[Objective] Current research on groundwater reserves in China primarily focuses on northern regions. Key challenges remain regarding the relationship between groundwater reserves and routine-emergency water supply, the construction of reserve systems, and the principles for reserve utilization. Focusing on the middle reaches of the Yangtze River—a region with favorable groundwater reserve conditions, dense urban distribution, and insufficient urban water supply security under extreme conditions—this study explores the connotation, hierarchical framework, and utilization strategies of groundwater reserves. The aim is to mitigate extreme water scarcity threats and enhance urban water supply security. [Method] Groundwater reserve volume was defined as the volume of groundwater stored to address water supply security threats in routine systems caused by extreme droughts or emergencies. The risks of extreme droughts and sudden pollution events, urban water supply security capacity, and groundwater occurrence and utilization in the middle reaches of the Yangtze River were analyzed. On this basis, a hierarchical groundwater reserve framework (HWUSR) was innovatively constructed based on five dimensions: crisis level of urban water supply security (How), urban scale (Where), reserve purpose (Use), reserve area scale (Scale), and groundwater renewability (Renewability). [Results] (1) Extreme droughts occur frequently in the middle reaches of the Yangtze River, severely threatening urban water supply systems that rely primarily on surface water. The region possesses massive groundwater storage, which can resolve the insufficient water supply security capacity for critical towns during extreme water scarcity events. (2) Groundwater reserves in important towns with abundant groundwater and low exploitation rates should follow a hierarchical approach. Priority should be given to megacities with dense populations and developed industries, as well as pilot zones with strong economic foundations, high population density, primary reliance on surface water, and inadequate emergency supply capacity. Domestic water needs for urban residents should be prioritized over industrial demands. Shallow aquifers with good water quality, high extractability, and strong renewability should be prioritized for reserves. (3) The utilization of groundwater reserves should adhere to the principles of confirmed necessity, dedicated use, shallow-deep complementarity, appropriate utilization, post-use recovery, and dynamic early warning. (4) Strategic reserve utilization must clarify responsibilities, implementation entities, timing, plans, and evaluations of effectiveness and environmental impacts. Following the principle of “whoever utilizes compensates, whoever benefits restores”, compensation and restoration measures must be implemented post-utilization. [Conclusion] These findings provide an important reference for the formulation and implementation of the groundwater reserve system and the construction of reserve demonstration projects in the Yangtze River Basin.
[Objectives] This study aims to quantify the respective contributions of suspended load and bed load to reservoir sedimentation over different operational periods, and to evaluate the impacts of upstream bed load supply rate, grain size distribution, and intra-annual distribution on reservoir sedimentation. The findings are intended to provide scientific support for the design, long-term operation, and numerical modeling of reservoir projects, especially in data-scarce mountainous regions. [Methods] The study takes the Tianzishan Reservoir in Hunan Province, China, as a case study. A one-dimensional water-sediment mathematical model, “HELIU-2” developed by the Changjiang River Scientific Research Institute (CRSRI), is employed to simulate reservoir sedimentation over a 300-year operational period. The model is set up using measured cross-sectional profiles along the entire reservoir reach (12.23 km, 31 sections), sediment gradation data from the dam site, and hydrological data at the dam site, with key empirical parameters adopted from established practices. Scenarios are designed to assess the influences of upstream bed load supply, bed load gradation, and intra-annual distribution of bed load supply. [Results] During the early operational period (first 10 years), bed load primarily deposits near the reservoir inlet (10 229-12 230 m from the dam), while suspended load dominates overall reservoir sedimentation, accounting for 89.78%-91.04% of total deposition over different operational years. In the near-dam reach (0-3 534 m from the dam), the proportion of bed load deposition increases gradually with operation time, reaching 40.98% at 300 years. In the inlet reach, bed load deposition reaches 100% after 100 years. In contrast, in the middle reaches (e.g., 5 884-7 110 m from the dam), the bed load deposition proportion declines after an initial increase, eventually disappearing as bed load migrates further downstream. Among the three factors examined—upstream bed load supply rate, bed load gradation, and intra-annual distribution of bed load supply—the supply rate has the greatest impact on reservoir sedimentation. A higher bed load supply rate leads to higher along-channel bed elevation and a steeper riverbed slope near the dam. The effect is more pronounced in reaches with initially milder bed slopes, where sediment deposition is more sensitive to changes in bed load supply. The influence of bed load supply rate becomes increasingly significant with longer operation time.In contrast, bed load gradation has a limited effect: coarser bed load gradation results in slightly higher bed elevation in the near-dam reach, but the overall impact on the longitudinal sedimentation profile is small. Similarly, the intra-annual distribution of bed load supply (flood-season-only versus year-round) shows negligible influence on both the sedimentation profile and bed slope. [Conclusions] (1) In the early stage of reservoir operation, bed load mainly deposits near the reservoir inlet, while suspended load dominates sedimentation in the reservoir area. As operation time increases, the proportion of bed load deposition in the near-dam reach gradually rises and reaches 100% in the inlet reach after 100 years. In the middle reaches, the proportion of bed load deposition first increases and then decreases.(2) The upstream bed load supply rate has a greater impact on reservoir sedimentation than bed load gradation and intra-annual distribution. A higher bed load supply rate leads to higher along-channel bed elevation and a steeper riverbed slope near the dam, with the effect being more pronounced in reaches of milder initial bed slope. This influence becomes increasingly significant with longer operation time. (3) Coarser bed load gradation results in slightly higher bed elevation in the near-dam reach, but its overall impact on the sedimentation profile is limited. The intra-annual distribution of bed load supply also shows negligible influence. For reservoirs with moderate sedimentation, neither factor is a controlling element.
[Objective] To ensure the safety of water supply and water quality for the South-to-North Water Diversion Project, this paper explores the collaborative governance mechanism for floating debris in the Danjiangkou Reservoir and clarifies the key points for enhancing the effectiveness of collaborative governance, thereby promoting the sustainable development of the ecological environment in the reservoir area. [Methods] First, based on the SFIC model and the characteristics of floating debris management in the Danjiangkou Reservoir, factors that influenced collaborative governance were identified. Second, structural equation modeling (SEM) was used to analyze the relationships among the influencing factors. Finally, system dynamics (SD) dynamic simulation was employed to analyze the trend of collaborative governance effectiveness and conduct sensitivity analysis. [Results] (1) SEM analysis results showed that starting conditions, facilitative leadership, and institutional design not only had a direct positive impact on collaborative governance effectiveness but also exerted indirect positive effects through collaborative processes. The order of impact of these factors on collaborative governance effectiveness was: collaborative process > institutional design > facilitative leadership > starting conditions. (2) System dynamics simulation results indicated that the effectiveness of collaborative governance first rose rapidly, then its growth rate slowed, and it finally gradually stabilized, with scores stabilizing between 4.7 and 4.8 (on a scale in which 5 represented the ideal state of collaborative governance effectiveness), which was close to the ideal level. This suggested that floating debris management in the reservoir area was likely to complete the nationally prescribed governance tasks within the planning period. (3) Sensitivity analysis results showed that collaborative governance effectiveness was most sensitive to changes in the collaborative process, followed by institutional design and facilitative leadership, with starting conditions being relatively weak. This finding was consistent with SEM conclusions. Additionally, the sensitivity analysis of items within each factor provided detailed guidance for formulating specific governance strategies. [Conclusion] Based on the above research findings, this paper proposes the “1+3” coordinated governance implementation pathway, which focuses on improving the collaborative process as the core approach, while simultaneously optimizing institutional design, strengthening facilitative leadership, and consolidating starting conditions through three complementary measures. This governance pathway aims to comprehensively improve the effectiveness of multi-stakeholder collaborative governance of floating debris in the Danjiangkou Reservoir and provides a theoretical basis and reference for research on multi-stakeholder collaborative governance in related fields.
[Objective] Located in the core of the Yangtze River Delta, the Taihu Basin is a typical plain tidal river network region characterized by numerous lakes. As a key watercourse in this basin, the Huangpu River serves as a representative case for studying ecological flow, which is critical for maintaining the stability and health of river-lake ecosystems. [Methods] Utilizing flow monitoring data from the Songpu Bridge section and hydrological records (including typhoons and rainfall) from 2007 to 2023, this study analyzed instances where the daily average flow fell below the sensitive ecological flow threshold of 90 m3/s. The ecological flow compliance rate was calculated as the ratio of days meeting the assessment indicator (≥90 m3/s) to the total days in the evaluation year. To accurately determine compliance, the analysis comprehensively accounted for force majeure events and major natural disasters (e.g., droughts, floods, typhoons, and earthquakes). Furthermore, factor analysis was employed to conduct a comprehensive assessment of the factors contributing to flow deficits relative to the sensitive ecological threshold. [Results] (1) Daily average flows at the Songpu Bridge section dropping below the sensitive ecological threshold occurred most frequently between August and October (Gregorian calendar), peaking in September. These occurrences were also more prevalent prior to the semi-monthly high tides (specifically the 3rd and 18th days of the lunar month). (2) Factor analysis indicated that the primary causes for flows falling below the sensitive threshold were force majeure factors, specifically the backwater effect of astronomical tides and storm surge-induced water level rises caused by typhoons and cold air. (3) During periods of sub-threshold flow, chloride ion concentrations at the section remained significantly lower than 250 mg/L, satisfying the ecological protection targets for tide prevention and saltwater intrusion control. Consequently, after accounting for force majeure and water quality compliance, the ecological flow assessment compliance rate for the Huangpu River from 2007 to 2023 was 100%. [Conclusions] Adopting a basin-scale perspective, this study establishes a comprehensive methodology and mitigation strategies for evaluating ecological flow maintenance in tidal river systems. Given its critical geographical position, the Huangpu River is identified as a representative tidal watercourse within the Taihu Basin. This research elucidates the influence of tidal processes on riverine ecosystems, offering a reference framework for similar estuarine reaches. We recommend integrating digital twin watershed construction to facilitate a closed-loop management system characterized by “technological intelligence, refined dispatching, and diversified objectives”, providing a reference for establishing ecological flow guarantee systems in water-abundant regions.
