[Objective] The Pearl River Basin is an important economic and densely populated area in China, frequently affected by extreme temperature and precipitation events. Studying the spatiotemporal variations of compound extreme events is vital for regional climate adaptation management. [Methods] Based on meteorological observation data from 1961 to 2020, this study employs the relative threshold method and the Standardized Precipitation Evapotranspiration Index(SPEI) to identify four types of compound extreme events: hot-dry, hot-wet, cold-dry, and cold-wet. Trend analysis and spatial distribution studies were conducted to reveal the spatiotemporal distribution characteristics and trends of these compound events. [Results] The result indicate a significant negative correlation between temperature and SPEI in the Pearl River Basin from 1961 to 2020, particularly prominent in the western and southern regions during summer and winter. Notably, there has been a substantial increase in hot-wet events and a significant decrease in cold-dry events, with the most pronounced changes occurring in the early 21st century. Hot-wet events were mainly concentrated in the southeastern and southern coastal areas of the basin, and hot-dry events frequently occur in the western and southern regions. Cold-wet events are common in the northwest, and cold-dry events are most frequent in the northeast. In terms of intensity, hot-dry events are mainly classified as strong or medium, with their duration far exceeding that of hot-wet, cold-wet, and cold-dry events. From 1991 to 2020, the occurrence frequencies of hot-wet and hot-dry events increased by 24% and 99%, respectively, while those of cold-wet and cold-dry events decreased by 9% and 41%. Regionally, hot-dry events are most frequent in plains and hilly areas, particularly in Hainan Island and the South China Sea Islands, whereas cold-wet events are more prominent in mid-altitude and high-altitude areas. The changes reveal a significant transformation in the regional climate. [Conclusion] The findings indicate a notable increase in the frequency of hot-dry and hot-wet events in the Pearl River Basin, especially in the southern and western regions, reflecting high-risk exposure in these areas under future climate warming. Conversely, the decreasing trend of cold-dry and cold-wet events suggests a shift toward a warmer and wetter climate. Through an in-depth analysis of the spatiotemporal characteristics and evolution of compound extreme events, this study provides important scientific evidence and references for climate prediction, response strategies to compound disaster events, and enhancing disaster prevention and mitigation capabilities in the Pearl River Basin.
[Objective] Floods are natural disasters triggered by factors such as heavy rainfall, rapid snow and ice melt, and storm surges, often resulting in significant economic losses and severe disruption to daily life. Conventional flood prediction primarily relies on traditional hydrological method and experience-based statistical models. However, in areas lacking long-term and continuous hydrological monitoring data, alternative data-driven method for flood prediction are essential. [Methods] Machine learning algorithms based on decision trees, including Random Forest, XGBoost, and LightGBM, demonstrated excellent performance in classification and regression tasks due to their interpretability and strong functions, making them suitable for flood prediction. A dataset containing 50 000 records and 21 variables was used to evaluate the flood prediction performance of these three algorithms, namely Random Forest, XGBoost, and LightGBM. Their performance was assessed based on prediction accuracy and key variable identification, with the ROC-AUC curve used for comparative analysis. [Results] The result showed that all three models achieved high prediction accuracy. Among them, the XGBoost model exhibited the lowest mean squared error(0.000 186 2) and the highest coefficient of determination(0.925 2). Moreover, the LightGBM model achieved the highest AUC value(0.99) in the ROC-AUC curve. The Random Forest model underperformed the other two across all indicators. [Conclusion] The findings indicate that XGBoost delivers optimal performance for flood probability prediction with lowest prediction errors, while LightGBM is the optimal choice for binary classification tasks, such as predicting flood occurrence.
