The changing environment alters river water quantity. Scientifically clarifying the change trend of water quantity is fundamental for water resources management in river basins. This paper focuses on 23 rivers for water allocation in the Yangtze River Basin. Utilizing measured runoff data, we analyzed the annual and monthly flow trends of major rivers and the change trends of outbound flow across different provinces within the basin. Results indicate that since 2012, the annual water volume of rivers for water allocation in the Yangtze River Basin has remained stable with an upward trend in 2020, followed by a significant shift from wet period to moderately dry period. The monthly runoff of each river exhibited an obvious unimodal pattern, yet the annual water volume gradually concentrated towards non-flood season. The outbound water volume across provinces in the basin showed multiple alternations between dry and wet seasons, generally presenting a fluctuating upward trend that peaked in 2020. After that, the outbound water volume across provinces continuously declined to the present state. The annual overall compliance rate of Guizhou and Hunan showed a downward trend, while Hubei followed behind.
Accurately predicting the development trend of river patterns and leveraging their advantages is a prerequisite for ensuring stable river function. After the operation of the Three Gorges and other reservoirs, the continuous long-term erosion of channels in the middle and lower reaches of the Yangtze River and the drastic adjustments of local river regimes may lead to river pattern transformation. Such transformation will have a series of impacts on river functions such as flood control, ecology, water supply, and navigation. This paper reviews the causes, classification and discrimination, river pattern transformation mechanisms, evolution laws, and prediction methods of different river patterns in the middle and lower reaches of the Yangtze River under long-term erosion. It also scrutinizes the impacts of river pattern transformation and corresponding governance strategies. On this basis, several directions for future research are proposed: the refinement of river pattern subcategories, the responses of shape parameters of different river patterns to changes in water and sediment conditions under discontinuous constraint boundaries, effects of longitudinal erosion adjustment of long channels on river pattern transformation, the quantitative identification of critical conditions for river pattern transformation, and prediction methods for century-scale river pattern transformation as well as trend estimation.
Strengthening the management of river sand mining and preserving healthy rivers and lakes are crucial steps in building a solid barrier for national ecological security. On the basis of a comprehensive and in-depth investigation of sand mining cases in the Tibet Autonomous Region, we summarize the current status of river sand mining management and identifies major problems. In terms of the management system and mechanism, problems include an incomplete system, ineffective coordination mechanisms, and unsmooth management processes. Regarding sand mining planning, problems such as limited planning coverage, poor-quality planning reports with low guidance value exist. In the licensing process, the license content is non-standard. During the implementation of sand mining, on-site management is irregular, and supervision is lacking. For the mining business model, it is characterized by a simple operation model and low management capabilities. Based on field investigations and statistical analyses, we propose targeted countermeasures and suggestions. These suggestions can assist the water administration department of the Tibet Autonomous Region in enhancing river sand mining management.
Analyzing the spatiotemporal variations of potential evaporation and runoff is crucial for accurately understanding the actual evaporation and water balance changes in climate-sensitive areas. However, due to the inter-annual and seasonal variations of the global climate, predicting and evaluating potential evaporation and runoff at the watershed scale are challenging. In this study, we selected the source area of the Yellow River, a climate-sensitive region, as the research target. We utilized the Comprehensive Differential Sample Method (CDSST) to classify the dry and wet states of the watershed into wet years, dry years, and mixed years. Then, we constructed monthly-scale abcd hydrological models for each state. We also investigated how the uncertainty in four potential evaporation algorithms (Haregreaves, Makkink, Penman-Monteith, and Jensen-Haise) affects the prediction uncertainty of potential evaporation and runoff in the watershed. Subsequently, by employing hydrological indicators such as the unevenness coefficient, concentration degree, and relative variation range, we revealed the influence of these four different potential evaporation algorithms on watershed water resource prediction. Results show that, compared with the period before 1990, the number of years when the watershed was in a dry state increased (from 4 years to 10 years). The corresponding proportions of wet years, dry years, and mixed years were 25.86%, 24.14%, and 50%, respectively. Meanwhile, the uncertainty of potential evaporation algorithms alters the spatiotemporal distribution characteristics of watershed evaporation and runoff. These findings are essential for scientifically grasping the dynamic changes of watershed hydrological processes under changing environments, as well as for regional water resource management and ecological restoration.
