the marginal distribution function of natural inflows across different periods. A five-dimensional joint distribution model of staged flows is then established through Copula functions， and the relationship between staged frequencies and annual design standards is analyzed. Then， operation simulation model is applied to derive the staged low flow considering upstream reservoir group regulation effect. Finally， addressing the issue of non-uniqueness in seasonal drought limited water levels corresponding to the same annual design standard， an optimization model is developed with the objective of maximizing the multi-year average power generation. The results show that： the joint distribution model， constructed with Frank Copula and C-Vine structure， achieves strong agreement between theoretical and empirical frequencies （correlation coefficients > 0.99）. Multiple staged frequency combinations can be derived under given annual design standards， and the operation simulation model successfully quantifies upstream regulation impacts and determines the staged low flow. With a replenishment flow of 7 000 m3/s， the seasonal drought limited water levels capable of coping with a once-in-a-century drought are 146， 148， 162， 175 and 156 m， respectively. The corresponding multi-year average power generation reaches 91.12 billion kWh， representing a 1.05% increase compared to conventional dispatch. These findings provide a reference for drought operation and drought-limited water level design.

With the intensification of global warming， the frequency of Compound Drought and Heatwave Events （CDHEs） in Northwest China has increased significantly， posing a severe threat to the balance of regional water supply and demand and the security of ecosystems. Based on multi-source high-resolution remote sensing meteorological data， this study constructed an index identification system for CDHEs. By comprehensively considering the occurrence frequency， duration， temperature difference characteristics and intensity level of the events， it systematically evaluated the spatiotemporal evolution characteristics and regional response differences of CDHEs in the five provinces of Northwest China from 1981 to 2022. Meanwhile， using CMIP6 multi-model and multi-scenario simulations， the study predicted the future development trends of CDHEs and the distribution of high-risk areas under different emission pathways. The results show that： ① From 1981 to 2022， CDHEs intensified significantly， with all four indicators showing a marked upward trend. Particularly in 2022， spring was the dominant season for CDHEs， followed by summer， while the risks in autumn and winter gradually emerged. Notably， the intensity of CDHEs in humid areas has increased remarkably in recent years. ② The spatial pattern of CDHEs in Northwest China presents an obvious gradient. The frequency decreases from west to east， while the intensity is most prominent in the oasis areas of the central and eastern regions. In terms of temporal evolution， the impact of CDHEs is expanding from the core arid areas to the ecologically fragile northern edge and southern high-altitude areas. The trends of scope expansion， intensity enhancement and hot spot transfer indicate that under the background of climate warming， the dry and hot stress faced by Northwest China is becoming more widespread， intense and intricate. ③ The intensity of future CDHEs will continue to rise in the future， and emission scenarios dominate the differences in trends. Under the SSP5-8.5 scenario， the number of CDHE days by 2100 will be approximately 41.7% higher than that under the SSP2-4.5 scenario. The intensity of high-frequency hot spot areas will continue to escalate， while the spatial distribution pattern remains stable， showing the characteristics of “dominance of quantitative change and stability of pattern”.The research results provide theoretical support for understanding the evolution mechanism of compound extreme climate events in arid areas and their future risks， and also offer a scientific basis for the formulation of regional water resource management and ecological adaptation strategies.

To investigate the changes in terrestrial water storage and their influencing factors in the source regions of rivers， and to address the common systematic biases of evapotranspiration products in alpine regions， this study took the source regions of the Yangtze and Yellow Rivers as the research area. The long short-term memory （LSTM） neural network was used to interpolate the missing terrestrial water storage data from April 2002 to December 2020. A ratio deviation correction method was constructed to correct evapotranspiration products and evaluate water balance errors. The influence of precipitation （P）， evapotranspiration （E）， temperature （T）， elevation （DEM）， and normalized difference vegetation index （NDVI） on terrestrial water storage was assessed using the Geographical Detector. The results showed that： ① The LSTM model filled the gaps in satellite observations effectively. The correlation coefficients between the reconstructed terrestrial water storage and surface water storage calculated by GLDAS during the missing periods reached 0.54 and 0.77， and were 0.50 and 0.72 in the entire study period. ② The ratio deviation correction improved the accuracy of evapotranspiration data. After correction， annual RMSE decreased by 65.04% and 80.09%， and MAE decreased by 67.80% and 82.78% in the two source regions. The correction effect was the best in summer on the seasonal scale， with all error reduction rates exceeding 68.91%. The water balance error was controlled within 28.42 mm. ③ The spatial distribution of TWSA in the source areas of the Yangtze River and the Yellow River was mainly dominated by P and E， with explanatory power （q） of 0.46 and 0.44 respectively. The interactions among factors were mainly characterized by nonlinear or two-factor enhancement effects. Among them， the q of E∩T， P∩T， E∩DEM and P∩DEM were 0.66， 0.63， 0.63 and 0.62 respectively， and their explanatory power for TWSA was particularly prominent. The single-factor explanatory power of temperature was limited， but temperature regulates key hydrological processes such as precipitation and evapotranspiration，and it showed stronger influence in interaction. Meanwhile， P∩NDVI （q=0.55） and E∩NDVI （q=0.56） also showed a strong influence.