[Objective] Traditional evaporation models are limited in floating ball coverage scenarios by complex parameterization, reliance on sensible heat flux data, and cumbersome aerodynamic calculations. This study aims to develop a highly accurate evaporation prediction formula with simplified parameters, adaptable to varying coverage ratios, to provide a reference for water resource quantification. [Method] Based on the Priestley-Taylor model, a modified model was developed by introducing correction term g(m) to address evaporation prediction under floating ball coverage. By analyzing the relationship between coverage ratio and latent heat flux, exponential (for high-temperature seasons) and linear (for transition seasons) correction forms were determined. Robust regression was employed to minimize the impact of outliers. The optimal model was selected through model comparison and cross-validation. [Result] (1) The response relationship between latent heat flux and floating ball coverage ratio exhibited significant monthly variations. At high-temperatures during high-radiation season (June-August), the exponential correction yielded the best fit (R2≥0.985), whereas the linear correction performed better during the transition seasons (March-May and September-October) (R2≥0.986). (2) The exponential model demonstrated superior adaptability and predictive advantages for floating ball evaporation. Over the entire experimental period, it achieved a Willmott’s index of agreement (D) of 0.986, a Nash-Sutcliffe efficiency (NSE) of 0.947, a root mean square error (RMSE) of 0.54 mm/d, and a mean absolute error (MAE) of 0.43 mm/d, with strong parameter stability and no systematic bias. (3) The model also showed good adaptability during external validation under high-coverage conditions (NSE=0.734, D=0.890). [Conclusion] The proposed modified model establishes a unified prediction framework based on basic meteorological parameters for varying floating ball coverage ratios. It enables precise estimation of evaporation processes across different coverage levels, providing an efficient and practical computational method for water resource management in arid regions.
[Objective] Water level variations in tidal river reaches are highly complex and influenced by both upstream runoff and downstream tides. Accurate prediction of these variations is crucial for flood control, disaster reduction and ecological security. Taking the Nanjing Hydrological Experimental Station in the tidal reach of the Yangtze River as a case study, this paper aims to develop a model that can capture the nonlinear, dynamic and complex fluctuation characteristics of water levels and achieve real-time prediction. [Methods] A hybrid CNN-LSTM-KAN model is proposed, which combines a Convolutional Neural Network (CNN), a Long Short-Term Memory (LSTM) network, and a Kolmogorov-Arnold Network (KAN). First, the CNN is used to extract spatial features from the water level sequence. Then, the LSTM captures the dynamic temporal dependencies of water level changes. Finally, KAN is introduced to further enhance the model’s ability to represent nonlinear and dynamic characteristics. [Results] The hybrid model shows strong real-time prediction performance on the measured data from the Nanjing station. It outperforms conventional deep learning methods such as optimized LSTM and CNN-LSTM in terms of prediction accuracy, peak capture capability and robustness to input data. On the test set, the model achieves a root mean square error (RMSE) of 0.065 4 m, a mean absolute error (MAE) of 0.042 9 m, a mean absolute percentage error (MAPE) of 0.023 5, and a Nash-Sutcliffe efficiency coefficient (NSE) of 0.995 1. [Conclusions] The proposed CNN-LSTM-KAN hybrid model has a simple structure and is easy to implement. It provides strong technical support for flood and drought disaster prevention and sustainable socio-economic development in the tidal reaches of the lower Yangtze River.
[Objective] There are significant differences between source reservoirs and receiving reservoirs in arid regions of Northwest China in terms of spatial distribution and functional positioning. Traditional single-level operation models are difficult to achieve the global optimal allocation of water resources. To improve the scientific basis of reservoir operation and the efficiency of intensive water resources utilization, this study constructs a joint operation model for reservoir groups based on bi-level optimization. [Methods] A bi-level optimization model was established for the joint operation of reservoir groups, and the particle swarm optimization (PSO) algorithm was used to solve the model. The upper-level model takes the source reservoir as the operation core and aims to minimize water transfer deviation and evaporation-seepage losses. The lower-level model takes the receiving reservoirs as the regulation objects and aims to minimize water shortage in irrigation districts. Hydrological methods were used to quantitatively evaluate ecological flow, which was incorporated into the operation model as a rigid constraint. [Results] The variation trends of water transfer amount and water diversion amount were consistent, and the ratio between them remained within the range of 0.87-1.07. The multi-year monthly average evaporation-seepage loss decreased from 413.14×104 m3 before optimization to 348.46×104 m3 after optimization, with a reduction of 15.66%. The water supply reliability of each sub-irrigation district reached 87.33%-95.63%. [Conclusions] The proposed reservoir group joint operation model based on bi-level optimization can coordinate the relationship between source reservoir regulation and receiving reservoir water demand, reduce evaporation-seepage losses, and improve irrigation water supply reliability under ecological flow constraints. The research results can provide a reference for the joint operation of reservoir groups in similar regions.
[Objective] This study aims to quantitatively assess the contributions of individual meteorological factors to hydrological drought evolution in the Yuanjiang River Basin, a typical humid region in the middle reaches of the Yangtze River, and to reveal the climatic mechanisms. [Methods] We integrated historical hydrometeorological observations (1975-2022) with future climate projections (2026-2050) derived from five CMIP6 global climate models (GCMs) under three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP370). To ensure regional accuracy, the future meteorological projections were bias-corrected using the quantile delta mapping method. Subsequently, we developed and validated a monthly streamflow prediction model across six hydrological stations utilizing the XGBoost algorithm. This model incorporated a comprehensive set of predictors, including meteorological variables (precipitation, relative humidity, radiation, and temperature) and human activity indicators (reservoir operation and land use changes). To ensure the interpretability of the machine learning model, the SHAP (SHapley Additive exPlanations) framework was employed to quantify the marginal contributions and unravel the directional influences of each driving factor on streamflow variations. Finally, the validated XGBoost model was driven by the bias-corrected future data to project long-term streamflow dynamics. Based on these projections, hydrological drought events were characterized using the standardized runoff index (SRI) at 3- and 6-month scales, with key drought features (frequency, duration, severity, and intensity) systematically extracted via the run theory. [Results] From 1975 to 2022, the XGBoost model demonstrated excellent performance, achieving average Nash-Sutcliffe efficiency (NSE) values of approximately 0.90 for calibration and 0.80 for validation. SHAP analysis revealed precipitation as the dominant driver of streamflow variability, accounting for 69.8% of the total explained variance, followed by relative humidity (11.9%). Furthermore, analysis of the daily-to-monthly aggregation methods showed that the monthly mean contributed 58.2% of the variance, whereas metrics capturing intra-monthly fluctuations and extreme distributions (std, p90, and p10) cumulatively contributed 41.8%. Specifically, standard deviation (20.8%) and the 90th percentile (14.4%) had high explanatory weights, highlighting the critical importance of intra-monthly meteorological variability. Historically, hydrological droughts exhibited distinct spatiotemporal heterogeneity, with more frequent occurrences in winter and spring and greater frequency and severity at upstream stations (e.g., Taoyi). Under future climate scenarios (2026-2050), precipitation was projected to follow a “drier dry season and wetter wet season” pattern, with reductions of approximately 25 mm from September to November and increases of up to 50 mm from April to August. Concurrently, relative humidity was projected to decline throughout the year, with pronounced decreases of 0.05-0.10 during the dry season. Consequently, hydrological droughts were expected to worsen, with projected increases in frequency (0.04-0.06), duration (0.2-0.4 months), and intensity (0.05). A significant structural shift in drought categories was also anticipated: moderate drought frequency increased markedly (0.04-0.08), while severe drought frequency generally declined, dropping by approximately 0.06 at Taoyi Station. This structural transition from “long-duration, high-intensity” to “high-frequency, moderate-intensity” droughts in the future was driven by seasonal moisture dynamics. Sharp declines in precipitation and relative humidity in June acted as the trigger for drought onset (with the combined SHAP negative contribution reaching approximately -230 m3/s), followed by continuous moisture deficits from July to October that drove the progression into moderate drought. However, steady moisture increases from November to May created a significant compensation effect, interrupting the deep accumulation of streamflow deficits and preventing the evolution into severe droughts. These findings highlighted that future drought mitigation in the Yuanjiang River Basin should prioritize managing seasonal consecutive droughts and the cumulative impacts of moderate droughts. [Conclusion] Precipitation and relative humidity are the dominant meteorological factors controlling hydrological drought in the Yuanjiang Basin. The XGBoost-SHAP framework effectively quantifies their individual contributions and reveals that the seasonal coupling between precipitation reduction and enhanced evapotranspiration due to declining relative humidity is the primary mechanism driving future drought intensification. Future hydrological drought across the basin is projected to intensify, characterized by increased frequency, prolonged duration, and a structural shift toward more frequent moderate drought events. Machine-learning interpretability approach provides a valuable and robust supplement to traditional physical models for understanding and projecting climate change impacts on hydrological drought. This integrated analytical framework offers scientific support for adaptive water resource management and drought mitigation strategies in the Yuanjiang River Basin and similar humid regions facing escalating drought risks under climate change.