The evaluation of regional water resources security plays a crucial role in guiding the scientific management of water resources and promoting the green development of the social-economy and water resources. The traditional water resources ecological footprint method lacks the connection between water resource ecology and socio-economic development. To address this, we introduce the water resources ecological benefit ratio to improve the emergy analysis method for water resource ecological footprint and then analyze the water resource security and sustainable development status of Xi’an City. Results reveal that from 2011 to 2019, the water resources carrying capacity fluctuated in line with annual rainfall. The average annual water resources ecological carrying capacity was 0.160 hm2/cap, while the average annual water resources ecological footprint was 1.521 hm2/cap. An obvious decreasing ecological deficit of water resources was observed. The regional water ecological footprint per 10 000 yuan of GDP declined from 0.057 hm2/cap in 2011 to 0.019 hm2/cap in 2019, indicating an improvement in water resources utilization efficiency. During 2011-2019, the water resources ecological tension index remained relatively high but decreased from 5.79 to 4.39, approaching a relatively safe level. Moreover, the water resources ecological benefit increased as the water resources stress alleviated. However, the water resources security situation in Xi’an remains severe. In the future, it is necessary to optimize the economic development mode and water resource allocation to promote high-quality sustainable development in Xi’an.
Snowmelt water is a significant component of spring runoff in the Lancang River basin, making it crucial to understand the variation of snow cover in the upper reaches of the Lancang River and accurately simulate snowmelt runoff processes for the scientific scheduling of water resources for cascade hydropower stations. Using remotely sensed snow cover data from 2000 to 2019, we applied the Mann-Kendall trend test to analyze the spatio-temporal variations of snow cover in the upper Lancang River basin and established a snowmelt runoff model (SRM) to simulate the snowmelt runoff process in 2018 and calibrated the model parameters using the Particle Swarm Optimization (PSO) algorithm. Results indicate that 1) Snow cover in the upper reaches of the Lancang River exhibited insignificant upward trend in spring, autumn, and winter, but an insignificant decline in summer. The average annual snow cover for spring, summer, autumn, and winter was 0.16, 0.06, 0.13, and 0.17, respectively. 2) Snow cover increased in the southwest and north of the Lancang River source area across all seasons, but decreased in the southeast. The northwest region experienced the largest increase in winter, reaching 3% per year. 3) The SRM demonstrated good applicability in the upper Lancang River basin, with coefficient of determination values reaching 0.87 and 0.78 for the calibration and verification periods, respectively, from January to May. These findings provide valuable insights for simulating snowmelt runoff in alpine regions.
The investigation of the evolution and non-stationarity of precipitation structures is essential for understanding regional water cycle variations. Based on daily precipitation data from 50 meteorological stations from 1960 to 2021 in the Jinsha River Basin, we analyzed the spatiotemporal evolution and non-stationarity of total precipitation, extreme precipitation, and precipitation events of various intensities by using the Mann-Kendall non-parametric test and the Pettitt test. Results demonstrate that: 1) The trend in total precipitation in the Jinsha River Basin was not significant, but extreme precipitation increased at a rate of 2.67 mm/decade (P<0.05). Both total and extreme precipitation exhibited evident non-stationarity. 2) Light, moderate, and heavy rainfall predominated in the Jinsha River basin, with an overall incidence rate of 94% and a contribution rate of 70% to total precipitation. 3) As precipitation intensity increased, the peak centers of incidence and contribution rate shifted gradually from north to south. 4) The incidence and contribution rate of light rainfall showed a significant downward trend, while the incidence rate of moderate and higher-grade precipitation events and the contribution rate of heavy rainfall exhibit significant upward trends(P<0.05). 5) Compared to the contribution rate, the incidence of precipitation displayed more pronounced non-stationarity, particularly in stations located in the lower section of the Jinsha River and the Yalong River Basin. These findings indicate significant changes in the precipitation structure of the Jinsha River Basin from 1960 to 2021, characterized by an increase in heavy precipitation and a decrease in light precipitation. Therefore, scientific allocation and management of local water resources are necessary in the future.
