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The Northeast part of India is experiencing an increase in infrastructure projects as well as landslides. This study aims to prepare the landslide susceptibility map of Tamenglong and Senapati districts, Manipur, India, and evaluates the state of landslide susceptibility along the Imphal-Jiribam railway corridor. Efficient statistical methods such as frequency ratio (FR), information value (IoV), weight of evidence (WoE), and weighted linear combination (WLC) were used in model preparation. A total of 322 landslide points were randomly divided into training (70%) and testing (30%) datasets. Nine causative factors were utilized for landslide susceptibility mapping (LSM). The importance of which was obtained using the information gain (IG) method. FR, IoV, WoE, and WLC were used to prepare the LSM using the training datasets and nine causative factors. Moreover, the accuracy and consistency were evaluated using AUC-ROC, precision, recall, overall accuracy (OA), balanced accuracy (BA), and F-score. The validation results showed that all methods performed well with the highest AUC and precision values of 0.913 and 0.95, respectively, for the IoV method, while the WLC method had the highest OA, BA, and F-score values of 0.808, 0.81, and 0.812, respectively. Finally, the results from LSM were used to evaluate the state of landslide susceptibility along the Imphal-Jiribam railway corridor. The results showed that 34% of the areas had high and very high susceptibility, while 40% were under less and significantly less susceptibility. The Tupul landslide area lay in medium susceptibility where the disastrous landslide occurred on 30 June 2022. Susceptibility values around the Noney and Khongsag railway station ranged from high to very high susceptibility. Thus, the study manifests the need for LSM preparation in rapidly constructing areas, which in turn will help the policymakers and planners for adopting strategies to minimize losses caused due to landslides., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The flash flood-induced erosion is the primary contributor to soil loss within the Indian Himalayan Region (IHR). This phenomenon is exacerbated by a confluence of factors, including extreme precipitation events, undulating topographical features, and suboptimal soil and water conservation practices. Over the past few decades, several flash flood events have led to the significant degradation of pedosphere strata, which in turn has caused landslides along with fluvial sedimentation in the IHR. Researchers have advocated morphometric, hydrologic, and semi-empirical methods for assessing flash flood-induced soil erosion in hilly watersheds. This study critically examines these methods and their applicability in the Alaknanda River basin of the Indian Himalayan Region. The entire basin is delineated into 12 sub-watersheds, and 13 morphometric parameters are analyzed for each sub-watershed. Thereafter, the ranking of sub-watersheds vulnerability is assigned using the Principal Component Analysis (PCA), compounding method (CM), Geomorphological Instantaneous Unit Hydrograph (GIUH), and Revised Universal Soil Loss Equations (RUSLE) approaches. While the CM method uses all 13 parameters, the PCA approach suggests that the first four principal components are the most important ones, accounting for approximately 89.7% of the total variance observed within the dataset. The GIUH approach highlights the hydrological response of the catchment, incorporating dynamic velocity and instantaneous peak magnifying the flash flood susceptibility, lag time, and the time to peak for each sub-watershed. The RUSLE approach incorporates mathematical equations for estimating annual soil loss utilizing rainfall-runoff erosivity, soil erodibility, topographic, cover management, and supporting practice factors. The variations in vulnerability rankings across various methods indicate that each method captures distinct aspects of the sub-watersheds. The decision-maker can use the weighted average to assign the overall vulnerability to each sub-watershed, aggregating the values from various methods. This study considers an equal weight to the morphometric, hydrological GIUH, and semi-empirical RUSLE techniques to assess the integrated ranking of various sub-watersheds. Vulnerability to flash flood-induced landslides in various sub-watersheds is categorized into three classes. Category I (high-priority) necessitates immediate erosion control measures and slope stabilization. Category II (moderate attention), where rainwater harvesting and sustainable agricultural practices are beneficial. Category III (regular monitoring) suggests periodic community-led soil assessments and afforestation. Sub-watersheds WS11, WS8, WS5, and WS12 are identified under category I, WS7, WS4, WS9, and WS6 under category II, and WS1, WS3, WS2, and WS10 under category III. The occurrence of landslides and flash-flood events and field observations validates the prioritization of sub-watersheds, indicating the need for targeted interventions and regular monitoring activities to mitigate environmental risks and safeguard surrounding ecosystems and communities., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

On 4 October 2023, a glacier lake outburst flood (GLOF) occurred at South Lhonak Lake in the northwest of Sikkim, India, posing a severe threat to downstream lives and property. Given the serious consequences of GLOFs, understanding their triggering factors is urgent. This paper conducts a comprehensive analysis of optical imagery and InSAR deformation results to study changes in the surrounding surface of the glacial lake before and after the GLOF event. To expedite the processing of massive InSAR data, an InSAR processing system based on the SBAS-InSAR data processing flow and the AI Earth cloud platform was developed. Sentinel-1 SAR images spanning from January 2021 to March 2024 were used to calculate surface deformation velocity. The evolution of the lake area and surface variations in the landslide area were observed using optical images. The results reveal a significant deformation area within the moraine encircling the lake before the GLOF, aligning with the area where the landslide ultimately occurred. Further research suggests a certain correlation between InSAR deformation results and multiple factors, such as rainfall, lake area, and slope. We speculate that heavy rainfall triggering landslides in the moraine may have contributed to breaching the moraine dam and causing the GLOF. Although the landslide region is relatively stable overall, the presence of a crack in the toparea of landslide raises concerns about potential secondary landslides. Our study may improve GLOF risk assessment and management, thereby mitigating or preventing their hazards. [ABSTRACT FROM AUTHOR]

