To investigate the characteristics and propagation patterns of cracks in water diversion tunnels during operation, this study utilized tunnel engineering projects in southwestern China. Based on the intelligent inspection data for lining surface defects, combined with mechanical models and numerical simulation methods, the distribution characteristics and propagation processes of lining cracks under internal water pressure were analyzed. The results indicate that cracks are the primary defect type, with lengths concentrated in the range of 0–5 m, and are predominantly distributed at the top of the tunnel entrance section. Owing to the influence of the surrounding rock stress, the tunnel top becomes a high-risk area for crack initiation, and the local stress significantly increases after the lining cracks. Meanwhile, the circumferential stress exhibited a decreasing trend along the tunnel length, resulting in the formation of more cracks at the inlet. Numerical simulations show that the deformation of cracks at the top of the tunnel entrance is most severe, and the crack propagation follows a pattern of “depth extension-penetration through the lining-turn toward the inner wall”. The simulation results agree well with the mechanistic analysis of the inspection results.

Physics-informed neural networks (PINNs) have shown potential for modeling open-channel flows; however, their performance deteriorates in dam-break problems due to strong nonlinearities and flow discontinuities. This study proposes a Gradient-Weighted and Residual-based Adaptive Refinement PINN (GW-RAR-PINN), which combines gradient-weighted loss modulation with residual-based adaptive refinement to enhance shock resolution in dam-break flows. One-dimensional dry-bed and wet-bed dam-break problems were simulated to assess the performance of GW-RAR-PINN against the baseline PINN and other representative variants. The results indicate that the proposed GW-RAR-PINN framework achieves the best overall performance among the compared models, with reductions in both MAE and RMSE for water depth and discharge/velocity. For the dry-bed dam-break case, the MAE and RMSE of water depth decrease by 25.2% and 16.0%, respectively, and those of discharge decrease by 38.7% and 26.3%. For the wet-bed case, the MAE and RMSE of water depth decrease by 1.2% and 15.1%, respectively, while the MAE and RMSE of velocity decrease by 4.3% and 18.6%. The GW module reduces the contribution of regions with steep gradients in the loss function, alleviating over-enforcement of governing equations near discontinuities, while the RAR module adaptively enriches collocation points in high-gradient regions to better resolve shock structures. The combined strategy stabilizes network training and improves the representation of localized hydraulic features. These findings demonstrate that GW-RAR-PINN provides a robust and accurate framework for simulating strongly nonlinear dam-break flows.

Quantifying the joint impacts of climate change and intensive cascade regulation on river thermal regimes is critical for managing ecological risks and optimizing hydropower production. However, most existing attribution studies primarily document broad, seasonally asymmetric warming and cooling patterns, offering limited mechanistic understanding of how specific reservoir operation strategies—particularly the widely implemented clear-water impoundment in China—regulate cross-seasonal heat storage and downstream winter warming. Here we developed an LSTM-based attribution framework to reconstruct counterfactual “no-dam” river temperatures and to quantify the relative contributions of anthropogenic regulation (ΔANT), long-term climatic warming (ΔTrend), and intra-annual climatic variability (ΔNCV) to downstream temperature changes in the lower Jinsha River, China. In addition, a suite of thermal-timing metrics is proposed to characterize seasonal heat states and to diagnose the cross-seasonal heat-storage processes responsible for the pronounced winter warming.Results indicate that anthropogenic regulation (ΔANT) is the dominant driver of observed downstream thermal changes, inducing substantial autumn–winter warming of up to ~2°C while dampening summer temperature extremes. Long-term climatic warming (ΔTrend) provides a persistent background increase, whereas intra-annual climatic variability (ΔNCV) imposes strong seasonal and interannual modulation. Notably, the magnitude of winter warming varies markedly among years and is strongly controlled by antecedent thermal-storage conditions, with thermal-timing metrics linking earlier autumn impoundment and greater cumulative heat storage to enhanced downstream winter temperatures (Pearson's r≈0.62). Overall, these findings elucidate the coupled roles of climate change and cascade reservoir regulation in shaping river thermal regimes and provide a mechanistic basis for optimizing multi-reservoir operations to balance hydropower generation with downstream thermal and ecological requirements.

