Overview assessment of risk evaluation and treatment technologies for heavy metal pollution of water and soil

[1] Tide effects on the structure of the near-field Changjiang River plume and on the extension of the far-field plume have often been neglected in analysis and numerical simulations, which is the focus of this study. Numerical experiments highlighted the crucial role of the tidal forcing in modulating the Changjiang River plume. Without the tidal forcing, the plume results in an unrealistic upstream extension along the Jiangsu coast. With the tidal forcing, the vertical mixing increases, resulting in a strong horizontal salinity gradient at the northern side of the Changjiang River mouth along the Jiangsu coast, which acts as a dynamic barrier and restricts the northward migration of the plume. Furthermore, the tidal forcing produces a bidirectional plume structure in the near field, and the plume separation is located at the head of the submarine canyon. A significant bulge occurs around the head of the submarine canyon and rotates anticyclonically, which carries a large portion of the diluted water toward the northeast and merges into the far-field plume. A portion of the diluted water moves toward the southeast, which is mainly caused by tidal rectification. This bidirectional plume structure is more evident under certain wind conditions. During the neap tide with the reduced tidal energy, the near-field plume extends farther offshore, and the bulge becomes less evident. These dynamic behaviors are maintained and are fundamentally important in the region around the river mouth even under the summer monsoon and the shelf currents, although in the far field the wind forcing and shelf currents eventually dominate the plume extension.

Tidal modulation on the Changjiang River plume in summer

As one of nature’s most destructive calamities, floods cause fatalities, property destruction, and infrastructure damage, affecting millions of people worldwide. Due to its ability to accurately anticipate and successfully mitigate the effects of floods, flood modeling is an important approach in flood control. This study provides a thorough summary of flood modeling’s current condition, problems, and probable future directions. The study of flood modeling includes models based on hydrologic, hydraulic, numerical, rainfall–runoff, remote sensing and GIS, artificial intelligence and machine learning, and multiple-criteria decision analysis. Additionally, it covers the heuristic and metaheuristic techniques employed in flood control. The evaluation examines the advantages and disadvantages of various models, and evaluates how well they are able to predict the course and impacts of floods. The constraints of the data, the unpredictable nature of the model, and the complexity of the model are some of the difficulties that flood modeling must overcome. In the study’s conclusion, prospects for development and advancement in the field of flood modeling are discussed, including the use of advanced technologies and integrated models. To improve flood risk management and lessen the effects of floods on society, the report emphasizes the necessity for ongoing research in flood modeling.

/pdf/comprehensive-overview-of-flood-modeling-approaches-a-review-biw6ievg.pdf

Comprehensive Overview of Flood Modeling Approaches: A Review of Recent Advances

The management of water resources depends heavily on hydrological prediction, and advances in machine learning (ML) present prospects for improving predictive modelling capabilities. This study investigates the use of a variety of widely used machine learning algorithms, such as CatBoost, ElasticNet, k-Nearest Neighbors (KNN), Lasso, Light Gradient Boosting Machine Regressor (LGBM), Linear Regression (LR), Multilayer Perceptron (MLP), Random Forest (RF), Ridge, Stochastic Gradient Descent (SGD), and the Extreme Gradient Boosting Regression Model (XGBoost), to predict the river inflow of the Garudeshwar watershed, a key element in planning for flood control and water supply. The substantial engineering feature used in the study, which incorporates temporal lag and contextual data based on Indian seasons, leads it distinctiveness. The study concludes that the CatBoost method demonstrated remarkable performance across various metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2) values, for both training and testing datasets. This was accomplished by an in-depth investigation and model comparison. In contrast to CatBoost, XGBoost and LGBM demonstrated a higher percentage of data points with prediction errors exceeding 35% for moderate inflow numbers above 10,000. CatBoost established itself as a reliable method for hydrological time-series modelling, easily managing both categorical and continuous variables, and thereby greatly enhancing prediction accuracy. The results of this study highlight the value and promise of widely used machine learning algorithms in hydrology and offer valuable insights for academics and industry professionals.

Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models

Influence of suspended sediment front on nutrients and phytoplankton dynamics off the Changjiang Estuary: A FVCOM-ERSEM coupled model experiment

Urban flooding has become an increasingly frequent and fatal natural hazard and numerical modeling techniques play a vital role in its prediction and management. We review urban flood numerical simulations by systematically summarizing the calculation methods of surface runoff, drainage systems, and coupled models. Following the review, accuracy and computational efficiency are found to be the two key areas hindering the quality improvement of urban flood models, so an investigation of the key trends in the improvement of model accuracy and computational efficiency is conducted. It is found that the 1D-2D coupling model, finite volume method, unstructured meshing method, and hybrid parallel computing applications are the most effective strategies. Furthermore, the complex coupling of models and the lack of validation data are still crucial challenges in the development of urban flood modeling. This result can be used as a guideline for hydrologists in choosing the proper method of urban flood numerical simulation according to the task. • The methods and applicability of urban flood numerical simulation are summarized to provide support for related research. • We propose that the urban flood model can be improved in two aspects: accuracy and calculation speed model. • The future development of urban flood numerical simulation tends to be modular with a complex coupling model. 

Urban flood numerical simulation: Research, methods and future perspectives

Phytoplankton Blooms off a High Turbidity Estuary: A Case Study in the Changjiang River Estuary

Modulation of Shelf Circulations Under Multiple River Discharges in the East China Sea

It has been frequently observed that there are surface cold patches (SCPs) in the Yellow Sea in summer. Although previous studies based on monthly mean temperature distribution found that these SCP...

Different Generating Mechanisms for the Summer Surface Cold Patches in the Yellow Sea

With the localizing gradient enhancement, a damage model for quasi-brittle materials is able to achieve regularized softening responses, with localized damage profiles corresponding to the development of macroscopic cracks, to resolve the numerical spurious effects induced by the conventional gradient enhancement. The typical implementation strategy for a gradient enhanced model is to solve the system of governing equations simultaneously. Focusing on the finite element (FE) package Abaqus, a user element subroutine is required to define the finite elements with additional degrees of freedom for the nonlocal field. Moreover, with user elements, additional effort is required to visualize the numerical results. To an inexperienced engineer/researcher, these requirements can be challenging. In this paper, a simple implementation of the localizing gradient damage model is elaborated. By utilizing the in-built coupled thermo-mechanical elements in Abaqus, the user only needs to define the material constitutive laws, as well as the sensitivity terms with respect to the field variables. Post-processing of results can be done directly in Abaqus. The applicability and ease of implementation are demonstrated via several examples, including those that utilize the Abaqus features of element deletion, contact between surfaces, as well as the incorporation of cohesive elements. Sample files can be downloaded from https://github.com/leonghien/Localizing-Gradient-Damage-with-UMAT-UMATHT

A simple implementation of localizing gradient damage model in Abaqus

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