The Pearl River Estuary (PRE) frequently experiences the impacts of typhoons, storm surges, and saltwater intrusion. While previous research has mainly focused on saltwater intrusion during the dry season, there is limited research on saltwater intrusion caused by storm surges in the PRE. In this study, we systematically investigate the effects of ten typical autumnal typhoons and associated storm surges on saltwater intrusion in the Modaomen Waterway using in situ data of water level, river discharge, and chloride concentrations from 2006 to 2022. We introduce the concept of Storm surge-Induced Salinity Augmentation (SISA) and analyze its characteristics and primary influencing factors. Our findings reveal that SISA primarily occurs in autumn, with reduced upstream river discharge and the dominance of high-salinity water in the estuary. SISA occurs immediately after storm surges and grows rapidly and violently, with a time lag of 2–4 h, but rapidly recedes after the typhoon passage due to heavy rainfall and high freshwater discharge. Typhoons with a westward trajectory have a greater influence, and the southeastern winds outside the estuary during typhoon events are the primary factors determining the intensity of SISA. Pre-typhoon river discharge affects the range and duration of saltwater intrusion. Moreover, the coupling effect of extreme river dryness, spring tide, and storm surges significantly enhances saltwater intrusion. Further research is needed to quantify the spatiotemporal characteristics of SISA accurately.

In the low-lying, river-rich Pearl River Delta in South China, an extensive network of flood defense levees, spanning over 4400 km, plays a crucial role in urban flood management. These levees are designed to withstand floods and storm surges, yet their failure can lead to significant human and economic losses, highlighting the need for robust urban flood defense strategies. This necessitates the development of a sophisticated geographic information system for the levee network and rapid, accurate assessment methods for levee conditions to support water management and flood mitigation efforts. This study focuses on the levees along the Hengmen waterway in the Pearl River Delta, utilizing airborne Light Detection and Ranging (LiDAR) technology to gather 3D spatial data of the levees. Employing the Cloth Simulation Filter (CSF) algorithm, non-ground point cloud data were extracted. The study improved upon the region-growing algorithm, using a seed point set approach for the automatic extraction of levee point cloud data. The accuracy and completeness of levee extraction were evaluated using the quality index. This method achieved effective extraction of four levee types, showing significant improvements over traditional algorithms, with extraction quality ranging from 72% to 83%. Key research outcomes include the development of a novel method for detecting localized levee depressions based on the computation of the variance of angles between normal vectors in single-phase levee point cloud data. An adaptive optimal neighborhood approach was utilized to accurately determine the normal vectors, effectively representing the local morphology of the levee point clouds. Applied in three levee depression detection experiments, this method proved effective, demonstrating the capability of single-phase data in identifying regions of levee depression deformation. This advancement in levee monitoring technology marks a significant step forward in enhancing urban flood defense capabilities in regions such as the cities of the Pearl River Delta in China.

Background: Sleep staging is important for assessing sleep architecture and supporting the diagnosis of sleep-related disorders. Manual scoring is time-consuming and subject to inter-scorer variability, while joint modeling of EEG channel relationships and frequency characteristics remains challenging. Methods: We propose DCMG-Net (dual-branch CNN and context-gated multi-view graph network), a frequency-aware network combining multi-view graph convolution with context gating for five-class sleep staging using EEG alone. Multiscale convolutions and attention extract channel features. Functional, globally learnable, and four physiological frequency-band adjacency matrices define six graph views, whose representations are adaptively fused by context gating. Results: On Sleep-EDF-20, ISRUC-S3, and the Sleep Heart Health Study (SHHS), overall accuracies were 91.2%, 90.0%, and 86.3%; macro-F1 scores were 84.4%, 88.7%, and 77.2%; and Cohen’s κ values were 0.88, 0.87, and 0.81, respectively. Macro-F1 exceeded the highest values reported by the compared literature methods by 1.9, 4.2, and 0.7 percentage points, respectively. In the ablation experiments, gating improved macro-F1 over direct concatenation by 6.1 and 2.3 percentage points on ISRUC-S3 and SHHS. Conclusions: Frequency-band topology and context gating jointly exploit EEG frequency information and channel relationships to improve multi-view classification, providing a methodological basis for portable sleep monitoring and computer-assisted clinical scoring.