The variation in the water surface of Dongting Lake directly affects flood routing pathways， the water surface elevation at water intakes， and the exposure of sandbars. Its characteristic river-lake transition morphology—“a vast expanse during floods， a narrow line during dry seasons”—poses significant challenges to flood control and disaster mitigation， water supply safety， and wetland conservation in the lake area. Understanding the water surface variation characteristics during Dongting Lake’s river-lake phase transition period is crucial for scientifically advancing water security， aquatic ecological restoration， and water environment management. However， most existing studies primarily rely on multispectral satellite remote sensing data， which are susceptible to cloud interference， to analyze long-term trends in the lake’s water surface. There remains a lack of high-temporal-resolution research on Dongting Lake’s water surface during its river-lake phase transition period. To address this gap， this study constructed a Sentinel-Landsat virtual constellation to overcome the spatial limitations of hydrological stations and the constraints of optical remote sensing satellites being easily affected by meteorological factors. Additionally， a method for reconstructing the water surface area when remote sensing images do not fully cover Dongting Lake was proposed， improving the average usable remote sensing image cycle for Dongting Lake from 13.4 days per image to 4.4 days per image. Based on the Dongting Lake water surface dataset derived from the Sentinel-Landsat virtual constellation imagery and combined with measured hydrological data from various stations around Dongting Lake， the study identified four main stages in the lake’s water surface morphology changes： “fully lake phase，” “ river-lake phase transition with river dominance，” “fully river phase，” and “river-lake phase transition with lake dominance.”Furthermore， the water level at Lianhuatang was found to be the key driver of Dongting Lake’s river-lake phase transition. Specifically， the Lianhuatang water level below 24 m corresponds to the “fully river phase.” The Lianhuatang water level between 24–28 m indicates the “river-lake phase transition with river dominance.” The Lianhuatang water level between 28–32 m represents the “river-lake phase transition with lake dominance.” And the Lianhuatang water level above 32 m signifies the "fully lake phase."

Coastal sluices play a crucial role in coastal disaster prevention and mitigation， water resource regulation， and shipping safety. However， many coastal sluices often face the problem of missing meteorological data such as wind speed and air pressure due to insufficient monitoring equipment or harsh environments. This leads to low accuracy of traditional tide level prediction models that rely on multi-source data， which may early warning in extreme scenarios， exacerbating flood risks and losses. Therefore， this study constructs a tide level prediction method suitable for scenarios with missing meteorological data to improve prediction reliability. The study uses three-year hourly historical tide level data from a coastal sluice in eastern China as the only input. After preprocessing such as outlier removal， missing value imputation， and harmonic analysis to repair the data， the Informer time series prediction model is introduced to predict the tide level for the next 24 hours when meteorological data is missing， and is compared with the widely used LSTM model. The dataset is divided into training， validation， and test sets in an 8∶1∶1 ratio， and the performance of the model is evaluated using two key indicators： mean absolute error （MAE） and root mean square error （RMSE）. The results show that the Informer model has significant advantages. Compared with the LSTM model， the MAE of the Informer model is reduced by 63.9%， and the RMSE is reduced by 53.1%， significantly improving the prediction accuracy. In addition， due to the optimization of the self-attention mechanism， the Informer reduces the computational complexity of processing long sequences， thereby significantly shortening the training time. In conclusion， the Informer model effectively addresses the limitations of traditional models in scenarios with missing data， enhances the ability to predict extreme tide levels and prediction reliability， provides a practical solution for areas with weak meteorological monitoring， and also lays the foundation for subsequent integration of multi-source data to optimize predictions.

Flood forecasting for small mountainous catchments presents a significant challenge within hydrological science. Conceptual hydrological models， typified by the Xin’anjiang （XAJ） model， often struggle to accurately capture the intricate runoff generation and routing processes in mountainous terrain due to their simplified structures， frequently resulting in suboptimal forecasting accuracy. While data-driven models， such as Long Short-Term Memory （LSTM） networks， exhibit powerful fitting capabilities， their inherent “black-box” nature provides limited support from physical mechanisms. To overcome these limitations， this study develops a hybrid physics-data model that leverages an LSTM network to correct the forecast residuals of the XAJ model and incorporates SHAP for an interpretable attribution analysis. The model was validated using 15 flood events from 2015 to 2018 in the Qiaodong Village catchment， a typical small mountainous watershed in Zhejiang Province. Results indicate the following： ① All XAJ-LSTM hybrid models demonstrated a significant improvement over the standalone XAJ model， which served as a baseline. The configuration that integrated the XAJ-forecasted discharge and soil moisture state variables yielded the optimal performance， with the Nash-Sutcliffe efficiency coefficient markedly increasing from 0.55 to 0.77. ② The introduction of more information does not necessarily enhance model performance； supplementing the optimal configuration with additional runoff component data resulted in performance degradation due to informational redundancy. ③ From a mechanistic perspective， the SHAP analysis confirmed that antecedent observed discharge and XAJ-forecasted discharge are the key drivers influencing the residual correction model’s decisions. This reveals that the integration of physical information effectively guides the data-driven model's learning process， shifting its focus from mere statistical fitting to reliance on more robust physical constraints. The proposed physics-data hybrid model， which combines high accuracy with strong interpretability， offers a new paradigm for the development of reliable and trustworthy intelligent hydrological forecasting models.

