Search Results (150)

Maintenance scheduling of hydropower units is essential for ensuring the operational security and stability of large-scale cascaded hydropower systems and for improving the efficiency of water energy utilization. This study takes the Cascaded Hydropower System of the Lower Jinsha River (CHSJS) as a representative case, identifying four key challenges facing maintenance planning: multi-dimensional influencing factor coupling, spatial and temporal conflicts with generation dispatch, coordination with transmission line maintenance, and compound uncertainties of inflow and load. To address these issues, four strategic recommendations are proposed: (1) identifying and quantifying the impacts of multi-factor influences on maintenance planning; (2) developing integrated models for the co-optimization of power generation dispatch and maintenance scheduling; (3) formulating coordinated maintenance strategies for hydropower units and associated transmission infrastructure; and (4) constructing joint models to manage the coupled uncertainties of inflow and load. The strategy proposed in this study was applied to the CHSJS, obtaining the weight of the impact factor. The coordinated unit maintenance arrangements of transmission line maintenance periods increased from 56% to 97%. This study highlights the critical need for synergistic optimization of generation dispatch and maintenance scheduling in large-scale cascaded hydropower systems and provides a methodological foundation for future research and practical applications. Full article

Hydropower infrastructure has profoundly altered riverine connectivity, posing challenges to the migratory behavior of aquatic species. This study examined the post-passage migration efficiency of Schizothorax wangchiachii in a regulated river system, focusing on upstream and downstream reaches of the Songxin Hydropower Station on the Heishui River, a tributary of the Jinsha River. We used radio-frequency identification (RFID) tagging to track individuals after fishway passage and coupled this with environmental monitoring data. A Cox proportional hazards model was applied to identify key abiotic drivers of migration success and to develop a predictive framework. The upstream success rate was notably low (15.6%), with a mean passage time of 438 h, while downstream success reached 81.1%, with an average of 142 h. Fish exhibited distinct diel migration patterns; upstream movements were largely nocturnal, whereas downstream migration mainly occurred during daylight. Water temperature (HR = 0.535, p = 0.028), discharge (HR = 0.801, p = 0.050), water level (HR = 0.922, p = 0.040), and diel timing (HR = 0.445, p = 0.088) emerged as significant factors shaping the upstream movement. Our findings highlight that fishways alone may not ensure functional connectivity restoration. Instead, coordinated habitat interventions in upstream tributaries, alongside improved passage infrastructure, are crucial. A combined telemetry and modeling approach offers valuable insights for river management in fragmented systems. Full article

To investigate the spatiotemporal characteristics of periphytic algae community structure and the Benthic Index of Biotic Integrity (B-IBI) in the Jinsha River, this study conducted two sampling surveys on periphytic algae and physicochemical factors at 15 representative sampling sites in November 2023 (dry season) and May 2024 (normal water period). Results showed that a total of 118 species of periphytic algae belonging to 59 genera and 7 phyla were detected, including 48 species from 5 phyla in the dry season of 2023 and 95 species from 6 phyla in the normal water period of 2024. Spatially, the distribution trends of total species richness and abundance of periphytic algae were basically consistent, both showing a gradually increasing trend from the downstream reservoir section of the Jinsha River to the upstream conservation section of the Yangtze River. Temporally, both the abundance and species richness of periphytic algae in the normal water period were higher than those in the dry season. Overall, the physicochemical indices of the Jinsha River water showed a decreasing trend from the reservoir areas to the river channels, with slightly higher values in the normal water period than in the dry season. Through parameter value distribution range analysis, discriminant ability analysis, and redundancy analysis of candidate parameters, the B-IBI index system for the study area was determined. The baseline values of the periphytic algae integrity index were 6.04 in the dry season of 2023 and 6.62 in the normal water period of 2024. The water ecological health status of the conservation section of the upper reaches of the Yangtze River is generally in a healthy state, and the overall water ecological health status gradually improves with the increase of the distance from the cascade reservoirs in the lower reaches of the Jinsha River. Full article

