Landslides are one of the most hazardous geological processes due to their difficult-to-predict nature and destructive effects, often leading to significant loss of life, infrastructure damage, and environmental disruption. In the Southern Andes of Chile, landslides are particularly frequent and destructive due to a combination of factors, including high seismic activity, steep topography, and the presence of weak, unconsolidated pyroclastic soils. Unfortunately, the geomechanical control of landslide initiation in the Southern Andes is still poorly understood, creating a significant source of uncertainty in developing accurate landslide susceptibility or risk models. This study evaluates the geological and geotechnical factors that control the generation of landslides in pyroclastic soils using in situ data, laboratory analysis and remote sensing approaches. The study area covers the surroundings of the Mocho-Choshuenco Volcanic Complex (MCVC), one of the most explosive volcanoes in the Southern Andes. The results show that the landslides are placed on slopes covered by multiple explosive eruptions that include a period of more than 12 ka. Landslide activity is related to pyroclastic soils with significant weathering and halloysite content. In addition, the geotechnical characteristics show very light soils, with highwater retention capacity, which is vital to induce mechanical instability. The detected deformation may be associated with seasonal precipitation that would increase the pore water pressure and reduce the shear strength of the soil, promoting slow-moving landslides. The geological and geotechnical characteristics of the soils suggests that slow-moving landslides would be extended to a large part of the Southern Andes. Finally, this study contributes to improving hazard assessment to mitigate the impact of landslides on the population, infrastructures and natural resources in the Southern Andes.

The 2018 Sulawesi Earthquake and Tsunami serves as a backdrop for this work, which employs simple and straightforward remote sensing techniques to determine the extent of the destruction and indirectly evaluate the region's vulnerability to such catastrophic events. Documenting damage from tsunamis is only meaningful shortly after the disaster has occurred because governmental agencies clean up debris and start the recovery process within a few hours after the destruction has occurred, deeming impact estimates unreliable. Sentinel-2 and Maxar WorldView-3 satellite images were used to calculate well-known environmental indices to delineate the tsunami-affected areas in Palu, Indonesia. The use of NDVI, NDSI, and NDWI indices has allowed for a quantifiable measure of the changes in vegetation, soil moisture, and water bodies, providing a clear demarcation of the tsunami's impact on land cover. The final tsunami inundation map indicates that the areas most affected by the tsunami are found in the urban center, low-lying regions, and along the coast. This work charts the aftermath of one of Indonesia's recent tsunamis but may also lay the groundwork for an easy, handy, and low-cost approach to quickly identify tsunami-affected zones. While previous studies have used high-resolution remote sensing methods such as LiDAR or SAR, our study emphasizes accessibility and simplicity, making it more feasible for resource-constrained regions or rapid disaster response. The scientific novelty lies in the integration of widely used environmental indices (dNDVI, dNDWI, and dNDSI) with threshold-based Decision Tree classification to delineate tsunami-affected areas. Unlike many studies that rely on advanced or proprietary tools, we demonstrate that comparable results can be achieved with cost-effective open-source data and straightforward methodologies. Additionally, we address the challenge of differentiating tsunami impacts from other phenomena (et, liquefaction) through index-based thresholds and propose a framework that is adaptable to other vulnerable coastal regions.

Rwanda, in eastern tropical Africa, is a small, densely populated country where climatic disasters are often the cause of considerable damage and deaths. Landslides are among the most frequent hazards, linked to the country's peculiar configuration including high relief with steep slopes, humid tropical climate with heavy rainfall, intense deforestation over the past 60 years, and extensive use of the soil for agriculture. The Karongi region, in the west-central part of the country, was affected by an exceptional cluster of more than 700 landslides during a single night (6-7 May 2018) over an area of 100 km2. We analyse the causes of this spectacular event based on field geological and geomorphology investigation and CHIRPS and ERA5-Land climate data. We demonstrate that (1) the notably steep slopes favoured soil instability; (2) the layered soil and especially the gravelly, porous C horizon allowed water storage and served as a detachment level for the landslides; (3) relatively low intensity, almost continuous rainfall over the previous two months lead to soil water-logging; and (4) acoustic waves from thunder or mechanical shaking by strong wind destabilized the water-logged soil through thixotropy triggering the landslides. This analysis should serve as a guide for forecasting landslide-triggering conditions in Rwanda.