The accuracy of the underlying surface runoff generation process fundamentally determines the simulation precision and forecast reliability of urban waterlogging models， which is of critical importance for urban flood control and drainage， as well as for enhancing comprehensive disaster prevention and mitigation capabilities under the context of climate change. In current urban waterlogging research， for areas lacking rainfall and flood data， the comprehensive runoff coefficient method is commonly used to derive a fixed runoff coefficient for runoff generation calculations. However， due to the combined influence of various rainfall conditions and complex underlying surface factors， the actual surface runoff generation process exhibits inherent dynamic characteristics during individual rainfall events， making static runoff coefficients inadequate for accurately capturing the true response process. To address this， this study takes the Dakeng River Basin in Guangzhou City as a case study and employs both the Hydrus-1D model and the MIKE URBAN model to investigate the importance of considering the temporal variability of the runoff coefficient during two distinct rainfall events in 2023 （the “8.24” event with a duration of 14.75 h and rainfall amount of 72.15 mm； the “9.7” event with a duration of 34.42 h and rainfall amount of 225.5 mm）. The results demonstrate that the MIKE URBAN model using static parameters performed well in simulating the runoff process for the short-duration rainfall event， with an average R2 of 0.95 and an average Nash-Sutcliffe efficiency （NSE） of 0.94. However， when applied to the long-duration event， although the overall trend of the simulated runoff process remained largely consistent with observations （average R2 of 0.95）， the average NSE decreased to 0.88， with a minimum value of only 0.76， indicating a decline in simulation accuracy. Further analysis revealed that the model systematically underestimated runoff during the middle and later stages of the rainfall， highlighting the inability of static parameters to effectively capture the dynamic changes in the runoff generation mechanism during these periods. After adopting segmented dynamic parameters， the simulation performance improved significantly， with the average R2 increasing by 1.6% and the average NSE increasing by 14.2%. Scenario simulations using the Hydrus-1D model further revealed that the variation of the runoff coefficient during a rainfall event is jointly controlled by rainfall conditions and the saturation level of the underlying surface. During continuous rainfall， as infiltration proceeds， the saturation of the underlying surface gradually increases， leading to a corresponding increase in the runoff coefficient. After the soil approaches full saturation， the change in the runoff coefficient stabilizes. For short-duration， low-volume rainfall events， the variation in the runoff coefficient is relatively small， and static parameters can still provide a reasonable fit. In contrast， for long-duration， high-intensity rainfall events， the runoff coefficient shows a significant increasing trend， which static parameters fail to capture， consequently leading to deviations in simulation accuracy. This research demonstrates the objective existence of temporal variability in the runoff generation process at the rainfall event scale and the necessity of accounting for this characteristic， providing a theoretical basis for refined urban waterlogging simulation and forecasting.

The Xiluodu Hydropower Station serves as a crucial component of China’s “West-East Electricity Transmission” project. Nevertheless， the region where it is situated exhibits prominent spatial discrepancies in water and sediment sources， resulting in a significant imbalance in sediment contribution between the main channel and tributaries. Therefore， understanding sediment erosion and deposition laws is essential for the long-term operational sustainability of the power station. Based on measured data since its operation， this study conducts a systematic analysis of the total sediment accumulation and distribution patterns in the reservoir. The results show that considering the uncontrolled areas， the total sediment deposition amounted to 760 million tons from 2013 to 2023， with an average annual sediment discharge ratio of 3.1%. The process of sediment erosion and deposition has resulted in a 9.6% loss of the dead storage capacity， while the flood control capacity has seen a slight increase. Since 2021， with the operation of upstream hydropower stations， the incoming sediment load into Xiluodu Reservoir has significantly decreased compared with the value designed in the feasibility study. Coupled with progressive sediment consolidation， this factor has led to the occurrence of an “erosion” phenomenon in the main channel of the reservoir. In contrast， the tributaries continue to experience persistent sedimentation and have become the primary areas for sediment deposition. The longitudinal profile of the main channel has risen by an average of 14.9 meters. Due to the influence of natural hidden ridges in the river channel， the sediment deposition thickness in the 15-kilometer reach upstream of the dam generally does not exceed 3 meters. Based on the law of conservation of mass， the estimated consolidation settlement in typical reservoir sections ranges between 0.1 and 1.5 meters. Compared with the Three Gorges Reservoir， Xiluodu Reservoir is characterized by continuous longitudinal deposition， low sedimentation intensity near the dam， and a single adjustment form of cross-sections.

The asynchronous propagation characteristics of flood and sediment peaks have an important influence on the evolution of river. Based on the measured hydrological data of the Jianli-Hankou reach in the middle Yangtze River from 1990 to 2023， and under the premise of a systematic understanding of the asynchronous characteristics of flood and sediment peaks in the Yichang-Jianli reach， this study analyzes the spatiotemporal evolution and formation mechanisms of the asynchronous propagation of flood and sediment peaks in the Jianli-Hankou reach before and after the impoundment of the Three Gorges Reservoir （TGR） and the implementation of small-to-medium flood regulation. Results indicate that prior to TGR impoundment， sediment peaks in the Jianli-Chenglingji reach predominantly lagged behind flood peaks， whereas in the Chenglingji-Hankou reach， sediment peaks generally preceded flood peaks. After impoundment， the lagging phenomenon of sediment peaks in the Jianli-Chenglingji reach weakened， yet the characteristic of sediment peaks leading flood peaks downstream of Chenglingji–Hankou remained dominant. The asynchronous behavior of flood and sediment peaks along the Jianli-Hankou reach is jointly governed by the confluence of the Dongting Lake （which delays the propagation of flood peaks in the main stem）， progressive channel-bed erosion and sediment replenishment along the reach， and alterations in the flood hydrograph. Among these factors， the delay effect of Dongting Lake inflow on the main-stem flood peak constitutes the primary driver for the advance of sediment peaks downstream of Chenglingji. These findings provide a scientific basis for optimizing TGR operation and fluvial morphological evolution research in the middle Yangtze River.

