[Objective] With the continuous expansion and deepening of development in the Pearl River Estuary and the sustained rapid economic growth, 1.8 to 2 billion tons of industrial and domestic wastewater are discharged into the estuary annually, exacerbating local water pollution. At the same time, shoreline variations during engineering construction have made the process of water quality evolution more complex. The aim is to investigate the spatiotemporal distribution characteristics of typical pollutants in the Pearl River Estuary and their response patterns to shoreline variations during the development processes such as industrial development, engineering construction, and land reclamation from 1990 to 2020. [Methods] A three-dimensional hydrodynamic and water environment coupled model for the Pearl River Estuary was established to simulate the transport and transformation processes and the spatiotemporal distribution of key water quality parameters under shoreline conditions in 1990 and 2020. [Results] The simulation result showed that from 1990 to 2020, under the influence of the narrowing of Lingdingyang Bay, chlorophyll-a concentration decreased in the upper estuary but increased from the central region to the estuary mouth. The land connection project at Gaolan Island impeded water exchange and chlorophyll-a transport between Huangmaohai Sea and the open sea, leading to an increase in chlorophyll-a concentration in the upper Huangmaohai Sea. Additionally, the distribution of suspended sediment and nutrients underwent significant variations due to the alterations in residual flow structures and chlorophyll-a distribution. Compared with the 1990 shoreline condition, under the 2020 shoreline condition, the average concentrations of nitrate and ammonium in the entire estuary during summer decreased by 28.8% and 33.8%, respectively, while the average concentrations of chlorophyll-a, suspended sediment, and silicate increased by 12.6%, 15.5%, and 1.9%, respectively. [Conclusion] During the development of the Pearl River Estuary, shoreline variations have led to increased flow velocities within the estuary while decreasing residual flows at the estuary mouth, thereby altering the spatiotemporal distribution of water quality parameters throughout the estuary, especially near the engineering areas and the estuary mouth.

[Objective] To investigate the adsorption and desorption behaviors and associated environmental risks of polyethylene(PE) microplastics toward four antibiotics(SDZ, SMX, NOF, OFL) under hydrodynamic conditions. [Methods] A horizontal oscillating grid tank was employed to simulate actual aquatic environments. Adsorption-desorption experiments were performed to analyze the adsorption and desorption characteristics of PE toward antibiotics. Additionally, the effects of microplastic concentration and turbulence intensity on the adsorption process were examined. [Results] The adsorption process reached equilibrium after 40 hours. Kinetic model fitting result indicated that the adsorption was mainly governed by chemisorption and jointly controlled by internal and external diffusion, while the desorption process was dominated by physical desorption. An increase in PE concentration ultimately reduced the unit adsorption capacity by about 30%, and a gradual increase in turbulence intensity led to an approximate 10% increase in adsorption capacity. [Conclusion] Both PE concentration and hydrodynamic turbulence intensity affect the adsorption capacity of PE: High PE concentration leads to particle aggregation, thereby reducing adsorption efficiency. Strong turbulence conditions enhance adsorption by accelerating molecular diffusion and particle dispersion. The desorption process exhibits potential chemical interactions, which may increase the environmental transfer risk of antibiotics. The dual role mechanism of PE as a carrier of antibiotics under hydrodynamic conditions is revealed, providing a theoretical basis for assessing the ecological risks of combined pollution.

[Objective] To investigate the characteristics of aquatic community structure and water quality in the Chishui River during the wet season, thereby providing important references for establishing water ecological assessment standards in the Yangtze River Basin. [Methods] During the wet season in 2023, field sampling surveys were conducted along the mainstream of the Chishui River. Redundancy analysis(RDA) and Pearson correlation were used to analyze the community structure characteristics of phytoplankton, zooplankton, zoobenthos, and periphytic algae. Water quality was assessed using aquatic biological indicators(dominant species indicator method and biodiversity indices) and physicochemical indices(water quality index, WQI, and comprehensive pollution index, CPI). [Results] The result showed that a total of 69 species of phytoplankton, 45 species of zooplankton, 30 species of zoobenthos, and 34 species of periphytic algae were collected. Dominant species included 7 phytoplankton, 7 zooplankton, 4 zoobenthos, and 3 periphytic algae. Physicochemical assessment indicated that the river was generally lightly polluted. Biological assessments, primarily based on plankton biodiversity indices, indicated light to moderate pollution. RDA and Pearson correlation analyses demonstrated that phytoplankton were affected by total nitrogen, total phosphorus, permanganate index, and conductivity. Zooplankton were affected by conductivity, total nitrogen, and ammonia nitrogen. Zoobenthos were influenced by ammonia nitrogen and dissolved oxygen. Periphytic algae were affected by dissolved oxygen and five-day biochemical oxygen demand. [Conclusion] During the wet season, plankton are significantly affected by environmental factors such as nutrients and pollutants. Both biological and physicochemical assessments indicate the presence of pollution in the Chishui River during the wet season. Continuous monitoring shows that there is a certain degree of recovery after the end of the wet season.

