Rising infrastructure density and transportation networks along the riverbank landslide alter critical stress and horizontal displacement in riverbank soils, contributing to erosion. Early warning systems can detect structural changes in soil to help mitigate damage. However, there is still a lack of studies evaluating horizontal pressure in landslide masses under the influence of load and horizontal displacement causing erosion or externally induced stress. This study presents a monitoring system based on wireless transmission technology combined with sensors embedded in the soil to track the displacement of the soil mass along the riverbank. The system uses tilt, soil moisture, and earth pressure sensors to collect real-time data on the mechanical properties of the soil. Experimental results show that a load of 17.5 kPa can destabilize the slope, with tilt angles increasing significantly as soil mass shifts toward the canal. The maximum recorded horizontal soil pressure is 2.77 kPa. The analysis reveals significant discrepancies between analytical methods and finite element method (FEM) in predicting soil behavior under loads, highlighting the superior accuracy of FEM, especially at higher loads. This research contributes to developing a reliable information system for managing landslide risks as well as externally induced stress, protecting people and infrastructure.

The Himalayan foothills are highly prone to rainfall induced flash floods. This research focuses on the August 19-20, 2022 flash flood event in Song watershed of Doon valley, Uttarakhand caused significant damages to buildings and a road bridge. The study aims to assess the flood intensity through flood simulation in a semi-distributed hydrological model by utilizing rainfall data, land use and soil data. Further, the flood hydrographs generated through hydrological modelling were used to simulate hydrodynamic model to estimate flood depth. Pre and post-flood inundation assessments were conducted using PlanetScope and Sentinel-1 imagery. Furthermore, development activities on river courses were analyzed utilizing Google earth and Bing maps high resolution imagery. Cumulative rainfall observations revealed 344 mm rainfall in Rishikesh and 225 mm in Sahastradhara on 19-20 August for the 24 hrs, contributed in a peak flood discharge 2679 m(3)/s at the Rishikesh outlet. The simulated flood depth depicted 4.81 m flood depth at the damaged Thano-Bhogpur bridge. The PlanetScope satellite imagery showed 182 m expansion in the cross-sectional width of river at Maldevta after the flood. A 5.36 sq. km. flood area observed throughout the entire Song catchment in two days post event Sentinel-1 imagery. Analysis of high-resolution imageries revealed increasing development activities in floodplains of the catchment, which got affected by flood. The findings indicate urgent need of floodplain management by implementing comprehensive flood risk management plans including early warning systems, land-use regulations based on flood hazard zonation and flood resilient infrastructure to mitigate future flood exposure to society.

The development of real-time early warning systems is crucial for mitigating landslide risks. Although internet coverage is extensive in urban areas, it often fails to reach remote locations such as mountainous regions. The low-power wide area (LPWA) communication network offers a viable alternative for transmitting data from landslide early warning system (LEWS) sensors to a central server. To develop an accurate and reliable LEWS, it is essential to establish appropriate thresholds for warning triggers. This study conducted a series of laboratory experiments on slope models, both with and without vertical cracks. The models were subjected to varying rainfall intensities to investigate the mechanisms of slope failure. The objective of this paper was to evaluate a cost-effective and sustainable LEWS based on internet of things using the Internet (WiFi) and LPWA for data transmission, and to monitor slope vulnerability. During the experiments, volumetric water content, pore water pressure, and tilt angle were measured. Thresholds for critical volumetric water content, pore water pressure rate, and tilt rate were proposed to define warning stages. The results contribute to enhancing the advancement of early warning systems, which are crucial for mitigating the risks associated with landslides.

Economic and human losses from flooding have had a significant global impact. Undeveloped nations often require extended periods to recover from flood-related damage, exacerbating the climate poverty trap, specifically in flood-prone regions. To address this issue, early warning systems (EWS) provide lead time for preparedness and measures to reduce vulnerability. However, EWS are mainly empirical at large scales and often do not incorporate hydrodynamic behaviors in flood forecasting, at least in developing regions with a lack of information. This study presents an open-source system integrating a hydrodynamic model with satellite rainfall data (PERSIANN PDIR-Now) and weather prediction data (GFS). It functions as a near real-time Digital Twin (DT) and Early Warning System for high-resolution flood forecasting. Simulated data can be compared with gauge stations in real-time through the model monitoring interface. A proof-of-concept was made by assessing the model capabilities in two case studies. First, the system simulated two consecutive extreme events (hurricanes ETA and IOTA) over the Sula Valley, Honduras, showing fidelity in streamflow responses. Second, the system worked as a DT and EWS to monitor the current and future hydrological states for two periods in 2022 and 2023. Results indicate that satellite data coupled with DT can provide up-to-date system conditions for flood forecasts for regions of lack of data for extreme rainfall events. This tool offered insights to enhance civil protection and societal engagement through warning dissemination against extreme events to build resilience to cope with the increasing magnitude and frequency of disasters in regions with data scarcity.