The runoff and confluence processes in many gullies of Northwest China are characterized by very small base flows under normal conditions， but by rapid flood responses during the rainy season， featuring high flow peaks， large flow variations， and considerable sediment loads. For runoff observation stations built on such gullies， flow-measuring weirs must be capable of adapting to wide fluctuations in flow magnitude and the influences of sediment-laden water. In this study， a newly constructed compound-section flow-measuring weir at a runoff station in a gully of Northwest China was selected as the research object. A 1∶32 physical model was established to carry out flow capacity tests and analyze the stage–flow relationships across a large flow range of 4~1 000 m3/s. The test results were verified using an electromagnetic flowmeter to ensure measurement accuracy. The results indicate that during high-flow conditions， the entire cross-section of the weir conveys flow， and the relationship between measured flow and flow depth follows a quadratic function. The measured water level agrees well with the designed stage， and the fitting accuracy is satisfactory. Under low-flow conditions， water passes only through the measuring trough of the compound section， where the stage–flow relationship still maintains a quadratic pattern， but the rate of flow increase is significantly reduced. A distinct inflection occurs as the flow transitions from the triangular section to the rectangular section. Near the two boundary transition points of the weir， the stage–flow relationship becomes less stable due to complex hydraulic interactions and local energy losses. Furthermore， the influence of sediment concentration on the flow capacity and flow coefficient was analyzed. When the sediment concentration is below 60 kg/m3， the flow coefficient of sediment-laden flow is nearly identical to that of clear water. As sediment concentration increases from 60 to 200 kg/m3， the flow coefficient decreases gradually with rising sediment content. However， when the sediment concentration exceeds 200 kg/m3， changes in the rheological properties of the sediment-laden flow lead to reduced turbulence intensity and energy dissipation， resulting in a subsequent increase in the flow coefficient with higher sediment concentration. Based on statistical regression analysis， empirical relationships between the flow coefficient of sediment-laden flow and discharge under different sediment concentrations were established. These relationships were further validated through flow measurements during the 2024 flood season. The findings provide a reliable scientific basis for the design and hydraulic calibration of flow-measuring weirs at runoff observation stations in similar environments.

Traditional single-station modeling paradigms ignore the spatiotemporal propagation characteristics of watershed water and sediment processes， leading to insurmountable physical deficiencies. Taking the Taohe River Basin as a case study， this research develops a deep learning prediction framework that incorporates spatiotemporal propagation constraints for water and sediment processes， achieving a transition from single-point independent prediction to multi-point collaborative prediction. The Dempster-Shafer （D-S） evidence fusion method integrates the results of Pearson correlation coefficient （PCC） analysis， maximal information coefficient （MIC） mutual information， and recursive feature elimination （RFE） to select 6~7 core predictive factors from 10 candidate factors， achieving a dimensionality reduction rate of 30%~40%. A Stacking ensemble model based on multilayer perceptron （MLP）， long short-term memory network （LSTM）， and Transformer is constructed， with a three-layer physical constraint mechanism designed to include temporal propagation time constraints， spatial continuity constraints， and spatiotemporal water-sediment coupling constraints. A four-stage spatial propagation prediction chain is established connecting Xibagou Station， Minxian Station， Lijiacun Station， and Hongqi Station. The SHapley Additive exPlanations （SHAP） method is employed for multi-dimensional driving mechanism analysis to identify the spatiotemporal evolution patterns of key driving factors. The results demonstrate that： the Nash-Sutcliffe efficiency coefficient （NSE） for runoff prediction consistently exceeds 0.85， while NSE for sediment prediction reaches 0.782~0.815，which are increased by 18.2% and 21.0% respectively compared to traditional methods， achieving excellent prediction performance； the physical constraint mechanisms effectively ensure the rationality of prediction results with constraint violation rates below 2.7%； current-month precipitation， antecedent runoff， and temperature are the most important driving factors， though their importance exhibits significant spatiotemporal differentiation； the driving mechanism has shifted from natural dominance to artificial intervention， with climate factor weights decreasing from 92.4% to 85.8% and artificial factor weights increasing fourfold. This study provides novel theoretical methods for watershed water and sediment process prediction and holds significant scientific value for accurate and reliable long-term runoff and sediment forecasting as well as watershed water resources management.