Accurately and efficiently predicting lake water quality is vital for water resource protection, ecological balance, and economic development. We propose a combined prediction model for total nitrogen (TN) concentration in lakes, integrating modal decomposition, multidimensional feature selection, Temporal Convolutional Network (TCN), self-attention mechanism, bidirectional long short-term memory (BiLSTM), and bidirectional Gate Recurrent Unit (BiGRU). First, we apply variational mode decomposition to break down the original TN sequence into intrinsic mode functions (IMFs) of different frequencies. This step effectively reduces the complexity and non-stationarity of the original sequence. Next, we use the random forest algorithm to select highly correlated features for each IMF. Then, we feed the filtered feature matrix into the TCN-BiLSTM hybrid network equipped with a self-attention mechanism for modeling. This network extracts key temporal information from the hidden data. Finally, to enhance the model’s prediction accuracy, we employ the BiGRU network to learn the detailed features of the residual sequence. We then fuse the residuals with the model’s prediction results to obtain the final prediction value. We conduct an experimental analysis using the water quality data from the Duchang Monitoring Station in Poyang Lake. The results demonstrate that, compared with other models, our model significantly improves the prediction accuracy of TN concentration. Specifically, its mean absolute error (MAE) is 0.03 mg/L, root mean square error (RMSE) is 0.049 mg/L, and coefficient of determination (R2) is 0.992.
Human activities, including reservoir construction, river diversion, embankment construction, and hydrological regulation in the upper reaches of the Yangtze River, have severely impacted fish habitats, causing environmental degradation and substantial habitat loss. To gain deeper insights into this issue, we selected habitat data over the past three years from four typical river sections at Zhutuo Station, which is in the middle part of the Yangtze River protection zone. We analyzed the hydrological features, water environment quality, flow velocity, water depth, and spatial distribution patterns of rare and endemic fish species to comprehensively characterize fish habitats. Based on the analysis results, we propose to establish comprehensive measures for the proliferation and release of rare and endemic fish species. Our research shows that 99% of the rare and endemic fishes aggregate in areas with flow velocities of 0.0-1.4 m/s, among which approximately 80% gather in slow-flow zones with velocities below 0.8 m/s. During the wintering period, the average water depth where fishes gather is 44.28 meters, with a minimum of 15 meters and a maximum of 57.47 meters. When the absolute water depth in the upper reaches of the Yangtze River ranges from 23 to 56 meters, it fails to provide suitable fish habitats. However, during the water-storage period, raising the water level can improve the living conditions of rare and endemic fish species while meeting requirements for flow velocity, water depth, and minimal interference. Studying the habitat characteristics of rare and endemic fish resources in the upper reaches of the Yangtze River enables us to better understand fish distribution and migration patterns, facilitating the rational planning and management of fishery resources. Simultaneously, researching proliferation measures can guide management actions such as artificial release, aiding in the restoration and enhancement of fish populations and ensuring the sustainable development of fisheries.
Exploring the spatiotemporal evolution patterns of Net Primary Productivity (NPP) of vegetation in Hutuo River Basin is of great significance for understanding and improving the surrounding ecological environment. Based on MOD17A3 NPP data, the spatiotemporal evolution of vegetation NPP in Hutuo River Basin from 2003 to 2022 was analyzed using methods such as univariate linear regression analysis and coefficient of variation. The vegetation NPP was combined with land cover types and terrain factors for zoning statistics. Results show that from 2003 to 2022, the average vegetation NPP in Hutuo River Basin ranged from 300 gC/(m2·a) to 400 gC/(m2·a). The maximum NPP peak occurred in 2020, reaching 828 gC/(m2·a), while the average NPP peak was in 2022, at 424.33 gC/(m2·a). Areas where vegetation NPP increased linearly from 2003 to 2022 accounted for 96.46% of the study area. The relative annual change rate of vegetation NPP mainly fell within the range of 20%-40%, and the long-term stability of vegetation NPP was characterized by low fluctuations. Among the land-cover types in the Hutuo River Basin from 2003 to 2022,agricultural land had the lowest average vegetation NPP,at 331.92 gC/(m2·a), whereas grassland had the highest, at 384.40 gC/(m2·a). Vegetation NPP increased with rising elevation and slope. In terms of aspect, planes featured the lowest vegetation NPP.