[Objective] Dongting Lake, a crucial ecological barrier in China, not only plays a key role in regulating regional climate and maintaining biodiversity, but also has significant carbon sequestration capacity. Carbon stock and its variation trends in Dongting Lake area are analyzed in this study, and the effect of different hydrometeorological factors on the variation of carbon stock is investigated, aiming to provide a scientific basis for regional carbon management and ecological protection. [Methods] Based on the remote sensing data from 2003 to 2022 and the modified carbon density data, combined with the InVEST model, the dynamic variations of carbon stock in Dongting Lake area were quantitatively analyzed, and the spatiotemporal distribution characteristics of carbon stock and the influencing factors of carbon stock variations were discussed. [Results] (1) From 2003 to 2022, the average carbon stock in Dongting Lake was 7.605 6×108 t, with the lowest value in 2007 at 7.368 4×108 t and the highest in 2005 at 7.904 7×108 t. Overall, the regional carbon stock showed a declining trend, with an average annual decrease of 0.12%.(2) Carbon stock was significantly affected by land use. The average values of carbon stock per unit area in hilly regions(forest land), plain regions(grassland and cultivated land), and water areas(water bodies and floodplains) were 27.05 kg/m2, 14.27 kg/m2, and 1.99 kg/m2, respectively.(3) Variations in carbon stock were affected by hydrometeorological factors such as temperature and precipitation. Average annual temperature showed a negative correlation with carbon stock variations, while precipitation in winter(October to December) exhibited a positive correlation with carbon stock. [Conclusion] From 2003 to 2022, carbon stock in Dongting Lake area shows an overall declining trend, indicating a weakening of its carbon sink function. Significant differences are observed in carbon stock across different areas. The hilly regions contribute the most to carbon sink capacity due to their high forest coverage, while the water areas contribute the least. Land use changes, climate fluctuations, and hydrological conditions have significant effects on carbon stock. Rising temperatures inhibit carbon stock, while winter precipitation enhances carbon sequestration. Addressing climate change effectively and optimizing land use structure are key strategies for improving the carbon sink function of Dongting Lake area.
[Objective] Industry is one of the sectors with the highest concentration of water and energy use and is also one of the most carbon-intensive. The rapid development of the industrial economy has intensified the exploitation of water and energy resources, leading to a sharp increase in greenhouse gases such as carbon dioxide. To explore the water-saving and carbon-reduction potential of high-water-consuming industry in Jiangsu Province and to promote the rational development and utilization of water resources and energy, as well as the healthy, green development of industry, [Methods] adopting a comprehensive “water intake—water production—water distribution—water use—water discharge” perspective in Jiangsu Province, the social water system is simplified into five subsystems: water intake, water production, water distribution, water use, and water discharge. Energy consumption analysis for each subsystem under different water resource behaviors is conducted, establishing a relationship between water saving, energy consumption, and carbon reduction. Using the SFA model, the water-saving potential of high-water-consuming industry in Jiangsu Province is calculated, and scenarios with baseline, moderate, and high levels are analyzed to assess water-saving and carbon-reduction potential. [Results] The result show that in three scenarios, water conservation reduced energy consumption by 4.46×108 kWh, 4.50×108 kWh, and 4.54×108 kWh, respectively, and decreased carbon emissions by 347,000 tons, 349,700 tons, and 352,500 tons. [Conclusion] The findings indicate that water conservation in high-water-consuming industry will play an increasingly important role in supporting energy savings and reducing carbon emissions. The social water system reveals the relationship between industrial water conservation and energy savings at various stages but does not establish a direct link to carbon reduction. The SFA model can comprehensively consider factors such as population, economy, and water usage to calculate the potential for water conservation in high-water-consuming industry, offering significant application value. The analysis of water conservation and carbon reduction in high-water-consuming industry in Jiangsu Province demonstrates the substantial impact of water conservation on carbon reduction and provides a reference for industrial water management and energy distribution in Jiangsu Province.
[Objective] Vegetation net primary productivity(NPP) is a crucial component in the carbon cycle of terrestrial ecosystems. Investigating its spatiotemporal evolution and driving factors is of great significance for promoting regional ecological civilization development. [Methods] Taking the Jialing River Basin as the study area, the spatiotemporal evolution patterns and driving factors of vegetation NPP were analyzed based on the coefficient of variation, Theil-Median trend method, Mann-Kendall statistical test, R/S analysis, geodetector, and PLS-SEM model. [Results] NPP showed a fluctuating upward trend, with spatial variation increasing and then decreasing with elevation. The overall trend remained stable, with fluctuations characterized by “higher in the south and lower in the north”. Historically, extremely significant increases predominated, while future trends were divided into two types: continuous increase and shift to decrease. Spatial differentiation was mainly dominated by natural factors such as temperature(Temp), normalized difference vegetation index(NDVI), and digital elevation model(DEM), while anthropogenic factors such as GDP also had significant effects. In interaction detection, Temp∩Pre, NDVI∩GDP, and Temp∩NDVI demonstrated the strongest explanatory power. PLS-SEM revealed that topography indirectly promoted NPP the most by inhibiting climate deterioration and promoting vegetation growth. Climate directly inhibited NPP but indirectly exerted a positive regulatory effect through vegetation growth, while indirectly inhibiting NPP through human activities. Human activities had negative effects both directly and indirectly. Based on future risk zoning, vegetation and urban landscapes should be optimized in the Sichuan Basin. A synergistic artificial-natural restoration system should be constructed in the northwest alpine area. Ecological redlines and corridors should be strengthened in mountainous and hilly areas. Ecological compensation and soil and water conservation projects should be coordinated across the entire basin. [Conclusion] Vegetation NPP in the Jialing River Basin is mainly increasing, with spatial differentiation dominated by natural factors. Temp∩Pre, NDVI∩GDP, and Temp∩NDVI have the strongest explanatory power. Topography indirectly promotes NPP by inhibiting climate deterioration and promoting vegetation growth. Climate directly inhibits NPP but indirectly regulates it positively through vegetation growth. Human activities inhibit NPP both directly and indirectly. Future strategies should focus on zoned management.