Underground engineering construction in karst regions is often affected by subsurface rivers， cavities， and other karst structures， making it highly susceptible to water and mud inrush disasters during the construction process. The geometric characteristics of karst structures， controlled by climatic， hydrological， and geological conditions within the region， directly govern the occurrence and movement of karst groundwater. However， the influence of karst structure morphology on water inrush processes in deep-buried tunnels remains insufficiently understood. In this study， based on site data from a tunnel excavation project， computational fluid dynamics （CFD） simulations were conducted to investigate water inrush processes induced by different karst structures under gravitational conditions， with a focus on the effects of conduit and cavity geometric parameters on tunnel water inrush behavior. The results indicate that， for a given inrush volume， the duration of the water inrush is primarily controlled by the cross-sectional area of the conduit： smaller cross-sections lead to slower flow and longer inrush durations， while the cavity cross-section has little effect on water level variation. The greater the distance between the tunnel and the water-bearing karst structure， the shorter the inrush duration. On the other hand， when the gradient of the subsurface river increases， the sensitivity of inrush duration to this distance decreases. Under equal conduit cross-sectional area， cavity-induced water inrush processes are characterized by slower flow and longer duration， whereas river-induced inrush events exhibit the opposite behavior. This study quantifies the inrush duration of tunnels under various karst structural and geometric conditions， elucidates the water level evolution in adits， analyzed the characteristics of the possible karst structures in the site based on simulation results， and provides a scientific basis for the optimization of underground engineering construction schemes.

To address the issue of unsatisfactory flow conditions in the forebay caused by a 90° bend connection between the intake channel and the diversion channel due to terrain and functional constraints， this study combines physical modeling and numerical simulation to optimize the flow patterns in the forebay of a medium-sized pump station， using velocity uniformity as the evaluation metric. The results show that the proposed composite optimization scheme—“arc-straight combined guide walls + perforated flow-rectifying sill + extended intake pool guide walls”—effectively eliminates problems such as recirculation zones and vortices present in the original design， improving the velocity uniformity to 86.75%. The optimized solution demonstrates significant effectiveness， and the findings can provide valuable references for the design and management of similar projects.

In this study， a representative elementary volume （REV）-scale coupled Darcy–Brinkman–Biot and Volume of Fluid （VOF） model is developed to investigate the hydrodynamic characteristics of bore-like dam-break flows impacting porous media over a wet bed， and systematically discusses the influence of downstream water depth on the hydrodynamic behaviors during the impact process. Simulation results reveal that， under high porosity conditions， the internal flow within the porous medium is dominated by inertial and gravitational forces， which induce discontinuous gas-liquid interfaces， easily forming air cavities， and viscous fingering structures. As the downstream water depth increases， the infiltration volume increases. Under the domination of capillary force， the maximum impact force shows a clear linear correlation with porosity. The total potential energy of upstream dam-break water decreases with the increase of downstream water depth， and the interaction between the wave front and downstream water is enhanced， resulting in the reduction of total impact force. Moreover， a good linear relationship exists between the maximum impact force and the dimensionless water depth ratio.

Numerical simulation of underwater jet-induced scour faces significant computational efficiency challenges due to the strong coupling of water-sediment multiphase flow， transient hydrodynamic behavior， and the discrete nature of sediment particles. To address this issue， this study employs a coupled Computational Fluid Dynamics and Discrete Element Method （CFD-DEM） approach to simulate the jet scouring process on a non-cohesive uniform sand bed. The simulations reveal strong correlations between scour performance and key physical parameters， including average shear stress， maximum shear stress， average shear velocity， maximum shear velocity， and average total pressure. Based on this finding， an innovative simplified strategy is proposed， which decouples the complex two-phase flow problem into a computationally more efficient simulation involving only single-phase water jet flow. Utilizing this strategy， the effects of key jet parameters—nozzle diameter， shape， and spacing—on scour efficiency are systematically investigated. The results indicate that， under the optimal nozzle spacing， square nozzle configurations， coupled with either increased flow rate or reduced diameter， significantly enhance the key physical quantities acting on the bed， thereby leading to improved scour performance. The proposed decoupled simulation method offers an efficient alternative for tackling underwater jet scour problems and opens new avenues for theoretical research and practical applications in related fields.

Under China's "Dual Carbon" strategy， high-head Pelton turbine units play a crucial role in hydropower development in the southwestern region. The lifespan degradation of jet needles due to sediment erosion under small-opening conditions deserves special attention. This study focuses on a high-altitude hydropower station， addressing the lifespan degradation of jet needles under small-opening conditions. Based on laboratory sediment tests， a hybrid modeling approach combining the Analytic Hierarchy Process （AHP） and Support Vector Regression （SVR） is proposed. The AHP method is employed to extract weightings for key operational features such as sediment concentration and particle size distribution， while the SVR algorithm is used to construct a predictive model for jet needle lifespan degradation. The model’s accuracy is validated using actual power plant data. The results indicate that under small-opening conditions （opening<0.4）， the additional lifespan degradation of jet needles increases significantly， with particle size exerting a dominant influence under low sediment concentrations.

In pumping stations of water transfer projects， multiple pumps are generally installed and operated in parallel. In order to quickly determine the water transfer flow rate， graph theory and recursive algorithms are introduced in the numerical solution method. The introduction of recursive algorithms makes the written computer program code concise， easy to read and understand， and with the use of graph theory， it can also make the program universal. When there are changes in different pump stations or the number of pumps participating in the operation of the same pump station， only the network topology diagram of the initial pump station device needs to be modified， and universal subroutines can be called to complete the calculation. It can provide reference for pump station design， operation and management， as well as smart pump station software development.