Taking Landsat images in 2000, 2010, and 2020 as data sources, we examined the dynamic changes of ecosystem types in the Ali prefecture in Tibet autonomous region over the past two decades by using ecosystem dynamics degree and transfer matrix model. Within the “Sensitivity-Pressure-Resilience” assessment framework, we selected 14 indicators to establish a sensitivity evaluation index system to delineate ecological sensitive zones, aiming to maintain ecosystem stability and scientifically build ecological security barriers. The findings demonstrate that 1) All eight ecosystem types experienced varying degrees of changes.The degree of dynamics for urban ecosystem and agricultural ecosystem displayed the most significant changes. 2) Deserts and grasslands served as crucial “sources” and “sinks” during the ecosystem evolution in the Ali prefecture. 3) The ecological sensitive areas mainly consisted of medium- and high-sensitivity zones, covering an area of 168 800 km2, which accounted for nearly 50% of the total. Spatially, the northwest part of Ali prefecture exhibited high sensitivity, while the southeast part low sensitivity, with low-sensitivity areas encircling high-sensitivity areas.
The navigation flow conditions of canal directly impact ship navigation safety. Channel regulation measures can effectively alleviate unfavorable flow conditions. In the Pinglu Canal project, after the original channel was widened and deepened, a significant bottom elevation difference emerged between the canal and the tributaries along the original river course. These tributaries, entering the confluence with a large drop, adversely affect the navigable flow conditions of the mainstream. Xinpingshui is a representative tributary with a large riverbed elevation difference and a wide intersection angle with the canal. To ensure smooth navigation in the Pinglu Canal, we conducted fixed-bed model tests to investigate the navigable flow conditions at the confluence of the Xinpingshui tributary and the main canal. Based on the test results, we proposed corresponding regulation measures. Results indicate that, on the basis of the canal construction, widening both the main and tributary channels and installing additional dissipation pools and diversion dikes at the tributary mouths significantly improve the navigational flow conditions under various flood scenarios. The findings of this study can serve as a reference for improving navigation channels in similar river reaches.
The accuracy of predicting the peak flood flow at the breach of earth-rock dam is crucial for dam break analysis. To improve the prediction accuracy of the post-breach peak flood flow, this paper presents a prediction model based on the General Regression Neural Network (GRNN), optimized by the Fennec Fox Optimization (FFA) algorithm for hyperparameters, to forecast the peak flood flow caused by dam breaches. Using a database of domestic and international dam failure cases, the model selects three factors as input variables: the reservoir capacity above the breach bottom, the water depth above the breach bottom, and the breach depth, to build the FFA-GRNN prediction model. To evaluate the model’s precision and fitting accuracy in predicting peak flood discharge at dam break, we compared it with four other intelligent algorithms. Results show that the proposed FFA-GRNN model has a lower Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and a higher coefficient of determination (R2) than other models, indicating superior computational precision and fitting performance.
Seepage field is a primary factor inducing the seepage instability of tailings dams. Intensifying research on the seepage stability of tailings dams is crucial for ensuring their safety and reducing the risk of dam failure. This paper reviews relevant domestic and international research data from the following perspectives: seepage instability failure types and influencing factors, seepage stability analysis methods, and safety monitoring and early-warning. It points out the existing problems in the research on seepage stability of tailings dams, including the lack of research on seepage failure discrimination techniques, insufficient comprehensive investigations into the factors affecting the seepage field, deficiencies in macro-micro multi-scale model experimental measurement methods and their generalized zoning, and the inadequacy of comprehensive stability analysis and evaluation methods, as well as safety monitoring and early-warning technologies. Given these deficiencies, this paper further clarifies future research priorities and issues requiring in-depth discussion, providing insights for future research.
