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.

Most of the sections of the Western Ghats ( in Kerala) are highly susceptible to landslides, particularly during extreme rainfall events (EREs). The massive landslide that occurred in the Mundakkai-Chooralmala region of Wayanad district on 30 July 2024 is the latest in a series of devastating landslides in this region. This event caused extensive damage, resulting in over 225 fatalities, more than 273 injuries, about 131 individuals reported missing, and the destruction of 1555 houses, making it one of the deadliest landslides in India. This investigation synthesizes the formation mechanisms, landslide characteristics and impacts of the event through the analysis of field observations, aerial photographs, satellite imageries and rainfall data. Results indicate that the region has been prone to landslides including those events in 1924, 1984, and the recent ones since 2018. The Mundakkai-Chooralmala landslide is the largest landslide that occurred in the Kerala, with an impact area of approximately 6.5 x 10(5) m(2) and a horizontal runout distance of similar to 7 km. Rainfall analysis shows that the landslide-affected region received extreme rainfall amounting to 373 mm within 24 h of the event. The antecedent rainfall for three days and five days was 586 mm and 809 mm, respectively. This extreme rainfall, combined with highly weathered and sheared geological conditions and unique slope morphological characteristics, triggered the landslide. The affected areas are characterized by loose, unconsolidated sediments, including a thick layer of weathered, soft lateritic soil (exceeding 30 m in thickness) and micaceous kaolinitic plastic clay, resting on highly weathered and jointed bedrock, such as charnockites and gneisses. These conditions, combined with prolonged and intense rainfall, increase pore water pressure, significantly reducing overburden shear strength. The concave slopes of the terrain further exacerbate this risk by accumulating surface runoff, making these slopes more susceptible to future failures. Field evidence also indicates the formation of debris dams in narrow sections of the valley due to dislodged material, including trees and large boulders. The breaching of these natural dams caused widespread damage throughout the affected areas. These findings underscore the urgent need for comprehensive landslide risk management strategies, including the implementation of early warning systems, improved land use planning, and community preparedness to mitigate the impact of future landslides in the Western Ghats region.

This study aims to investigate the sliding mechanism of slopes along railways in loess regions under the coupling effect of extreme rainfall and train vibration. Using the Baotou-Xi'an railway as a case study, a physical model of slopes along railways was developed to account for the impacts of dry-wet cycles, extreme rainfall, and train vibration. The experiments revealed that during the dry-wet cycle phase, the pore fractal dimension of the slope soil decreases from 2.95 to 2.81, indicating an increase in macropores, which enhances water transport efficiency in the soil. Following extreme rainfall, the pore water pressure and moisture content data of the soil approach peak levels, suggesting increased soil saturation and weakened stability. Subsequent vibration loading results in highly saturated soil, as evidenced by fluctuations in volumetric moisture content (from 48 % to 50.7 %) and pore water pressure (from 1.6 to 1.8 Kpa). Train vibration contributes to crack formation and expansion, while water infiltration establishes a pore-crack-seepage network. This network, combined with rainfall and train vibrations, destabilizes the soil structure and triggers landslides in loess regions along railways. The continuous application of vibration load further expands the sliding range. Meanwhile, an equation was derived to determine the sliding distance in relation to the number of vibratory loads applied. The sliding mechanism of slopes along railways under the combined influence of rainfall and train vibration has been preliminarily verified through micro, meso, and macroscopic perspectives.

Heavy rainfall is the main factor inducing the failure of loess slopes. However, the failure mechanism and mode of terraced loess slopes under heavy rainfall have not been well investigated and understood. This paper presents the experimental study on the deformation and failure of terraced loess slopes with different gradients under extreme rainfall conditions. The deformation and failure processes of the slope and the migration of the wetting front within the slope during rainfall were captured by the digital cameras installed on the top and side of the test box. In addition, the mechanical and hydrological responses of the slope, including earth pressure, water content, pore water pressure, and matric suction, were monitored and analyzed under rainfall infiltration and erosion. The experimental study shows that the deformation and failure of terraced loess slopes under heavy rainfall conditions exhibit the characteristic of progressive erosion damage. In general, the steeper the slope, the more severe the deformation and failure, and the shorter the time required for erosion failure. The data obtained from sensors embedded in the slope can reflect the mechanical and hydraulic characteristics of the slope in response to rainfall. The earth pressure and pore water pressure in the slope exhibit a fluctuating pattern with continued rainfall. The failure mode of terraced loess slopes under extreme rainfall can be summarized into five stages: erosion of slope surface and formation of small gullies and cracks, expansion of gullies and cracks along the slope surface, widening and deepening of gullies, local collapse and flow-slip of the slope, and large-scale collapse of the slope. The findings can provide preliminary data references for researchers to better understand the failure characteristics of terraced loess slopes under extreme rainfall and to further validate the results of numerical simulations and analytical solutions.