A full-flow-channel transient simulation was conducted on a prototype giant Francis turbine to investigate the evolution of the draft tube vortex rop e during the start-up transient process and monitor pressure pulsations across the unit. By visualizing the morphology and intensity of the vortex rope at different time instants and analyzing pressure pulsations at typical locations of hydraulic components， this study elucidates the evolutionary mechanisms of the draft tube vortex rope during turbine start-up and clarifies the sources of pressure pulsations along with their characteristic frequency components at distinct positions. Furthermore， the propagation patterns of pressure pulsation components within the main flow passage are summarized， providing insights for optimizing the operational stability of the turbine during start-up transients.

Research on the energy-saving and consumption-reducing optimization scheduling algorithm of pumping stations has significant practical significance for achieving the economic operation of pumping stations. In view of the shortcomings of high computational complexity and tendency to fall into local optimum in the common intelligent optimization algorithms used in the optimal scheduling of multi-unit and multi-constraint pumping stations， this paper applies the Multi-Objective Stochastic Paint Optimizer （MOSPO） to this field for the first time， and verifies its applicability by comparison with the NSGA-III and MOCS algorithms. Taking the minimum power consumption cost of the pumping station and the minimum sum of the squares of the water shortage in the water plant as the optimization goals， three algorithms were applied to solve the optimal scheduling model of the Weituo Pumping Station in the Western Chongqing Water Resources Allocation Project. Pareto solution sets under different load scenarios were obtained， and the optimal schemes of the MOSPO algorithm under 100%， 90%， and 80% load scenarios were selected as the final decision-making basis. The results show that the MOSPO algorithm outperforms MOCS and NSGA-III algorithms in terms of convergence index and uniform distribution index. The MOSPO algorithm shows significant energy-saving effects under different loads. The energy-saving rates of the optimal schemes under 100%， 90% and 80% load scenarios are 6.0%， 2.3% and 8.2% respectively， showing obvious optimization effect. Moreover， the dispatching scheme generated by the MOSPO algorithm conforms to the actual rule that “electricity consumption cost is positively correlated with water-lifting load”， verifying its applicability and feasibility in optimal dispatching of pumping stations.

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

This paper proposes a multi-stage filtering and speed deadband collaborative control method based on state decision-making， aimed at suppressing high-frequency noise， pulse noise， and pressure pulsation interference in the head signal of variable-speed pumped-storage units. Initially， median filtering and notch filters are employed to eliminate pulse noise and specific-frequency pressure pulsations， respectively. Subsequently， the operating condition state is dynamically detected using a sliding standard deviation. Finally， a combination of sliding weighted filtering and a first-order inertial link achieves deep noise reduction in steady-state and rapid transient response. The control precision and stability of the unit under complex conditions such as load sudden changes are ensured by a linear interpolation gradual switching strategy and speed deadband design. Simulation results indicate that this strategy improves the signal-to-noise ratio of the head signal， reduces the steady-state fluctuation amplitude， and shortens the transient response time. This method enhances the optimization accuracy of the coordinated controller and the dynamic condition adaptation capability of the unit.

To more accurately evaluate the economic benefits of hybrid pumped storage power stations in systems with high-proportion of new energy， this paper proposes a refined operating cost model for hybrid pumped storage power stations based on flow optimization. The model establishes a refined optimization objective based on unit flow， transforming the flow optimization， which is determined by power， and the avoidance of unit vibration zones into quantifiable economic targets. An improved particle swarm optimization algorithm with multiple integrated mechanisms is employed for solving the model， and Latin Hypercube Sampling and K-means clustering are used to generate multiple scenarios for validation. Taking a practical engineering case as reference， the results indicate that， compared to installing conventional units， the hybrid pumped storage power station achieves lower internal economic costs and higher economic benefits while ensuring no wind and solar curtailment and maintaining the supply-demand balance of the power grid. This study more scientifically reveals the significant economic value of pumped storage units in mitigating fluctuations and enhancing system stability， providing a more precise decision-making basis for the planning and benefit assessment of hybrid pumped storage power stations.

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

The Shule River Basin Irrigation District is a typical arid oasis-desert ecosystem and the utilization of cultivated land in this district is of great significance for coordinating regional cultivated land protection， sustainable agricultural development and ecological security. Based on high-resolution remote sensing images from 2017 and 2022， this study analyzed the spatiotemporal evolution characteristics of actually cultivated farmland， abandoned farmland， and saline-alkali farmland in the basin’s irrigation districts. The results are as follows： ① From 2017 to 2022， the area of actually cultivated farmland in the basin increased by 5 376.48 hm2， with the largest growth in the Huahai Irrigation District of 2 041.21 hm2. Among the newly added area， 67.37% originated from the improvement of abandoned and saline-alkali farmland. ② Abandoned farmland increased by 1 091.45 hm2， with a significant growth of 930.00 hm2 in the Shuangta Irrigation District and a decrease of 549.84 hm2 in the Danghe Irrigation District. Of the newly added abandoned farmland， 67.76% came from the conversion of unused land and cultivated land， and 78.73% of the transferred-out area was reclaimed into cultivated land. Spatially， it presented a “low fragmentation and high aggregation” pattern. Remarkable effects were achieved in the treatment of local patches in Huahai and Qiaozhi Irrigation Districts and the overall direction of centroid migration of abandoned farmland was opposite to that of actually cultivated farmland. ③ Saline-alkali farmland decreased by 1 213.59 hm2， with 542.14， 169.91 and 224.98 hm2 improved in the Shuangta， Danghe， and Huahai Irrigation Districts， respectively， while such farmland in the Changma Irrigation District continued to increase. 92.67% of its transferred-out area was converted into actual cultivated land or abandoned land. Saline-alkali farmland in each irrigation district was limited in quantity and relatively concentrated spatially.