The observation of precursory signals of the 2021 Chamoli rock-ice avalanche provides an opportunity to investigate the multidisciplinary analysis approach of rock failure. On 7 February 2021, a huge rock-ice mass detached from the Raunthi peak at Chamoli district in Uttarakhand, India. The tragic catastrophe resulted in more than 200 deaths and significant economic losses. Here, we analyse radon concentration and seismic signals to characterise the potential precursory anomalies prior to the detachment. Continuous peaks of radon anomalies were observed from the afternoon of 5 to 7 February and decreased suddenly after the event, while a cumulative number of seismic tremors and amplitude variations are more intensified similar to 2.30 h before the main event, indicating a static to dynamic phase change within the weak zone. This study not only characterises abnormal signals but also models the rock failure mechanisms. The analysis unveils three time-dependent nucleation phases, physical mechanisms of signal generation and a complete scenario of physical factors that affected the degree of criticality of slope failure. The results of this study suggest gradual progression of rock cracks/joints, subsequent material creep and slip advancement acceleration preceded the final failure. Furthermore, the study highlights the importance of an early warning system to mitigate the impact of events like the 2021 Chamoli rock-ice avalanche.

During recent decades there has been an increase in extreme flood events and their intensity in most regions, mainly driven by climate change. Furthermore, these critical events are expected to intensify in the future. Therefore, the improvement of preparedness, mitigation, and adaptation counterparts is mandatory. Many scientific fields are involved in this task, but from a meteorological and hydrological perspective, one of the main tools that can contribute to mitigating the impact of floods is the development of Early Warning Systems. In this sense, this paper presents a scientific literature review of some of the most representative Flood Early Warning Systems worldwide, many of which are currently fully operational, with a special focus on the numerical modeling component when it is developed and integrated into the system. Thus, from basic to technically complex, and from basin or regional to continental or global scales of application, these systems have been reviewed. In this sense, a brief description of their main features, operational procedures, and implemented numerical models is also depicted. Additionally, a series of indications regarding the key aspects of the newly developed FEWSs, based on recent trends and advancements in FEWSs development found in the literature, are also summarized. Thus, this work aims to provide a literature review useful to scientists and engineers involved in flood analysis to improve and develop supporting tools to assist in the implementation of mitigation measures to reduce flood damage for people, goods, and ecosystems and to improve the community resilience.

In the laying of long-distance pipelines, it is sometimes impossible to avoid one or more areas that are prone to frequent geological disasters, such as landslides. In the case of such a disaster, the buried pipeline is likely to undergo large displacement leading to plastic deformation, subsequent leakage, explosions, and other accidents that may result in its failure. In order to ensure the safety of pipeline transportation, in this work, a remote real-time system for monitoring the status of pipelines was designed on a cloud service platform to realize stress-strain analysis and to provide an early warning of pipeline damage after a landslide. The results of the stress-strain analysis of a pipeline buried under a landslide were used to establish a numerical calculation model based on the shell element and nonlinear soil springs. The deformation distribution characteristics of the pipeline, based on multiple factors, were studied, and the effects of the landslide width, buried depth, ultimate soil resistance, diameter thickness ratio, and internal pressure on vertical displacement, as well as the axial strain and bending strain of the pipeline were obtained. According to the results of the finite-element method, the plastic deformation position of the pipeline under the action of landslide was determined, the software and hardware configuration of the pipeline strain monitoring scheme was designed, and the installation of the pipeline strain monitoring system was carried out. The processing results of the field data showed that the model had a good noise reduction effect. Moreover, the results showed that the system achieved stable real-time data acquisition, efficient data remote transmission, convenient operation, and rich terminal monitoring capabilities, thus effectively providing an evaluation of the operating status of the pipeline, improving landslide disaster warning, and ensuring the safe operation of the pipeline. Buried pipelines are likely to undergo large displacement under the action of geological disasters such as landslides, which can lead to accidents such as pipeline leakage and explosion. In order to ensure the safety of pipeline transportation, a numerical calculation model of a buried pipeline based on shell elements and soil springs is established to analyze the stress and strain of a pipeline under a landslide. The model can reflect the deformation distribution characteristics of the pipeline and analyze the influence of landslide width, buried depth, and other factors on the deformation of the pipeline. Based on the presented method, the dangerous points of plastic deformation of pipeline under landslide can be determined. Furthermore, combined with the actual situation of a landslide site, a monitoring system has been designed and installed that can operate stably for a long time in the landslide disaster site. The system can realize the acquisition, transmission, and evaluation of pipeline data, and ensure the smooth operation of buried pipelines.