[Objective] High water quality ensures the survival and reproduction of aquatic organisms and maintains biodiversity. Variations in the water quality of inflow rivers of the Miyun Reservoir are closely related to the ecological protection of its upstream watershed. [Methods] Water quality monitoring data from 2000 to 2020 at Daguan Bridge Station on the Bai River and Xinzhuang Bridge Station on the Chao River located upstream of the Miyun Reservoir(Beijing's drinking water source) were used. The Seasonal Mann-Kendall(S-MK) trend analysis, Spearman correlation analysis, and Long Short-Term Memory(LSTM) prediction model were applied to reveal the characteristics and evolution trends of water quality variations in the inflow rivers of the Miyun Reservoir. [Results] The result indicated that total nitrogen at Daguan Bridge increased year by year from a minimum of 1.85 mg/L to 2.09 mg/L, and at Xinzhuang Bridge from 4.89 mg/L to 5.93 mg/L. From 2000 to 2020, total nitrogen failed to meet Class Ⅱ standards and was the main influencing factor of the water quality variations in the inflow rivers of the Miyun Reservoir. Specifically, population growth and GDP contributed over 90% to water quality variations, while precipitation, temperature, and evapotranspiration contributed approximately 9%, indicating human factors as the primary drivers of water quality. LSTM predictions showed that in the next ten years, the water quality of inflow rivers of the Miyun Reservoir would generally remain within Class Ⅱ-Ⅲ standards. Total nitrogen and nitrate nitrogen could reach a maximum value of 8.36 mg/L in certain months, showing significant fluctuations. Seasonal variations in water quality required particular attention. [Conclusion] By analyzing water quality variations, exploring driving factors, and predicting future water quality trends, the findings provide references for water resource management in the watershed of Miyun Reservoir and for water quality protection research in rivers in northern China.

[Objective] Under the new hydrological conditions, the groundwater level in Beijing has been continuously rising and has reached a historical high, posing a threat to urban safety. Extreme precipitation has further accelerated this groundwater rise, making it urgent to investigate the response mechanism of shallow groundwater to extreme precipitation events, assess the impact of groundwater level rise on urban resilience, and explore strategies for groundwater regulation. [Methods] Taking 2021 and 2023—two years with extreme precipitation events—as examples, the percentile thresholds of extreme precipitation were calculated, and the spatiotemporal distribution characteristics of precipitation in the multi-year average(1983—2023), 2021, and 2023 were analyzed. Based on data from 360 shallow groundwater monitoring wells in the study area, the response process of shallow groundwater levels to extreme precipitation events in extremely wet years was quantitatively analyzed. [Results] The result showed that:(1) the annual average precipitation in Beijing over the past 40 years, in 2021, and in 2023 was 564.9 mm, 864.5 mm, and 690.0 mm, respectively. Precipitation was mainly concentrated between June and September, with 2021 being the wettest year in the past 40 years.(2) Among 20 typical meteorological stations in Beijing, the 95% thresholds for extreme precipitation in 2021 and 2023 were 23.0~56.6 mm and 19.3~89.7 mm, respectively. The number of extreme precipitation days during the year ranged from 4 to 6 in 2021 and from 3 to 5 in 2023. The annual contribution of extreme precipitation ranged from 35.5% to 56.7% in 2021 and from 28.1% to 67.1% in 2023. The intensity of extreme precipitation ranged from 45.3 to 123.1 mm·d^-1 and from 25.8 to 217.8 mm·d^-1, respectively. The annual maximum daily precipitation ranged from 48.4 to 223.3 mm and from 37.9 to 306.8 mm, respectively.(3) A comparison of groundwater levels between the wet and dry seasons in 2021 and 2023 showed that the groundwater level generally increased by 0~5 m, with the maximum annual increase exceeding 15 m.(4) The groundwater depth in urban areas was generally less than 20 m, with leakage occurring along some metro lines. The infiltration capacity of rainwater storage and flood regulation project in the western area was significantly reduced. [Conclusion] Due to differences in surface permeability, piedmont areas are more receptive to atmospheric precipitation recharge than those in the eastern and southern plains. However, different rainfall types result in different recharge effectiveness for groundwater. Long-duration continuous precipitation provides more effective groundwater recharge than short-duration intense rainfall. Elevated groundwater levels affect the normal operation of metro systems, underground structures, and urban flood control projects. Coupled with rapid recharge and slow discharge of groundwater, the additional recharge from extreme precipitation poses a serious challenge to urban resilience. Therefore, it is essential to develop scientifically grounded urban resilience plans that consider groundwater recharge and discharge characteristics and the impacts of extreme precipitation.