The daily operation of cascade hydropower stations induces periodic water level fluctuations (WLFs) that propagate as gravity waves, significantly affecting the hydrodynamics of reservoirs. Previous studies have mainly focused on the effects of individual stations, with little attention paid to the combined impacts of upstream and downstream operations. Taking the Wudongde Reservoir on the Jinsha River as a case study, we used a one-dimensional hydrodynamic model and cross-correlation analysis to simulate flow fluctuation patterns under joint daily operations. The results show that fluctuations from upstream stations attenuate rapidly in the reservoir, with greater attenuation during the dry season. Under joint operations, wave energy decayed exponentially near the reservoir tail and linearly in the main reservoir area, leading to a further reduction in the WLF amplitudes. The interactions between upstream- and downstream-propagating waves enhance energy dissipation. The wave type transitioned from kinematic to dynamic as the water depth increased. During the wet and dry seasons, the average wave velocities were approximately six and nine times higher, respectively, than those under natural conditions. Joint operations expand the range of potential slope instability but reduce the WLF rate compared to natural flows. These findings provide a scientific reference for optimising the daily operations of cascade hydropower stations and mitigating their ecological impacts. Full article

Medium-voltage cables in hydropower plants are typically arranged in multi-circuit configurations to ensure reliability, yet their exposure to harsh operational conditions accelerates insulation degradation and increases partial discharge risks. Traditional fault localization methods, such as the traveling wave method using wavelet transform to process fault signals, suffer from wavefront distortion due to inter-line reflections and noise interference in multi-circuit systems, because wavelet-based techniques are limited by preset basis functions and environmental noise. To address these challenges, a fault localization method for multi-circuit parallel cables based on the Teager–Kaiser Energy Operator (TKEO) is proposed in this paper. First, the fault signal is decoupled using Clarke transformation to suppress common-mode interference, obtaining the α component. Subsequently, the α component is subjected to wavelet transform to obtain the high-frequency components, which are then optimized using the TKEO. The TKEO is applied to optimize the wavelet-transformed signal, enhancing transient energy variations to precisely identify the arrival time of the fault wavefront at measurement points, thereby enabling accurate fault localization. The results of the four types of fault experiments indicate that the use of the TKEO to optimize the wavelet transform of the traveling wave method improved the accuracy of fault localization. Full article

Anthropogenic activities (represented by dams and land use change) and climate change have disrupted the delicate balance between natural and anthropogenic factors affecting riverine material transport, yet their effects across different river basins remain underexplored. This study investigated multi-decade (1980–2023) variations in sediment and particulate carbon (C), nitrogen (N), and phosphorus (P) exports from the Jinsha (JSR) and Jialing River (JLR) basins, two cascading developmental river systems in Southwestern China, and evaluated the cumulative impacts of land use change and dam construction. The results revealed significant decreases in particulate fluxes from both basins, despite stable water discharge. Particulate material fluxes declined by 90.9–99.6% in the JSR (last decade vs. 1980–1989, with an abrupt change occurring during 2002–2003) and by 54.0–79.3% in the JLR (with an abrupt change occurring in 1994). Over time, the influence of precipitation and water discharge on material transport has diminished, whereas land use change and dams have become increasingly dominant. Key drivers include forest expansion, increased impervious surfaces, reservoir construction, and reductions in grassland and farmland; however, there are spatial differences in the relative importance of these drivers. This study provides crucial insights for decision making on regional ecological conservation and cascading development. Full article

The Jinsha River Basin in Yunnan serves as a crucial ecological barrier in southwestern China. Objective ecological assessment and identification of key driving factors are essential for the region’s sustainable development. The Remote Sensing Ecological Index (RSEI) has been widely applied in ecological assessments. In recent years, interpretable machine learning (IML) has introduced novel approaches for understanding complex ecological driving mechanisms. This study employed Google Earth Engine (GEE) to calculate three vegetation indices—NDVI, SAVI, and kNDVI—for the study area from 2000 to 2022, along with their corresponding RSEI models (NDVI-RSEI, SAVI-RSEI, and kNDVI-RSEI). Additionally, it analyzed the spatiotemporal variations of these RSEI models and their relationship with vegetation indices. Furthermore, an IML model (XGBoost-SHAP) was employed to interpret the driving factors of RSEI. The results indicate that (1) the RSEI levels in the study area from 2000 to 2022 were primarily moderate; (2) compared to NDVI-RSEI, SAVI-RSEI is more susceptible to soil factors, while kNDVI-RSEI exhibits a lower saturation tendency; and (3) potential evapotranspiration, land cover, and elevation are key drivers of RSEI variations, primarily affecting the ecological environment in the western, southeastern, and northeastern parts of the study area. The XGBoost-SHAP approach provides valuable insights for promoting regional sustainable development. Full article