[Objective] Danjiangkou Reservoir is the core water source of China’s South-to-North Water Diversion Middle Route Project, with a 378.82 km2 water-level-fluctuation zone (WLFZ) where agricultural planting covers 45.39% of the 158-170 m area, threatening adjacent reservoir bay water quality. Existing studies lack clarity on region-specific planting impacts and risks from pathways like rainfall runoff and submergence. This study aimed to: (1) quantify total nitrogen (TN) and total phosphorus (TP) changes in soils and bay waters before/after planting and rainfall; (2) measure TN/TP release rates from soils under submergence via simulations; (3) provide targeted support for WLFZ water quality protection. [Methods] A total of 10 topsoil samples (0-15 cm) and 9 bay water samples (0.5 m below surface) were collected during pre-planting (Sept. 6-7, 2024), post-planting/pre-rainfall (Oct. 22-23, 2024), and post-planting/post-rainfall (Nov. 16-17, 2024). Soil TN/TP and water quality indicators were tested following national standards. Static immersion experiments used soil columns (5 cm height) with 2 L reservoir water, sampling every other day until concentration stabilization. [Results] (1) Soil TN concentrations rose by 159-890 mg/kg, with the mean value increasing from 790 mg/kg (pre-planting, mostly Grade Ⅴ-Ⅵ, low nutrient level) to 1 246 mg/kg (post-planting, mostly Grade Ⅲ-Ⅳ, medium level). Soil TP concentrations increased by 46-546 mg/kg, with the mean rising from 608 mg/kg (pre-planting) to 669 mg/kg (post-planting); only unfertilized sites showed no increase or a decrease. After rainfall, soil TN decreased by an average of 266 mg/kg (range: 80-698 mg/kg), with the largest drop (698 mg/kg) in Shengwan Town. Soil TP decreased by an average of 149 mg/kg (range: 58-332 mg/kg), with the largest decline (332 mg/kg) in Jiuchong Town.(2) Rainfall led to significant water quality deterioration: the proportion of Ⅰ-Ⅱ class water (per GB3838-2002) dropped from 88.81% (pre-rainfall) to 33.33% (post-rainfall). Water TN increased by an average of 14% (range: 0.07-0.47 mg/L). Water TP surged by an average of 255% (range: 0.01-0.10 mg/L). This indicated that TP (dominated by particulate phosphorus) was more vulnerable to rainfall erosion than TN.(3) Planting significantly enhanced TN and TP release from soils. Annual TN release rates increased by 65%-386%: from 16.1 mg/(m2·d) to 26.62 mg/(m2·d) (Jiuchong Town D2-166), 16.77 mg/(m2·d) to 81.51 mg/(m2·d) (Xianghua Town D1-166), and 7.36 mg/(m2·d) to 33.1 mg/(m2·d) (Cangfang Town D3-163). Annual TP release rates increased by 6%-232%: from 3.59 mg/(m2·d) to 3.79 mg/(m2·d) (Jiuchong Town D2-166), 0.38 mg/(m2·d) to 1.26 mg/(m2·d) (Xianghua Town D1-166), and 0.48 mg/(m2·d) to 1.10 mg/(m2·d) (Cangfang Town D3-163). Nutrient release followed a consistent pattern: rapid release in the early stage and gradual stabilization later, though release amounts varied significantly across different planting sites. [Conclusion] This study innovatively clarifies region-specific pollution risks and nutrient release of agricultural planting in the Danjiangkou Reservoir WLFZ. Agricultural planting increases soil TN/TP accumulation, while rainfall runoff and submergence accelerate their migration to water bodies—with TP more susceptible to rainfall erosion. The marked rise in nutrient release rates post-planting elevates water quality risks during WLFZ submergence. Targeted recommendations include: (1) strengthening intensive water quality monitoring in high-risk bays (Xianghua, Shengwan, Jiuchong Towns) during peak planting seasons (April-May, October-November); (2) implementing strict fertilizer controls and regular inspections in these key regions; (3) prioritizing measures to reduce particulate phosphorus loss and mitigate eutrophication. These findings fill gaps in region- and pathway-specific pollution risk assessments and provide critical support for safeguarding the South-to-North Water Diversion Project’s water quality.
[Objective] Nitrogen, a critical limiting nutrient in aquatic ecosystems, exhibits spatiotemporal heterogeneity that profoundly influences water quality dynamics and eutrophication evolution in the Danjiangkou Reservoir. This study aims to clarify the spatiotemporal differentiation patterns and driving mechanisms of key nitrogen forms in Danjiangkou Reservoir, focusing on seasonal (non-flood vs. flood) and vertical (surface, middle, bottom) variations. It focuses on critical yet underexplored issues, including seasonal dynamics of nitrogen forms, vertical data gaps, and tributary-reservoir coupling, to directly support the water quality security goals of the South-to-North Water Diversion Project. [Methods] This study systematically collected surface water samples from tributaries discharging into the reservoir and stratified samples (surface, middle, and bottom layers) during the non-flood (March 2024) and the flood (September 2024) seasons. By quantifying total nitrogen, nitrate, ammonium, and dissolved organic nitrogen, we investigated the spatiotemporal differentiation mechanisms of nitrogen forms. [Results] In the non-flood season, nitrate nitrogen ($\mathrm{NO}_{3}^{-}-\mathrm{N}$) was the dominant form of nitrogen in the water of the Danjiangkou Reservoir area and its inflow tributaries, accounting for 63.3% to 88.6% of the total nitrogen and holding an absolute advantage. In the longitudinal direction of the reservoir area, after the inflow from the Han Reservoir entered the Danjiangkou Reservoir, the concentration of $\mathrm{NO}_{3}^{-}-\mathrm{N}$ generally showed a decreasing trend along the direction of water flow. In the vertical direction, the average concentration of $\mathrm{NO}_{3}^{-}-\mathrm{N}$ followed the order of bottom layer > middle layer > surface layer, which may be attributed to photochemical denitrification in the surface water. Dissolved organic nitrogen (DON) was the second most abundant form of nitrogen; its concentration in the Han Reservoir was significantly higher than that in the Dan Reservoir. Vertically, DON concentration showed a decreasing trend toward the bottom in the Han Reservoir, while it showed an increasing trend toward the bottom in the Dan Reservoir. This phenomenon may be caused by the adsorption of DON by fine suspended sediments and their transport to the reservoir bottom under the slow flow conditions of the Dan Reservoir. After entering the flood season, DON concentrations in the inflow tributaries increased significantly. In both the Danjiangkou Reservoir area and its tributaries, the dominant forms of nitrogen became $\mathrm{NO}_{3}^{-}-\mathrm{N}$ and DON, with $\mathrm{NO}_{3}^{-}-\mathrm{N}$ accounting for 13.90% to 90.44% and DON accounting for 1.04% to 74.18%. Compared with the non-flood season, the content of $\mathrm{NO}_{3}^{-}-\mathrm{N}$ decreased significantly, which may be due to the enhanced photochemical denitrification in the surface water and microbial denitrification in the bottom water of the reservoir during the flood season. In the longitudinal direction, the distribution of $\mathrm{NO}_{3}^{-}-\mathrm{N}$ showed no obvious regularity; however, in the vertical direction, the average concentration of $\mathrm{NO}_{3}^{-}-\mathrm{N}$ still followed the order of bottom layer > middle layer > surface layer. [Conclusion] The results show that nitrate nitrogen is the main form of nitrogen in the water body during the non-flood season of the reservoir. After entering the flood season, the content of dissolved organic nitrogen increases significantly and jointly dominates the occurrence of nitrogen in the water body with nitrate nitrogen. Longitudinally, after the water from the Han Reservoir enters the Danjiangkou Reservoir, the concentration of nitrate nitrogen generally shows a decreasing trend along the direction of water flow, reflecting the regulatory effect of the hydrodynamic process on the transport of nitrogen. Vertically, the average concentration of nitrate nitrogen in both the non-flood season and the flood season shows the typical pattern of bottom layer > middle layer > surface layer, and the vertical difference in the flood season is more significant. This pattern may be closely related to the reduction of nitrate nitrogen by photochemical denitrification in the surface water body and the inhibition of vertical mixing due to thermal stratification in the flood season.
[Objective] In order to clarify the spatiotemporal distribution characteristics of total nitrogen (TN) and total phosphorus (TP) loads from non-point source pollution in the Guanshan River watershed, as well as to identify their major pollution source contributions, and simultaneously to address the problem of insufficient estimation accuracy of the traditional export coefficient model when applied to watersheds with complex terrain conditions, this study introduced a rainfall factor (α) and a topography factor (β) to improve the traditional export coefficient model. Based on this improved model, the nitrogen and phosphorus loads from non-point source pollution in the Guanshan River watershed from 2021 to 2024 were estimated, and their temporal and spatial variation characteristics were systematically evaluated and analyzed. [Methods] In this study, the rainfall factor (α) was extracted by utilizing the daily runoff data recorded at the hydrological station located at the watershed outlet of the Guanshan River watershed during the period from 2021 to 2024, in combination with the observed rainfall data collected from the Dama Station, Xihe Station, Yuanjiahe Station, and Gushan Hydrological Station. Meanwhile, the topography factor (β) was extracted based on the Digital Elevation Model (DEM) data of the Guanshan River watershed. Subsequently, both the rainfall factor (α) and the topography factor (β) were incorporated into the framework of the traditional export coefficient model, thereby constructing an improved export coefficient model with enhanced applicability to the study area. [Results] (1) The improved export coefficient model effectively enhanced the simulation accuracy of nitrogen and phosphorus load estimation. Specifically, the NSE coefficients for total nitrogen and total phosphorus increased significantly from 0.18 and -2.65 to 0.98 and 0.93, respectively, while the relative error was reduced from a maximum value of 291.89% to a controlled range within 10%, demonstrating a substantial improvement in model performance.(2) During the period from 2021 to 2024, the total nitrogen and total phosphorus loads in the Guanshan River watershed decreased by 67.84% and 80.06%, respectively. These reductions further revealed the contribution rates of nitrogen and phosphorus loads from three major source categories, namely land use sources, rural domestic sources, and livestock sources. Among these categories, land use sources contributed the largest proportion, accounting for 81.32% to 94.94% of the total nitrogen and phosphorus loads. Within land use types, forest land made the highest contribution to both nitrogen and phosphorus loads, while the total nitrogen load intensity from cropland reached 2 778.63 kg/km2, and the total phosphorus load intensity from construction land reached 709.30 kg/km2. (3) On the temporal scale, both total nitrogen and total phosphorus loads exhibited an overall declining trend throughout the period from 2021 to 2024. On the spatial scale, Baihe Town and Guanshan Town were identified as the key priority areas for nitrogen and phosphorus pollution prevention and control within the watershed, primarily due to their diverse land use types and relatively higher intensity of human activities compared to other regions. [Conclusions] This study successfully constructed an improved export coefficient model by incorporating both the rainfall factor and the topography factor, which effectively enhanced the estimation accuracy of nitrogen and phosphorus loads from non-point source pollution in the Guanshan River watershed. The results demonstrate that the total nitrogen and total phosphorus loads in the Guanshan River watershed generally showed a decreasing trend from 2021 to 2024, thereby verifying the positive effects of the water source protection measures implemented in the region. However, it should be noted that the effectiveness of pollution control remains jointly influenced by multiple interacting factors, including interannual rainfall variability, changes in land use types, and livestock activities. Therefore, in the future, it is necessary to adopt precise zoned and categorized management strategies tailored to different sub-regions and pollution source types. This study provides a quantitative scientific basis for the effective prevention and control of non-point source pollution in the Guanshan River watershed, and in doing so, offers a valuable scientific reference for safeguarding the long-term water quality security of the South-to-North Water Diversion Project.