The stagnation point, defined as the location of maximum pressure, is a key characteristic of oblique submerged circular impinging jets. This study presents a theoretical analytical model to predict the stagnation point location based on mass and momentum conservation principles. An approximate linear solution is further derived from the model for practical application. Both the numerical and approximate linear solutions are validated against experimental and numerical simulation results available in the literature. Potential errors associated with model assumptions are evaluated, and the model's applicability is defined for specific conditions: impingement angle beta >= 45 degrees and impingement distance H <= 50 times the nozzle diameter (d), or 45 degrees >=beta >= 30 degrees and H <= 25d. Within these limits, the model achieves a root-mean-square error (RMSE) of 0.149 for the analytical solution and 0.160 for the linear solution, with a relative accuracy exceeding 80% even in extended scenarios. The analysis of model results reveals that the stagnation point location varies approximately linearly with impingement distance at fixed impingement angles and exhibits an approximate cotangent relationship with the impingement angle at fixed distances. This analytical model offers new insights into the dynamic behavior of oblique submerged circular impinging jets and serves as a practical tool for the design and optimization of hydraulic structures.

This study proposes a two-dimensional mechanistic model to predict concrete abrasion under oblique submerged sand-laden jets, using sand-phase momentum as a key indicator. Improved formulations for velocity and axial concentration account for incomplete particle settling, with validation against published data yielding a mean relative error of 3.20 % and a correlation coefficient of 0.99. The model captures spatial momentum distributions and directional abrasion features across impact angles (20 degrees-90 degrees). Submerged experiments confirmed that abrasion concentrates near the jet axis, with geometry shaped mainly by normal momentum and elongated by tangential momentum. Critical normal momentum thresholds were quantified, revealing a time-dependent exponential decay. Overall, it offers new insights into submerged jet dynamics and robust framework for spatial abrasion prediction.

Salinity distribution is a critical determinant in the management of water resources and the protection of ecological integrity in coastal regions, drawing particular concerns in the context of increasing saltwater intrusion. This study aims to establish a framework for the efficient and robust simulation of hydrodynamic-salinity distribution profiles in tidal river systems. The proposed modelling approach, termed Subgrid FD, is developed based on a two-dimensional subgrid methodology to enhance computational efficiency. It combines a flow resistance mechanism to ensure computational convergence when addressing wetting-drying boundaries and dispersion effects to account for the impact of uneven velocity distributions. The applicability of the Subgrid FD model is validated through its implementation in the Sanya River, China, where it exhibits robust predictive performance, achieving optimal root mean square error values of 0.04 m for hydrodynamic simulations and 1.7 parts per thousand for salinity predictions. Using the calibrated model, a comprehensive correlation analysis between hydrodynamic parameters and salinity distribution reveals that salinity demonstrates dynamic variability in response to tidal cycles and geomorphological features. The present work provides a theoretical and methodological basis for aquatic environmental management in tidal river systems; however, further refinement of subgrid-scale parameterization in low-gradient reaches is required to enhance model applicability.

Over the past three decades, lake water level fluctuations have intensified due to climate change and increasing water demand, creating an urgent need for accurate and efficient prediction methods. However, existing deep learning-based surrogates often suffer from two major limitations: the lack of physically informed guidance for hyper-parameter selection, which increases computational costs, and the scarcity of extreme water level samples, which leads to imbalanced datasets and reduced accuracy. To address the limitations, this study proposes a novel Physics-Informed Neural Network (PINN) framework that integrates data augmentation with physically guided hyper-parameter selection. The framework employs boundary water level time series as input, incorporates mass-conservation constraints, and applies a clustering-based augmentation method to enrich extreme event samples. Its applicability was validated in the Lower Lake of Nansi Lake in China. Evaluation using Root Mean Squared Error (RMSE) and Nash-Sutcliffe Efficiency (NSE) shows that incorporating physical constraints robustly improves predictive accuracy, with performance even surpassing that of a classical LSTM model. Physically guided hyper-parameter selection further enhances both training efficiency and accuracy, and the proposed augmentation method reduces RMSE by 69.1 % under extreme conditions. Compared with an existing augmentation method, the proposed method can shorten training time by 63.35 % with better prediction performance. The final surrogate achieves RMSE = 0.021 m and NSE > 0.94 (against observations), requiring only 2.42 % of the computational time of a traditional hydrodynamic model. These results highlight the framework's potential for reliable real-world water level prediction and its transferability to other hydrological systems.