The soil used for the core dam of a reservoir in Xinjiang is dispersive soil. To treat this soil, 1% lime is added. To ensure project quality and achieve uniform mixing, a ZB05 cold recycling paver was employed, with mixing cycles set at 1, 2, 4, and 6, respectively. The SG-6 multifunctional direct-reading calcium meter was used for rapid and random detection of soil lime content to control the uniformity of the lime-treated soil. The influence of aging time on the compaction characteristics of the improved soil was also investigated. In the field construction environment, the impact of curing age on the physical and mechanical properties of the improved soil was evaluated through indoor compression and direct shear tests. Results show that, when rolled 8 times with a spread thickness of 30 and 35 cm, the settlement of the soil material stabilizes, and the dry density after rolling meets the requirement of ≥1.73 g/cm3, with a compaction degree of ≥99%. When the mixing cycle is 6, the uniformity of the lime mixing stabilizes, with a coefficient of variation ( Cv ) of 0.28. As the aging time increases, the lime-treated soil becomes less favorable for rolling. To maintain a compaction degree of 99%, the aging time should not exceed 9 hours. The compressibility and shear strength of the improved soil are significantly influenced by curing age. As curing age increases, the compressibility of the lime-treated soil decreases, whereas shear strength increases, consistent with laboratory test results.
This study investigates the development of cumulative strain and cumulative pore pressure of expansive soil reinforced with oyster shell powder (OSP) under cyclic loading. A GDS dynamic triaxial test system was employed to simulate vehicle dynamic actions on the soil. The experiment examines axial cumulative strain and cumulative pore pressure under particle size (dosp), dosage (Fosp), and cyclic stress ratio (CSR). Findings reveal that for particle sizes dosp<0.5 mm, both axial cumulative strain and cumulative pore pressure in the modified soil significantly improve with increasing OSP content. However, when Fosp=9% and dosp=0.5 mm, the dynamic performance of the modified soil is relatively stable. Conversely, for particle sizes dosp>0.5 mm, the dynamic performance of the modified soil deteriorates compared to plain soil, with this degradation becoming more pronounced as both OSP content and particle size increase. Based on these results, if OSP-modified soil is used as roadbed filler for ballasted track railways with design speeds below 200 km/h, it is recommended to control the OSP content at Fosp=9%, with particle sizes limited to the range of (0.25,0.5] mm to maintain optimal dynamic performance.
To address the challenge of determining the optimal timing for initial support during layered excavation in large underground powerhouses, we present a concept of plastic volume ratio to quantify the volume of rock mass undergoing plastic deformation to the total volume of excavated rock mass. Based on the lateral deformation characteristics of the entire cross-section during tunnel excavation, we propose a method for determining the timing of initial support in layered excavation processes based on the plastic volume ratio. This method takes into account the displacement release rate, along with the characteristics of the transverse displacement release rate and the longitudinal convergence deformation curve(LDP) under different stress release rates. The research demonstrates that the evolution of rock mass deformation, as described by the plastic volume ratio, offers a more intuitive understanding. This method overcomes the challenges associated with identifying the critical state of displacement evolution and the difficulty in determining the timing of initial support for different layers during layered excavation, as encountered when using the displacement release rate method. By conducting two-dimensional and three-dimensional numerical calculations for excavations in the underground main powerhouse of a pumped-storage power station, we demonstrate consistent performance of the proposed method. It can serve as a valuable reference for the design and construction of layered excavation in large underground projects.
Some aeolian sandy soils in the Mu Us Desert area are collapsible, affecting engineering structural stability due to large settlements induced by plunges in its strength when exposed to water. Investigating the collapsibility of aeolian sandy soil in the Mu Us Desert is of great practical significance. This study focuses on the aeolian sandy soil at the southern edge of the Mu Us Desert. We conducted indoor collapse tests on undisturbed and remolded aeolian sandy soil samples collected from the southern edge of the Mu Us Desert. Through single-factor analysis, we explored the influencing factors of the collapsibility of aeolian sandy soil and their influence patterns. Results indicate the following: 1) The aeolian sand in the southern edge of the Mu Us Desert is slightly collapsible when exposed to water. 2) The collapsibility decreases rapidly as the dry density increases, and declines with an increase in moisture content, but rises with an increase in clay content, and first increases and then decreases as the test vertical pressure increases. 3) The boundary indicators for determining the collapsibility of the aeolian sand in the southern edge of the Mu Us Desert are a dry density of 1.5 g/cm3, a moisture content of 8%, and a relative density of 40.5%.