[Objective] Accurately estimating the friction velocity within the backwater region of deposit body is crucial for deriving hydraulic parameters and predicting river scour and deposition. While classical friction velocity methods (e.g., the logarithmic velocity method) are well-established for uniform flows, their applicability and the associated velocity-turbulence parameter characteristics in backwater regions with non-uniform deposits remain systematically underexplored in current research. [Methods] We conducted a series of flume experiments, employing three distinct flow rates to simulate conditions ranging from low to heavy rainfall during the rainy season. A plexiglass deposit model, characterized by a 45° slope and a channel width contraction ratio of 0.5, was utilized. Three-dimensional velocities and water levels were measured at 19 cross-sections using an Acoustic Doppler Velocimeter (ADV) to systematically analyze the hydraulic characteristics of the deposit-induced backwater region. Four classical methods-the Single-point Reynolds Stress Method, Three-dimensional Turbulent Kinetic Energy Method, Vertical Turbulent Kinetic Energy Method, and Logarithmic Velocity Method were selected to estimate friction velocity, and their relationships with backwater characteristics were investigated. The Karman constant (k) and integration constant (A) were fitted based on the measured velocity distributions. Additionally, empirical coefficients D and λ were derived from turbulence intensity measurements to elucidate their influencing factors. [Results] The key findings of this study are as follows: (1) The Three-dimensional Turbulent Kinetic Energy Method and the Single-point Reynolds Stress Method proved most effective in characterizing bed friction under the influence of deposits, yielding the smallest relative deviations for the estimated mean friction velocities. Furthermore, a significant monotonic negative correlation was observed between friction velocity and the backwater parameter (h/h0). (2) While the vertical velocity profiles within the backwater region still adhered to a logarithmic distribution, the mean Karman constant (k) fitted across the entire water depth was lower than the conventionally recommended value of 0.4. Consequently, the integration constant (A) is proposed to be revised to 7.5-9.5 to better align with engineering requirements. (3) The empirical coefficient D for turbulence intensity was found to be jointly influenced by both flow rate and the friction velocity model, exhibiting a stable mean ratio of Du∶Dv∶Dw=1.41∶1.00∶0.40. In contrast, λ was solely driven by flow rate. The observed decaying trend of vertical, longitudinal, and transverse turbulence intensities with increasing water depth further corroborated the reliability of the friction velocity estimations. [Conclusion] This research successfully identified the optimal method for estimating friction velocity in backwater regions characterized by non-uniform deposits and refined the empirical values of velocity-turbulence parameters. The findings offer crucial experimental evidence for advanced hydraulic modeling, flood control engineering design, and river management strategies in mountainous river sections affected by deposits. Furthermore, this study holds significant implications for river restoration efforts following earthquake-induced secondary geological hazards.
[Objective] The water hammer pressure in pipeline systems is a key factor that endangers the safe and stable operation of the system. Especially for the viscoelastic pipes, their time-dependent mechanical behavior makes the analysis and calculation of water hammer pressure more complicated. This study aims to 1) explore the direct water hammer pressure response characteristics of viscoelastic pipes under rapid valve closing by combining experiment and theory, 2) modify the existing theoretical calculation model, and introduce an intelligent algorithm to predict the pressure, and 3) finally to establish a more accurate and practical calculation method for water hammer pressure in viscoelastic pipes. [Methods] The classical Joukowsky formula for direct water hammer pressure was modified and the creep compliance function of pipeline was introduced to derive the direct water hammer pressure calculation formula suitable for viscoelastic pipelines. The formula was then modified by using experimental data. Based on the modified pressure rise formula, the nonlinear fitting function of a backpropagation (BP) neural network was used to predict the direct water hammer pressure rise value of the same type of pipe with the valve closing time, initial flow rate and pipeline material parameters as input features and the peak value of water hammer pressure as the output target, and an intelligent prediction model of direct water hammer pressure in viscoelastic pipeline was established. [Results] In viscoelastic pipes, the shorter the valve closing time, the greater the water hammer pressure value. The delayed strain of viscoelastic material was introduced into the viscoelastic calculation formula. The mechanical response of the pipe wall included two parts: instantaneous elastic strain and delayed strain, resulting in a higher peak value of water hammer pressure, which made the calculated value of the viscoelastic water hammer pressure formula higher than that of the classical water hammer pressure formula. The calculation results showed that the classical elastic theory was no longer suitable for the calculation of direct water hammer pressure in viscoelastic pipelines. The comparison showed that the maximum error between the calculated value and the measured value of the modified formula was small under all test conditions, indicating that the proposed modified formula could better characterize the direct water hammer pressure in viscoelastic pipeline. In the BP neural network prediction model, the prediction results were compared with the calculation results of the modified viscoelastic pipe direct water hammer pressure calculation formula, and it was found that the error between the two was kept in the range of 1% to 4%. [Conclusion] In the water hammer test of PMMA pipeline, the deviation between the calculation results of classical viscoelastic water hammer pressure rise formula and the measured values is large, with a maximum deviation of 15.1%. Based on the viscoelastic theory derivation and experimental data fitting, a modified direct water hammer pressure calculation formula is proposed, which effectively characterizes the influence of pipe viscoelasticity and valve operation rate on the pressure peak. Verification shows that the maximum error of the calculation is only 3.3%, which is significantly better than the classical elastic theory. Furthermore, a hybrid prediction method combining theoretical model and BP neural network is constructed in this study. The theoretical formula is embedded in the network structure, and the prediction error of pressure peak is controlled within 0.1 m under various flow velocity conditions, showing good accuracy, reliability, and adaptability to working conditions. This provides a feasible reference method for pressure prediction and water hammer risk assessment in engineering practice.
[Objective] Extremal theory is an important approach for predicting the hydraulic geometry of stable channels. However, multiple theories coexist, and their applicability and accuracy under different bed-material conditions (sand-bed and gravel-bed rivers) remain unclear, posing challenges for model selection in engineering practice. To address this issue, this study selects the maximum flow efficiency (MFE), minimum Froude number (MFN), and maximum entropy and minimum energy dissipation rate (ME & MEDR) theories as the research objects, aiming to: (1) clarify the computational accuracy and applicability limits of different extremal theories; (2) reveal the intrinsic relationship between theory applicability and riverbed type; and (3) provide guidance for future researchers in predicting the hydraulic geometry of stable channels. [Methods] A total of 351 datasets from both sand-bed and gravel-bed rivers were used to evaluate the MFE, MFN, and ME & MEDR theories. Model performance was assessed using the mean relative error, geometric mean deviation, and correlation coefficient. [Results] (1) Among the three extremal theories, the MFE theory considered the largest number of parameters and involved the most complex calculations, whereas the MFN theory established a stable channel-width equation, requiring only discharge and median sediment size to predict the hydraulic geometry of stable channels.(2) The validation results showed that, for sandy stable channels, both the MFE and ME & MEDR theories underestimated stable channel width when the channel width was less than 2 m and overestimated stable water depth when the water depth was less than 0.1 m. For gravel-bed stable channels, the MFE theory underestimated stable channel width and overestimated stable water depth; the MFN theory underestimated stable water depth; and the ME & MEDR theory overestimated stable channel width while underestimating stable water depth.(3) Error analysis showed that the MFE theory generally exhibited larger errors than the other two theories. The MFN theory achieved the highest accuracy in predicting stable channel width, whereas the ME & MEDR theory achieved the highest accuracy in predicting stable water depth. [Conclusion] This study clarifies the optimal applicability of different extremal theories and provides a quantitative basis and recommended approaches for stable channel design under specific riverbed conditions. For predicting the hydraulic geometry of stable sandy channels, all three extremal theories provide satisfactory accuracy. For gravel channels, however, the MFN and ME & MEDR theories are recommended.
[Objective] Fractured strata during deep foundation pit excavation can easily lead to instability in the surrounding soil. To investigate the deformation response patterns of nearby subway stations during deep foundation pit excavation in fractured strata, this study examines a deep excavation project in fractured strata near Shenzhen Metro Line 5. [Methods] We conducted an in-depth investigation into the deformation of nearby subway stations caused by unloading during foundation pit excavation, employing theoretical analysis, numerical simulation, and field monitoring. Mindlin solution was employed to calculate additional stresses. Based on the stress release method, assuming full or partial stress release in the surrounding and pit-bottom soils caused by excavation, we comprehensively considered the support effect of the excavation retaining structure. Stresses were multiplied by a reduction factor to derive equivalent released stresses for the sidewalls. A theoretical formula for station deformation calculation was established, revealing its deformation mechanism. Three-dimensional modeling analysis was conducted through numerical simulation. Grey relational analysis was employed to perform sensitivity analysis on factors influencing station deformation, determining the sensitivity of each factor to station deformation. [Results] According to numerical calculation results, the maximum settlement deformation of the main structure of the subway station during each construction process was approximately 2.5 mm, and the maximum horizontal deformation was approximately 4.5 mm. Excavation unloaded the soil adjacent to the station structure, inducing horizontal displacement toward the pit center. Numerical simulations indicated that significant longitudinal deformation occurred primarily near the excavation zone, while structures farther away exhibited negligible deformation. Vertical deformation followed a pattern of greater displacement at the top and smaller displacement at the bottom. Comparative analysis between measured data from the central excavation zone and theoretical calculations/numerical simulations yielded the following station horizontal displacement results: Compared to monitoring curves, numerical simulation results were approximately 6% smaller; compared to theoretical calculation curves, numerical simulation results were approximately 8% larger. [Conclusion] Comparative analysis demonstrates that numerical analysis methods provide a reliable basis for subsequent factor impact assessments. Simplified calculations omitted factors such as peripheral loading around the excavation pit and delayed support construction, resulting in monitoring values exceeding both numerical and theoretical calculations. Therefore, construction must strictly adhere to avoiding peripheral loading and promptly installing supports to minimize impacts on station deformation. Sensitivity analysis of factors affecting station deformation using grey relational analysis reveals high correlations with diaphragm wall moment of inertia (0.874), overburden thickness (0.816), and excavation depth (0.759). Among these, diaphragm wall moment of inertia warrants particular control emphasis. During excavation, factors such as economy and safety must be comprehensively considered. Selecting a reasonable structural stiffness can achieve good deformation control, but beyond a certain threshold, this control effect diminishes significantly. The horizontal displacement of the station decreases as the moment of inertia of the diaphragm wall increases, the excavation depth decreases, and the overburden thickness increases, exhibiting a nonlinear relationship. Grey relational analysis identifies that diaphragm wall moment of inertia is the major influencing factor.