[Objective] Achieving the coupling coordination of ecosystem services and human well-being is crucial for the development of regional ecological civilization. [Methods] By establishing an evaluation index system of human well-being, the spatiotemporal coupling coordination relationship and spatial effects in the Poyang Lake Basin from 2005 to 2023 were analyzed by using the modified equivalent factor method, coupling coordination degree model, and spatial econometric model. [Results] (1) During the study period, the Poyang Lake Basin was in the stage of “fluctuating decline in ecosystem service values and continuous improvement in human well-being”. The coupling coordination degree increased from 0.393 to 0.531, transitioning from moderate imbalance to coordination, with a spatial distribution of higher values in the north and lower values in the middle.(2) The coupling coordination degree showed significant positive clustering, with spatial correlation showing a trend of initial decrease, followed by an increase, and then another decrease. Overall, high-value clustering remained relatively stable, while low-value clustering shifted: high-value clustering types were mainly distributed around Poyang Lake, while low-value clustering types shifted from the vicinity of Yingtan City to industrial-focused districts and counties such as Ruichang City and Anyi County, with an expansion trend around the Yichun City area.(3) The increase in local coupling coordination degree was primarily influenced by positive spillover effects of vegetation quality, industrial structure, industrial development, government intervention, and economic vitality in neighboring districts and counties. Direct effects of industrial structure, industrial development, and economic vitality also contributed, while vegetation quality and government intervention had negative effects on the local coupling coordination development. [Conclusion] From 2005 to 2023, the overall coupling coordination in the study area has improved steadily. In the future, efforts should focus on mitigating the negative effects of government intervention and vegetation quality on local coupling coordination development. Additionally, achieving integrated improvement in the coupling coordination development of the Poyang Lake Basin can be facilitated by adjusting industrial structure and promoting industrial development.
[Objective] Urban flood disasters have caused significant damage to lives and property. An objective and accurate quantitative risk assessment of these disasters is crucial for enhancing urban resilience. [Methods] Huai'an City was selected as the study area. The urban flood risk indicator system was established using the “Hazard-Exposure-Vulnerability(H-E-V)” framework adopted by the IPCC. The relative importance of flood factors was calculated using the random forest algorithm to determine objective weights, while subjective weights were assigned using the Analytic Hierarchy Process(AHP). The Kendall coefficient was applied to test the consistency between the weights, and the optimal combined weight was calculated. The refined indicator weights were then used to conduct a detailed risk assessment of urban flood hazards in Huai'an City. [Results] The results showed that:(1) The Kendall test confirmed a coordination coefficient of W=0.145 6, indicating consistency between objective and subjective weights at the 0.05 significance level.(2) The influence of exposure and vulnerability was found to be significantly higher than that of hazard, particularly in areas near major water systems and in regions with high population density.(3) Medium to high-risk areas in Huai'an City were closely associated with the distribution of major rivers and water systems, while Qingjiangpu District, northwest Lianshui County, eastern Huai'an County, and southeastern Huai'an District were also identified as medium to high-risk zones due to the effects of population density and per capita GDP. [Conclusion] Validation using recent extreme disaster points indicated that 88% of these points were located in the medium to high-risk zones of the combined risk level map. These findings are expected to provide valuable insights for improving urban flood disaster management in the future.