[Objective] China is one of the countries most severely affected by heavy rainfall from tropical cyclones,incurring annual economic losses amounting to hundreds of billions of yuan.Understanding the variation characteristics of these events is crucial for flood prevention and disaster mitigation efforts. [Methods] Based on hourly rainfall data from 1 185 meteorological stations in China and the best track dataset of tropical cyclones,heavy rainfall events were defined using an absolute threshold method.Linear regression was employed to analyze the characteristics of hourly-scale heavy rainfall events induced by landfalling tropical cyclones in China from 1980 to 2020.The analysis focused on the spatiotemporal distribution of these events and the interannual variation trends in frequency,intensity,and duration of events with different durations (short-duration:1~6 h,medium-duration:7~12 h,and long-duration:>12 h). [Results] (1) Heavy rainfall events induced by landfalling tropical cyclones mainly occurred in Hainan Island and the southeastern coastal areas of China,with their intensity weakening toward inland and northern areas.The overall intensity of such events showed an increasing trend.The duration of heavy rainfall events tended to decrease from south to north and from coastal to inland areas,with Hainan exhibiting the longest duration (6.22 h/a).Areas with increasing trends in event frequency,intensity,and duration were mainly located along the southern coast,the lower reaches of the Yangtze River,and Shandong Province.(2) In rainfall events induced by landfalling tropical cyclones with different durations,long-duration events accounted for the highest proportion (39.75%) and showed an increasing trend in frequency along with short-duration events.The rainfall intensity of both types was increasing,but the duration of long-duration events continued to rise,while that of short-and medium-duration events was decreasing. [Conclusion] From 1980 to 2020,the intensity of heavy rainfall events induced by landfalling tropical cyclones has increased,and event durations have been prolonged,exerting profound impacts on the socioeconomic development and ecological security in some regions of China.The increasing frequency,intensity,and duration of long-duration heavy rainfall events pose significant challenges for disaster prevention in both coastal and inland areas of the country.

[Objective] To address the intensifying drought under global climate warming and the limitations of existing drought monitoring models that consider overly simplistic factors, a Comprehensive Drought Monitoring Model based on Ensemble Learning Algorithms(CDMM_MLEA) for the Yellow River Basin is proposed. [Methods] This model integrates multi-source remote sensing data, comprehensively considers the impacts of crop canopy temperature, crop morphology, vegetation greenness dynamics, soil moisture fluctuations, and crop canopy water status on drought monitoring, and accurately characterizes the spatiotemporal evolution of drought in the Yellow River Basin over the past 20 years. [Results] The result showed that:(1) in drought monitoring of the Yellow River Basin, CDMM_MLEA outperformed other models, particularly in the upper reaches, with an average correlation coefficient of 0.46 and a root mean square error as low as 0.81.(2) Compared with the three-month-scale Standardized Precipitation Evapotranspiration Index(SPEI03), demonstrated significant advantages in monitoring drought events in 2002 and 2010. It accurately identified the boundaries of moderate drought areas in the upper reaches and improved the continuity of drought extent. In the middle reaches, it effectively captured abrupt changes in drought intensity and complex fluctuation characteristics. In the lower reaches, it provided smoother transitions in drought severity and significantly enhanced spatial continuity.(3) revealed the seasonal variation characteristics of drought in the Yellow River Basin from 2001 to 2023. In spring, 35.2% of the upper reaches showed humidification, while 28.7% of the middle reaches experienced intensified drought. In summer, the Loess Plateau in the middle reaches showed a pattern of “slow in the east and rapid in the west”. In autumn, drought frequency increased by 15% in the middle reaches and locally expanded by 15% in the lower reaches. During the growing season, drought intensified across the entire river basin, with the middle reaches most severely affected. Temporally, after 2011, drought intensity across the river basin decreased by 25%, but in the lower reaches, drought duration extended from 2.55 per event to 2.32 months per event. The model effectively captured the spatiotemporal variability of drought across the river basin. [Conclusion] The findings provide scientific method ological support for accurate regional drought monitoring and serve as a basis for optimizing decision-making on drought prevention and mitigation strategies.

[Objective] To study the movement characteristics and key influencing parameters of debris flow, construct a debris flow disaster risk prediction model, and provide data support for assessing the disaster risk of transportation infrastructure caused by debris flows. [Methods] Taking the debris flow disaster in Wei Ziping Village, Xi'an City, Shanxi Province as the research object, a debris flow movement model based on the depth-integrated continuum mechanics theory was established. The friction coefficient and debris flow discharge were selected as key parameters to explore the variation laws of debris flow velocity and mud depth in the accumulation area, and evaluate the risk level of the area where the debris flow exits the gully. Combined with the multiple linear regression, polynomial regression and support vector machine models, the prediction equations for the relationship between debris flow discharge, friction coefficient and flow velocity, and mud depth were established. [Results] There is a significant positive correlation between the flow velocity and the flow rate of debris flow. The flow velocity fluctuates under different flow rate conditions, with the maximum flow velocity ranging from 2.58 m·s^-1 to 8 m·s^-1. The mud depth keeps increasing with the increase of flow rate, and the maximum mud depth range is 0.5 m to 4 m. The friction coefficient is negatively correlated with both the flow velocity and the mud depth of the debris flow. With the increase of the friction coefficient, the maximum flow velocity and the maximum mud depth show an overall downward trend. The risk prediction result show that the risk of debris flow increases significantly with the increase of debris flow. The increase of the friction coefficient can effectively reduce the risk, and the effect is significant at a lower debris flow, while the effect of frictional resistance is not significant at a larger debris flow. The support vector machine model has a better prediction effect on the maximum flow velocity of debris flow, while the polynomial regression model has the best prediction effect on the maximum mud depth in the debris flow accumulation area. [Conclusion] Monitoring the flow of debris flows to predict the flow velocity and the depth of the accumulated sediment in the affected area plays a crucial supporting role in pre-disaster assessment of the risk level of debris flow and post-disaster auxiliary evaluation of the severity of debris flow.