An intelligent prediction model for shield tunneling parameters accounting for geological conditions is presented by employing a geological data processing technique which integrates in-situ stress extraction and tunneling section stratum information coding. The method encompasses data acquisition, preprocessing and decomposition, and model construction, training and testing, as well as result evaluation and analysis. The model is applied to predict the shield parameters for the large-diameter slurry shield tunnel project of the Luyuan North Street section on the Beijing-Harbin Expressway within the Beijing East Sixth Ring Road reconstruction project. Findings reveal that accounting for geological conditions enhances the prediction accuracy of shield thrust and cutter-head torque by 38.53% and 44.86%, respectively. This improvement secures the construction safety of subsequent shield tunneling operations. The research outcomes can serve as a reference for future similar projects.
Continuous slope monitoring in reservoirs and dams using ground-based synthetic aperture radar interferometry (GB-InSAR) is vulnerable to atmospheric environmental fluctuations. These fluctuations can cause inaccuracies in deformation results derived from interferogram sequences. Moreover, processing large volumes of continuous GB-SAR images is time-consuming, which negatively affects the overall efficiency of GB-InSAR and the feasibility of quasi-real-time deformation analysis applications. To tackle these problems, this paper presents a uniform grid sampling method and interferometric stacking technique based on the phase gradient building on the conventional polynomial atmospheric correction method. A polynomial atmospheric correction method based on downsampled high-quality pixels (HQPs) is then constructed. This method is applied to monitor the deformation of the high slope on the right bank during the construction of the Huangdeng Hydropower Station. Experimental results show that the root mean square error (RMSE) of the binary polynomial model averages 0.039 5 rad, significantly outperforming that of the unitary model and other conventional correction methods. The average RMSE of the proposed method is 0.024 0 rad, comparable to the accuracy before downsampling. However, the overall solution time reduces notably from 2.32 h to 0.80 h. This indicates that the proposed method can significantly improve the efficiency of continuous image atmospheric correction while maintaining modeling accuracy, offering effective technical support for slope safety monitoring.
To achieve reliable analysis of gravity dams with limited statistical data, a non-probabilistic reliability assessment method was developed. This method requires only the upper and lower bounds of uncertain parameters. The correlated relationship of these parameters was described using an inscribed ellipsoid model. By introducing a scaling factor, the non-probabilistic reliability calculation model was transformed into a constrained optimization problem. The Kriging model, known for its effectiveness in fitting highly nonlinear functions, was employed to model the functional behavior of gravity dam elements. Additionally, the Whale Optimization Algorithm (WOA) was used for reliability optimization. Case validation confirmed that the non-probabilistic reliability calculation method, based on the convex set scaling factor and the WOA-Kriging model, effectively analyzes the reliability of gravity dams.
To offer a reference for the safety monitoring of a large-scale hydropower station, we conducted a vibration characteristics analysis of surface radial gate of the dam using finite element calculation software ANSYS. We primarily investigated how the elasticity of water stop structures affects the vibration modes and natural frequency of the gate. The analysis reveals that the elasticity of water stop structures has a certain degree of influence on the gate’s vibration characteristics. Compared with the calculation results neglecting the elasticity of water stop structures, the frequencies of some vibration modes closely related to the constraint conditions on both sides of the gate can differ by up to 46.80%. Within a certain range, as the foundation stiffness coefficient which characterizes the elasticity of water stop structures decreases, the vibration frequency of the same vibration mode declines. When the foundation stiffness coefficient ranges from 0.1 to 0.2 N/mm3, the simulation results align with the observed prototype gate’s vibration response. Additionally, we also proposed a method to quantify the elasticity of water stop structures, providing basic data for selecting correlation coefficient values, and potentially offering a reference for the safety monitoring of the studied hydropower station and the vibration analysis of gates in other projects.
The fine structure of grass-planted concrete plays a crucial role in determining its compressive strength. Understanding its physicochemical properties is essential for enhancing the performance of porous grass-planted concrete. We investigated the pore structure using Rapid Air 457 device, examined the SEM, XRD diffraction, and mechanical properties of grass-planted concrete. Results revealed that increasing the dosage of silica fume powder and fly ash reduced the finescale pore content to 0.85% and 0.22%, respectively. The average pore size decreased to less than 80 μm, and the spacing coefficient was significantly altered, which enhanced the 28-day maximum compressive strength of the grass-planted concrete up to 10.1 MPa and 11.3 MPa, respectively. SEM and XRD diffraction tests together with Dessication Susceptibility (DES) analysis unveiled that the mass ratio of Ca to Si in grass-planted concrete declined, indicating that the hydration products of silica fume powder and fly ash densified the internal structure of the cementitious material of grass-planted concrete, positively affecting its compressive strength. This research fills a gap in the study of the fine pore structure of grass-planted concrete.