Erosion of hydraulic concrete induced by submerged sediment-laden jets constitutes a representative surface damage problem that is strongly governed by physical processes while exhibiting limited textural contrast, representing a multiphase sediment-structure interaction process relevant to sediment management and operation-maintenance of hydropower infrastructure. Its spatial heterogeneity and graded erosion patterns arise from the coupled effects of sediment momentum transfer and erosion evolution. Conventional erosion assessments predominantly rely on integral metrics such as mass or volume loss, which are insufficient to describe the two-dimensional spatial structure and graded characteristics of erosion damage. These erosion patterns represent a localized surface-morphological response of hydraulic concrete surfaces in sediment-laden jet environments. Although computer vision techniques have recently been applied to erosion detection, existing approaches remain largely texture-driven and data-centric, typically focusing on binary segmentation between damaged and undamaged regions. Such models are therefore inadequate for resolving multiple erosion grades and lack explicit incorporation of erosion mechanisms, leading to limited robustness and interpretability across varying hydraulic and sediment conditions. In this work, a physics-informed computer vision (PICV) framework is developed for intelligent segmentation of hydraulic concrete erosion, bridging mechanism-based sediment action modelling with data-driven image segmentation. The framework is built upon a structured physical-visual representation that explicitly links erosion morphology with sediment-induced physical actions. Controlled submerged sediment-laden jet experiments are conducted under systematically varied jet velocities, impingement angles, sediment concentrations, particle sizes, and exposure durations to acquire high-resolution erosion surface images. Based on particle impact and cutting mechanisms, spatially distributed sediment-phase momentum fields, including normal and tangential components, are derived to characterize the intensity of particle-wall interactions, serving as modelling-informed multiphase sediment action descriptors. These momentum fields are spatially registered to the corresponding erosion images, forming a coupled two-dimensional representation in which erosion surface images serve as the visual carrier and are associated with aligned physical descriptors. This representation provides a physics-vision integrated dataset suitable for mechanism-aware visual learning. On the basis of this coupled representation, a PICV-oriented multi-modal segmentation framework is established, in which erosion images and sediment momentum fields are jointly exploited to enable concurrent learning of textural features and physically meaningful action intensity. Furthermore, a dimensionless erosion intensity indicator derived from experimentally measured mass loss rates is incorporated into the loss function as a soft-consistency regularization term, providing sample-wise adaptive guidance during model optimization. Rather than imposing strict physical constraints on the solution space, physical information is used to guide the learning process toward physically plausible spatial patterns. Compared with image-only baselines (U-Net and DeepLab), the proposed PICV model improves multi-class graded-segmentation performance (Pixel-wise precision: +10-15 percentage points) and notably reduces grade confusion in transition regions. Under cross-condition evaluation, PICV demonstrates enhanced stability and interpretability, linking predicted grade distributions to aligned momentum patterns. This framework provides a transferable pathway for robust, mechanism-aware erosion assessment under complex submerged sediment-laden jet environments, supporting erosion-risk evaluation and sediment-management decision-making for hydraulic infrastructure.

Understanding the impingement pressure characteristics of normal and oblique submerged circular impinging jets is crucial for the safe operation of hydraulic structures. This study experimentally investigates these characteristics across a broad range of impingement angles (20°–90°) and velocities (1.03 m/s–6.77 m/s), with the Reynolds numbers of 20 457–134 459. The time-averaged impingement pressure, instantaneous maximum pressure, and Root Mean Square (RMS) of pressure fluctuations are examined as key parameters. Results indicate that the corresponding dimensionless pressure coefficients for the impingement pressure characteristics are independent of the Reynolds number. A unified framework for the distribution of dimensionless pressure coefficients in the impingement region is established by extending the time-averaged impingement pressure distribution to both the instantaneous maximum pressure and the RMS of pressure fluctuations, and validating against experimental data. The effects of the impingement angle and distance on the distribution parameters are further analyzed. Under normal impingement, all three pressure characteristics exhibit self-similar distributions. As the impingement angle decreases, asymmetry between upstream and downstream distributions increases, leading to lower peak pressures and flatter profiles. The distribution parameters for all three pressure descriptors follow a power-law relationship, with the magnitude RMS > instantaneous maximum > time-averaged pressure, reflecting distinct attenuation behaviors of the mean and fluctuating flows. Notably, the stagnation point pressure is proportional to the normal component of the jet velocity, and the dimensionless distribution parameters remain consistent across varying impingement distances. These findings enhance the understanding of submerged impinging jet behavior and offer practical guidance for the hydraulic design and protection of engineering structures.