[Objective] Total water consumption control serves as a key indicator of the most stringent water resource management system. Faced with continuously fluctuating water demand, traditional single prediction models fail to effectively capture such dynamic adjustments. [Methods] To enhance the accuracy of total water consumption prediction, based on current research progress in this field, the development trends of existing prediction method for total water consumption and their applicability are systematically reviewed from the dimensions of statistical patterns, water consumption mechanisms, water consumption quotas, and model construction. [Results] The results show that China's early prediction result of medium-and long-term total water consumption are generally higher than the actual data. Water consumption predictions based on statistical patterns rely heavily on historical data, demonstrating better reference value for short-term predictions. The prediction method based on water consumption mechanism is suitable for experimental sites or at relatively small-scale areas, but cannot be directly applied to macro-planning. The prediction method based on water consumption quota is applicable to medium-and long-term, regional predictions for different water users. The model construction-based prediction method, while requiring high-quality data and strong dependence, demonstrates high prediction accuracy and strong adaptability. [Conclusion] Early predictions of long-term total water consumption suffer from limited accuracy. Furthermore, the water consumption trend in China demonstrates nonlinear evolution characteristics, and the existing prediction method have limitations in applicability and accuracy. Therefore, future research should focus on comprehensive integration of multiple method or multilayer model construction to enhance scientific validity and practical applicability.

[Objective] Topographic data of river channels is fundamental for hydrological monitoring. However, in data-scarce regions, existing method struggle to obtain high-accuracy river channel information, which has become a key bottleneck for hydrological observation. Using a 7-km river segment upstream and downstream of the Benzilan hydrological station on the Jinsha River as the study area, this study applies an integrated multi-source remote sensing approach to construct river channel topography, aiming to provide new technical pathways and solutions for terrain modelling in complex river segments and data-scarce settings. [Methods] Based on an unmanned-aerial-vehicle-derived digital elevation model(UAV-DEM) and measured cross-sectional elevation data from the hydrological station, river channel topography within water-level fluctuation ranges from dry and flood seasons was constructed by integrating surface water elevation data from the Surface Water and Ocean Topography(SWOT) satellite and shoreline data extracted from high-resolution remote sensing imagery. Additionally, comparative analysis was conducted with the river channel topographic data(GF7-DEM) generated from GF-7 stereo imagery during the dry season. [Results] The result showed that after correction, the GF7-DEM showed improved accuracy, with RMSE reduced from 0.717 8 m to 0.601 9 m, MAE reduced from 0.561 5 m to 0.476 2 m, and STD from 0.717 5 m to 0.596 m, and R² increased from 0.909 5 to 0.930 1, indicating enhanced representation capability for large-scale river channel morphology.(2) The multi-source remote sensing-derived DEMs—RBF-DEM, EBK-DEM, and IDW-DEM all showed good spatial consistency when compared with UAV-DEM. The RBF method showed optimal performance, achieving the lowest errors(MAE=1.063 9 m, RMSE=1.330 5 m), the least dispersion(STD=1.284 9 m), and the relatively high correlation.(3) For large-scale river channel morphology representation, the corrected GF7-DEM(RMSE=0.601 9 m, MAE=0.476 2 m, STD=0.596 m, and R²=0.930 1) outperformed the RBF-DEM(RMSE=1.330 5 m, MAE=1.063 9 m, STD=1.284 9 m, and R²=0.792 9) in both accuracy and stability. However, when GF7 data were incomplete, the RBF-DEM served as a more effective alternative for terrain reconstruction in data-scarce regions. [Conclusion] GF7-DEM demonstrates strong capability in representing river channel topography, but it has high data acquisition requirements, necessitating the use of dry-season data for DEM generation and offering limited spatial coverage. In contrast, SWOT satellite data are abundant. Together with GF7-DEM, they complement each other to form an approach for acquiring topographic data in data-scarce regions. This provides fundamental support for research applications and management in different fields, including remote sensing-based runoff monitoring, river hydrodynamic process simulation, digital twin watersheds, river habitat evolution.