[Objective] The primary objective of this research is to develop and optimize a novel, sustainable, and cost-effective binder for the stabilization/solidification (S/S) treatment of cadmium (Cd)-contaminated soils by utilizing a multi-component system comprising red mud (RM), granulated ground blast furnace slag (GGBS), carbide lime residue (CCR), and phosphogypsum (PG), all of which are industrial waste materials. A secondary objective is to compare the effectiveness of response surface methodology (RSM) with traditional orthogonal experimental design (OED) in optimizing the binder composition for achieving both high unconfined compressive strength (UCS) and low Cd leaching concentrations. The study also aims to elucidate the mechanisms governing Cd stabilization within the treated soil matrix via advanced microstructural characterization. [Methods] Artificially Cd-contaminated soil was prepared and treated with varying proportions of the RM-GGBS-CCR-PG binder. Two parallel optimization approaches were used: (1) OED (L9 (34) orthogonal array) to identify the key factors affecting the S/S performance and (2) RSM (Box-Behnken design) to model the complex relationships between binder components (CCR, PG content, and RM/GGBS ratio) and responses (UCS and Cd leaching). Following curing, the treated soil samples were subjected to UCS testing and leaching tests. The experimental data obtained from RSM were used to develop quadratic polynomial models, perform analysis of variance (ANOVA), and generate response surface plots. The optimal binder composition was determined by maximizing UCS and minimizing Cd leaching. X-ray diffraction (XRD) and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS), was performed to identify the reaction products and elucidate the Cd stabilization mechanisms. [Results] The RM-GGBS-CCR-PG binder effectively stabilized Cd in the contaminated soil. Both OED and RSM identified the RM/GGBS ratio as the most significant factor influencing both UCS and Cd leaching. RSM provided a more refined optimization, leading to a binder composition of 13.77% CCR, 10% PG, and a RM/GGBS ratio of 0.57∶1. At this optimal composition, the treated soil achieved a UCS of 2.12 MPa and a Cd leaching concentration of 1.57 mg/L. ANOVA results for the RSM models showed that the models were highly significant (p<0.000 1) and exhibited a good fit (R2>0.99). XRD analysis revealed the formation of hydration products such as C-(A)-S-H, ettringite (AFt), and Cd-containing precipitates (Cd(OH)2, CdCO3, Cd3(PO4)2). SEM-EDS confirmed the encapsulation of Cd within the C-(A)-S-H gel matrix and the formation of Cd-bearing precipitates. Replacing 1 kg PC with 1 kg of the waste-based binder lowered CO2 emissions by 0.81 kg, embodied energy by 3.93 MJ, and natural resource consumption by 1.73 kg. At a 10% dosage, material cost for S/S was approximately 24.7 CNY/m3 for the RM-GGBS-CCR-PG system versus approximately 99.95 CNY/m3 for PC—an approximately 75% reduction—reflecting the low processing burden of industrial by-products (with GGBS requiring only modest grinding) relative to clinkerized binders. Therefore, the optimized mix simultaneously advanced mechanical performance, contaminant immobilization, and environmental-economic metrics. [Conclusion] This study demonstrates the feasibility of utilizing a novel multi-component binder composed entirely of industrial waste materials (RM, GGBS, CCR, and PG) for the effective stabilization/solidification of Cd-contaminated soils. The RM-GGBS-CCR-PG binder exhibits superior performance compared to Portland cement in terms of both mechanical strength and Cd immobilization. The application of response surface methodology (RSM) proves to be a powerful tool for optimizing the binder composition, providing a more precise solution than the traditional orthogonal experimental design. The study elucidates the mechanisms of Cd stabilization, highlighting the roles of physical encapsulation, chemical precipitation, ion exchange, and surface complexation. Furthermore, the carbon footprint and cost analysis demonstrate the substantial environmental and economic benefits associated with the use of this waste-based binder.
[Objective] The effectiveness of microbially induced carbonate precipitation (MICP) in saline soils is frequently compromised by the high-salt environment. The presence of salt ions inhibits microbial activity and disrupt ion migration, leading to reduced calcium carbonate precipitation efficiency and non-uniform spatial distribution. These limitations hinder the enhancement of mechanical properties in the stabilized soil and undermine the reliability of engineering applications. To address these challenges, this study introduces sodium alginate (SA) to develop an SA-MICP composite stabilization system aimed at improving the curing performance of saline soil. [Methods] The research focuses on investigating the variation patterns of calcium carbonate precipitation behavior and mechanical properties under different SA dosages, while elucidating the mechanism of SA-mediated synergistic regulation of microbial mineralization through multi-scale experimental approaches. Sodium alginate was incorporated at varying mass fractions (0% to 6%) relative to the dry soil weight. The quantity of precipitated calcium carbonate was quantified using both solution-based precipitation assays and soil column reaction tests. Mechanical performance of the stabilized soil at curing ages of 3, 7, and 14 days was evaluated via unconfined compressive strength (UCS) and Brazilian tensile strength (BTS) tests. The morphology, mineralogical composition, and spatial distribution of calcium carbonate were systematically analyzed using particle size analysis, scanning electron microscopy (SEM), and X-ray diffraction (XRD). [Results] The incorporation of sodium alginate significantly enhanced the MICP-based stabilization of saline soil, with the extent of improvement being strongly dosage-dependent. Calcium carbonate precipitation initially increased with SA dosage, reaching a maximum at 3% SA, where the yield was approximately 58% higher than that achieved by MICP alone. Beyond this threshold, further increases in SA dosage resulted in decreased precipitation, suggesting that excessive SA elevated system viscosity and excessively sequestered calcium ions, thereby impairing reaction efficiency. Mechanical test results aligned with this trend: both UCS and BTS increased with SA dosage up to 3%, peaking at 14 days of curing. Specifically, UCS improved by approximately 35.2%, while BTS exhibited a substantial increase of up to 194.8%. The pronounced enhancement in tensile strength indicated that SA played a critical role in improving fracture resistance and inter-particle bonding continuity. [Conclusion] Microstructural analyses further validate the synergistic stabilization mechanism of the SA-MICP system. SA-MICP-treated soil displays a continuous and dense “crystal bridge-encapsulation” cementation structure, with more uniform calcium carbonate distribution effectively filling interparticle voids. This study demonstrates that sodium alginate effectively synergizes with MICP to overcome key challenges such as low mineralization efficiency and heterogeneous precipitation in saline environments. The overall SA-MICP stabilization process follows a synergistic pathway characterized as “microbial mineralization-polymer-confined regulation-crystal bridging cementation,” ultimately yielding a densely structured soil matrix with superior mechanical performance. At a 3% SA dosage, the system achieves optimal balance between precipitation efficiency and mechanical enhancement, particularly excelling in tensile strength and crack resistance. However, excessive SA adversely affects reaction kinetics and structural homogeneity, underscoring the importance of precise dosage control. Overall, the SA-MICP composite approach significantly improves the applicability and engineering reliability of MICP in high-salt conditions.
[Objective] To address the insufficient understanding of the mechanical contact behavior at the interface between a cut-off wall and a clay core wall in earth-rockfill dams, this study aims to investigate the nonlinear normal contact relationship developed during the penetration of a cut-off wall into a clay core wall. The mechanical interaction between the cut-off wall and the core wall is a key factor affecting the deformation compatibility and overall stability of seepage-control systems in earth-rockfill dams. However, the wall-end resistance mobilization mechanism and its dependence on overburden pressure have not been fully clarified. Therefore, this study focuses on establishing an experimentally validated nonlinear normal contact model that can describe the relationship between wall-end pressure and penetration displacement, and further evaluates its applicability through numerical simulation. [Methods] A self-developed model test apparatus was designed to simulate the penetration process of a cut-off wall into a clay core wall. Clay specimens were prepared to represent the core wall material, and three levels of overburden pressure (100, 300, and 500 kPa) were applied to investigate the influence of vertical stress conditions on the penetration behavior. A hyperbolic nonlinear normal contact model was proposed and calibrated to describe the mechanical relationship between wall-end pressure and penetration displacement. The proposed model was introduced into finite element numerical simulations to reproduce the penetration process to verify its effectiveness and applicability. [Results] The relationship between wall-end pressure and penetration displacement exhibits significant nonlinear characteristics during the penetration of the cut-off wall into the clay core wall. Under a constant overburden pressure, the wall-end resistance increases rapidly at the initial stage while the penetration displacement changes only slightly, indicating that the clay core wall provides a high initial resistance to wall penetration. As penetration continues, the increase rate of wall-end pressure gradually decreases, and the displacement begins to increase more noticeably. At the later stage, the penetration displacement increases substantially, whereas the growth rate of wall-end pressure becomes much smaller. This behavior indicates that the soil structure near the wall end is progressively disturbed and damaged, and large deformation of the clay core wall can occur without a proportional increase in contact pressure. The test results also demonstrate that overburden pressure has a significant influence on the penetration behavior. As the overburden pressure increases from 100 to 500 kPa, the overall bearing stiffness of the clay core wall increases markedly. This pressure-dependent behavior reflects the compaction effect and pressure-hardening characteristics of clay under higher vertical stress conditions. The proposed hyperbolic normal contact model effectively describes the measured relationship between wall-end pressure and penetration displacement. The coefficient of determination of the model fitting reaches 0.936 9, indicating good agreement between the fitted curves and the experimental data. The finite element results obtained using the proposed nonlinear normal contact model are generally consistent with the model test results, further confirming the rationality and effectiveness of the proposed contact model. [Conclusions] The penetration of a cut-off wall into a clay core wall is governed by a strongly nonlinear normal contact mechanism, rather than a simple linear pressure-displacement relationship. The wall-end resistance develops rapidly at the initial stage and then gradually approaches a slower growth trend as soil disturbance and local structural damage progress. Overburden pressure is a key controlling factor in this process, because higher vertical pressure enhances the compactness, stiffness, and resistance capacity of the clay core wall. The hyperbolic nonlinear normal contact model proposed in this study provides an effective mathematical representation of the wall-end pressure-penetration displacement relationship and can reasonably reflect the pressure-dependent deformation characteristics of clay. Its successful application in finite element analysis indicates that the model can serve as a reliable theoretical and numerical tool for analyzing the interaction between cut-off walls and clay core walls.