Abrasion damage caused by sand-laden flows poses a serious challenge to the durability and maintenance of hydraulic structures. Accurate segmentation of abrasion regions remains difficult because abrasion damage typically exhibits irregular morphology, ambiguous boundaries, and strong visual variability, while most existing methods remain largely data-driven and lack explicit physical guidance. To overcome this challenge, this study developed a dual-branch deep learning framework inspired by physical principles for abrasion damage segmentation, integrating abrasion-rate-based weighting loss function and morphological structure-guided enhancement. The proposed model jointly exploits raw visual information and morphological structure features to improve the representation of irregular abrasion regions and complex boundaries. In addition, an abrasion-rate-based coefficient is incorporated into the loss function as a physics-inspired regularization term. Rather than directly predicting physical variables, this term modulates the strength of structural supervision according to abrasion severity, thereby establishing an implicit relationship between abrasion dynamics and pixel-level damage delineation. By strengthening the coupling between abrasion morphology and the underlying physical process, the design improves both segmentation robustness and interpretability. Experimental results show that the proposed method outperforms several baseline models, reaching a precision of 0.952, which indicates a strong ability to suppress false positives and accurately delineate abrasion regions. This work provides a mechanism-to-learning framework for intelligent abrasion damage segmentation, offering a transferable design principle for related damage-oriented segmentation tasks and methodological support for vision-based structural health monitoring of hydraulic infrastructure.

This study presents an experimental and mechanistic investigation of concrete abrasion under submerged sand-laden jet impingement. Tests were performed across impact angles (20 degrees-90 degrees), velocities (10-20 m/s), distances (0.1-0.3 m), durations (0-8 h), and a fixed concrete compressive strength (48.8 MPa). An improved apparatus was developed, incorporating a flow isolation baffle to minimize external fluid interference and sloped bottom to ensure effective water-sand mixing. A quantitative particle replacement protocol was established to improve test consistency. High-resolution three-dimension scanning identified two abrasion regions: elliptical major region from jet impingement and parabolic minor region caused by wall jet. Multidimensional quantitative analysis revealed that impact angle primarily governs abrasion morphology via redistributing jet velocity components and enhancing flow asymmetry. With decreasing angles, the abrasion range elongated, peak depth shifted downstream (73 % at 45 degrees), and eccentricity increased (0.93 at 20 degrees). Peak depth (35.2 mm) and weight (251.9 g) occurred at 60 degrees, reflecting a balance between jet momentum and flow resistance. Abrasion weight and volume followed near-cubic power-law trends with velocity (exponents: 2.91 and 3.02), highlighting the energy-dominated nature of the process. Increasing impact distance expanded abrasion range but reduced depth due to jet attenuation, with peak abrasion weight (251.9 g) at 0.2 m. Time-dependent growth followed power-law trends, while peak depth location and eccentricity remained angle-dependent. Proposed semi-empirical equations based on dimensional analysis predicted abrasion length and width with high accuracy (mean relative error < 4.05 % on experimental dataset). These results advance mechanistic understanding and offer predictive tool for abrasion-prone zones in hydraulic structure design and maintenance.

