From July 26 to July 28, 2024, a rare heavy rainfall associated with Typhoon Gaemi triggered widespread clustered landslides in Zixing City, Hunan Province, China. The severe disaster caused 50 fatalities and 15 missing persons across 26 villages, damaging 11,869 houses and affecting a total of 128,000 individuals. Timely and accurate event analysis is essential for deepening our understanding of landslide clustering mechanisms and guiding future disaster prevention efforts. To achieve this, remote sensing analysis using satellite and unmanned aerial vehicle (UAV) aerial images was conducted to assess the distribution pattern of landslide clusters and explore their relationship with environmental factors. Field investigations were subsequently carried out to identify the failure mechanisms of representative landslides. The results identified three main landslide clustering areas in the eastern mountainous forest region of Zixing City. The landslides are predominantly shallow soil slides, with their distribution closely linked to rainfall thresholds and lithology. The clustering areas typically received cumulative precipitation exceeding 400 mm during the extreme rainfall event. Lithology significantly influences the composition and thickness of slope soils, which in turn controls sliding patterns and affects landslide distribution density and individual landslide size. Granite residual soils contributed to the highest landslide density, with many large individual landslides. Topography and vegetation also play important roles in landslide formation and movement. This study provides preliminary insights into the clustered landslide event, aiding researchers in quickly understanding its key features.

In the transitional waters of 30 to 90 m, jacket foundation has great application potential due to its advantages of light weight, high structural stiffness and good stability. In addition to the long-term normal wind and waves, the wind turbines will suffer from typhoons and waves in extreme bad weather. Currently, research on the dynamic response of jacket supported OWTs in clay under severe typhoons is very rare. The study develops a numerical method to calculate the dynamic response and fatigue damage of jacket supported OWTs under typhoon loads by incorporating a simplified single bounding surface model of clays. Through three-dimensional numerical analysis across various scenarios, this study investigates the dynamic response characteristics of jacket supported OWTs on clay soil. It also examines the impact of wind-wave coupling effects on the fatigue damage experienced by these structures. It was found that severe typhoons can lead to notable permanent tilting of the jacket foundation, thereby failing to meet the requirements of normal serviceability limits. The most critical nodes of the OWT are situated at the mudline of the pile foundations, followed closely by the bottom of the tower structure. The most significant fatigue damage occurs for wind-wave co-directional coupling loading along the orthogonal direction of the OWT. The research outcomes provide valuable guidance for enhancing the typhoon-resistant design of jacket supported OWTs.

After landfall, tropical cyclone (TC) remnants may maintain or even rejuvenate and incur catastrophic disasters. What leads to the revival of TC remnants over land remains elusive. In this study, the revival mechanism of Typhoon Doksuri (2023) remnants is extensively explored. Doksuri brought severe damage to the Chinese mainland after its landfall. The remnants vortex of Doksuri sustained an inland trajectory for 3 days and underwent a total maintenance of 60 h, with a revival of 18 h. Based on multi-source observations and ERA5 reanalysis data, by calculation of moist potential vorticity and analysis of slantwise vorticity development (SVD), this study unveils that while maintaining a significant warm-core structure over the course of maintenance and revival, the Doksuri remnants transported sufficient moisture in the mid-lower troposphere, which intensified the north-south temperature and humidity gradients, causing tilting of the isentropic surfaces remarkably. According to the SVD theory, the tilting gave rise to vorticity development and forced upward air motion on the northern side of the remnant vortex. Moreover, numerical sensitivity experiments based on the WRF model reveal that the topography of Taihang Mountains and the diabatic heating associated with surface and convective latent heat fluxes also played important roles in the revival of the Doksuri remnants. The dynamic and thermodynamic mechanisms derived by this study will help improve understanding and prediction of the disasters induced by TC remnants.

In the context of global climate change, shallow landslides induced by strong typhoons and the ensuing rainstorms have increased significantly in China's eastern coastal areas. On 27 May 2022, more than 700 liquefied landslides were induced by the rain gush in Wuping County, Longyan City, Fujian Province, SE China. In light of their widespread occurrence and the severe damage caused, detailed field investigations, UAV surveys, trench observations, in situ tests, and numerical simulation are conducted in this work. The cascading landslides are classified as channelized landslides and hillslope landslides. Long-term rainfall, the influence of vegetation roots under wind load, and differences in the strength and structure of surficial soil are the dominant controlling factors. The sliding surface is localized to be the interface at a depth of 1-1.5 m between the fully weathered granite and the strongly weathered granite. Kinetic analysis of a channelized landslide shows that it is characterized by short runout, rapid velocity, and strong impact energy. The maximum velocity, impact energy, and impact force of the Laifu landslide are 29 m/s, 4221.35 J, and 2110 kPa. Effective excavation is usually impossible in this context. This work highlights the escalating issue of shallow landslides in eastern China's coastal areas, exacerbated by climate change and extreme weather events like typhoons. By conducting comprehensive investigations and analyses, the research identifies key factors influencing landslide occurrence, such as rainfall patterns and soil characteristics. Understanding the dynamics and impact of these landslides is vital for improving risk assessment, developing effective early warning systems, and informing land management policies in this region. Further exploration concerning hydro-meteorological hazard early warning should be encouraged in this region.