[Objective] High arch dams constructed in alpine gorge areas are equipped with numerous observation instruments. Developing objective deformation analysis models and safety monitoring criteria has important theoretical significance and application value. This study aims to overcome the deficiencies of existing methods by proposing a novel method for monitoring the deformation safety of in-service high arch dams. [Methods] We constructed a deformation principal component (DPC) analysis model taking observation point group (OPG) with similar spatiotemporal deformation patterns as research object. Adaptive adjustment approach, Gaussian mutation disturbance, and Tent chaotic disturbance were introduced to optimize the search ability of the sparrow search algorithm (SSA). Using the Improved SSA (ISSA), the parameter optimization approach of the DPC model was established. A 4-level monitoring criterion system was proposed by comprehensively considering the deviation degree between the DPC and the elastic state and the combined control limit of DPCs. Using the radial deformation observation data of the studied dam, the effectiveness of the proposed methodology was verified. [Results] (1) Compared with SSA, ISSA showed improvements in convergence speed and optimization accuracy according to the results of benchmark function testing. (2) Deformation regularities of observation points located in the same area were similar, whereas those at different locations varied largely. (3) The DPC model optimized by ISSA had the most stable and the best generalization performance. For the fitting results of observation groups A-C, the values of the multiple correlation coefficient were 0.997 7, 0.998 1, and 0.997 0, respectively, the values of residual standard deviation were 0.200 6, 0.182 3, and 0.257 3, respectively, and the values of mean absolute percentage error were 0.152 9, 0.269 9, and 0.294 3, respectively. (4) The DPC criteria enabled the 4-level precision monitoring. The physical significance and the probabilistic interpretation were clear. If an abnormal state occurred, it indicated that the deformation similarity characteristics of OPGs changed to some extent. [Conclusion] (1) The established DPC model shows good performance in characterizing the main deformation patterns of OPGs with similar spatiotemporal deformation characteristics. (2) ISSA shortens the computation time, avoids premature convergence, and improves the DPC model performance. (3) The proposed DPC criteria exhibit greater rigor in probabilistic and physical significance compared with the information entropy criterion. Compared with the confidence ellipsoid method, the proposed criteria provide greater practicality for engineering applications. In engineering applications, the most appropriate countermeasures should be determined through scientific analysis, practical application experience, and site-specific conditions. In future research and practice, it is essential to strengthen studies on the deformation feedback mechanisms of high arch dams and adjacent mountain slopes. Special attention should be paid to the impacts of strong earthquakes, cold-wave shocks, freeze-thaw cycles, dissolution, and carbonation, as well as their coupled effects. In addition, an intelligent database and sharing platform should be established. This will facilitate the efficient management and utilization of in-situ observation data of high arch dams, which can improve the efficiency of 4-level precision monitoring.
[Objective] The primary objective of this study is to develop a high-precision deformation prediction method capable of effectively handling the inherent nonlinearity and dynamic variations in monitoring data. Traditional single models and static ensemble methods often struggle to adapt to the shifting data distributions and sudden changes characteristic of real-world deformation scenarios, such as landslides and structural settlements. This research aims to overcome these limitations by proposing a Dynamic Weighted Ensemble Learning (DWEL) model that intelligently integrates multiple base learners and dynamically adjusts their contributions based on recent performance, thereby enhancing prediction accuracy, robustness, and generalization ability in complex environments. [Methods] The proposed DWEL model integrates four diverse and complementary base learners: Long Short-Term Memory (LSTM) networks, Support Vector Regression (SVR), Random Forest (RF), and XGBoost. This selection ensures comprehensive feature extraction from different perspectives. The core innovation of DWEL lies in its two-stage optimization mechanism: (1) Dynamic Weighting Mechanism. A sliding window is employed to continuously track the prediction error (absolute error) of each base learner over a recent period. (2) Two-Level Fusion Strategy. A novel “weight-then-regress” hybrid fusion strategy is implemented. Experiments were conducted on two real-world deformation monitoring datasets: a landslide monitoring dataset (Dataset A) characterized by high non-stationarity and abrupt changes, and a tunnel settlement dataset (Dataset B) with relatively stable long-term trends but periodic fluctuations. The model’s performance was evaluated against single models (LSTM, SVR, RF,XGBoost) and traditional ensemble methods (Averaging, Bagging, Static Stacking) using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R2). Ablation studies were performed to validate the contribution of each core component. [Results] (1) Experimental results on both datasets demonstrated the superior performance of the proposed DWEL model.On the complex Landslide Dataset A, the complete DWEL model (DWEL-2) achieved the best results with an RMSE of 2.72 mm, MAE of 2.08 mm, and R2 of 0.951. This represented a significant improvement over the best single model (LSTM: RMSE=3.92 mm, R2=0.897), reducing the prediction error by approximately 30.6%. It also outperformed traditional ensemble methods like Static Stacking (RMSE=3.52 mm, R2=0.918). Similarly, on the Tunnel Settlement Dataset B, DWEL-2 attained an RMSE of 0.63 mm, MAE of 0.52 mm, and R2 of 0.951, again surpassing all competitors and reducing the error of the best single model (LSTM) by 32.3%.(2) Analysis of the dynamic weight changes revealed the model’s adaptive capability. For instance, during the accelerated deformation phase (hours 150-160) in Dataset A, the weights of LSTM andXGBoost increased significantly, indicating their stronger ability to capture nonlinear mutations, while RF and SVR maintained higher weights during stable periods. This visual analysis confirmed that the dynamic weighting mechanism effectively allocated influence based on temporal data characteristics.(3) The impact of the sliding window length (w) was systematically analyzed. For the highly dynamic Landslide Dataset A, a medium window length (w=16) yielded the optimal balance between responsiveness and stability (RMSE=2.72 mm), whereas shorter (w=8) or longer (w=24) windows led to increased errors due to noise sensitivity or delayed response, respectively. For the more stable Tunnel Dataset B, a longer window (w=24) performed best, demonstrating the method’s adaptability to different data dynamics. (4) Ablation studies conclusively proved the necessity of both innovative components. Using static weights instead of dynamic weighting increased the RMSE on Dataset A by 15.8% compared to the full DWEL-2 model. Using only the meta-learner without dynamic weights performed similarly to Static Stacking. Furthermore, the two-level fusion (DWEL-2) provided an 11.7% reduction in RMSE compared to using only dynamic weighted averaging (DWEL-1), highlighting the significant contribution of the regression-based residual correction in the second fusion layer. [Conclusions] This study successfully developed and validated a novel Dynamic Weighted Ensemble Learning (DWEL) model for deformation prediction. The key innovations include: 1) A dynamic weighting mechanism based on sliding window error feedback, which effectively addresses the lag issue of static ensemble methods during data distribution shifts, improving response speed at mutation points by over 60%; and 2) A two-level “weighted average followed by regression” fusion strategy that synergizes rapid dynamic adaptation with enhanced non-linear fitting capability, reducing prediction errors by an additional 11.7% compared to single-level fusion.The experimental results robustly demonstrate that the DWEL model significantly outperforms existing single and traditional ensemble models across different deformation scenarios (landslide and tunnel settlement). It exhibits remarkable accuracy, robustness, and generalization ability, particularly during critical periods of trend mutation. The method provides an effective, reliable, and high-precision prediction tool for geological hazard early warning and structural health monitoring. Future work will focus on the adaptive optimization of the weighting function and the integration of multi-physics field coupling modeling to further enhance prediction reliability in even more complex environments.
[Objective] This study aims to address two core challenges in the digital governance of complex water networks: insufficient integration of physical topological relationships with multidimensional hydraulic characteristics, and limited multi-level routing analysis capabilities, which collectively weaken the support for water diversion decision-making. Focusing on the Beijing Section of the Middle Route of South-to-North Water Diversion Project and its supporting municipal projects, the research seeks to construct a knowledge graph that realizes the integrated expression of spatial topology and hydraulic attributes, and develop a scalable query system to meet diverse scheduling demands, thereby providing technical support for digital twin water network construction. [Methods] The research adopts a systematic approach for knowledge graph construction and application: (1) Knowledge modeling: A novel “topology-hydraulics-business” three-dimensional framework is proposed to integrate engineering entities, relational entities, and attribute entities, with core concepts aligned with the national standard SL/T 213—2020. (2) Knowledge extraction: Data from structured documents, semi-structured texts, and GIS spatial datasets are processed through standardization (unified units/coordinate system), missing value imputation, and synonym dictionary optimization to ensure data quality, followed by triple extraction using regular expressions. (3) Knowledge fusion: Entity alignment (resolving duplicates like “Miyun Reservoir Sluice” and “Miyun Reservoir Gate”), attribute integration (unified units: m3/s for flow, km for length), and relationship disambiguation (clarifying multi-semantic terms like “Yongding River”) are conducted manually. (4) Knowledge storage: The graph is stored in Neo4j for efficient visualization and querying. (5) Query system development: A three-level query system is built using Cypher language, integrated with the Dijkstra algorithm for path optimization. (6) Performance evaluation: Comparative experiments with Floyd-Warshall and SPFA algorithms are conducted to assess efficiency, convergence, and stability. [Results] The constructed knowledge graph achieves comprehensive semantic expression of the study area, encompassing 437 engineering entities, 149 relationship entities, and 6 attribute types. The three-level query system demonstrates strong applicability: (1) Basic topological analysis successfully retrieves direct downstream nodes of Guanting Reservoir (i.e., “Guanting Reservoir Sluice” and “Guanting Reservoir Spillway”) and the complete path of the Hexi Branch Line (from Daning Reservoir to Shimeng Gate Station via 3 pumping stations). (2) Conditional constraint filtering identifies 5 pipelines over 40km and generates an alternative path for water transfer from Guanting Reservoir to Zhuwo Reservoir when the spillway fails. (3) Multi-objective optimization yields optimal solutions: the shortest path between Huairou and Miyun Reservoirs is 30.33 km; the path with the highest water transmission efficiency (L/Q=3.658 5) from Tuancheng Lake to Huairou Reservoir; and the optimal path meeting dual constraints. Performance evaluation shows the Dijkstra-based algorithm outperforms competitors: average runtime of 0.05 s (vs. 1.655 s for Floyd-Warshall), average relaxation steps of 19 (vs. 34 for SPFA), and zero response time standard deviation, confirming high efficiency and stability. [Conclusions] This study makes two key innovations: (1) The “topology-hydraulics-business” three-dimensional modeling framework resolves knowledge fragmentation by integrating physical structure, hydraulic characteristics, and scheduling rules. (2) The three-level query system supports dynamic constraint filtering and multi-objective optimization, adapting to scenarios like emergency scheduling and daily management. The results validate the knowledge graph’s effectiveness in integrating multi-source data and supporting hierarchical decision-making, providing a replicable technical route for the digital governance of large-scale water diversion projects. Future research will focus on dynamic topology update mechanisms, logical modeling of complex scheduling rules, and human-machine interaction interface development, advancing the knowledge graph into an intelligent decision tool with spatiotemporal dynamic response capabilities to promote precision and intelligence in digital twin water network governance.