[Objective] The issue of sustainable utilization of water resources in Shanxi Province is expected to become increasingly severe under the combined influence of natural and human factors. Therefore, accurate prediction of the development trend of the water resources ecological footprint in the province is essential for ensuring sustainable utilization of water resources. [Methods] A system dynamics model for the sustainable utilization of water resources in Shanxi Province was established using the water resources ecological footprint method and system dynamics method. Four scenarios were designed based on the result of parameter sensitivity analysis: continuation of the status quo(DS1), economic development(DS2), water conservation and pollution prevention(DS3), and comprehensive development(DS4). These scenarios were used to predict the level and degree of sustainable utilization of water resources in Shanxi Province from 2023 to 2050. [Results] The result showed that both the per capita water resources ecological footprint and the ecological carrying capacity in the four scenarios exhibited an increasing trend during the forecast period. However, the average value of the ecological footprint was more than 4.850 times that of the ecological carrying capacity, leading to a water resources deficit. The water resources ecological footprint per 104 RMB of GDP showed a decreasing trend over the years, indicating an effective improvement in water resources utilization efficiency. Despite this, the result of the ecological pressure index of water resources indicated that the pressure on water resources consumption remained high in the study area, and the current utilization was unsustainable. Predictions using the Tapio decoupling model indicated that the relationship between the water resources ecological footprint and economic development remained coordinated and sustainable in most years. [Conclusion] Through comprehensive comparison, scenario DS4 is identified as the most suitable future scenario for the study area. The development indicators associated with this scenario are conducive to promoting the sustainable development of both the socio-economic environment and water resources in Shanxi Province. However, for future water consumption, it is necessary to optimize the water consumption structure, improve water consumption efficiency across industries, and strengthen water conservation awareness across society to promote the sustainable utilization of water resources in the study area.
[Objective] Designing an accurate and efficient groundwater depth dynamics prediction model is crucial for the effective application of intelligent monitoring and early warning systems for substation drainage systems and for ensuring the safe and stable operation of substations. [Methods] Focusing on the pilot study project of the 220kV substation in the industrial park, a comprehensive evaluation was conducted on three machine learning models: Extreme Gradient Boosting(XGBoost), Random Forest(RF), and Long Short-Term Memory(LSTM). The performance of these models in predicting groundwater depth dynamics under heavy rainfall scenarios was analyzed in detail. The training data for the models were derived from a calibrated and validated groundwater flow numerical model, using prediction result of groundwater depth dynamics under various rainfall scenarios as benchmark reference values. To thoroughly assess the prediction accuracy and reliability of these models, the Nash-Sutcliffe Efficiency Coefficient(NSE), Root Mean Square Error(RMSE), Pearson Correlation Coefficient, and Mean Absolute Error(AE) were used as evaluation indicators. [Results] The research result showed that XGBoost, RF, and LSTM models could simulate groundwater depth dynamics consistent with the benchmark result over the time scale, with NSE, RMSE, and Pearson correlation coefficients reaching 0.999 8, 0.003 1 m, and 0.999 9, respectively. However, the spatial performance varied significantly. The AE simulated by the RF model was less than 0.01 m, the AE simulated by the XGBoost model was less than 0.26 m, and the AE given by the LSTM model was less than 0.12 m. When using model data from 20% of the grid points for machine learning training, the RF model still showed the best performance, and the time efficiency of model training and prediction improved by 5 times. [Conclusion] The groundwater depth dynamics prediction model based on machine learning models demonstrates excellent performance and shows promising application prospects in the intelligent monitoring and early warning systems for drainage systems.
[Objective] The issues of navigation obstruction and ecological degradation are caused by uneven hydrodynamic distribution in the river section of the Ganjiang River estuary. To address these, the optimization of flow diversion ratios after the impoundment operation of the Nanchang Hydraulic Hub is quantitatively analyzed, thereby improving the uneven hydrodynamic distribution in the river section of the estuary. [Methods] Based on hydrodynamic numerical simulation, a two-dimensional hydrodynamic model of the river section of Ganjiang River estuary was established. The hydrodynamic response characteristics were simulated under four operating conditions of historical flow diversion ratios from 1983 to 2018 when the hub impoundment raised the water level at Waizhou station to 15.5 m. The flow velocity variation patterns in each channel were analyzed. Combining navigation requirements, ecological flow demands, flow velocity requirements, and the flow capacity of each branch in the river section of the estuary, multi-objective constraints for water allocation were defined. The Max-Min criterion(maximization of minimum velocity method) was applied to optimize the flow diversion ratios. [Results] The result showed that after the hub impoundment, when the flow at Waizhou station was less than or equal to 2 000 m3/s, the water level difference between Waizhou and the downstream gates of each branch remained within 0.5 m, with smaller differences observed at lower flow rates. Under the optimized flow diversion ratio scheme, when the flow rate at Waizhou station was 500 m3/s, the minimum flow velocity in each branch increased by over 18.9% compared to the four historical flow diversion schemes. At 1 000 m3/s, the lower limit of flow velocity increased by more than 12.3%. At 2 000 m3/s, the lower limit of flow velocity in each branch increased by more than 15.4%, maintaining velocities within the range of 0.16 to 0.19 m/s and avoiding extreme flow velocity differences caused by other flow diversion ratio schemes. When the flow reached 4 000 m3/s, the upper limit of flow velocity reduced, keeping the velocities of all branches below 0.46 m/s, thereby alleviating the scouring of riverbeds caused by high flow velocity compared to other schemes. [Conclusion] Flow regulation via the Nanchang Hub effectively improves the uneven hydrodynamic characteristics among the branches. While meeting the basic ecological and navigation requirements of each branch, this regulation raises the lower limit of flow velocity, controls the upper limit, and addresses the issue of seasonal flow interruptions in the branches, thereby providing a decision-making basis for multi-objective coordinated water resource scheduling.