[Objective] To explore the constraints of water resources during the rapid urban development, analyze the interdependent relationships between human, water, and city, and investigate the coupling mechanisms of the human-city and water-city pathways, thereby providing references for determining a reasonable scale for urban development. [Methods] Taking Ningxia as the study area, historical urban development data from 2006 to 2022 and the constraints of urban water supply were integrated. The human-city MP index, binary regression analysis of population size and economic structure, and the comprehensive land water use indicator method were used to analyze the reasonable range of built-up area. By balancing the rigid constraints on water resources with urban population and economic development demands, the “water-based city development” scheme was determined. [Results] In 2025, the urban water use constraint of Ningxia was projected to be 1.001 billion m³, with an urban population of 4.748 4 million. Based on these figures, the reasonable controlled area for built-up areas was calculated to be 763.33 km², representing an increase of 85.87 km² compared to the current situation(2022). To meet the control requirements of “water-based city development” scheme, the average water use in built-up areas needed to reach 1.41 m³/m², an increase of 79.7% compared to the current level. The water use per capita for the entire region was expected to be 0.43 m³/d, an increase of 23.1% compared to the current level. It was indicated that the urban water use intensity significantly improved. [Conclusion] Urban development is closely related to water resource conditions. By analyzing the relationship between rational urban scale and water resource constraints through the “water-based city development” scheme, it effectively balances the relationship between urban population growth, economic development, and sustainable water resource utilization. This provides both theoretical support and practical guidance for the scientific development of cities in water-scarce regions, promoting the coordination among population, resources, and environment.

[Objective] Precipitation input errors are the major source of flood forecasting errors, and integrating multi-source precipitation information is an important approach to reduce such errors. Satellite precipitation products with high spatiotemporal resolution can better capture the spatiotemporal distribution of precipitation events, but their application is limited due to large point estimation errors. A multi-source precipitation fusion method based on machine learning and Bayesian model averaging(BMA) is proposed to improve the accuracy of precipitation data at spatiotemporal scales. [Methods] Firstly, a bilinear interpolation method was used to perform spatial downscaling of three satellite precipitation products(GSMaP, IMERG, and PERSIANN). Then, the light gradient boosting machine(LGBM) algorithm was utilized for precipitation bias correction. Finally, the corrected precipitation products were fused based on the seasonal-scale BMA to obtain precipitation data with higher accuracy. [Results] The river basin upstream of Xiashan station in the upper reaches of Ganjiang River was selected for case validation. The result showed that:(1) the fused precipitation was significantly better than the original satellite products in all six evaluation indicators(with a root mean square error of 4.73 mm, a correlation coefficient of 0.92, and a false alarm ratio of 0.28).(2) Compared with the original satellite products, the spatial accuracy of the fused precipitation data was significantly improved and showed better consistency with ground observation stations.(3) The root mean square error values of both the single-satellite corrected precipitation and the multi-satellite fused precipitation were significantly lower than those of the original satellite products under five precipitation magnitudes: light rain, moderate rain, heavy rain, rainstorm, and heavy rainstorm. [Conclusion] Overall, the multi-satellite fused precipitation data integrates the advantages of each corrected precipitation product and performs well across different precipitation magnitudes, providing accurate precipitation input data support for hydrological simulation and forecasting.

[Objective] Sand-gravel strata distribute widely in flash-flood-prone valleys. Under the combined action of flash-flood impact and local scour, the evolution mechanism of bearing capacity of inclined pile group is not clear at present. In order to explore the influence of complex geological formation conditions on the bearing capacity of pile group, finite element simulations of a 2×2 inclined pile group under different working conditions is carried out, and the bearing capacity curves of inclined pile group under different loads are obtained. [Methods] This study considers the heterogeneity of soil along the depth direction with Gibson soil model, and subsequently builds a finite element model taking the flash-flood impact, local scour, and nonlinear pile-soil interaction into account. The model is validated with published data of in-situ test. [Results] The result show that increase of the inclination of piles during an appropriate range could reduce the settlement and increase the vertical bearing capacity of the pile group; When the inclination of piles exceeds a certain value, the embedment depth of piles in the gravel stratum will decrease significantly, and consequently the pile group settlement will increase as a result of declined vertical bearing capacity; Due to the fact that the gravel stratum has a large particle size and high stiffness in comparison with the sandy stratum, the decrement of pile group settlement is not obvious when increasing the inclination of piles; The increase of inclination of piles could effectively improve the horizontal bearing capacity of pile group; The settlement and horizontal displacement of pile group increase significantly with respect to the formation of local scour hole, and consequently will lead to the deterioration of bearing performance of pile group. [Conclusion] After all, the bearing capacity of inclined pile group does not increase monotonically with the increase of inclination angle under the condition of composite strata with cobble at the bottom. With the increase of inclination angle, the bearing capacity of pile group subjected to the impact force of flash flood increases obviously. Therefore, the inclined pile group in sand-gravel strata should be designed with a reasonable inclination angle with regard to the engineering geological condition, with which the inclined pile group could attain expected bearing performance under the action of vertical load and flash flood.