Increasingly, Climate Change (CC) is yielding more adverse climatic conditions that lead to the occurrence of natural hazards. Within these CC-related phenomena, it is possible to list global warming, flooding events, and urban heat islands. These scenarios generate damage to road infrastructure to a greater or lesser extent. Consequently, the CC-related phenomena affect the interconnection of production centers with cities and other communities. In this way, as CC causes potential damage to the pavement structures, socio-economic growth rates are correspondingly decreased. The preceding reveals the importance of designing CC-resilient asphalt pavements, which represent the vast percentage of transport infrastructure built worldwide. In this regard, this literature review aims to summarize the leading technologies and strategies developed in the state-of-the-art to mitigate the impacts of CC, as well as promote disaster risk reduction. Thus, this manuscript explains the following resilient design alternatives: anti-rutting asphalt mixtures, multilayer cool coatings, less temperature- sensitive asphalt mixtures, high-inertia pavements, flame retardancy of asphalt binders, anti-fatigue asphalt mixtures, self-healing asphalt mixtures, self-deicing asphalt mixtures, road-heating systems, fast-draining asphalt pavements, hydrophobic asphalt pavement, anti-ageing additives, solar pavements, and cool pavements. Furthermore, several constitutive models capable of simulating soil behaviour under CC-related events are introduced throughout this paper. This review highlights critical advancements in pavement engineering and encourages the adoption of sustainable, resilient design practices to safeguard infrastructure and ensure longterm socio-economic stability. The findings from this investigation provide a valuable resource for pavement designers, civil engineers, and policymakers, offering practical guidance for adapting road infrastructure to future climatic conditions.

Slope failures are an ongoing global threat leading to significant numbers of fatalities and infrastructure damage. Landslide impact on communities can be reduced using efficient early warning systems to plan mitigation measures and protect elements at risk. This manuscript presents an innovative geophysical approach to monitoring landslide dynamics, which combines electrical resistivity tomography (ERT) and low-frequency distributed acoustic sensing (DAS), and was deployed on a slope representative of many landslides in clay rich lowland slopes. ERT is used to create detailed, dynamic moisture maps that highlight zones of moisture accumulation leading to slope instability. The link between ERT derived soil moisture and the subsequent initiation of slope deformation is confirmed by low-frequency DAS measurements, which were collocated with the ERT measurements and provide changes in strain at unprecedented spatiotemporal resolution. Auxiliary hydrological and slope displacement data support the geophysical interpretation. By revealing critical zones prone to failure, this combined ERT and DAS monitoring approach sheds new light on landslide mechanisms. This study demonstrates the advantage of including subsurface geophysical monitoring techniques to improve landslide early warning approaches, and highlights the importance of relying on observations from different sources to build effective landslide risk management strategies.

Due to favorable natural conditions and human impact, the territory of North Macedonia is very susceptible to natural hazards. Steep hillslopes combined with soft rocks (schists on the mountains; sands and sandstones in depressions), erodible soils, semiarid continental climate, and sparse vegetation cover give a high potential for soil erosion and landslides. For this reason, this study presents a multi-hazard approach to geohazard modeling on the national extent in the example of North Macedonia. Utilizing Geographic Information Systems, relevant data about the entire research area were employed to analyze and assess soil erosion and susceptibility to landslides and identify areas prone to both hazards. Using the Gavrilovi & cacute; Erosion Potential Method (EPM), an average value of 0.36 was obtained for the erosion coefficient Z, indicating low to moderate susceptibility to erosion. However, a significant area of the country (9.6%) is susceptible to high and excess erosion rates. For the landslide susceptibility assessment (LSA), the Analytical hierarchy process approach is combined with the statistical method (frequency ratio), showing that 29.3% of the territory belongs to the zone of high and very high landslide susceptibility. Then, the accuracy assessment is performed for both procedures (EPM and LSA), showing acceptable reliability. By overlapping both models, a multi-hazard map is prepared, indicating that 22.3% of North Macedonia territory is highly susceptible to erosion and landslides. The primary objective of multi-hazard modeling is to identify and delineate hazardous areas, thereby aiding in activities to reduce the hazards and mitigate future damage. This becomes particularly significant when considering the impact of climate change, which is associated with increased landslide and erosion susceptibility. The approach based on a national level presented in this work can provide valuable information for regional planning and decision-making processes.