[Objective] Axial flow pumps play an important role in flood control and drainage, farmland irrigation, and other fields. The aim is to address the issues of internal flow and energy dissipation characteristics of axial flow pumps under partial load conditions. [Methods] An axial flow pump was selected as the research object. The internal flow, entropy production distribution, and energy dissipation characteristics of the axial flow pump were quantitatively analyzed under 80%, 70%, 65%, and 60% design flow conditions using numerical simulation and entropy production analysis. [Results] The result showed that the external characteristic calculations obtained from the numerical simulation matched well with the experimental result, with consistent curve trends, indicating that the numerical model and meshes used in the simulation were reasonable and reliable. The energy dissipation in the axial flow pump, from highest to lowest, was ranked as follows: impeller, guide vane, guide vane hub, 60-degree bend pipe, outlet pipe, inlet pipe, and water guide cone. Among them, the combined entropy production of the impeller and guide vane accounted for up to 75.93%, mainly due to turbulent dissipation caused by pulsating velocity. As the flow rate decreased, the entropy production in the impeller and guide vane regions increased, and the proportion of entropy production in the impeller increased, especially near the impeller rim. [Conclusion] The result indicate that the high entropy production regions in the impeller are concentrated near the leading edge, rim, and trailing edge on the suction side of the blades, extending from the leading edge to the trailing edge, with the maximum near the impeller rim. Flow separation occurs near the leading edge of the blades, and the recirculation vortex and wake flow on the suction side are the primary factors contributing to high entropy production in these regions. In the guide vane, high entropy production regions are concentrated near the leading edge of the blade inlet, within the flow passage, and near the trailing edge, resulting from flow impact on the blade inlet, passage vortices, and wake flow. As the flow rate decreases, the turbulent dissipation on the suction side at the same impeller cross-section increases, especially near the rim, due to increased flow separation and vortices forming on the suction side at low flow rates. As the flow rate decreases, the turbulent dissipation in the guide vane increases, which is caused by the increase of flow impact, passage vortices, and wake flow near the inlet of the guide vane at low flow rates. The research findings can provide a reference for understanding energy dissipation characteristics and optimizing the hydraulic design of axial flow pumps.
[Objective] To address the issues of low accuracy and poor stability in traditional hydropower generation capacity forecasting, [Methods] a hybrid prediction model for power generation capacity of hydropower systems, combining coupled mode decomposition, machine learning, and swarm intelligence was proposed in this paper. First, the original output sequence was decomposed and denoised using successive variational mode decomposition(SVMD) to extract multi-scale feature signals for classification and modeling. Subsequently, a least squares twin extreme learning machine(LSTELM) was employed to predict each decomposed signal, and an Improved Grey Wolf Optimization(IGWO) algorithm was used to optimize the model parameters, enhancing the prediction performance. Finally, the prediction result of each sub-sequence were integrated and aggregated to obtain the final result. [Results] The result demonstrated that the proposed method achieved accurate and reliable predictions for three hydropower stations. At the Chitan Hydropower Station, for 1-day forecast period, the proposed model improved the Nash-Sutcliffe efficiency(NSE) compared to the extreme learning machine model by 12.88% and 12.11% for direct and multi-input multi-output strategies, respectively. As the forecast period increased from 1 to 8 days, the NSE of the traditional method gradually decreased from 0.884 0 and 0.888 5 to 0.573 5 and 0.567 1, while the NSE of the two proposed strategies decreased from 0.997 9 and 0.996 1 to 0.942 3 and 0.928 6. [Conclusion] The findings indicate that the proposed model is highly stable and generalizable in predicting the generation capacity of complex hydropower systems. SVMD effectively reduces noise influence in the generation capacity sequence, and the least squares method and twin structure enhance the generalization ability of the LSTELM model. The SVMD-IGWO-LSTELM model demonstrates higher prediction accuracy for hydropower stations with stable hydrological characteristics, while prediction accuracy slightly decreases for stations with complex hydrological features, but still remains high. This model provides an effective approach for predicting hydropower system generation capacity under changing environmental conditions.