[Objective] As an important hydraulic phenomenon, hydraulic jumps are widely used for energy dissipation in water conservancy projects. Hydraulic jumps with low Froude numbers tend to form wavy structures, and the energy dissipation rate is often unsatisfactory, resulting in severe scouring and damage to the downstream energy dissipation and anti-scouring facilities. [Methods] Therefore, the weakening mechanisms of energy dissipation by hydraulic jumps with low Froude numbers are analyzed and summarized from the perspectives of mechanical mechanisms and external conditions. The understanding of the structure and energy dissipation pathways of hydraulic jumps with low Froude numbers is deepened by considering water body structure, bubble entrainment, and their interactions. Based on the understanding of the weakening mechanisms, key technologies and mechanisms related to stilling basin design, stilling basin structure optimization, auxiliary energy dissipator application, and aeration rectification energy dissipation are reviewed to guide engineering design. Additionally, key technologies are highlighted for addressing derivative problems such as cavitation erosion of auxiliary energy dissipators, sediment deposition in stilling basins, and fish mortality caused by supersaturated dissolved gases in water. [Results] It is found that due to the inherent complexity of hydraulic jump structures and the limitations of modern flow measurement techniques, systematic knowledge gaps remain in the research on the turbulent structures and energy dissipation mechanisms of hydraulic jumps with low Froude numbers. In engineering applications, there is a lack of universal design criteria to guide engineering optimization, and the establishment of an engineering ecological impact assessment system is urgently needed. [Conclusion] In the future, physics-informed neural networks and machine learning technologies can be used to carry out further research in three aspects: in-depth investigation of two-phase flow mechanisms of hydraulic jumps, optimization of energy dissipator design method, and the establishment of multi-objective evaluation systems for engineering schemes.

[Objective] Local flow disturbances caused by changes in gate leaf combinations and water intake layers in stepless stratified water intake systems are difficult to quantitatively characterize using traditional head loss methods. To address this challenge, their dissipation characteristics and spatial distribution patterns under complex structural conditions are investigated, thereby providing methodological support for the identification and optimization of energy losses under complex hydraulic structures. [Methods] Based on the entropy production theory, an analytical framework for sub-item energy consumption was constructed. On the basis of verification through a 1∶20 physical model test, three-dimensional numerical simulation was used to quantitatively analyze the viscous dissipation(EPDD), turbulent dissipation(EPTD), and wall friction dissipation(EPWS) of the inlet system under typical operating conditions. [Results] The results showed that the total entropy production of the system increased with the upward movement of the water intake layer. High-level water intake triggered drop impact and shaft flow reconstruction. The shaft section was the main dissipation zone, and the maximum entropy production of the system reached 2 800.468 W·K^-1. The entropy production contribution of the stepless stratified water intake device remained below 8% under different operating conditions, indicating good hydraulic stability. EPTD was the dominant dissipation mechanism, accounting for more than 98% of the entropy production in all operating conditions. Local high-dissipation regions were mainly concentrated in the gate leaf-cross brace junction, shaft drop zone, and pipeline inlet region. [Conclusion] Entropy production theory can effectively reveal the spatial distribution and underlying causes of energy loss in complex hydraulic structures, offering greater diagnostic depth and optimization guidance than traditional head loss methods. The findings clarify the relationship between structural disturbances and energy consumption distribution in stepless stratified water intake systems, providing theoretical support for system structural optimization and operational scheduling and holding significant implications for improving system energy efficiency and operational safety.

[Objective] With the ongoing advancement of energy transition, the “low-inertia and low-damping” characteristics of power systems have become increasingly prominent, and a large number of virtual synchronous generators(VSGs) have been integrated into power systems. However, existing research has rarely considered the impact of VSGs on the transient stability of power systems. To maintain the stable and economic operation of power systems, the transient stability-constrained optimal power flow(TSCOPF) model is optimized for power systems incorporating VSGs, and a VSG-transient stability-constrained optimal power flow(V-TSCOPF) model is proposed. [Methods] First, the VSG model was embedded into the traditional TSCOPF model to characterize the dynamic characteristics of VSGs. Second, the input features were optimized, and the virtual rotor angle of VSGs was incorporated into the scope of the transient stability index(TSI). A spatiotemporal graph attention network(ST-GAT) was employed to extract the relationships between input features and TSI. Then, the ST-GAT was embedded into the TSCOPF model containing VSGs to form the V-TSCOPF model based on optimized features, and the model was solved efficiently using a quantum genetic algorithm(QGA). Finally, simulation validations were conducted on the 10-machine 39-bus and 16-machine 68-bus systems. [Results] The results showed that in the 10-machine 39-bus and 16-machine 68-bus systems, the V-TSCOPF model based on optimized features significantly improved system stability compared to the TSCOPF model based on traditional features. During the simulation validation of the obtained operating modes, the operating mode obtained by the V-TSCOPF model caused all generator rotor angles to converge after actual fault occurred, while the operating mode obtained by the TSCOPF model exhibited rotor angle divergence after the fault. The ST-GAT model achieved an R² value of 0.988 4, converged after 22 iterations, and the optimized system cost was 422 919 yuan RMB. The QGA algorithm performed excellently in terms of convergence speed and solution accuracy. [Conclusion] The results show that the V-TSCOPF model based on optimized features effectively ensures power system stability by optimizing input features and integrating the virtual rotor angle of VSGs, realizing online adjustment of VSG parameters, and providing novel insights for addressing transient stability issues in VSG grid-connected systems. The synergistic application of ST-GAT and QGA achieves precise characterization of transient stability constraints and rapid solving of complex models, providing solutions for the secure and economic operation of new-type power systems.