This study investigates the abrasion of hydraulic concrete under submerged conditions using the water-borne sand impact method. Experiments were conducted to examine the effects of impact time (0-8 h), angle (20 degrees-90 degrees), velocity (10-20 m/s), distance (0.1-0.3 m), concrete strength (C10-C40), and sand content (0-92.55 kg/m(3)) on the abrasion rate. Results show that abrasion evolves through three stages: an initial stage with nearly constant rate, a developmental stage marked by > 20 % rate reduction, and steady stage with negligible abrasion. During the initial stage, the maximum abrasion rate occurred at 60 degrees due to the combined normal and tangential abrasion. Abrasion rate showed a strong linear correlation with sand content, confirming the dominant role of particle kinetic energy. A power-law relationship with velocity (exponent 2.96) was observed, where the sub-cubic exponent reflects a secondary but non-negligible contribution from water-phase stress. The abrasion rate peaked at an intermediate distance (0.2 m), driven by the trade-off between decreasing velocity and increasing impingement area. Tensile strength exhibited stronger correlation with abrasion resistance than compressive strength. An improved prediction model was developed by extending the classical framework to separately quantify particle and water flow contributions, account for velocity attenuation and expanding impingement area, and adopt tensile strength as the material parameter. Model validation using experimental and literature data showed over 85 % prediction accuracy (mean relative error =14.21 %), outperforming two existing representative models by 12.61 % and 10.73 %. The findings offer mechanistic insights and a practical tool for design optimization and durability management in hydraulic structures.

Algal blooms pose increasing threats to lakes and reservoirs worldwide, with harmful species such as cyanobacteria and dinoflagellates releasing toxin that endanger ecosystem and human health. However, the dominant bloom type varies across systems due to differences in climatic conditions and morphometric characteristics. This study aims to identify the key drivers influencing algal bloom types in freshwater systems. We compiled a global dataset of 160 lakes and reservoirs that have experienced either cyanobacterial or dinoflagellate blooms, incorporating climate variables, morphometric features, and physico-chemical water quality parameters. Using XGBoost and Logistic Regression models, we found that lake morphology, particularly depth and surface area, as well as wind speed are critical determinants of bloom type. Notably, a simple depth-area function (H=7.8A0.3) effectively differentiate between the two bloom categories, underscoring the strong influence of lake morphology on bloom dynamics. In addition, the dimensionless morphometric index Cs=Hπ/A, combined with wind speed, further improves classification performance. Given that lake morphology reflects underlying climatic, hydrodynamic, and biogeochemical conditions, these findings offer practical guidance for assessing bloom risk and developing targeted management strategies.

The Nansi Lake is one of the key storage reservoirs in the Eastern Route of the South-to-North Water Diversion Project (SNWDP-ER) in China, which was divided into the Upper Lake and Lower Lake by the Erji Dam. Due to water diversion being conducted only during the dry season, Nansi Lake experiences periodic flow reversals during diversion and non-diversion periods, leading to frequent water exchange between the Upper and Lower Lakes. A comprehensive hydrodynamic and water quality simulation of Nansi Lake and its connected canals is essential for understanding water quality evolution and variations during different operational periods. Based on the T-UWMM model for complex water systems, a multi-region solver was constructed to sequentially simulate the Upper and Lower Lakes. An internal boundary mechanism was implemented in the Erji Dam to enable the exchange of flow and substance concentrations between the two lakes in order to establish an integrated numerical model for Nansi Lake. Numerical simulations of the hydrodynamic and water quality conditions in Nansi Lake for 2021 showed that during the water diversion period, water stages were higher in the south, with flow moving south-to-north, while the opposite occurred during the non-diversion period, consistent with observed conditions. The water quality simulation revealed that the Lower Lake had slightly better water quality than the Upper Lake, suggesting that diverted water from the Lower Lake during the diversion period can improve the Upper Lake's water quality, demonstrating a positive impact of SNWDP-ER. Additionally, water quality was more stable during the diversion period, whereas CODMn concentrations fluctuated significantly during the non-diversion period, with tributary pollution being more pronounced in the latter, emphasizing the importance of tributary management. These findings provide a quantitative basis for analyzing water flow and material exchange processes between the Upper and Lower Lakes and offer theoretical support for operational management and water quality improvement in the Nansi Lake Basin.