Floods in southwestern Saudi Arabia, especially in the Asir region, are among the major natural disasters caused by natural and human factors. In this region, flash floods that occur in the Wadi Hail Basin greatly affect human life and activities, damaging property, the built environment, infrastructure, landscapes, and facilities. A previous study carried out for the same basin has effectively revealed zones of flood risk using such an approach. However, the utilization of the HEC-HMS (Hydrologic Engineering Center-Hydrologic Modeling System) model and IMERG data for delineating areas prone to flash floods remain unexplored. In response to this advantage, this work primarily focused on flood generation assessment in the Wadi Hail Basin, one of the major basins in the region that is frequently prone to severe flash flood damage, from a single extreme rainfall event. We employed a fully physical-based, distributed hydrological model run with HEC-HMS software version 4.11 and Integrated Multi-satellite Retrievals of Global Precipitation Measurement (IMERG V.06) data, as well as other geo-environmental variables, to simulate the water flow within the Wadi Basin, and predict flash flood hazard. Discharge from the wadi and its sub-basins was predicted using 1 mm rainfall over an 8-h occurrence time. Significant peak discharge (3.6 m3/s) was found in eastern and southern upstream sub-basins and crossing points, rather than those downstream, due to their high-density drainage network (0.12) and CNs (88.4). Generally, four flood hazard levels were identified in the study basin: 'low risk', 'moderate risk', 'high risk', and 'very high risk'. It was found that 43.8% of the total area of the Wadi Hail Basin is highly prone to flooding. Furthermore, medium- and low-hazard areas make up 4.5-11.2% of the total area, respectively. We found that the peak discharge value of sub-basin 11 (1.8 m3/s) covers 13.2% of the total Wadi Hail area; so, it poses more flood risk than other Wadi Hail sub-basins. The obtained results demonstrated the usefulness of the methods used to develop useful hydrological information in a region lacking ungagged data. This study will play a useful role in identifying the impact of extreme rainfall events on locations that may be susceptible to flash flooding, which will help authorities to develop flood management strategies, particularly in response to extreme events. The study results have potential and valuable policy implications for planners and decision-makers regarding infrastructural development and ensuring environmental stability. The study recommends further research to understand how flash flood hazards correlate with changes at different land use/cover (LULC) classes. This could refine flash flood hazards results and maximize its effectiveness.

Aerosol-cloud interactions, also known as aerosol indirect effect (AIE), substantially impact rainfall frequency and intensity. Here, we analyze NEX-GDDP, a multimodel ensemble of high-resolution (0.25 degrees) historical simulations and future projections statistically downscaled from 21 CMIP5 models, to quantify the importance of AIE on extreme climate indices, specifically consecutive dry days (CDD), consecutive wet days (CWD), and simple daily intensity index (SDII). The 21 NEX-GDDP CMIP5 models are classified into models with reliable (REM) and unreliable (UREM) monsoon climate simulated over India based on their simulations of the climate indices. The REM group is further decomposed based on whether the models represent only the direct (REMADE) or the direct and indirect (REMALL) aerosol effects. Compared to REMADE, including all aerosol effects significantly improves the model skills in simulating the observed historical trends of all three climate indices over India. Specifically, AIE enhances dry days and reduces wet days in India in the historical period, consistent with the observed changes. However, by the middle and end of the 21st century, there is a relative decrease in dry days and an increase in wet days and precipitation intensity. Moreover, the REMALL simulated future CWD and CDD changes are mostly opposite to those in REMADE, indicating the substantial role of AIE in the future projection of dry and wet climates. These findings underscore the crucial role of AIE in future projections of the Indian hydroclimate and motivate efforts to accurately represent AIE in climate models. We investigate the impacts of aerosol on India's wet and dry climate. High-resolution downscaled CMIP5 models were used to calculate extreme indices like CDD (consecutive dry days), CWD (consecutive wet days), SDII (precipitation intensity). From the group of 22 models, 12 reliable models were chosen based on their fidelity to the observations. Amongst the reliable models, certain models incorporate only aerosol-radiation interaction (REMADE), while others have both aerosol-radiation and aerosol-cloud interaction (REMALL). We found that the simulated trends in the REMAll were similar to the observed trends. In the current period (1975-2005), the aerosol-cloud interactions led to the reduction in rainfall (both frequency and intensity wise) and enhanced the dry days, however in the future projections, the reduction in aerosol emissions leads to a wetter climate (increase in wet days and rainfall intensity) over India.

Infrastructure and transportation systems on which northern communities rely are exposed to a variety of climatic hazards over a broad range of scales. Efforts to adapt these systems to the rapidly warming Arctic climate require high-quality climate projections. Here, a state-of-the-art regional climate model is used to perform simulations at 4-km resolution over the eastern and central Canadian Arctic. These include, for the first time over this region, high-resolution climate projections extending to the year 2040. Validation shows that the model adequately simulates base climate variables, as well as variables hazardous to northern engineering and transportation systems, such as degrading permafrost, extreme rainfall, and extreme wind gust. Added value is found over coarser resolution simulations. A novel approach integrating climate model output and machine learning is used for deriving fog-an important, but complex hazard. Hotspots of change to climatic hazards over the next two decades (2021-2040) are identified. These include increases to short-duration rainfall intensity extremes exceeding 50%, suggesting Super-Clausius-Clapeyron scaling. Increases to extreme wind gust pressure are projected to reach 25% over some regions, while widespread increases in active layer thickness and ground temperature are expected. Overall fog frequency is projected to increase by around 10% over most of the study region by 2040, due to increasing frequency of high humidity conditions. Given that these changes are projected to be already underway, urgent action is required to successfully adapt northern transportation and engineering systems located in regions where the magnitude of hazards is projected to increase.