Introduction: The Indian Himalayas' susceptibility to landslides, particularly as a location where climate change effects may be event catalysts, necessitates the development of dependable landslide susceptibility maps (LSM). Method: This study diverges from traditional binary classification models, framing LSM as a positive-unlabeled learning problem. This approach acknowledges that regions without recorded landslides are not necessarily at low risk but could simply have not experienced landslides yet. The study utilizes novel positive-unlabeled learning-enhanced algorithms—Random Forest, K-Nearest Neighbor, and Decision Tree—to create LSM for Chamoli district, India. Eleven causative factors for landslides are identified, including elevation, aspect, slope, geology, geomorphology, distance to lineament, lithology, NDVI, distance to river, distance to road and residential land use. To address spatial correlation biases, instead of randomly splitting the dataset, the study adopts spatial splitting to get the training and testing datasets. Conclusion: The study reveals that positive-unlabeled learning substantially improves the Area Under Curve and recall, leading to a more conservative LSM compared to binary classification methods. Analysis shows that the southern region of Chamoli exhibits high recall but lower accuracy, suggesting a latent high landslide susceptibility despite a lack of historical landslides in this region. The study also quantifies the impact of human activity on landslide risk, indicating an elevated threat to life and the local economy, especially in Chamoli's southwestern areas. [ABSTRACT FROM AUTHOR]

Floods have devastating consequences for human life and economies worldwide. The present study focuses on flash flooding in the Himalayan environment, specifically in Dharamshala, Himachal Pradesh, India. In July 2021, a low-pressure system developed in the area, intensifying into a deep depression and resulting in extreme rainfall on July 12, 2021. This extreme rainfall event resulted flash flooding, landslides, and extensive damage in Dharamshala. Utilizing satellite observation and geospatial technology, this study examined the heavy rainfall events and the vulnerability of the geology of Dharamshala to flash flooding. The area received a cumulative rainfall of more than 370 mm, with over 35 mm recorded at 09:00 Universal Time Coordinated (UTC). The steep slopes of the region, combined with sudden heavy downpours from multiple extreme rainfall events, contributed to flash flooding and landslides. The consequences were severe, resulting in the loss of seven lives and impacting thousands of people in the study area. The research findings emphasize the significance of satellite-borne remote sensing applications and geospatial technology for real-time short-lived localised rainfall events in the study area. The study also highlights the role of Bhagsu Nala in exacerbating flood risks. This research provides a valuable model for implementing effective disaster mitigation measures. Moreover, it contributes to global efforts to reduce the risk of natural catastrophes, particularly in Himachal Pradesh. The findings offer valuable insights into disaster management and mitigation strategies. [ABSTRACT FROM AUTHOR]

Pressure Probe (PreP) is in effect a simplified penetrometer: it measures the mechanical resistivity of the soil at shallow depths dropping the probe always from the same height. The resolution of this technique is very good. It is able to detect e.g., fractures due to their decreased mechanical resistance if they are covered, but in a shallow depth with a reasonable sampling distance. An example will be demonstrated where fracture system of a slowly-moving landslide was characterized enabling also its delineation. In spite of that its maximal penetration is only about 0.5 m it may also give information from remarkable depths in case if fractures reach close to the surface indicating the borders of geological structures in larger depth. An example from India will be shown to present such results. All results have been verified by geoelectric measurements. PreP may also indicate any kind of hidden holes indirectly. [ABSTRACT FROM AUTHOR]

The mountain state of Uttarakhand constitutes a part of the North-western Indian Himalayan region and is inherently vulnerable to natural disasters. It is witnessing faster melting of glaciers and increased occurrences of disasters like cloudbursts, flash floods, landslides, landslide/glacial lake outburst floods, rock–ice avalanches, and debris flows. This study provides a comprehensive assessment of the influence of changing climate on the occurrence of natural hazards. It assesses the status of two major rapidly developing infrastructures in the state (viz., hydropower plants and transportation network) under climate variability. Detailed causative analysis of two major disaster events (in Kedarnath and Chamoli in 2013 and 2021, respectively) and associated destruction is presented to understand the vulnerability of infrastructure and communities to hazards. Based on the challenges identified, a unique two-fold framework is proposed, and recommendations are articulated for building resilient and sustainable mountain infrastructures and communities in the region. [ABSTRACT FROM AUTHOR]

Landslides cause significant damage to property and life in the West Jaintia Hills district in Meghalaya, India, due to mountains, slopes, and extreme rainfall. Landslide susceptibility maps are highly efficient and helpful for disaster preparation and management. The determination of this work is to create a landslide susceptibility map (LSM) for West Jaintia Hills using the analytical hierarchy process (AHP) method. The previous landslide data are required for effective management of the current situation, and these data are divided into training samples (75%) and testing samples (25%) for predicting efficiency. The different landslide causative factors are investigated in this study: rainfall, slope, geomorphology, elevation, lineament density, LULC, distance from a road, NDWI, MSAVI, and NDVI. The causal factors are subdivided and weighted according to AHP, and a susceptibility map is prepared. According to the developed map, 19% of the region's total area is low susceptible, 52% moderate, 25% high, and 4% very highly susceptible. Field data and the receiver operating characteristics (ROC) approach are used to validate the produced map. The outcomes indicate that the predicted susceptibility map closely reflects previous and recent landslide events. The created map is accurate and can be applied to future land-use planning. [ABSTRACT FROM AUTHOR]