[Objective] The Mekong Delta is one of the globally vital biodiversity hotspots, while long-term coastal geomorphic evolution driven by natural sediment transport and intensive anthropogenic interference has triggered prominent spatial differentiation in shoreline progradation and retrogradation across the delta. This study aims to quantitatively characterize the long-term spatio-temporal evolution characteristics of sandbars and shorelines in the Mekong Delta, identify the spatial heterogeneous patterns of coastal accretion and erosion, and further clarify the coupling driving mechanisms of natural hydrological processes and human activities behind delta geomorphic changes. The research is expected to provide reliable long-term baseline data and decision-making references for coastal ecological conservation, integrated water resource regulation, coastal risk prevention and regional sustainable governance in the transboundary Mekong coastal zone. [Methods] We acquired 35-year continuous Landsat remote sensing images covering the period from 1988 to 2023. The Normalized Difference Water Index (NDWI) was adopted to enhance the contrast between water bodies and terrestrial surfaces, and the Otsu’s method (maximum between-class variance algorithm) was applied to realize the automatic threshold segmentation of water-land boundaries, so as to extract multi-phase vector datasets of shorelines and sandbars accurately. On this basis, multiple quantitative indicators including sandbar total area and patch number, total shoreline length, inter-annual shoreline change rate and net shoreline movement distance were calculated to systematically reveal the temporal variation trends of sandbar geomorphology and the spatial differential features of shoreline dynamics between river channels and estuarine regions. Combined with historical sediment data, tidal-hydrodynamic records and regional socio-economic engineering statistics, We further summarized the dominant natural and anthropogenic driving factors responsible for the spatially differentiated geomorphic evolution of the Mekong Delta. [Results] During 1988-2023, the total terrestrial area of the Mekong Delta showed a significant increasing trend, accompanied by an obvious decline in the number of sandbar patches, which indicated the geomorphic evolution characteristic of small scattered sandbars gradually merging into large contiguous tidal flats. The total shoreline length exhibited dramatic inter-annual fluctuations within the 35-year research period, with a net total growth of 63.39 km across the whole study time span. Obvious disparities existed in shoreline change rates between both banks of inland river channels and estuarine zones. The net horizontal migration distance of shorelines presented spatially asymmetric progradation and discontinuous dynamic features, where most estuarine segments advanced seaward remarkably, whereas the coastal zone in the southeastern Ca Mau Peninsula suffered continuous shoreline retreat. The spatio-temporal evolution of the Mekong Delta shoreline displayed prominent spatial differentiation: estuarine coasts experienced persistent seaward accretion, while the southeastern coast of the Ca Mau Peninsula underwent severe erosion and retreat. Such two opposite geomorphic processes were jointly controlled by the long-term coupling effects of natural sedimentation, tidal hydrodynamic evolution, sea level rise, land subsidence, mangrove degradation, hydraulic engineering construction, tidal flat reclamation and groundwater over-exploitation. [Conclusions] This study systematically supplements a long-term sequential geomorphic monitoring dataset for the Mekong Delta and clarifies the spatially divergent evolution law of delta sandbars and shorelines under the joint disturbance of natural processes and human activities. The reduction in sandbar quantity alongside the growth in total delta area verifies that sediment convergence and small sandbar amalgamation dominate the geomorphic accretion process of the Mekong estuary, while asymmetric shoreline migration reveals that regional hydrodynamic redistribution and anthropogenic engineering have reshaped the traditional natural sediment transport pattern of the delta. From the practical perspective, the severe coastal erosion occurring in the Ca Mau Peninsula reminds local management authorities to strengthen mangrove wetland protection, restrict excessive groundwater exploitation and optimize the layout of coastal hydraulic projects to mitigate land subsidence and wave-induced coastal retreat. The research innovatively integrates sandbar morphological metrics and multi-temporal shoreline dynamic indicators to interpret delta geomorphic evolution, which can provide methodological references for long-term coastal geomorphic monitoring in other large river deltas worldwide.
[Objective] Global warming has reshaped the characteristics of snow cover across the Xizang plateau, including snow depth, duration, and spatial distribution. The spatial heterogeneity of snow evolution and the underlying mechanisms governing snow-climate interactions remain insufficiently understood. Specifically, the role of precipitation phase transitions under warming conditions has not been fully clarified. To address these gaps, we analyzed the long-term trends of snow phenology in the source regions of the Yangtze River and Yellow River from 1980 to 2020, aiming to reveal the spatial patterns of snow depth and snow cover duration, and identify the dominant climatic and environmental drivers controlling snow cover variation. [Methods] Multi-source datasets were employed to investigate snow dynamics in the study area. Daily snow depth datasets with spatial resolutions of 0.25° (1980-2020) and 0.05° (2000-2020) were acquired from the National Tibetan Plateau Data Center. To ensure spatial consistency, all datasets were resampled to a uniform 0.25° resolution. Snow phenology indices were derived from daily snow depth records. Temporal trends were quantified using the Theil-Sen slope estimator, and their statistical significance was evaluated via the Mann-Kendall test. Pearson correlation analysis was conducted to examine the relationships between snow cover characteristics and climatic factors (i.e., temperature and precipitation). Precipitation phase was identified using a wet-bulb temperature-based parameterization scheme to distinguish rainfall and snowfall events and compute the snowfall-to-precipitation ratio. [Results] (1) Snow cover in the source regions of the Yangtze River and Yellow River generally exhibited a decreasing trend during 1980-2020. Snow cover duration and snow-covered area also decreased but did not reach statistical significance. Spatially, snow phenology across the study area showed a consistent pattern characterized by delayed snow onset, earlier snowmelt, shortened snow seasons, and reduced snow depth. (2) Despite the overall decreasing trend, clear spatial heterogeneity existed. In the northern part of the Yangtze River source region, some areas showed earlier snow onset and delayed snowmelt, suggesting a localized extension of the snow season. Similarly, parts of the central and headwater regions of the Yellow River basin exhibited slight increases in snow duration and snow depth. Elevation-dependent variations were also evident. In the Yangtze River source region, snow depth decreased significantly below 5 000 m, while snow duration slightly increased above this elevation. In the Yellow River source region, snow depth and duration generally showed decreasing trends, although moderate increases occurred in some mid-elevation zones. (3) Correlation analysis indicated that snow characteristics were generally negatively correlated with temperature: weak negative correlations were observed in the Yangtze River source region, whereas weak positive correlations in the Yellow River source region. This difference was mainly associated with precipitation phase changes. In the Yangtze River source region, the snowfall-to-precipitation ratio decreased markedly, indicating that a larger proportion of precipitation occurred as rainfall rather than snowfall. In contrast, snowfall still accounted for a considerable proportion of precipitation in the Yellow River source region, allowing precipitation increases to contribute to snow accumulation. [Conclusion] Significant long-term changes in snow phenology are evident in the source regions of the Yangtze River and Yellow River from 1980 to 2020. The region generally exhibits a shortened snow season characterized by delayed snow onset, earlier snowmelt, and decreasing snow depth. Temperature rise is the dominant driver of snow reduction, while the transition of precipitation phase from snowfall to rainfall plays an important role in shaping basin differences in snow-precipitation relationships. Spatial heterogeneity in snow evolution is influenced by elevation gradients and precipitation phase changes. Continued warming may further reduce snow resources and affect seasonal runoff regimes in the Xizang Plateau headwaters.
[Objective] Base flow is an important component of runoff. Studies investigating changes in river base flow and the associated mechanisms in the source region of the Yangtze River remain limited. The recharge conditions and influencing factors of river base flow have not been fully elucidated, and no relevant studies have yet examined the effects of climate oscillations on river base flow. Using observed hydrological data from the source region of the Yangtze River, this study aims to analyze the evolution of river base flow and its response to the Pacific Decadal Oscillation (PDO), explore the relationship between river base flow and PDO and the possible underlying mechanisms, and provide a reference for understanding the hydrological cycle and protecting water resources in the region under climate change. [Methods] The modified Kalinin method was employed to separate base flow from the measured runoff at the Zhimenda hydrological station. Correlation analysis and cross-wavelet analysis were used to analyze the response of river base flow to PDO and the correlation between them. Meteorological observations from the source region of the Yangtze River were analyzed to identify the main factors driving changes in river base flow. The potential mechanism by which PDO affected river base flow in the source region of the Yangtze River was explored from the perspective of atmospheric circulation. [Results] (1) Seasonal and annual river base flow in the source region of the Yangtze River increased significantly. Before 2000, base flow showed alternating high- and low-flow conditions but remained relatively stable. After 2000, river base flow showed a marked increasing trend that has continued to the present.(2) River base flow in the source region of the Yangtze River was negatively correlated with PDO. Annual base flow was significantly negatively correlated with the PDO index for the original series and the 3- and 5-year moving averages, with all correlations significant at the 99% confidence level. The two series exhibited an antiphase resonance at the 8-16-month timescale throughout the study period.(3) PDO had a significant influence on river base flow in the source region of the Yangtze River, with a typical negative correlation between them. When PDO was in a positive phase, a negative base-flow anomaly was more likely, indicating below-normal flow conditions. Conversely, when PDO was in a negative phase, a positive base-flow anomaly was more likely, indicating above-normal flow conditions.(4) Precipitation and temperature were the primary factors driving changes in base flow in the source region. Beginning around 2000, temperature increased significantly in the source region, followed by an abrupt increase in precipitation around 2008. The base-flow trend was generally consistent with the corresponding trends in precipitation and temperature.(5) Through teleconnections and atmospheric wave trains, PDO induced the formation of high-pressure ridges and anticyclonic circulation anomalies over the Tibetan Plateau, which in turn altered temperature, precipitation, and other meteorological variables in the source region. During the negative PDO phase, sea surface temperature anomalies in the central and western North Pacific induced anticyclonic circulation anomalies. Atmospheric teleconnections and wave-train propagation through the westerlies strengthened the anticyclonic anomalies over the Tibetan Plateau, resulting in surface warming, increased precipitation, and consequently increased river base flow in the source region of the Yangtze River. The opposite occurred during the positive PDO phase. [Conclusion] River base flow plays an important role in maintaining both aquatic and terrestrial habitats in the source region of the Yangtze River. This study provides an exploratory statistical assessment of the influence of PDO on river base flow. Future studies may combine numerical simulations, such as coupled climate-hydrological models, to further verify the chain of processes involving high-pressure ridges, anticyclonic circulation anomalies, changes in temperature and precipitation, and the river base-flow response.