[Objective] The morphological evolution mechanism of weakly tidal estuarine channels is complex. Clarifying its response to runoff and sediment dynamics is crucial for river channel regulation and flood control. [Methods] Systematic physical model experiments were conducted to observe the complete dynamic evolution process of meandering channels under different runoff and sediment conditions. Based on the mechanisms revealed by the tests, and combined with river resistance theory, parameters including channel slope, Froude number, and bed sediment size were introduced to construct characteristic morphological parameters. Historical hydrological cross-sectional data from the lower reaches of the Yellow River were classified using support vector machine(SVM), and quantitative criteria for river pattern identification were established. [Results] The experimental result indicated that river channel evolution exhibited a typical three-stage pattern: stable, slight change, and strong change. Runoff and sediment conditions dominated the evolution mode, where high flow triggered intense erosion and channel avulsion, while high sediment concentration led to deposition and channel widening. Based on this, quantitative thresholds for identifying wandering, braided, and meandering river patterns were established. Validation using recent river channel data confirmed the effectiveness of these criteria in identifying pattern transitions. For instance, the Sunkou-Aishan section was identified as transitioning towards a braided pattern, which was consistent with interpretations from remote sensing imagery. [Conclusion] Physical modeling is an effective approach for revealing river channel evolution mechanisms. The constructed morphological parameter model facilitates the transition from qualitative description to quantitative discrimination, offering theoretical guidance for river channel management.

[Objective] The Kissimmee River Restoration Project aims to address ecological degradation caused by human activities by rehabilitating riverine habitats, restoring biodiversity within the basin, and reducing pollution to downstream lakes, while simultaneously maintaining the river's flood-control function. [Methods] To restore hydraulic connectivity between rivers and lakes and between rivers and floodplains, during the restoration of Kissimmee River, water flow was first redirected into the original river channel by backfilling artificial river channels, and then the discharge volume of the upstream reservoir was controlled to simulate historical hydrological conditions of the downstream river. The scouring effect of the water flow addressed the issue of nutrient accumulation in the original river channel. Meanwhile, the natural channel morphology of the original river channel and the erosion and sedimentation in the hydrological process were leveraged to restore the meandering of the river. In order to restore the biodiversity of the river basin, the project regulated discharge of the upstream reservoir to reinstate seasonal flood inundation patterns in the riparian zone. Additionally, the extensive floodplain wetlands downstream were reconstructed, providing habitats for numerous animals, plants, and microorganisms. [Results] After more than 20 years of implementation, the Kissimmee River Restoration Project achieved remarkable result. Water quality improved notably, with a significant increase in biodiversity across the river basin ecosystem. Riparian wetlands largely restored their water storage and flood control functions, while also purifying natural water bodies and providing habitats for wildlife. [Conclusion] The Kissimmee River Restoration Project has not only rectified the destructive consequences of human intervention in the natural environment, but has also become an important milestone in global ecological management and restoration practices. The successful implementation of the project has improved biodiversity and water quality, while bringing positive benefits to the local society and economy, such as protecting agricultural development and promoting natural tourism. This case demonstrates the key role of hydrogeomorphic restoration in the ecological restoration of rivers and lakes. The underlying restoration logic of the project conforms to the current principles of Nature-based Solutions, integrating flood control with ecological environment protection to achieve the stability and sustainable development of the river ecosystem.