As a major agricultural water-using province， Guangdong Province relies heavily on improving irrigation efficiency to ensure food security. Based on the quarterly water consumption data of 453 large and medium-sized irrigation districts surveyed in the province in the past five years， this study reveals the temporal and spatial dynamic patterns of irrigation water consumption across the province by using statistical indicators， trend analysis and Getis-Ord Gi* hotspot analysis. The results show that： the province’s large and medium-sized irrigation areas showed a significant decreasing trend in both irrigation water consumption and actual irrigated area between years， with an average annual decrease of 4.2% in irrigation water consumption （R2=0.886 5）， but the decreasing trend in irrigation water consumption per unit area is not significant （R2=0.439 1）， and the potential of water conservation remains to be explored. Affected by the water demand law of paddy rice， the third quarter each year is the peak water consumption period for large and medium-sized irrigation districts in the province， and the gross irrigation water consumption per unit area of large irrigation areas is slightly higher than that of medium-sized irrigation areas. The spatial differentiation of gross irrigation water consumption per unit area is significant， with the highest in the northern Guangdong， medium in the Pearl River Delta， fluctuations of high in winter and low in summer in the Leizhou Peninsula， and peak water use for late rice in the fourth quarter in the Chaoshan Plain. Irrigation efficiency is the dominant driving factor of irrigation water consumption per unit area in large and medium-sized irrigation areas of Guangdong Province. Particularly from the second to the fourth quarter， there is a significant negative correlation between irrigation efficiency and gross irrigation water consumption per unit area. Gross irrigation water consumption per unit area is negatively correlated with precipitation in all four quarters， but the correlation is significant only in the fourth quarter. Rice proportion is positively correlated with water consumption throughout the year （r=0.394~0.455 in the late rice season）， though not significantly. The proportion of vegetable contributes positively to reducing water use through water-saving techniques， while the proportion of other crops has insignificant effects. The results of the study revealed the spatial and temporal dynamic distribution pattern and influencing factors of irrigation water consumption in large and medium-sized irrigation areas of Guangdong Province， which can provide a basis for decision-making on intensive utilization and sustainable development of regional agricultural water resources.

To improve the prediction accuracy of machine learning algorithms for crop water requirements （CWR） in small and medium-sized irrigation districts with typical crops as representatives， this study constructs an iTransformer-TimesNet hybrid model. The Seasonal-Trend decomposition procedure using Loess （STL） method is employed to decompose and analyze historical meteorological data and irrigation records. Comparative validation is conducted against existing models including iTransformer， Transformer， and TimesNet. Taking the Hanyan Canal irrigation district of Qingtongxia as the study area， the research analyzes interannual CWR trends for spring wheat， spring maize， and rice—selected as representative crops—based on meteorological data from Yinchuan Station （2014-2023） and collected irrigation data. Multiple models are trained to predict 2024 CWR. Results demonstrate that the hybrid model achieves superior performance with an R2 of 0.95. Its mean absolute error （MAE） reaches 0.77 mm， outperforming iTransformer （1.08 mm）， Transformer （1.05 mm） and TimesNet （0.91 mm）. The root mean square error （RMSE） of 1.92mm also surpasses iTransformer and TimesNet （both 1.99 mm）. In CWR prediction， the hybrid model exhibits faster convergence and smaller deviations than all baseline models， confirming its effectiveness for regional CWR forecasting. Finally， the SHAP analysis method is used to analyze the contribution of each meteorological impact factor to CWR prediction in the hybrid model. SHAP analysis reveals that average temperature and solar radiation are the dominant meteorological factors influencing CWR predictions in this region. This study provides a novel and implementable approach for CWR prediction in small/medium irrigation districts.

To manage disinfection by-products （DBPs） risks and optimize disinfection strategies in small-scale rural water supply systems （e.g.， single-village water stations）， this study selected two typical single-village stations in Zhejiang Province as research subjects. Three gradients of sodium hypochlorite dosage （high， medium， and low） were applied， combined with seasonal sampling in summer and winter， to analyze the effects of source water type， season， and distribution distance on residual chlorine decay， disinfection efficacy， and DBPs formation. By measuring residual chlorine， microbial indicators， and concentrations of six DBPs， a water quality model was developed to simulate water quality at the consumer endpoint， and a targeted disinfection strategy based on peak-off-peak water consumption patterns was proposed. The results demonstrated that： ① Due to significantly higher organic matter concentrations in stream-weir water sources compared to groundwater （p<0.01）， DBPs detection rates at the pipeline endpoints were higher； ② Seasonal influence was significant： trichloromethane concentrations were higher in summer （up to 54.52 μg/L）， while trichloroacetic acid was more readily formed in winter （up to 156.53 μg/L）. Redundancy analysis indicated that the influencing factors followed this order： season ≈ permanganate index ≈ TOC > residual chlorine > pH； ③ Disinfectant dosage determined risk level： low dosage resulted in low microbial compliance rates at endpoints （e.g.， only 37.5% at Station A）， while high dosage significantly increased DBPs risks. Medium dosage achieved a balance between the two； ④ Model simulations based on peak-valley water consumption patterns （peak， normal， and low-use periods） showed that dynamically adjusting the effluent residual chlorine according to time periods could improve sensory experience while ensuring compliance with terminal residual chlorine and DBPs standards. In conclusion， controlling source water organic matter should be prioritized for single-village water stations， with a recommended effluent residual chlorine ≤ 1.5 mg/L and minimum concentrations determined based on the specific supply-consumption characteristics of each system. Implementing time-based targeted disinfection in accordance with peak-off-peak water use patterns can simultaneously ensure safety and user experience.