An increase in precipitation due to climate change has given rise to the number of landslide occurrences. Vetiver, which is a perennial grass, is becoming increasingly popular all over the world as a vegetation-based soil bioengineering tool for preventing landslides. Sunshine Vetiver grass, also known as Chrysopogon zizanioides is noninvasive and does not compete with other indigenous plants growing in the area. Even though it is a tropical grass, Vetiver can grow in a wide range of climate conditions, including those that are quite harsh in terms of both soil and climate. The roots can grow up to 3 m in length in a dense bushy root network under optimal conditions. In this review, the authors have studied the impact of Vetiver on landslide mitigation as a climate-adaptive slope repair tool based on the research undertaken so far. Furthermore, the authors have addressed the future potential and constraints associated with the use of Vetiver for landslide mitigation. It is seen that the use of Vetiver reduces pore water pressure. The high tensile strength of Vetiver roots provides reinforcement for slopes and enhances soil shear strength. Vetiver increases saturated hydraulic conductivity and reduces surface runoff and slip surface depth. Being a vegetation-based climate-adaptive technology, this grass exhibits great promise in its ability to effectively address landslide problems. However, the magnitude of the root impact diminishes as the depth increases, rendering Vetiver a more promising remedy for shallow landslide occurrences. In addition, Vetiver grass has a wide range of practical uses due to its unique characteristics, which provide additional benefits. Employment of Vetiver is cost-effective compared with traditional engineering methods, and it requires less initial maintenance, which implies that community-based initiatives can effectively address landslide prevention through Vetiver implementation. Vetiver grass has a long bushy network of roots that can grow up to 3 m in length. The Sunshine Vetiver grass is not invasive and does not compete with indigenous plants. Although Vetiver is a tropical grass, this grass can survive in various climates and soil conditions. Vetiver is a vegetation-based climate-adaptive technology that can prevent slope failure and reduce surface runoff. Additionally, growing Vetiver can generate income for local communities because the fragrant roots can be utilized in the extraction of essential oils for the perfume industry and from the manufacture and trade of other commodities derived from Vetiver. The grass's green leaves contribute to the aesthetic appeal of the landscape. Implementing Vetiver on slopes does not require heavy machinery and is cost-effective compared with traditional engineering methods. It also requires less initial maintenance, making it an ideal solution for community-based initiatives aiming to address slope failure prevention through Vetiver implementation.