In drinking water distribution systems (DWDSs), localized flow disturbances induced by pipe joints and elbows lead to variations in shear stress, which can influence bacterial colonization and the subsequent development of biofilms on pipe surfaces. Despite the importance of these dynamics, the effects of microscale hydrodynamic characteristics on bacterial colonization remain inadequately understood, primarily due to the challenges associated with concurrently addressing hydrodynamics and microbiological factors. This study strategically integrates a recirculation pipe testing system with computational fluid dynamic (CFD) simulations to comprehensively investigate the impact of wall shear stress on bacterial colony formation. The results indicate that fluctuations in wall shear stress, resulting from flow disturbances, are correlated with increased bacterial diversity. Within a specific section of the pipe, bacterial communities at the bottom demonstrate a degree of similarity, whereas the highest diversity is observed in samples collected from the mid-sections. Among the bacterial species detected, shear-resistant Caulobacter and metabolically adaptable Aquabacterium emerge as the predominant taxa in environments with highly variable shear stress. To maintain the microbiological safety of water within DWDSs, it is recommended to implement thorough cleaning around pipe connections and to pay particular attention to sediment accumulation at the bottom of the pipelines. Future investigations should incorporate a variety of pipe structural components and different flow regimes to provide a more comprehensive foundation for water quality protection.

Hydraulic structures require advanced inspection methods to address aging-related defects for operational safety. The emergence of deep learning has revolutionized defect identification through enhanced computational efficiency and superior model performance in complex engineering scenarios. This systematic review evaluates deep learning-based approaches for hydraulic structure assessment, focusing on three critical components: (1) the transition from manual inspections to unmanned systems for defect image acquisition and large-scale dataset construction in complex hydraulic infrastructures; (2) deep learning-based image enhancement methodologies, specifically convolutional neural networks (CNNs), generative adversarial networks (GANs), and Transformer architectures to effectively mitigate critical underwater imaging challenges including blurring, low contrast, and chromatic distortion; and (3) the application of classification, object detection, and segmentation algorithms in hydraulic structure defect identification, highlighting the high efficiency of YOLO series algorithms in object detection and the high precision of U-Net, DeepLab, and Transformer models in defect segmentation. Ultimately, this review systematically analyzes current technical challenges and proposes actionable research directions to advance defect identification in hydraulic infrastructures.

Lateral discharge serves as the primary pathway through which rivers receive sewage, and the permitted pollutant loadings, determined based on the pollutant mixing zone, represent critical parameters in discharge management. The adjoint equation method demonstrates substantial benefits in solving inverse problems in hydraulics. However, optimization objectives that rely on discrepancies between predicted and observed concentrations cannot be directly applied to determine the permissible loadings, limiting the application of the adjoint equation method to this issue. This study applies the adjoint equation method to derive both the control equation and boundary conditions specifically suited to lateral effluents utilizing the depth-averaged pollution transport equations for lateral discharges. Considering the narrow and elongated characteristics of the pollutant mixing zone in lateral discharges, a new formula for the error source term is introduced, with the length of the pollutant mixing zone defined as the primary objective. The adjustment value for lateral effluents is calculated by solving the adjoint equations and employing the BFGS optimization algorithm, which iteratively determines the permitted pollutant loadings from lateral discharges. The simulation of the forward problem establishes the foundation for solving the inverse problem. This research focuses on an outlet from a sewage treatment facility located in the upper reaches of the Yangtze River to evaluate the hydrodynamic and water quality model. The findings indicated that the water quality model accurately simulates the pollutant mixing zone, with the prediction error for the permanganate index (CODMn) maintained at 16.7%, meeting the precision requirements of water quality simulations in practical engineering. Following the accuracy verification in the forward problem, an experiment is conducted to evaluate the performance of the proposed inversion method. The inversion outcomes revealed that, after 18 iterations, the computational precision for the length of the pollutant mixing zone remains below 0.01 m despite two fluctuations during the convergence process due to inherent limitations of the BFGS method. In practical engineering applications, the required precision for controlling the mixing zone length is comparatively modest and is achieved within six iterations, reducing the error to 1 m. These results highlight the method's high computational accuracy and rapid convergence rate, providing valuable technical support for managing effluents in natural rivers.