[Objective] Accurately assessing the ecological and environmental effects of hydropower development in high-altitude, ecologically sensitive regions is essential for maintaining the ecological security barriers of these areas. Therefore, an improved Remote Sensing Ecological Index(IRSEI) model is proposed to study the effect of hydropower construction on regional ecological environment quality in a specific area of the Qinghai-Xizang Plateau. [Methods] Taking a hydropower station in the Qinghai-Xizang Plateau as the study object, and considering the fragile soil and complex topography of high-altitude regions, a normalized difference mountain vegetation index(NDMVI) was established. At the same time, soil erosion modulus was introduced as a soil erosion indicator. An improved remote sensing ecological index(IRSEI) for high-altitude hydropower development areas was proposed. The spatiotemporal dynamics of the IRSEI and its driving factors were analyzed using method such as Theil-Sen slope estimation, Mann-Kendall trend analysis, and Hurst exponent. [Results] By replacing NDVI with NDMVI and incorporating the soil erosion indicator, IRSEI was more suitable for ecological quality monitoring in areas with complex topography. The results showed that hydropower development significantly affected the landscape structure of the basin, particularly grasslands and forests, leading to a reduction in their area. The average IRSEI value decreased from 0.53 before hydropower construction to 0.42 after construction, with the most significant ecological degradation occurring in the second year of construction. During the operational period, the value increased to 0.57. The IRSEI grades of forests and grasslands in the basin were mainly excellent and good, while construction and cultivated land areas were mainly poor and very poor. The future development trend of IRSEI was expected to stabilize, with more than 60% of the basin area remaining unchanged. The main controlling factors for the spatial differentiation of IRSEI in the study area were annual precipitation, average annual temperature, NDVI, and land use changes. [Conclusion] During the construction phase, hydropower development significantly affects the landscape pattern and IRSEI of the study area. During the operation of the hydropower station, the ecological environment of the study area shows an overall positive change, with ecological deterioration mainly concentrated in urban areas. In the future, it is essential to enhance ecological restoration in this basin, taking into account human activities and climate change, and ecological protection and restoration efforts should be carried out. The research findings provide theoretical and data support for implementing targeted ecological protection and promoting high-quality development in the basin.
[Objective] Reservoir scheduling is an important non engineering measure for the comprehensive utilization of water resources at present. In recent years, with the improvement of hydrological forecasting technology, the reservoir optimal operation combined with hydrological forecasting has received increasing attention. However, the impact mechanism of flood control and power generation benefits under the reservoir rolling forecasting optimized operation is still unclear. [Methods] In response to this issue, a rolling forecast optimization scheduling model for reservoir flood control was established, and the control variable method was used to analyze the impact of different flood levels, forecast periods, and dynamic control upper limits of flood season water levels on reservoir flood control and power generation benefits taking Xiajiang Reservoir as a case. [Results] The results show that:(1) The peak shaving rate of floods gradually decreases with the increase of the upper limit of dynamic control of water level during flood season.(2) The power generation of the reservoir increases with the increase of the upper limit of the dynamic control of water level during the flood season, and the maximum discharge flow also increases.(3) The larger the magnitude of the flood, the shorter the forecast period required for reservoir operation to achieve maximum peak shaving effect.(4) The difference in the optimization scheduling result of flood control rolling forecasting under uncertain and deterministic inflow conditions is relatively small. [Conclusion] In summary, under the reservoir rolling forecasting optimized operation, there are patterns in the impact of flood magnitude, forecast period, and dynamic control upper limit of flood season water level on flood control and power generation. Combined with reliable forecast information, raising the flood season water level limit of the reservoir can improve power generation efficiency under the premise of controllable risk. Taking a 50-year design flood and the forecast period is 72 hours as an example, compared with the upper limit of dynamic control of water level during flood season being 46 m, when the upper limit is set at 43.5 m, the average peak shaving rate only increases by 0.46%(about 104 m3/s), but the average power generation decreases by 30.55%(about 15.56 million kWh).