Coreius guichenoti, once widely distributed in the upper reaches of the Jinsha River, has become a nationally protected species in China due to the profound impacts of cascade reservoirs. To assess the influence of substrate on the suitability of spawning habitat for C. guichenoti, this study develops a substrate-inclusive habitat model using fuzzy logic based on expert knowledge. Taking the Pingdi Town section of the lower Jinsha River—a historical spawning site for C. guichenoti—as a case study from March to July 2020, we simulated changes in the spawning habitat suitability index (HSI) and compared the results with those from traditional models that exclude substrate factors. The results showed that in the first and second halves of May, Weighted Usable Area (WUA) and Overall Suitability Index (OSI) increased by 42.31% and 38.73%, respectively, while MSP exhibited dramatic increases of 236.04% and 614.56%. These improvements were primarily observed along the riverbanks, where HSI increased by approximately 0.25. From a management perspective, the HSI results provide a scientific basis for optimizing ecological flow regulation. Incorporating substrate factors into spawning habitat models offers a more objective and comprehensive assessment of habitat quality. Habitat restoration measures, such as targeted substrate improvement in key riverbank areas, may further increase habitat suitability, providing additional opportunities for conservation planning in regulated rivers. Full article

To address the challenges posed by the direct integration of large-scale wind and solar power into the grid for peak-shaving, this paper proposes a short-term optimization scheduling model for hydro–wind–solar multi-energy complementary systems, aiming to minimize the peak–valley difference of system residual load. The model generates and reduces wind and solar output scenarios using Latin Hypercube Sampling and K-means clustering methods, capturing the uncertainty of renewable energy generation. Based on this, a new improved algorithm, Tent–Gaussian Enterprise Development Optimization (TGED), is introduced by incorporating chaotic initialization and Gaussian random walk mechanisms, which enhance the optimization capability and solution accuracy of the traditional enterprise development optimization algorithm. In a practical case study of a certain hydropower station, the TGED algorithm outperforms other benchmark algorithms in terms of solution accuracy and convergence performance, reducing the residual load peak–valley difference by over 600 MW. This effectively mitigates the volatility of wind and solar power output and significantly enhances system stability. The TGED algorithm demonstrates strong applicability in complex scheduling environments and provides valuable insights for large-scale renewable energy integration and short-term optimization scheduling of hydro–wind–solar complementary systems. Full article

This study assesses the effectiveness and limitations of publicly accessible runoff and river discharge reanalysis datasets through an intercomparison in the Upper Jinsha River, an alpine region with substantial hydropower potential on the eastern edge of the Tibetan Plateau. The examined datasets are the European Centre for Medium-Range Weather Forecast Reanalysis version 5 (ERA5-Land), the Global Flood Awareness System (GloFAS), the Global Reach-Level Flood Reanalysis (GRFR), and the China Natural Runoff Dataset (CNRD). These datasets are created using various meteorological forcing, runoff generation models, river routing models, and calibration methods. To determine the causes of discrepancies, additional simulations were carried out. One simulation, driven by meteorological forcing similar to that of ERA5-Land and GloFAS but utilizing the uncalibrated NoahMP land surface model at a higher spatial resolution, was included to evaluate the effects of meteorological inputs, spatial resolution, and calibration on runoff estimation. Runoff from all datasets was rerouted on a high-resolution river network derived from the 3-arcsecond Multi-Error-Removed Improved-Terrain Hydrography (MERIT-Hydro) dataset, allowing for a comparison between vector- and grid-based river routing models for discharge estimates. The intercomparison is grounded in observations from three gauging stations—Zhimenda, Gangtuo, and Benzilan—at monthly, daily, and hourly scales. The results suggest that model calibration has a more significant influence on runoff and discharge estimates than meteorological data. Calibrated datasets, such as GloFAS and GRFR, perform better than others, despite variations in the forcing data. The runoff characteristics-based calibration method used in GRFR exhibits superior performance at Zhimenda and Benzilan. However, at Gangtuo, GRFR’s performance is unsatisfactory, highlighting the limitation of the machine learning-based method in regions with rugged terrain and limited observations. Vector-based river routing models demonstrate advantages over grid-based models. GloFAS, which uses a grid-based routing model, encounters difficulties in simultaneously producing accurate runoff and discharge estimates. The intercomparison shows that GRFR’s river routing is sub-optimally configured. However, when GRFR’s runoff rerouted, the performance of discharge improves substantially, attaining a Kling–Gupta efficiency of approximately 0.9. These findings offer valuable insights for the further development of reanalysis datasets in this region. Full article