Under the dual pressures of climate change and agricultural restructuring， the dynamics of irrigation water demand have become increasingly complex， requiring multidimensional analysis of its drivers and evolution. This study focuses on Hubei Province， a key agricultural region in the middle Yangtze River， estimating annual irrigation water demand from 1990 to 2023 using crop area， yield， value， and meteorological data. By applying both additive and multiplicative Logarithmic Mean Divisia Index （LMDI） models， the dominant driving factors were identified across different periods and hydrological year types. Results show a general downward trend in irrigation demand， with an average annual decrease of 223 million m3. Economic water intensity and technological efficiency were the main restraining factors， while value transformation and land productivity factors promoted the increase of water demand. Planting structure and scale had varying regulatory effects across time. Spatially， irrigation trends converged structurally but varied in magnitude across cities. Hydrologically， normal years yielded the best water-saving performance， wet years offered more regulatory flexibility， and drought years need to rely on technological solutions. This study reveals the dominant mechanism and regional differences of irrigation water demand change in Hubei Province. These findings highlight that， while ensuring agricultural output， enhancing irrigation efficiency and reducing water consumption per unit value， and providing a basis for regionally adaptive and climate-resilient water management strategies are key approaches to realize agricultural water conservation and guarantee food security.

Throat width is a key structural parameter for measuring flumes to form critical flow. It significantly impacts the Froude number and directly relates to the hydraulic performance of measuring flumes. Currently， the selection of measuring flumes mostly relies on experience and lacks a quantitative correlation between these two factors， making it difficult to meet the requirements of precision irrigation. To address this issue， this study focused on elliptical-straight flumes and explored a flume selection method based on the Froude number for determining the throat width. Experiments were conducted using three different types of elliptical-straight flumes in U-shaped channels under four slope conditions. The dimensionless relationship between the Froude number and throat width was systematically derived through dimensional analysis. A calculation formula for the Froude number was established using multiple linear regression. The results show that the established Froude number formula exhibits high accuracy， with an average relative error of 2.27%， a maximum relative error of 6.34%， and a maximum absolute error of only 0.03. This formula establishes a quantitative relationship model between the Froude number and throat width. It can guide the scientific selection of throat width and clarify the upper limit of its reasonable value range. The study provides references for the optimal selection of flumes in irrigation district open channels.

Based on the goal of basically realizing rural water supply modernization by 2035， this study explores and excavates the connotation of rural water supply modernization， and proposes that the evaluation system of rural water supply modernization can be divided into two parts： regional rural water supply modernization and rural water supply engineering modernization. On this basis， four indicator systems are constructed， including sound systematic layout， intensive and safe facilities， standardized and professional management， and high-quality and efficient services. The three-level indicators and target values of regional and engineering rural water supply modernization are determined. Guided by the development of rural water supply construction with new development concepts， supported by advanced technology and equipment for engineering construction， and implemented through operation and management with modern management methods and systems， efforts shall be made to build a high standard rural water supply engineering facility system， a high-level rural water supply operation and management system， a high-quality rural water supply quality guarantee system， an efficient rural water supply service system， and an efficient rural water supply guarantee system， so as to achieve modernization of rural water supply.

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

With the rapid development of water diversion and transfer projects in China， water conveyance tunnels， as core hydraulic structures， play a pivotal role in achieving the overall benefits of the projects. However， due to the long distances and complex geological conditions leading to significant sectional differences along these tunnels， existing safety evaluation methods face challenges of inadequate adaptability and weak integration capabilities for monitoring data， making it difficult to accurately assess tunnel safety conditions. This paper proposes a novel safety evaluation method integrating K-means clustering and the cloud model： tunnel zoning is achieved using the K-means clustering method； a combination weighting method is employed to balance subjective and objective weights； and a safety evaluation system is constructed based on the cloud model. Taking a long-distance inter-basin water supply project as a case study， deformation monitoring data from a 1，350-meter section of the main tunnel were analyzed. The K-means algorithm partitioned the tunnel into five distinct zones （clusters）. Cloud model evaluation indicated that four zones （cluster 1， etc.） were in a "normal" state， while cluster 3 was "basically normal". The comprehensive evaluation concluded that the entire tunnel is currently in a "basically normal" state， aligning with its actual operational condition. This method， combining zonal evaluation with fuzzy processing， enhances assessment accuracy， provides a scientific basis for tunnel safety monitoring， and contributes to the safe and efficient operation of water diversion and transfer projects.