[Objective] On July 20th, 2024, a severe flash flood and debris flow disaster occurred in Xinhua Village, Malie Township, Hanyuan County, Ya'an City, Sichuan Province, affecting over 1 500 people and causing severe damage to infrastructure. A disaster review and analysis is conducted to investigate the formation mechanism of this compound flash flood and debris flow disaster, analyze the scope and degree of its impact, and propose targeted disaster prevention and mitigation measures, providing references for the prevention and control efforts of similar flash flood and debris flow disasters in the future. [Methods] Remote sensing measurement, field investigation, numerical simulation, and comparison with design indicators were employed. In the simulation analysis, the inferential formula method, area analogy method, and distributed hydrological simulation method were employed for multi-method comparative analysis to comprehensively determine the return period of this flood. [Results] Using the inferential formula method, the peak discharge of a 30-year return period design flood at the confluence of Dagotou and Malie Gully was calculated to be 286.00 m3/s. Based on surveyed flood marks, the peak discharge of this disaster was estimated to be approximately 263.00 m3/s using the area analogy method. By applying the distributed hydrological simulation method, the peak discharge of this disaster was calculated to be 249.70 m3/s. A comprehensive analysis indicated that the return period of this flood was approximately 30 years. [Conclusion] The analysis confirms that the intense rainfall is the primary triggering factor of this compound disaster. The unfavorable topographic, geomorphic, and geological conditions in the watershed exacerbate the severity of the disaster, while the blockage of bridges and culverts further expands the impact of the disaster. Moreover, deficiencies in early warning systems, emergency response mechanisms, and risk management during disaster response are key factors leading to the severe loss.
[Objective] It is of great significance for the study of karst collapse mechanism to clarify the role of water creep in overlying karst collapse. In order to determine the collapse mode of covered karst collapse under the action of underground corrosion, [Methods] A physical model device was designed and built, and the physical model test was carried out on the basis of field investigation, combined with the engineering geological conditions and drilling data in the study area, taking the collapse of Xinsheng Street and Minzhu North Road in the abandoned Yellow River area of Xuzhou as the research object. [Results] The result show that the collapse shape of Minzhu North Road is nearly circular, with an average collapse range of 35 cm and an average collapse depth of 26 cm. The collapse shape of Xinsheng Street is nearly circular, with an average collapse range of 23 cm and an average collapse depth of 17.75 cm. [Conclusion] The results show that the soil layer of Minzhu North Road has evolved from the initial “cave-type” collapse to the “hourglass-type” collapse, while the soil layer of Xinsheng Street has been an “hourglass” collapse from beginning to end. The size of the collapse pit of the “hourglass” collapse is related to the thickness of the soil layer; the thicker the soil layer, the larger the collapse range. The displacement curve of the surface layer of the soil at Minzhu North Road showed a fluctuating upward trend, while the displacement curve of the surface layer of the soil at Xinsheng Street showed a stepped upward trend. The faster the latent erosion rate, the faster the soil particle loss rate, the larger the soil settlement, and the more likely a ground collapse will occur. The larger the particle gradation of the original soil layer, the larger the particle gradation of the soil that passes through the pipeline after latent erosion, and vice versa. The experimental study of the evolution process and characteristics of karst collapse under latent erosion has practical significance and reference value for the prevention and control of karst collapse.
[Objective] Generally, the yield stress of debris flow is positively correlated with solid volume concentration and the enhancement of clay viscosity. It is also influenced by the properties of coarse particles as well. However, existing laboratory rheological experiments have not considered the influence of medium particle properties on yield stress. To investigate the influence of medium particle properties on yield stress and improve the yield stress calculation model, [Methods] the yield stress of debris flow was examined using four types of medium-particle quartz sand with different mesh sizes. By varying the solid volume concentration, medium particle size, and particle grading, debris flow slurry was prepared for laboratory rheological experiments, and the experimental data were statistically analyzed. [Results] Analysis and comparison result indicated that better medium particle grading result ed in lower yield stress, whereas poorer grading led to higher yield stress. When the solid volume concentration was C0< 0.47, medium particle size had little effect on yield stress. When C0≥0.47, medium particle size exhibited a negative correlation with yield stress. [Conclusion] The experimental result demonstrate that modifying medium particle grading and size, based on coarse particle studies, resulting a new volume concentration correction coefficient. This refinement further improves the yield stress calculation model, enhancing its scientific accuracy and reliability.