Yunnan Province, as a critical ecological security barrier in China, has long been highly susceptible to drought events. Characterizing the spatiotemporal distributions of drought and identifying its driving factors is crucial. Due to the complexity of drought occurrence, linear correlation analysis alone is insufficient to quantify drought drivers and their interactions. This study used the Standardized Precipitation Evapotranspiration Index (SPEI) as a drought indicator to analyze the spatiotemporal trends of drought across Yunnan and its six major river basins. The geographic detector model (GDM) and random forest (RF) were utilized to quantify the impacts of meteorological, topographical, soil, and human activities on drought, as well as the interactions among these factors. The results showed that 63.61% of the study area exhibits a significant drying trend (p-value < 0.05), with the Jinsha River Basin (JSRB) experiencing the highest frequency of extreme drought events. Precipitation (PRE), temperature, potential evapotranspiration (PET), vapor pressure deficit (VPD), and relative humidity (RH) were identified as the primary controlling factors of drought, with factor interactions displaying nonlinear enhancement effects. PRE plays a dominant role in driving drought across Yunnan, whereas elevation primarily influenced drought severity in the JSRB, Lancang River Basin (LCRB), and Nujiang River Basin (NJRB). The RF-based SPEI prediction model demonstrated superior performance in simulating short-term drought (SPEI_1, R² > 0.931, RMSE < 0.279), particularly in the JSRB (R² = 0.947 RMSE = 0.228). These findings provide a scientific basis for regional water resource management applications and drought early warning systems, offering a robust framework for understanding and mitigating drought impacts in ecologically sensitive regions. Full article

Vegetation dynamics significantly influence watershed ecohydrological processes. Physically based hydrological models often have general plant development descriptions but lack vegetation dynamics data for ecohydrological simulations. Solar-induced chlorophyll fluorescence (SIF) and the Normalized Difference Vegetation Index (NDVI) are widely used in monitoring vegetation dynamics and ecohydrological research. Accurately predicting long-term SIF and NDVI dynamics can support the monitoring of vegetation anomalies and trends. This study proposed a SWAT-ML framework, combining the Soil and Water Assessment Tool (SWAT) and machine learning (ML), in the Jinsha River Basin (JRB). The lag effects that vegetation responds to using hydrometeorological elements were considered while using SWAT-ML. Based on SWAT-ML, SIF and NDVI series from 1982 to 2014 were reconstructed. Finally, the spatial and temporal characteristics of vegetation dynamics in the JRB were analyzed. The results showed the following: (1) the SWAT-ML framework can simulate ecohydrological processes in the JRB with satisfactory results (NS > 0.68, R² > 0.79 for the SWAT; NS > 0.77, MSE < 0.004 for the ML); (2) the vegetation index’s mean value increases (the Z value, the significance indicator in the Mann–Kendall method, is 1.29 for the SIF and 0.11 for the NDVI), whereas the maximum value decreases (Z value = −0.20 for SIF and −0.42 for the NDVI); and (3) the greenness of the vegetation decreases (Z value = −2.93 for the maximum value and −0.97 for the mean value) in the middle reaches. However, the intensity of the vegetation’s physiological activity increases (Z value= 3.24 for the maximum value and 2.68 for the mean value). Moreover, the greenness and physiological activity of the vegetation increase in the lower reaches (Z value = 3.24, 2.68, 2.68, and 1.84 for SIF_max, SIF_ave, NDVI_max, and NDVI_ave, respectively). In the middle and lower reaches, the connection between the SIF and hydrometeorological factors is stronger than that of the NDVI. This research developed a new framework and can provide a reference for complex ecohydrological simulation. Full article