Typhoons are recurring meteorological phenomena in the southeastern coastal area of China, frequently triggering debris flows and other forms of slope failures that result in significant economic damage and loss of life in densely populated and economically active regions. Accurate prediction of typhoon-triggered debris flows and identification of high-risk zones are imperative for effective risk management. Surprisingly, little attention has been devoted to the construction of physical vulnerability curves in typhoon-affected areas, as a basis for risk assessment. To address this deficiency, this paper presents a quantitative method for developing physical vulnerability curves for buildings by modeling debris flow intensity and building damage characteristics. In this study, we selected the Wangzhuangwu watershed, in Zhejiang Province of China, which was impacted by a debris flow induced by Typhoon Lekima on August 10, 2019. We conducted detailed field surveys after interpreting remote sensing imagery to analyze the geological features and the mechanism of the debris flow and constructed a comprehensive database of building damage characteristics. To model the 2019 debris flow initiation, entrainment, and deposition processes, we applied the Soil Conservation Service-Curve Number (SCS-CN) approach and a two-dimensional debris flow model (FLO-2D). The reconstructed debris flow depth and extent were validated using observed debris flow data. We generated physical vulnerability curves for different types of building structures, taking into account both the degree of building damage and the modeled debris flow intensity, including flow depth and impact pressure. Based on calibrated rheological parameters, we modeled the potential intensity of future debris flows while considering various recurrence frequencies of triggering rainfall events. Subsequently, we calculated the vulnerability index and economic risk associated with buildings for different frequencies of debris flow events, employing diverse vulnerability functions that factored in uncertainty in both intensity indicators and building structures. We observed that the vulnerability function utilizing impact pressure as the intensity indicator tends to be more conservative than the one employing flow depth as a parameter. This comprehensive approach efficiently generated physical vulnerability curves and a debris flow risk map, providing valuable insights for effective disaster prevention in areas prone to debris flows.

Typhoon-induced slope failure is one of the most important geological hazards in coastal areas. However, the specific influence of typhoons on the stability of residual soil slopes still remains an open issue. In this study, the Feiyunjiang catchment in Zhejiang Province of SE China was chosen as the study area, and a downscaling physical model of residual soil slopes in the region was used to carry out the wind tunnel test. Our aim was to answer the question, How does the vegetation on the slope and slope stability respond during a typhoon event? For this purpose, multiple aspects were monitored and observed under four different wind speeds (8.3 m/s, 10.3 m/s, 13.3 m/s, and 17 m/s), including vegetation damage on the slope, macrocracks on the slope surface, wind pressure, wind load, permeability coefficient of the soil layer, and slope stability. The results showed that the plants on the slope could restore to their original states when the wind speeds ranged from 8.3 m/s to 13.3 m/s, but were damaged to the point of toppling when the wind speed increased to 17 m/s. Meanwhile, evident cracks were observed on the ground under this condition, which caused a sharp increase in the soil permeability coefficient, from 1.06 x 10-5 m/s to 6.06 x 10-4 m/s. The monitored wind pressures were larger at the canopy than that at the trunk for most of the trees, and generally larger at the crown of the slope compared with the toe of the slope. Regarding the wind load to the slope ground, the total value increased significantly, from 35.4 N under a wind speed of 8.3 m/s to 166.5 N under a wind speed of 17 m/s. However, the wind load presented different vector directions at different sections of the slope. The quantitative assessment of slope stability considering the wind load effect revealed that the safety factor decreased by 0.123 and 0.1 under the natural state and saturated state, respectively, from no wind to a 17 m/s strong wind. Overall, the present results explained the mechanism of slope failure during typhoon events, which provided theoretical reference for revealing the characteristics of residual soil slope stability under typhoon conditions.

In subtropical typhoon-prone regions, landslides are triggered by short-duration intense rainfall and prolonged periods of elevated pore-water pressure. However, fast-moving landslides pose a significant challenge for timely warning because of insufficient data on rainfall triggers and the identification of potential failure sites. Thus, our study introduces an integrated approach that combines a double-index intensity-duration (I-D) threshold, accounting for daily rainfall (R0) and 5-d effective rainfall (R5), with the MC-TRIGRS, a probabilistic physically based model, to analyze fast-moving landslide hazards at a regional scale. This approach is characterized by its innovative features: (i) it employs a double-index model to categorize rainfall events, differentiating between long-term continuous rainfall and short-term intense precipitation; (ii) it utilizes a comprehensive dataset from extensive field investigations to implement the grey wolf optimizer (GWO) -enhanced long short-term memory neural network (LSTM) to predict soil thickness distributions across the study area; and (iii) it adopts the classical Monte Carlo method to calculate failure probabilities under various rainfall scenarios, incorporating randomness in key soil parameters, such as cohesion and internal friction angle. By leveraging geotechnical data from both field and laboratory tests and integrating the accumulated knowledge, these models can be applied to the coastal mountainous basins of Eastern China, a region highly prone to landslides. Our goal was to augment the effectiveness of landslide early warning systems. Particularly, the synergistic use of rainfall empirical statistics and probabilistic physically based slope stability models is poised to bolster real-time control and risk mitigation strategies, providing a robust solution for short-term preparedness.