[Objective] Due to glacial melt and permafrost degradation, runoff series in the Yangtze River source region exhibits high non-stationarity and nonlinearity, which constrains prediction accuracy. To address this challenge, we develop a hybrid long short-term memory (LSTM) model incorporating variational mode decomposition (VMD) and Kolmogorov-Arnold Network (KAN) to improve forecasting precision. [Methods] Daily runoff data from the Zhimenda Hydrological Station spanning 2000-2019 and concurrent meteorological observations from six meteorological stations (including daily precipitation, mean temperature, maximum temperature, and minimum temperature) were used. LSTM, gated recurrent unit (GRU), KAN-LSTM, and KAN-GRU, were constructed to comparatively evaluate the effect of introducing KAN. In KAN-LSTM and KAN-GRU, traditional fully connected layers were replaced with KAN structures featuring spline-parameterized activation functions, enabling adaptive nonlinear mapping of extracted temporal features. The VMD-KAN-LSTM and VMD-KAN-GRU models followed a “decomposition-prediction-reconstruction” framework: the raw runoff series was first decomposed into multiple component sequences via VMD; each component sequence, combined with meteorological inputs, was then fed independently into KAN-LSTM or KAN-GRU for prediction; finally, the predictions of all components were aggregated to obtain the final runoff forecast. Model performance was assessed using the Nash-Sutcliffe efficiency (NSE), coefficient of determination (R2), and Kling-Gupta efficiency (KGE). [Results] (1) The introduction of KAN significantly improved model performance relative to the baseline architectures. The KAN-LSTM model attained NSE, R2, and KGE values of 0.850, 0.856, and 0.924, respectively, corresponding to gains of 0.019, 0.024, and 0.048 over the standard LSTM (NSE=0.831, R2=0.832, KGE=0.876). Similarly, KAN-GRU exhibited consistent improvements over GRU. With KAN integration, KAN-LSTM effectively mitigated the systematic overestimation observed in LSTM during low-flow periods while preserving its accuracy in peak-flow prediction. KAN-GRU, in turn, showed reduced volatility and enhanced stability compared to GRU. (2) After VMD preprocessing, model accuracy was further elevated. The VMD-KAN-LSTM model achieved the optimal performance, with NSE=0.883, R2=0.885, and KGE=0.937, exceeding KAN-LSTM by 0.033, 0.029, and 0.013, respectively. Parallel improvements were also observed for VMD-KAN-GRU. (3) VMD-KAN-LSTM demonstrated the best overall predictive performance among all compared models. Nevertheless, during extremely low-flow periods, the model occasionally yielded negative predictions, a consequence of algebraic error superposition during the reconstruction of IMFs. Despite this physical inconsistency under extreme low-flow conditions, the model maintained high fidelity to the observed hydrograph and significantly improved dry-season prediction accuracy relative to the non-decomposed approach. (4) A parallel-architecture VMD-KAN-LSTM-GRU model was tested, which delivered slightly superior metrics (NSE=0.908, R2=0.909, KGE=0.939) but demanded exponentially greater computational resources and training time. Hence, VMD-KAN-LSTM offered a more favorable balance between efficiency and performance. [Conclusion] This study proposes a VMD-KAN-LSTM coupled model for daily runoff prediction in the Yangtze River source region. The results confirm that KAN enhances nonlinear fitting capability compared to traditional fully connected layers, while VMD effectively separates multi-scale oscillatory components, thereby further improving runoff forecasting accuracy. VMD-KAN-LSTM exhibits the best overall predictive performance among all models compared, demonstrating its enhanced capacity to handle the inherent complexity and nonlinearity of hydrological time series, which in turn delivers more reliable and robust forecasts. The proposed VMD-KAN-LSTM model provides a reliable new methodology for daily runoff forecasting in the Yangtze River source region.
[Objective] The Qinghai-Xizang Plateau and its surrounding headwater regions constitute the source areas of major rivers, including the Yellow River, Yangtze River, and Yarlung Zangbo River, and play a critical role in downstream water resource regulation and ecological security. As an essential component of the cold-region hydrological cycle, snow cover exerts a key influence on river runoff, dry-season water supply, and ecosystem stability through its accumulation and melt processes. This study aims to characterize the spatial heterogeneity of snow cover changes across different headwater regions and to provide a scientific basis for water resource management and ecological protection in cold regions. [Methods] Based on snow depth and meteorological datasets from 1979 to 2020, this study systematically investigated the spatial distribution, interannual variability, and climatic controls of snow cover indicators, including snow start date (SSD), snow end date (SED), snow duration (SDs), mean snow depth (SD), and maximum snow depth (MSD), across nine representative headwater basins. Linear regression was used to estimate temporal trend slopes for each variable, and the Mann-Kendall (MK) test was applied to assess trend significance. To ensure spatial consistency between meteorological and snow datasets, all meteorological variables were resampled to match the spatial resolution of snow depth data using bilinear interpolation. To quantitatively characterize the combined effects of precipitation and temperature on snow cover changes, structural equation modeling (SEM) was employed to analyze the pathways linking climatic factors to snow cover indices. [Results] (1) Snow cover exhibited pronounced spatial heterogeneity. High-elevation headwater regions were characterized by earlier snow onset, later snow end, longer duration, and greater and more stable snow depth, whereas low-latitude or warmer basins showed shorter and more intermittent snow cover with shallow snow depth and high sensitivity to temperature. The spatial patterns of SDs and SD were highly consistent, with high values concentrated in cold, high-altitude headwater regions, while snow cover in low-altitude and warmer basins was more vulnerable and more sensitive to climate change.(2) Long-term snow evolution exhibited clear regional differences. Most regions showed earlier SSD, later SED, and an overall extension of the snow season. However, unstable regions such as central NX, western JYQ, and central ZMD exhibited delayed SSD and advanced SED, indicating a shortened snow season and a pronounced warming signal. SDs generally followed the trends of SSD and SED, whereas both SD and MSD decreased across nearly all sub-basins, indicating that snow depth was most sensitive to warming. In some basins, an “extended snow duration but reduced snow depth” pattern was observed, suggesting that snow duration and snow depth were governed by different climatic controls.(3) Climatic factors significantly influenced snow cover characteristics. Increased precipitation primarily prolonged snow duration by enhancing snow accumulation and persistence, whereas rising temperature mainly reduced snow depth by accelerating melt processes and suppressing solid precipitation accumulation. The combined effects of precipitation and temperature led to strong spatial heterogeneity in snow cover changes, with notable snow depth declines in high-elevation headwater regions, while some regions with increasing precipitation exhibited longer snow duration but continued reductions in snow depth. [Conclusion] This study systematically reveals the spatial patterns, long-term evolution, and climatic drivers of snow cover across nine major headwater regions of the Qinghai-Xizang Plateau and its surroundings, providing a scientific basis for water resource management, ecological protection, and climate change adaptation in cold-region headwaters.
[Objective] The alpine wetlands in the Changjiang River source region are extremely sensitive to climate change. This study aims to investigate the distribution characteristics of dissolved carbon in these wetlands. [Methods] Water samples were collected from the river network, lakes, and thermokarst ponds within the alpine wetlands of the upper reaches of the Dangqu River in the southern Changjiang source region. Total dissolved carbon (TDC), dissolved inorganic carbon (DIC), and dissolved organic carbon (DOC) were measured in laboratory. Their concentration changes and relative proportions were analyzed. [Results] (1) Dissolved carbon in various water bodies within the upper Dangqu River wetlands predominantly consisted of DIC, and the mainstream exhibited the highest proportion of DIC, reaching 80%. TDC concentrations in water samples from the wetlands in the upper reaches of the Dangqu River ranged from 27.843 mg/L to 91.922 mg/L. The minimum value 27.843 mg/L was recorded in the Chadangqu tributary; the maximum value was measured at the eastern edge of the study area. TDC concentrations in thermokarst ponds were significantly higher than those in the mainstream and tributaries. TDC concentrations gradually increased from upstream to downstream. The six left-bank tributaries of the Dangqu River exhibited an average TDC concentration of 39.000 mg/L. The four right-bank tributaries showed an average TDC concentration of 45.506 mg/L. TDC levels in right-bank tributaries significantly exceeded those in left-bank tributaries. (2) The minimum DIC concentration in all water samples was 17.840 mg/L, and the maximum was 52.820 mg/L, with an average of 34.337 mg/L and a coefficient of variation of 0.291. DIC concentrations in water samples along the mainstream of Dangqu River increased progressively from upstream to downstream. DIC concentrations in wetland water samples from the upper reaches of the Dangqu River ranged from 17.840 mg/L to 52.820 mg/L. Among tributary water samples, DIC concentrations in upstream tributaries were generally higher than those in downstream tributaries. The mean DIC concentration for the six left-bank tributaries of the Dangqu River was 27.978 mg/L, while the four right-bank tributaries averaged 28.624 mg/L, indicating that right-bank tributaries generally exhibited slightly higher DIC concentrations than their left-bank counterparts. (3) To further elucidate variations in TDC across different regions of the Three Rivers Source Region, we also investigated the average dissolved carbon concentrations in representative rivers and lakes. The average TDC concentration in the wetlands of the upper reaches of the Dangqu River, the southern source of the Changjiang River, reached 49.055 mg/L, significantly higher than those in the Lancang River source (32.88 mg/L) and the Yellow River source (17.7 mg/L), as well as the average concentration in other alpine rivers in the Three Rivers Source Region (17.03 mg/L). [Conclusion] These findings provide valuable in situ data for studies on carbon cycling in alpine wetland waters in China and offer a key reference for climate change research in high-altitude regions.