In the simulation of concrete thermal stress fields, thermal parameters are crucial for calculating the concrete temperature field. In actual construction, due to the adjustment of the concrete mixing ratio and the changing external environment (temperature fluctuations, cooling conditions, solar radiation, thermal insulation measures, etc.), there are significant differences between the thermal parameters obtained in tests and the actual working conditions, which affect the simulation accuracy. Therefore, the inverse analysis of concrete thermal parameters under real working conditions can be carried out based on the measured temperature data. A method for inverse analysis of thermal parameters of arch dams using the walrus optimization algorithm (WaOA) is proposed. To verify the accuracy of the inversion parameters, twelve classical test functions are used to compare the three algorithms to evaluate their fitness. The efficiency difference is analyzed by nonparametric methods such as Fredman and Wilcoxon rank sum test. The results consistently indicate that the walrus optimization algorithm performs better. Furthermore, the WaOA is utilized for the parameter inversion of an arch dam in the downstream area of the Jinsha River. We bring the inversion results into different dam sections to calculate the temperature field during construction, which effectively verifies the efficient solution ability of the WaOA for the inverse analysis of concrete thermal parameters under complex engineering backgrounds. Full article

In high-mountain canyon regions, many settlements are located on large, deep-seated ancient landslides. The deformation characteristics, triggering mechanisms, and long-term developmental trends of these landslides significantly impact the safety and stability of these communities. However, the deformation mechanism under the influence of human engineering activities remains unclear. SBAS-InSAR (Small Baseline Subset-Interferometric Synthetic Aperture Radar) technology, UAV LiDAR, and field surveys were utilized in this study to identify a large ancient landslide in the upper Jinsha River Basin: the Zhongxinrong landslide. It extends approximately 1220 m in length, with a vertical displacement of around 552 m. The average thickness of the landslide mass ranges from 15.0 to 35.0 m, and the total volume is estimated to be between 1.48 × 10⁷ m³ and 3.46 × 10⁷ m³. The deformation of the Zhongxinrong landslide is primarily driven by a combination of natural and anthropogenic factors, leading to the formation of two distinct accumulation bodies, each exhibiting unique deformation characteristics. Accumulation Body II-1 is predominantly influenced by rainfall and road operation, resulting in significant deformation in the upper part of the landslide. In contrast, II-2 is mainly affected by rainfall and river erosion at the front edge, causing creeping tensile deformation at the toe. Detailed analysis reveals a marked acceleration in deformation following rainfall events when the cumulative rainfall over a 15-day period exceeds 120 mm. The lag time between peak rainfall and landslide displacement ranges from 2 to 28 days. Furthermore, deformation in the high-elevation accumulation area consistently exhibits a slower lag response compared to the tensile deformation area at lower zones. These findings highlight the importance of both natural and anthropogenic factors in landslide risk assessment and provide valuable insights for landslide prevention strategies, particularly in regions with similar geological and socio-environmental conditions. Full article

The Shenyu River, as the tributary of the Jinsha River closest to the Wudongde Hydropower Station, has had seven landslides developed on its right bank, forming an interconnected landslide group system. The evolution and future development trends of the landslide group have a significant impact on the safe operation of the Wudongde Hydropower Station. Using geological field surveys and exploration data, we studied and analyzed the formation mechanism of the landslide group on the right bank of the Shenyu River in the reservoir area of the Wudongde Hydropower Station. The main conclusions are as follows: The local structure of the study area is mainly composed of north–south faults and folds, which control the development of rock mass unloading in the later stage of the study area, the formation of the stepped landform in the study area, and the formation process of geological disasters in the study area. The synclinore (Bellmouth fold) structure on the southern flank of the Shenyu River controls the spatial distribution of geological disasters in the study area, forming a spatial distribution pattern centered on the Dapingdi landslide and Dacun landslide, with the scale of geological disasters decreasing toward both sides. The research findings contribute to the improvement of the theoretical system of the formation mechanism of geological disasters. Full article