Landslides are a prevalent natural hazard in West Bengal, India, particularly in Darjeeling and Kurseong, resulting in substantial socio-economic and physical consequences. This study aims to develop a hybrid model, integrating a Genetic-based Random Forest (GA-RF) and a novel Self-Attention based Convolutional Neural Network and Long Short-term Memory (SA-CNN-LSTM), for accurate landslide susceptibility mapping (LSM) and generate landslide vulnerability-building map in these regions. To achieve this, we compiled a database with 1830 historical data points, incorporating a landslide inventory as the dependent variable and 32 geoenvironmental parameters from Remote Sensing (RS) and Geographic Information Systems (GIS) layers as independent variables. These parameters include features like topography, climate, hydrology, soil properties, terrain distribution, radar features, and anthropogenic influences. Our hybrid model exhibited superior performance with an AUC of 0.92 and RMSE of 0.28, outperforming standalone SA-CNN-LSTM, GA-RF, RF, MLP, and TreeBagger models. Notably, slope, Global Human Modification (gHM), Combined Polarization Index (CPI), distances to streams and roads, and soil erosion emerged as key layers for LSM in the region. Our findings identified around 30% of the study area as having high to very high landslide susceptibility, 20% as moderate, and 50% as low to very low. The vulnerability-building map for 244,552 building footprints indicated varying landslide risk levels, with a significant proportion (27.74%) at high to very high risk. Our model highlighted high-risk zones along roads in the northeastern and southern areas. These insights can enhance landslide risk management in Darjeeling and Kurseong, guiding sustainable strategies for future damage qualification.

This research comprehensively assesses the aftermath of Cyclonic Storm Mocha, focusing on the coastal zones of Rakhine State and the Chittagong Division, spanning Myanmar and Bangladesh. The investigation emphasizes the impacts on coastal ecology, shoreline dynamics, flooding patterns, and meteorological variations. Employed were multiple vegetation indices-Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Modified Vegetation Condition Index (mVCI), Disaster Vegetation Damage Index (DVDI), and Fractional Vegetation Cover (FVC)-to evaluate ecological consequences. The Digital Shoreline Assessment System (DSAS) aided in determining shoreline alterations pre- and post-cyclone. Soil exposure and flood extents were scrutinized using the Bare Soil Index (BSI) and Modified Normalized Difference Water Index (MNDWI), respectively. Additionally, the study encompassed an analysis of microclimatic variables, comparing meteorological data across pre- and post-cyclone periods. Findings indicate significant ecological impacts: an estimated 8985.46 km2 of dense vegetation (NDVI >0.6) was adversely affected. Post-cyclone, there was a discernible reduction in EVI values. The mean mVCI shifted negatively from -0.18 to -0.33, and the mean FVC decreased from 0.39 to 0.33. The DVDI underscored considerable vegetation damage in various areas, underscoring the cyclone's extensive impact. Meteorological analysis revealed a 245 % increase in rainfall (20.22 mm on May 14, 2023 compared to the May average of 5.86 mm), and significant increases in relative humidity (14 %) and wind speed (205 %). Erosion was observed along 74.60 % of the studied shoreline. These insights are pivotal for developing comprehensive strategies aimed at the rehabilitation and conservation of critical coastal ecosystems. They provide vital data for emergency response initiatives and offer resources for entities engaged in enhancing coastal resilience and protecting local community livelihoods.

Multicomponent or Macroscopic Cellular Automata (MCA) were conceived for modelling and simulating complex macroscopic phenomena such as surface flows, forest fires, bioremediation of soils, etc. Many MCA models were developed for risky surface flows of different typology (lava flows, debris/mud/granular flows, lahars, snow avalanches, pyroclastic flows, rain runoff), these models share many elementary processes, but differ in some specificities of the particular phenomenon to be simulated. These specificities could be generalized to a single model (SCURRI: Simulation byCellularUnits of the Rheological RIsks) valid for each surface flow we deal with. The base of SCURRI is given by SCIDDICA, including its derivativeMCAmodels LLUNPIY, VALANCA for simulating single phase surface flows (debris/mud/granular flows, lahars, snow avalanches). We introduce viscosity effects in SCURRI by adopting the approach of the MCA model SCIARA for simulating lava flows: a critical height, beyond which the flows become negligible, is introduced in SCURRI now version 01. SCURRI-01 was applied to a real event (of course) different from a lava flow: the secondary lahar of February 2005 of Vascun Valley from Tungurahua. This event had been simulated satisfactorily by LLUNPIY. Simulations were also performed by SCURRI-01with different values of critical height. Higher values of this parameter, produce clear viscosity increasing effects such as speed decrease which can reach so low values that we can no longer talk about lahars, but rather of slow flow landslide.