Winter precipitation in the Tuotuo River directly affects plateau snow cover, through heat and radiation fluxes, impacts the local climate and atmospheric circulation. We utilized the Arctic Oscillation Index to identify the years with positive and negative arctic oscillation anomalies and investigated the effects of these anomalies on winter precipitation at Tuotuo River meteorological station. We also analyzed the multi-time scale correlation between winter precipitation and arctic oscillation, and explored the possible response mechanism of winter precipitation to arctic oscillation anomalies. Results indicate that winter precipitation at Tuotuo River station is significantly affected by arctic oscillation, with a notable positive correlation on inter-decadal scale. Positive-anomaly years of arctic oscillation coincide with higher probability of abundant winter precipitation. This can be attributed to the weakening of east Asian winter monsoon over the plateau and the strengthening of the India-Burma trough in the southern part of the plateau which enhance the southwest warm and humid airflows from the northern Indian Ocean and the Bay of Bengal. Conversely, negative-anomaly years of arctic oscillation correspond with scarce winter precipitation. Because in negative-anomaly years, winter monsoon on the plateau intensifies, while the India-Burma trough weakens. As a result, the southwest warm and humid airflows are relatively weaker, hindering the northward movement of water vapor onto the plateau and causing a significant reduction in precipitation.
Alpine wetlands serve as substantial carbon sinks and play a crucial role in providing habitats for wildlife and maintaining ecological security on the plateau. Current research predominantly focuses on areas below 4 000 meters in altitude, while studies on the surface soil organic carbon (SOC) of alpine wetlands above 4 000 meters require further enhancement. In this study, we analyzed the spatial distribution of surface SOC contents and their proportions in the total carbon of alpine wetlands. We utilized samples from 20 monitoring sites (with 3 replicates at each site) in the Chadan wetland, the highest-elevation wetland (average elevation over 4500 m) in the source region of the Yangtze River. Additionally, we explored the spatial differences among different tributaries. Results indicated that the surface SOC content in the Chadan wetland ranged from 0.54% to 18.47%, with an average of 4.78%. Moreover, its proportion in the total carbon exceeded 80%. The spatial correlations of total carbon and total organic carbon in tributaries on the north and south banks are opposite. This study advanced our understanding of the spatial distribution of surface SOC in alpine wetlands and provided preliminary exploration and validation data for more accurate estimations of carbon sink storage in high-altitude alpine wetlands.
This study employs a nested principal component regression model to reconstruct the natural annual runoff series for the Yangtze River source region from 1433 to 2002. It explores historical variability, the evolution of wet and dry events, and periodic fluctuations. Using tree-ring data and observed runoff data, the model’s accuracy is validated through evaluation indicators (CRSQ, VRSQ, RE, and CE). Results indicate that over the past 570 years, the annual runoff in the Yangtze River source region has experienced significant fluctuations. Six wet periods and nine dry periods are identified, with the longest wet periods occurring from 1451 to 1510 and from 1596 to 1645, and the longest dry period spanning from 1848 to 1903. The dry periods during the reconstruction period align with droughts on the Tibetan Plateau and in other areas of the Yangtze River basin, suggesting that the reconstructed runoff changes in the Yangtze River source region reflect large-scale climate fluctuations. Furthermore, the reconstructed runoff series for the Yangtze River source region exhibits significant periodic fluctuations at intervals of 4-8 years, 16-32 years, 50-100 years, and 100-200 years. These fluctuations are likely driven by the combined effects of ENSO (El Niño-Southern Oscillation), the East Asian Summer Monsoon (EASM), the Pacific Decadal Oscillation (PDO), and the Atlantic Multidecadal Oscillation (AMO), and also reflect the impact of global climate change and the trends of glacier and snowmelt on the Tibetan Plateau.