[Objective] Landslides are a common type of geological hazard that may induce a series of severe secondary disasters when the sliding mass accumulates and blocks river channels. Previous studies have mainly focused on deposition characteristics of landslides in dry channels, while systematic understanding of deposition behavior under the influence of water remains inadequate. The influence of varying water depths on the deposition process and geometric characteristics of landslide materials still needs to be examined. [Methods] Through laboratory physical model experiments, the deposition characteristics of uniform quartz sand with a particle size of 2.5 mm under slope gradients of 35° and 45° were systematically investigated as it slid into dry river channels(water depth of 0 cm) and channels with different water depths(3 cm, 6 cm, 9 cm, and 12 cm). The landslide volumes were set at 500 cm³, 1 000 cm³, 1 500 cm³, and 2 000 cm³. High-speed photography and three-dimensional point cloud scanning techniques were employed to record the deposition process and morphology. The river channel deposition volume ratio λ was defined to characterize the degree of deposition. [Results] Water depth significantly affected the deposition process and morphology of the landslide materials. Under dry river channel conditions, the deposits exhibited an uneven thickness distribution, with a thicker front and thinner rear. In the presence of water, the deposit thickness became more uniform, and a “double landslide head” phenomenon was observed. The river channel deposition volume ratio λ decreased with increasing water depth. At θ = 35°, λ decreased from 0.36 under dry river channel conditions to 0.19 at H = 12 cm, representing a reduction of 47.2%. At θ = 45°, λ decreased from 0.72 to 0.41, representing a reduction of approximately 43.1%. A multiple regression analysis was conducted to establish a prediction model relating λ to water depth, slope gradient, and landslide volume, with their influence on λ ranked as: slope gradient > landslide volume > water depth. [Conclusion] The presence of water significantly reduces the proportion of landslide materials deposited in river channels by increasing resistance and providing a buffering effect. The value of λ increases with increasing slope gradient, and decreases with increasing landslide volume and water depth. It is recommended that further studies incorporate factors such as the gradation of landslide materials and dynamic changes in the riverbed to explore deposition characteristics under more complex conditions. These findings provide a quantitative basis for risk assessment and mitigation of landslide-induced river blockages.

[Objective] In light of the current challenges in predicting potential sliding surfaces of slopes, such as high difficulty and low accuracy, this study attempts to quantitatively predict the potential sliding surface of soil slopes using neural networks, based on real-time slope displacement monitoring data. [Methods] Based on the principle of stochastic potential sliding surface generation, this study employs the discrete element program 3DEC to simulate the entire process of slope instability under randomly generated slip surfaces. Subsequently, a neural network model capable of predicting potential sliding surfaces is developed by integrating sample data and a cascading algorithm, which is then validated through both laboratory tests and real-world engineering case studies. Finally, a dynamic slope safety early-warning system is established. [Results] The indoor test result show that both the established cascade correlation neural network(referred to as the “CC” neural network) model and the backpropagation feedforward neural network(referred to as the “BP” neural network) model can, to some extent, map the implicit relationship between slope displacement and the potential sliding surface. Compared to the BP neural network, the average consistency of the real-time sliding surface curve predicted by the CC neural network is 0.973, by comparison increase of 0.07. Moreover, the average relative error of the predicted cohesive force of the sliding surface soil is 14.99%, by comparison decrease of 9.93%, and the average relative error of the internal friction angle is 10.12%, by comparison decrease of 10.25%. The application of the CC neural network model in practical engineering projects shows that the consistency of the predicted real-time sliding surface curves is greater than 0.99, while the relative error of the real-time predicted values of soil shear strength is less than 14%, with the overall prediction accuracy exhibiting an upward trend. [Conclusion] The result indicate that the formation and stability state of the potential sliding surface in a slope can be further reflected by the distribution and evolutionary trend of surface displacement. Analysis of laboratory slope model tests shows that the CC neural network, owing to its unique self-adaptive architecture, achieves higher accuracy and stronger adaptability in predicting slope sliding surfaces. By integrating slope surface displacement with AI-powered neural network prediction technology, it is possible to rapidly and accurately intelligently predict potential sliding surfaces and their mechanical properties. This approach enables convenient, efficient, and reliable assessment of potential instability hazards in slopes.

[Objective] To fully utilize the temporal information of remote sensing imagery, dynamically identify irrigated areas and their spatial distribution, and provide data support for enhancing irrigation water management capabilities. [Methods] Taking the Yongji Irrigation Area in the Hetao Irrigation District of Inner Mongolia as the study area, spatiotemporal fusion of MODIS and Sentinel-2 data was performed using the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model(ESTARFM) to calculate and construct a daily time series of the Re-modified Perpendicular Drought Index(RPDI). To address the flattening of irrigation characteristics in spatiotemporal fused data, a Bi-directional Long Short-Term Memory(Bi-LSTM) network based on sliding window was used to identify irrigation events and dynamically monitor the irrigated area and its spatial distribution. [Results] From April to June in 2024, the actual irrigated areas in the Yongji Irrigation Area were 323.88 km², 462.67 km², 500.57 km², respectively. The irrigation event identification achieved an average overall accuracy of 89.82 % and an average kappa coefficient of 0.77, effectively reflecting the dynamic spatiotemporal variation of irrigation events within the irrigation area. [Conclusion] Spatiotemporal fusion provides a more continuous image data foundation for monitoring actual irrigated areas. The Bi-LSTM method based on sliding window effectively captures the temporal variations in soil moisture content during irrigation. It addresses the challenge of irrigation identification caused by the flattening of soil moisture changes in the fusion of different spatiotemporal resolution data, thereby improving the capability for continuous and dynamic monitoring of actual irrigated areas.