To address the challenges associated with the green recycling and reuse of blades(Glass Fiber Reinforced Polymer, GFRP) in the widespread use of wind turbines and their continuous capacity iteration processes, decommissioned wind turbine blades were processed into fiber rods through mechanical method, and their feasibility of application in hydraulic concrete was verified. Macroscopic, microscopic, and acoustic emission(AE) method were employed to investigate the effects of different fiber rod lengths and dosages on the workability, compressive strength, flexural strength, and fracture damage of concrete. The result indicate that a 60 mm-long fiber rod with a 0.5% volume dosage maximally enhanced the compressive strength of concrete by 21.35%, while an 80 mm-long fiber rod with a 1.5% volume dosage reduced the compressive performance by 10.42%. The flexural strength of concrete was enhanced by varying degrees ranging from 21.93% to 105.6%. AE signals revealed that the number of acoustic emission events significantly increased with the increase in fiber length and dosage, and these events were concentrated near the mid-span and cracks. Additionally, the fracture surfaces were uneven, and the cracks appeared more tortuous. Therefore, the mechanical bonding between the recycled fiber rods of wind turbine blades and the cement matrix is effective, exhibiting a strong bridging function. The feasibility of their application in hydraulic concrete is strong, and it is foreseeable that their large-scale recycling and utilization hold broad development prospects.
[Objective] To address the flood detention effect during the impoundment phase of reservoirs under construction and the coordination of flood control scheduling among cascade reservoirs, and to explore a dynamic allocation mechanism between impoundment in under-construction reservoirs and flood control storage capacity in existing cascade reservoirs to improve the efficiency of floodwater utilization and achieve coordinated optimization of flood safety and power generation benefits. [Methods] Taking the Yalong River cascade reservoirs as the study subject, a joint scheduling model for cascade reservoirs was developed. The Muskingum method was applied to simulate the flood process based on the design flood of a typical year. Combined with the storage constraints during the impoundment period of the Lianghekou Reservoir, the equivalent ratio between the impoundment volume of the Lianghekou Reservoir and the flood control storage capacities of the downstream Jinping I and Ertan Reservoirs was quantified. [Results] The result showed that when the Lianghekou Reservoir aimed for an impoundment level of 2 835 m during its initial operation period(2022), it could effectively replace 1.592 billion m3 of flood control capacity in the downstream cascade reservoirs. Under the premise of ensuring flood control safety in the basin(flood control capacity≥2.5 billion m3), the dynamic regulation range of the total usable capacity of the cascade reservoirs could reach 2.6~4.2 billion m3. In actual scheduling, joint operation of the cascade reservoirs raised the water levels of the downstream Jinping I and Ertan Reservoirs, thereby avoiding reservoir water spillage. [Conclusion] The result show that the flood detention effect during the impoundment phase of reservoirs under construction can be incorporated into the joint flood control scheduling system of cascade reservoirs. By optimizing the impoundment process, the floodwater utilization efficiency can be improved, and flood safety and power generation benefits can be coordinated. It provides a solid water resource foundation for ensuring power and water supply security, as well as scientific support and an engineering demonstration for joint flood control scheduling during the impoundment phase of under-construction reservoirs in the basin.
[Objective] A well-developed underground drainage system is crucial for enhancing the drainage capacity of underground pipeline networks in substations under heavy rainfall scenarios and ensuring the safe operation of power facilities. [Methods] Taking a planned substation in Jieyang City, Guangdong Province, as the study area, a new underground drainage system was designed. An equivalent permeability coefficient method, coupling the “circular pipe model” and the seepage model, was proposed. A numerical model was established to evaluate the drainage performance of the underground pipe network under episodic heavy rainfall scenarios. By calibrating parameters using groundwater survey data before the construction of the substation, the drainage flow, groundwater level dynamics, and the spatiotemporal distribution characteristics of water accumulation areas under six heavy rainfall scenarios were assessed. [Results] The result showed that the new underground drainage system could effectively drain water and reduce groundwater levels around the pipeline network. After heavy rainfall, the groundwater level in the core area of the substation exhibited a trend of gradual rise and slow decline, while the surrounding groundwater levels demonstrated a pattern of rapid rise and rapid decline. Under the 50-year and 100-year return period hourly and minute-scale heavy rainfall scenarios, the new underground drainage system exhibited excellent performance, with no water accumulation. However, under daily-scale heavy rainfall scenarios of the same return periods, significant water accumulation occurred in the southwestern part of the study area, and the accumulated water from a continuous 7-day rainfall event took 4 days to recede. [Conclusion] The new underground drainage system, simulated using the equivalent permeability coefficient method under episodic heavy rainfall scenarios, demonstrates good drainage capacity, which provides an effective approach for evaluating and optimizing the drainage capacity of substation drainage networks.