To simulate the dynamical structures of stratified shear flows, the high-resolution Total Variation Diminishing (TVD) method is necessary and widely-used due to its high-order spatial accuracy, oscillation control, and ability to capture the well-defined structures of vortices. Lack of understanding the TVD slope limiters usually results in inaccurate numerical simulation on stratified shear flows in terms of shear instability and spatiotemporal variations of mixing. In this study, the performances of four typical TVD slope limiters, namely the minmod, van Leer, Monotonized Central (MC), and superbee limiters, were investigated on modelling stratified shear flows based on the open-source non-hydrostatic model, NHWAVE. The four slope limiters are all commonly-used and have the typical numerical characteristics. All the limiters were respectively applied in two classical test cases, namely, shear instability and lock-exchange problem. The simulation results showed that the effects of slope limiters were correlated with their characteristics of numerical dissipation (or anti-dissipation), which can influence notably the model predictions of the generation of shear instability, the development of interfacial structures, and the mixing process. In the test cases, MC limiter’s performance was the best, because it could simulate the well-defined structures of instability while not introducing noticeable error. Minmod has an excessively large dissipation, which introduced noticeable numerical errors that can influence the model accuracy and can even suppress or omit the generation of interfacial vortices. Superbee limiter, the most anti-dissipative one, usually over-predicted the instability and mixing effects in time and space domain, and was likely to cause computational instability in some cases. The performances of van Leer and MC were similar, but their predictions of the evolutions of interfacial structures and mixing could be significantly different. Besides, the co-effects of grid resolution and slope limiters were also investigated; it was found that the refinement of grids may not help to reproduce a higher-quality result with a specific slope limiter.

Microplastic pollution in riverine systems has become a critical environmental concern, with complex hydrodynamic processes governing their transport and fate. This study presents a novel spatiotemporal graph neural network framework to elucidate the influence mechanisms of river hydrodynamics on microplastic transport processes. The methodology integrates graph-based river network representation with multi-scale temporal feature extraction, incorporating physics-informed constraints to ensure prediction consistency with fundamental transport principles. Field validation using microplastic concentration data from multiple monitoring stations demonstrates superior predictive performance, achieving correlation coefficients exceeding 0.89 compared to traditional numerical models (0.6–0.7). The model reduces computational time by approximately 92% while maintaining comparable accuracy. Sensitivity analysis reveals that flow velocity and bed shear stress constitute dominant controls, accounting for 62.9% of concentration variance. The framework effectively captures transport phenomena across multiple spatiotemporal scales, with optimal performance at 1–7 day forecasting horizons and 1–10 km spatial extents. This research provides significant contributions to environmental modeling methodologies and offers valuable capabilities for pollution source identification, real-time monitoring system design, and remediation strategy optimization in river environmental management.

The simulation-optimization method, integrating the numerical model and the evolutionary algorithm, is increasingly popular for identifying the release history of groundwater contaminant sources. However, due to the usage of computationally intensive evolutionary algorithms, traditional simulation-optimization methods always require thousands of simulations to find appropriate solutions. Such methods yield a prohibitive computational burden if the simulation involved is time-consuming. To reduce general computation, this study proposes a novel simulation-optimization method for solving the inverse contaminant source identification problems, which uses surrogate models to approximate the numerical model. Unlike many existing surrogate-assisted methods using the pre-determined surrogate model, this paper presents an adaptive surrogate technique to construct the most appropriate surrogate model for the current numerical model. Two representative cases about identifying the release history of contaminant sources are used to investigate the accuracy and robustness of the proposed method. The results indicate that the proposed adaptive surrogate-assisted method effectively identifies the release history of groundwater contaminant sources with a higher degree of accuracy and shorter computation time than traditional methods.

The law of pollutant emission and diffusion in stratified waters is a common issue. In this paper, numerical study on the interaction between vertical turbulent jets and the pycnocline is carried out to study the problems of jet’s emission through the large eddy simulation (LES). A trigonometric function disturbance (TFD) method is developed to ensure the velocity distribution of the jet in the horizontal plane yield to Gaussian profile. Numerical simulations are carried out in the range of 1.11 < Fr_p < 4.77, corresponding to 1393 < Re_p < 5979, where the Froude number Fr_p and the Reynolds number Re_p are defined at the entrance of pycnocline. The coherent structure and internal waves are observed at the pycnocline during the process of jets impinging. After the impingement, the destratification effects can be found. It can be found that Fr_p = 3 is a threshold value for the interaction between jets and the pycnocline. When Fr_p > 3, the interaction becomes intensely. Furthermore, the fitting formula of the radial momentum flux dissipation rate that is used to describe the decay of energy contained by the jets during the impinging process, is established through the dimensionless analysis. As a result, the influence range of the jet on the horizontal plane can be evaluated by Re_p. It is also found that the destratification of jets is mainly affected by the velocity of the internal wave induced by jets. In addition, by employing the dimensionless time T related to that velocity, the law of destratification varies with dimensionless time is obtained, which can be summarized as follows: Due to the influence of the first internal wave, the thickness of the pycnocline increases rapidly and reaches a critical value at T = 1.4, after that, the increase of the thickness of the pycnocline becomes linear.

Accurate, stable, and long-term water quality predictions are essential for water pollution warning and efficient water environment management. In this study, a hierarchical water quality prediction (HWQP) model was developed based on ‘data decomposition–predictor screening–efficient prediction’ via wavelet decomposition, Spearman correlation analysis, and long short-term memory network, respectively. The observed data from 14 stations in the Huaihe River–Hongze Lake system, including ammonia nitrogen (AN) and chemical oxygen demand (COD), were used to make long-term water quality predictions. The results suggested that, compared to existing water quality prediction models, the HWQP model has higher accuracy, with the root mean square errors of 6 and 17% for simulating AN and COD, respectively. The AN and COD concentrations will range from 0 to 1 mg/l and from 3 to 5 mg/l at 12 stations, respectively, and the COD concentrations will exceed the water quality target at Stations 4 and 5. The established model has great potential to address the challenges associated with the water environment.