Bridge failures are caused by various reasons, but the main reason is caused by scour at bridge pier. Debris accumulation on bridge piers is an ongoing problem nowadays. The study of debris accumulation on bridge piers has done by many researchers, but not any researches have done on debris accumulation on sacrificial piles, yet. The aim of this study is to estimate the effect of debris accumulation at sacrificial pile to bridge pier scour. Experiments were performed to understand the changes and interaction of scour over a range of flow depths and velocities in various cases like single pier cases, sacrificial piles cases, debris accumulated at single pier cases, and debris accumulated at sacrificial piles cases with different diameters and thickness of debris. Maximum scour depth in presence of debris accumulation at isolated pier and sacrificial piles have been compared to the maximum scour depth without the presence of debris accumulation. New relationships have been proposed to predict the effect of debris accumulation at sacrificial piles on bridge pier scour in term of relative maximum scour depth. The results of single pier with debris showed that the scour depth increased from 10% to 60% compared to single pier without debris. In sacrificial piles with debris the results showed that scour depth increased from 8% to 100% compared to sacrificial piles without debris. Through this experimental study the debris found to be effected to bridge pier scour. It was seen that the debris effected to the effectiveness of sacrificial piles. The effect of debris accumulation at sacrificial piles to bridge pier scour should be considered. Future researches are needed to consider more about the spacing between the pile, the angle of the pier to approach flow, and the angle of flow to the pier.

Because urban flooding occurs frequently, it is important to calculate the areal extent of flooding based on flood volumes and water levels, and thus, the extent of expected damage can be effectively estimated. In this study, urban inundation from heavy rainfall and the contribution of topographic scales to urban river drainage basins in Seoul, Korea are simulated. A light detection and ranging (LiDAR)‐based 1× 1 m digital elevation model (DEM) and a 1:5,000 digital map‐based 10 × 10 m DEM are used in this study for the storm water management model (SWMM). Using a multi‐dimensional flood damage analysis to estimate economic damage, the asset value of each building type is analyzed in connection with the construction cost of buildings, household ratio, and building area, and then, the building damage is calculated at each flood level based on the percentage of building damage. Based on the analysis, 119.6 and 55.8 M USD in damages are expected to occur according to the 1 and 10 m DEMs, respectively. The results of this study can be used to evaluate alternatives for urban flooding mitigation and an optimal mitigation measure can be suggested among the alternatives because the methods used for predicting floods are examined and the associated damages are accurately estimated using digital terrain data at precise scales.

High turbidity in rivers, intensified by extreme rainfall associated with climate change, poses a great challenge to water resource management globally. To manage and control turbidity events, prediction models have been developed utilizing suspended sediment (SS) data. However, traditional methods for measuring SS data, such as water sampling and laboratory analysis, are time-consuming and impractical for real-time applications. Although the turbidity–SS relationship is widely used, its accuracy depends on sediment particle size distribution. To address this limitation, we developed turbidity–SS equations adaptive to various SS fractions, using data from controlled circulating flume experiments designed to reflect sediment characteristics of a natural river. The results showed significant improvements in the linear turbidity–SS relationship, with R² values ranging from 0.60 to 0.99, depending on sediment fractions. These equations were applied to field data from the area upstream of Soyanggang Dam in South Korea, yielding error rates of 1–18%. This study highlights the importance of incorporating sediment fraction variability into turbidity–SS models, which significantly improves their accuracy and reliability. The proposed approach offers a practical and scalable solution for real-time and large-scale SS monitoring, contributing to improved water quality management in various riverine conditions.

Digital twin (DT) technology is gaining attention for effective water quality management by integrating diverse data sources and enabling real-time insights. The practical implementation of DT technology for intelligent river water quality management requires extensive spatiotemporal big data, underscoring the critical need to integrate physical sensors, soft sensors, and remote sensing technologies. Here, we synthesized recent advancements in hybrid physical–soft sensing systems and highlighted their potential to address the inherent limitations of conventional water quality monitoring methods, such as limited spatiotemporal resolution and high operational costs. Soft sensors, driven by machine learning (ML), estimated difficult-to-measure water quality parameters by leveraging easily measurable variables from physical sensors. Therefore, soft sensors significantly expanded the range of measurable parameters and improved data collection frequency. In addition, remote sensing offers broad spatial coverage, enabling large-scale monitoring of optically active constituents, algal blooms, and sediment dynamics. We critically review methodologies and applications that integrate these sensing technologies into DT frameworks, and identify critical knowledge gaps, particularly the lack of a fully unified integration framework combining these technologies for next-generation DT systems. By assessing the strengths and limitations of each approach and proposing integration strategies, this study offers practical guidance and integration recommendations for DT-based river management.

The potassium ferrate(VI) was applied to treat secondary wastewater and its performance as both a disinfectant and a coagulant was investigated. In order to compare oxidation potential of the ferrate(VI) with that of chlorine, the different condition of doses and contact times were applied to the experiment for the same coliform. The disinfection rate of ferrate(IV) was faster than that of chlorine at the same concentration. The effect of ferrate(VI) supplementation on coagulation of phosphorus was examined and compared with other common coagulants. Ferrate(VI) reduced more than 80% of total phosphorous in the range of doses between 5. and 25 mg-Fe/L. The removal efficiencies of the ferrate(VI) appeared to be similar to those of alum but higher than other iron coagulants. The removal efficiencies of other contaminants in secondary effluent were not notable.