The eastern route of the South-to-North Water Diversion Project (SNWDP-ER) in China, an open-channel cross-basin project, is exposed to multiple potential pollution sources and has changing hydrodynamic conditions between diversion and non-diversion periods. After a decade of pollution control efforts, it is imperative to perceive the water quality conditions and identify potential pollution factors for further maintenance. Combining the water quality identification index (WQII) and multivariate statistical techniques (MSTs), this study conducted an exploratory analysis in SNWDP-ER, based on the monitoring data in 2020–2021. The monitoring and WQII results show that the overall water quality in SNWDP-ER is satisfactory and meets the Class III requirements in China Environmental Quality Standard for Surface Water, except for total nitrogen (TN) which falls into Class V or even worse. The periodic alternations of flow direction between diversion and non-diversion periods tend to deteriorate water quality, particularly in the initial stage of alternations. Water quality appears more stable during the diversion period, and impounding lakes along the project are crucial to water environment restoration in this period. Due to the positive influence brought by the self-purification and environmental carrying in the Luoma Lake, Nansi Lake, and Dongping Lake, water quality exhibits an enhancing trend from upstream to downstream in the Lianghu and Jiaodong reaches. Besides, the monitoring stations in Shandong reach exhibit significant divisional heterogeneity under hierarchical cluster analysis (HCA) with the Nansi Lake and Dongping Lake as the demarcation points, and the results of factor analysis/principal component analysis (FA/PCA) further identified the contributions of mining, shipping, and land use to each section. Based on the findings, the study recommends implementing wastewater discharge control, abandoned mines remediation, and continued water quality monitoring for further maintenance and improvement in SNWDP-ER.

Achieving sustainable clear states in eutrophic shallow lakes is challenging due to the lag between nutrient load reductions and ecosystem response, often resulting in regime shifts. Submerged vegetation tends to fall off and float to the surface and block light due to the instability of freshly restored lakes, a key feature in influencing whether lakes deteriorate again. However, the mechanisms linking such transient shading to regime shifts remain unclear. This study conducted in situ experiments that quantified the shading effect of floating submerged vegetation leaves. We introduced the novel parameters, light interception coefficients and function that served as a crucial link between experimental findings and numerical models. Notably, we developed an innovative module specifically designed to assess the impacts of different clearing measures on aquatic ecosystems, which had been seamlessly integrated into the PCLake model. This practical model was applied to Xinglong Lake, recently ecologically restored, to simulate variations in key ecological indicators (total phosphorus (TP), total nitrogen (TN), chlorophyll-a (Chl-a), submerged vegetation biomass (DVeg)) and identify regime shift thresholds under different nutrient loads, initial time and time intervals of leaf clearing. The experimental results showed that light interception coefficients exhibited a subtle pattern, initially increasing slightly with water depth before declining, ranging from 0.573 m2/kg to 0.982 m2/kg for Vallisneria natans. The scenarios simulations demonstrated that prolonging clearing intervals from 0 to 120 days resulted in elevated TP, TN, and Chl-a concentrations, accompanied by a decline in DVeg, even causing the lake to a turbid state. Resuming daily clearing after a period of cessation proved ineffective in restoring the lake ecosystem if a regime shift had occurred. As nutrient loads and interception coefficients increased, the time intervals for triggering regime shifts shortened. We conservatively recommended that leaf clearing intervals should not exceed 10 days and ideally begin by March to ensure sufficient light for submerged vegetation. The study provides valuable insights into the impact of transient shading from floating leaves on regime shifts and offers scientific guidance for maintaining shallow lakes sustainably clear.

This study conducts a comparative analysis between detached eddy simulation (DES) and Unsteady Reynolds-averaged Navier-Stokes (URANS) models for simulating pressure fluctuations in a stilling basin, aiming to assess the URANS mode's performance in modeling pressure fluctuation. The URANS model predicts accurately a smoother flow field and its time-average pressure, yet it underestimates the root mean square of pressure (RMSP) fluctuation, achieving approximately 70% of the results predicted by DES model on the bottom floor of the stilling basin. Compared with DES model's results, which are in alignment with the Kolmogorov -5/3 law, the URANS model significantly overestimates low-frequency pulsations, particularly those below 0.1 Hz. We further propose a novel method for estimating the RMSP in the stilling basin using URANS model results, based on the establishment of a quantitative relationship between the RMSP, time-averaged pressure, and turbulent kinetic energy in the boundary layer. The proposed method closely aligns with DES results, showing a mere 15% error level. These findings offer vital insights for selecting appropriate turbulence models in hydraulic engineering and provide a valuable tool for engineers to estimate pressure fluctuation in stilling basins.